assessment and assignment what is the difference

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What is the Difference Between Assignment and Assessment

The main difference between assignment and assessment is that assignments refer to the allocation of a task or set of tasks that are marked and graded while a ssessment refers to methods for establishing if students have achieved a learning outcome, or are on their way toward a learning objective.  

Assignments and assessment are two important concepts in modern education. Although these two words are similar, they have different meanings. Assignments are the pieces of coursework or homework students are expected to complete. Assessment, on the other hand, refer to the method of assessing the progress of students. Sometimes, assignments can act as tools of assessment.

Key Areas Covered

1. What is an Assignment       – Definition, Goals, Characteristics 2. What is an Assessment      – Definition, Characteristics 3. Difference Between Assignment and Assessment      – Comparison of Key Differences

What is an Assignment

Assignments are the pieces of coursework or homework given to the students by teachers at school or professors at university. In other words, assignments refer to the allocation of a task or set of tasks that are marked and graded. Assignments are essential components in primary, secondary and tertiary education.

Assignments have several goals, as described below:

– gives students a better understanding of the topic being studied

– develops learning and understanding skills of students

– helps students in self-study

– develops research and analytical skills

– teaches students time management and organization

– clear students’ problems or ambiguities regarding any subject

– enhance the creativity of students

Generally, educators assign such tasks to complete at home and submit to school after a certain period of time. The time period assigned may depend on the nature of the task. Essays, posters, presentation, annotated bibliography, review of a book, summary, charts and graphs are some examples of assignments. Writing assignments develop the writing skills of students while creative assignments like creating posters, graphs and charts and making presentation enhance the creativity of students. Ultimately, assignments help to assess the knowledge and skills, as well as the students’ understanding of the topic.

What is an Assessment

Assessment refers to methods for establishing if students have achieved a learning outcome, or are on their way toward a learning objective. In other words, it is the method of assessing the progress of students. Assessment helps the educators to determine what students are learning and how well they are learning it, especially in relation to the expected learning outcomes of a lesson. Therefore, it helps the educator to understand how the students understand the lesson, and to determine what changes need to be made to the teaching process. Moreover, assessment focuses on both learning as well as teaching and can be termed as an interactive process. Sometimes, assignments can act as tools of assessment.

There are two main types of assessment as formative and summative assessment . Formative assessments occur during the learning process, whereas summative assessments occur at the end of a learning unit. Quizzes, discussions, and making students write summaries of the lesson are examples of formative assessment while end of unit tests, term tests and final projects are examples of summative assessment. Moreover, formative assessments aim to monitor student learning while summative assessments aim to evaluate student learning.

Difference Between Assignment and Assessment

Assignments refer to the allocation of a task or set of tasks that are marked and graded while assessment refers to methods for establishing if students have achieved a learning outcome, or are on their way toward a learning objective. 

Assignments are the pieces of coursework or homework students have to complete while assessment is the method of assessing the progress of students

Goal                

Moreover, assignments aim to give students a more comprehensive understanding of the topic being studied and develop learning and understanding skills of students. However, the main goal of assessment is monitoring and evaluating student learning and progress.

Assignments are the pieces of coursework or homework students have to complete while assessment refers to the method of assessing the progress of students. This is the main difference between assignment and assessment. Sometimes, assignments can also act as tools of assessment.

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Assignment vs. Assessment: What's the Difference?

Key Differences

Comparison chart, assignment and assessment definitions, can assignment and assessment be used interchangeably, are assignments only given in an academic context, is an assignment always individual work, does an assessment always affect the final grade, can an assessment be informal, can an assessment include physical tests, is an assignment always graded, do all assignments require submission of work, is an assessment only about grading, is an assessment always conducted by teachers, can an assessment be a self-evaluation, does every assignment involve writing, are all assessments standardized, can an assessment be a group activity, do assignments always have deadlines, can the word assignment refer to legal contexts, can an assignment be optional, is an assessment always planned, can assignments have multiple parts, can an assignment be verbal.

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What Is The Difference Between Assignments And Assessments?

The two central ideas of contemporary education are assignment and assessment. Assignments and assessments are essential components of a student’s academic career. However, a lot of students are unaware of the fundamental distinction between an assignment and an assessment. Assignment refers to the distribution of the numerous tasks that students must do to receive the best grades in their academic curriculums. In comparison, a teacher will assess students by giving them a variety of assessment tasks that may be of different types and observing what information and skills they have learned. A student can get to know various outcomes of their learning and how they are progressing with learning objectives by completing the assessment activity.

For the best results in their academic work, students pursuing a variety of courses at various colleges must deal with assignments and assessments. Therefore, they must complete these two tasks using the right format and procedure. Assessments include writing assignments, class exercises, quizzes, case studies, and group activities, whereas assignments consist of writing tasks like case studies, reports, essays, etc. As a result, both are equally important but approached in different ways. 

Let’s have a look at this in detail!

What Is An Assignment? 

Assignments are pieces of writing paper or homework that a lecturer or university gives to assess your knowledge and abilities. It may also be referred to as writing assignments that must be finished and submit in before the deadlines. This is a requirement for their academic work; thus, you must conduct extensive research to finish the assignment. Numerous tasks require you to select a topic before you begin writing on it, including essays, reports, a thesis, case study assignments, and many more. It aids in the development of your comprehension and learning abilities, and you can conduct your research to finish these assignments. Additionally, it develops research and analytical skills, which will help the students in the future. 

What Is An Assessment?

Assessment refers to the process by which a teacher evaluates the scholars’ knowledge and learning outcomes. In other words, multiple assessment assignments can be used to evaluate your academic development. It aids the professor in determining a student’s aptitude and degree of curricular compliance. Because of this, an assessment is an interactive process that focuses on both teaching and learning. An assignment may occasionally serve as an assessment tool.

Formative and summative assessments are the two main types of assessment. Summative evaluation takes place after each learning unit, whereas formative evaluation is undertaken throughout the learning process. Assessment includes tests, assignments, group projects, quizzes, and summaries.

What Is The Format Of An Assignment? 

Understanding the right format and structure is essential before beginning any work. The format is crucial in capturing the reader’s interest. You’ll be able to compose the assignment extremely precisely if you follow the right format for an assignment. As a result, the most crucial assignment writing format must be used.

• Executive summary:  The executive summary is crucial for making a good first impression on the reader; therefore, when a student begins writing an assignment, he needs to focus on it. It briefly describes an academic topic, such as a project proposal or business strategy. It provides a synopsis of the case study or reports writing and a solid structure for the writing techniques you’ll employ later on. 
• Table of content:  Each subsection in this section must be listed together with the relevant page number. It will surely be helpful for the reader to skip straight to the topic’s intriguing parts. Also, they can directly jump to that topic according to their interest. 
• Introduction:  The first section of your assignment must contain all of the crucial information related to the topic you have chosen for the assignment. In this section, you have to be very precise and clear while framing it. You need to mention all those details that you are going to explain in the further assignment. Therefore an introduction must create an impact on the reader’s mind and develop an interest in reading the whole assignment. 
• Body section:  After the introduction is complete, you must start on the body section. All of the crucial information should be mentioned in the assignment’s central section. When you reach this part, you need to be familiar with the major ideas, illustrations, and statistics.
• Conclusion:  In conclusion, you must be able to present a summary of all the data once the primary steps have been completed. Never provide extra information for the assignment.

What Are The Major Steps To Complete An Assessment Task? 

• Know the purpose of evaluation:  This stage clarifies the aim of the meeting to everyone in attendance. Additionally, it establishes the meeting’s objectives and tone. It also makes it clear how questions and remarks that should be shorter for the meeting’s format will be addressed. Use our recommended introduction in the description below, or write your own.
• Determine the work provided to you:  In this phase, the learner and you will review the pertinent responses you both filled out on your assessment form. The Educator should have gone over these in advance and taken any necessary notes.
• Discuss all your work and start writing it:  Items for homework are tasks that must be finished at home. To allow the learner and Educator enough time to complete the work, they are assigned homework. To answer questions from the learner and to make expectations clear, homework is discussed in this stage so that you can get the best answers for your assessment questions. 

If you are enrolled in a course or program offered by a reputable university, you must understand the assignment and assessment differences. Since you will be dealing with both tasks during your curriculum, it will aid you in writing them correctly. You can seek assistance from our  assessment help  services if you still need help understanding the difference and are unable to complete the assignment or assessment activity. Our most experienced expert will help you correctly write your assignment or assessment work. Our highly qualified experts are skilled at assessment and assignment help and finishing them before the deadlines.

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Types of Assignments and Assessments

Assignments and assessments are much the same thing: an instructor is unlikely to give students an assignment that does not receive some sort of assessment, whether formal or informal, formative or summative; and an assessment must be assigned, whether it is an essay, case study, or final exam. When the two terms are distinquished, "assignment" tends to refer to a learning activity that is primarily intended to foster or consolidate learning, while "assessment" tends to refer to an activity that is primarily intended to measure how well a student has learned. 

In the list below, some attempt has been made to put the assignments/assessments in into logical categories. However, many of them could appear in multiple categories, so to prevent the list from becoming needlessly long, each item has been allocated to just one category. 

Written Assignments:

• Annotated Bibliography : An annotated bibliography is a list of citations or references to sources such as books, articles, websites, etc., along with brief descriptions or annotations that summarize, evaluate, and explain the content, relevance, and quality of each source. These annotations provide readers with insights into the source's content and its potential usefulness for research or reference.
• Summary/Abstract : A summary or abstract is a concise and condensed version of a longer document or research article, presenting the main points, key findings, and essential information in a clear and brief manner. It allows readers to quickly grasp the main ideas and determine whether the full document is relevant to their needs or interests. Abstracts are commonly found at the beginning of academic papers, research articles, and reports, providing a snapshot of the entire content.
• Case Analysis : Case analysis refers to a systematic examination and evaluation of a particular situation, problem, or scenario. It involves gathering relevant information, identifying key factors, analyzing various aspects, and formulating conclusions or recommendations based on the findings. Case analysis is commonly used in business, law, and other fields to make informed decisions and solve complex problems.
• Definition : A definition is a clear and concise explanation that describes the meaning of a specific term, concept, or object. It aims to provide a precise understanding of the item being defined, often by using words, phrases, or context that distinguish it from other similar or related things.
• Description of a Process : A description of a process is a step-by-step account or narrative that outlines the sequence of actions, tasks, or events involved in completing a particular activity or achieving a specific goal. Process descriptions are commonly used in various industries to document procedures, guide employees, and ensure consistent and efficient workflows.
• Executive Summary : An executive summary is a condensed version of a longer document or report that provides an overview of the main points, key findings, and major recommendations. It is typically aimed at busy executives or decision-makers who need a quick understanding of the content without delving into the full details. Executive summaries are commonly used in business proposals, project reports, and research papers to present essential information concisely.
• Proposal/Plan : A piece of writing that explains how a future problem or project will be approached.
• Laboratory or Field Notes:  Laboratory/field notes are detailed and systematic written records taken by scientists, researchers, or students during experiments, observations, or fieldwork. These notes document the procedures, observations, data, and any unexpected findings encountered during the scientific investigation. They serve as a vital reference for later analysis, replication, and communication of the research process and results.
• Research Paper : A research paper is a more extensive and in-depth academic work that involves original research, data collection from multiple sources, and analysis. It aims to contribute new insights to the existing body of knowledge on a specific subject. Compare to "essay" below.
• Essay : A composition that calls for exposition of a thesis and is composed of several paragraphs including an introduction, a body, and a conclusion. It is different from a research paper in that the synthesis of bibliographic sources is not required. Compare to "Research Paper" above. 
• Memo : A memo, short for memorandum, is a brief written message or communication used within an organization or business. It is often used to convey information, provide updates, make announcements, or request actions from colleagues or team members.
• Micro-theme : A micro-theme refers to a concise and focused piece of writing that addresses a specific topic or question. It is usually shorter than a traditional essay or research paper and requires the writer to present their ideas clearly and concisely.
• Notes on Reading : Notes on reading are annotations, comments, or summaries taken while reading a book, article, or any other written material. They serve as aids for understanding, retention, and later reference, helping the reader recall essential points and ideas from the text.
• Outline : An outline is a structured and organized plan that lays out the main points and structure of a written work, such as an essay, research paper, or presentation. It provides a roadmap for the writer, ensuring logical flow and coherence in the final piece.
• Plan for Conducting a Project : A plan for conducting a project outlines the steps, resources, timelines, and objectives for successfully completing a specific project. It includes details on how tasks will be executed and managed to achieve the desired outcomes.
• Poem : A poem is a literary work written in verse, using poetic devices like rhythm, rhyme, and imagery to convey emotions, ideas, and experiences.
• Play : A play is a form of literature written for performance, typically involving dialogue and actions by characters to tell a story or convey a message on stage.
• Choreography : Choreography refers to the art of designing dance sequences or movements, often for performances in various dance styles.
• Article/Book Review : An article or book review is a critical evaluation and analysis of a piece of writing, such as an article or a book. It typically includes a summary of the content and the reviewer's assessment of its strengths, weaknesses, and overall value.
• Review of Literature : A review of literature is a comprehensive summary and analysis of existing research and scholarly writings on a particular topic. It aims to provide an overview of the current state of knowledge in a specific field and may be a part of academic research or a standalone piece.
• Essay-based Exam : An essay-based exam is an assessment format where students are required to respond to questions or prompts with written, structured responses. It involves expressing ideas, arguments, and explanations in a coherent and organized manner, often requiring critical thinking and analysis.
• "Start" : In the context of academic writing, "start" refers to the initial phase of organizing and planning a piece of writing. It involves formulating a clear and focused thesis statement, which presents the main argument or central idea of the work, and creating an outline or list of ideas that will support and develop the thesis throughout the writing process.
• Statement of Assumptions : A statement of assumptions is a declaration or acknowledgment made at the beginning of a document or research paper, highlighting the underlying beliefs, conditions, or premises on which the work is based. It helps readers understand the foundation of the writer's perspective and the context in which the content is presented.
• Summary or Precis : A summary or precis is a concise and condensed version of a longer piece of writing, such as an article, book, or research paper. It captures the main points, key arguments, and essential information in a succinct manner, enabling readers to grasp the content without reading the full text.
• Unstructured Writing : Unstructured writing refers to the process of writing without following a specific plan, outline, or organizational structure. It allows the writer to freely explore ideas, thoughts, and creativity without the constraints of a predefined format or order. Unstructured writing is often used for brainstorming, creative expression, or personal reflection.
• Rough Draft or Freewrite : A rough draft or freewrite is an initial version of a piece of writing that is not polished or edited. It serves as an early attempt by the writer to get ideas on paper without worrying about perfection, allowing for exploration and creativity before revising and refining the final version.
• Technical or Scientific Report : A technical or scientific report is a document that presents detailed information about a specific technical or scientific project, research study, experiment, or investigation. It follows a structured format and includes sections like abstract, introduction, methods, results, discussion, and conclusion to communicate findings and insights in a clear and systematic manner.
• Journal article : A formal article reporting original research that could be submitted to an academic journal. Rather than a format dictated by the professor, the writer must use the conventional form of academic journals in the relevant discipline.
• Thesis statement : A clear and concise sentence or two that presents the main argument or central claim of an essay, research paper, or any written piece. It serves as a roadmap for the reader, outlining the writer's stance on the topic and the key points that will be discussed and supported in the rest of the work. The thesis statement provides focus and direction to the paper, guiding the writer's approach to the subject matter and helping to maintain coherence throughout the writing.

Visual Representation

• Brochure : A brochure is a printed or digital document used for advertising, providing information, or promoting a product, service, or event. It typically contains a combination of text and visuals, such as images or graphics, arranged in a visually appealing layout to convey a message effectively.
• Poster : A poster is a large printed visual display intended to catch the attention of an audience. It often contains a combination of text, images, and graphics to communicate information or promote a particular message, event, or cause.
• Chart : A chart is a visual representation of data or information using various formats such as pie charts, bar charts, line charts, or tables. It helps to illustrate relationships, trends, and comparisons in a concise and easy-to-understand manner.
• Graph : A graph is a visual representation of numerical data, usually presented using lines, bars, points, or other symbols on a coordinate plane. Graphs are commonly used to show trends, patterns, and relationships between variables.
• Concept Map : A concept map is a graphical tool used to organize and represent the connections and relationships between different concepts or ideas. It typically uses nodes or boxes to represent concepts and lines or arrows to show the connections or links between them, helping to visualize the relationships and hierarchy of ideas.
• Diagram : A diagram is a visual representation of a process, system, or structure using labeled symbols, shapes, or lines. Diagrams are used to explain complex concepts or procedures in a simplified and easy-to-understand manner.
• Table : A table is a systematic arrangement of data or information in rows and columns, allowing for easy comparison and reference. It is commonly used to present numerical data or detailed information in an organized format.
• Flowchart : A flowchart is a graphical representation of a process, workflow, or algorithm, using various shapes and arrows to show the sequence of steps or decisions involved. It helps visualize the logical flow and decision points, making it easier to understand and analyze complex processes.
• Multimedia or Slide Presentation : A multimedia or slide presentation is a visual communication tool that combines text, images, audio, video, and other media elements to deliver information or a message to an audience. It is often used for educational, business, or informational purposes and can be presented in person or virtually using software like Microsoft PowerPoint or Google Slides.
• ePortfolio : An ePortfolio, short for electronic portfolio, is a digital collection of an individual's work, accomplishments, skills, and reflections. It typically includes a variety of multimedia artifacts such as documents, presentations, videos, images, and links to showcase a person's academic, professional, or personal achievements. Eportfolios are used for self-reflection, professional development, and showcasing one's abilities to potential employers, educators, or peers. They provide a comprehensive and organized way to present evidence of learning, growth, and accomplishments over time.

Multiple-Choice Questions : These questions present a statement or question with several possible answer options, of which one or more may be correct. Test-takers must select the most appropriate choice(s). See CTE's Teaching Tip "Designing Multiple-Choice Questions."  

True or False Questions : These questions require test-takers to determine whether a given statement is true or false based on their knowledge of the subject.

Short-Answer Questions : Test-takers are asked to provide brief written responses to questions or prompts. These responses are usually a few sentences or a paragraph in length.

Essay Questions : Essay questions require test-takers to provide longer, more detailed written responses to a specific topic or question. They may involve analysis, critical thinking, and the development of coherent arguments.

Matching Questions : In matching questions, test-takers are asked to pair related items from two lists. They must correctly match the items based on their associations.

Fill-in-the-Blank Questions : Test-takers must complete sentences or passages by filling in the missing words or phrases. This type of question tests recall and understanding of specific information.

Multiple-Response Questions : Similar to multiple-choice questions, but with multiple correct options. Test-takers must select all the correct choices to receive full credit.

Diagram or Image-Based Questions : These questions require test-takers to analyze or interpret diagrams, charts, graphs, or images to answer specific queries.

Problem-Solving Questions : These questions present real-world or theoretical problems that require test-takers to apply their knowledge and skills to arrive at a solution.

Vignettes or Case-Based Questions : In these questions, test-takers are presented with a scenario or case study and must analyze the information to answer related questions.

Sequencing or Order Questions : Test-takers are asked to arrange items or events in a particular order or sequence based on their understanding of the subject matter.

Projects intended for a specific audience :

• Advertisement : An advertisement is a promotional message or communication aimed at promoting a product, service, event, or idea to a target audience. It often uses persuasive techniques, visuals, and compelling language to attract attention and encourage consumers to take specific actions, such as making a purchase or seeking more information.
• Client Report for an Agency : A client report for an agency is a formal document prepared by a service provider or agency to communicate the results, progress, or recommendations of their work to their client. It typically includes an analysis of data, achievements, challenges, and future plans related to the project or services provided.
• News or Feature Story : A news story is a journalistic piece that reports on current events or recent developments, providing objective information in a factual and unbiased manner. A feature story, on the other hand, is a more in-depth and creative piece that explores human interest topics, profiles individuals, or delves into issues from a unique perspective.
• Instructional Manual : An instructional manual is a detailed document that provides step-by-step guidance, explanations, and procedures on how to use, assemble, operate, or perform specific tasks with a product or system. It aims to help users understand and utilize the item effectively and safely.
• Letter to the Editor : A letter to the editor is a written communication submitted by a reader to a newspaper, magazine, or online publication, expressing their opinion, feedback, or comments on a particular article, topic, or issue. It is intended for publication and allows individuals to share their perspectives with a broader audience.

Problem-Solving and Analysis :

• Taxonomy : Taxonomy is the science of classification, categorization, and naming of organisms, objects, or concepts based on their characteristics, similarities, and differences. It involves creating hierarchical systems that group related items together, facilitating organization and understanding within a particular domain.
• Budget with Rationale : A budget with rationale is a financial plan that outlines projected income and expenses for a specific period, such as a month or a year. The rationale provides explanations or justifications for each budget item, explaining the purpose and reasoning behind the allocated funds.
• Case Analysis : Case analysis refers to a methodical examination of a particular situation, scenario, or problem. It involves gathering relevant data, identifying key issues, analyzing different factors, and formulating conclusions or recommendations based on the findings. Case analysis is commonly used in various fields, such as business, law, and education, to make informed decisions and solve complex problems.
• Case Study : A case study is an in-depth analysis of a specific individual, group, organization, or situation. It involves thorough research, data collection, and detailed examination to understand the context, challenges, and outcomes associated with the subject of study. Case studies are widely used in academic research and professional contexts to gain insights into real-world scenarios.
• Word Problem : A word problem is a type of mathematical or logical question presented in a contextual format using words rather than purely numerical or symbolic representations. It challenges students to apply their knowledge and problem-solving skills to real-life situations.

Collaborative Activities

• Debate : A debate is a structured discussion between two or more individuals or teams with differing viewpoints on a specific topic or issue. Participants present arguments and counterarguments to support their positions, aiming to persuade the audience and ultimately reach a resolution or conclusion. Debates are commonly used in academic settings, public forums, and formal competitions to foster critical thinking, communication skills, and understanding of diverse perspectives.
• Group Discussion : A group discussion is an interactive conversation involving several individuals who come together to exchange ideas, opinions, and information on a particular subject. The discussion is typically moderated to ensure that everyone has an opportunity to participate, and it encourages active listening, collaboration, and problem-solving. Group discussions are commonly used in educational settings, team meetings, and decision-making processes to promote dialogue and collective decision-making.
• An oral report is a form of communication in which a person or group of persons present information, findings, or ideas verbally to an audience. It involves speaking in front of others, often in a formal setting, and delivering a structured presentation that may include visual aids, such as slides or props, to support the content. Oral reports are commonly used in academic settings, business environments, and various professional settings to share knowledge, research findings, project updates, or persuasive arguments. Effective oral reports require clear organization, articulation, and engaging delivery to effectively convey the intended message to the listeners.

Planning and Organization

• Inventory : An inventory involves systematically listing and categorizing items or resources to assess their availability, quantity, and condition. In an educational context, students might conduct an inventory of books in a library, equipment in a lab, or supplies in a classroom, enhancing their organizational and data collection skills.
• Materials and Methods Plan : A materials and methods plan involves developing a structured outline or description of the materials, tools, and procedures to be used in a specific experiment, research project, or practical task. It helps learners understand the importance of proper planning and documentation in scientific and research endeavors.
• Plan for Conducting a Project : This learning activity requires students to create a detailed roadmap for executing a project. It includes defining the project's objectives, identifying tasks and timelines, allocating resources, and setting milestones to monitor progress. It enhances students' project management and organizational abilities.
• Research Proposal Addressed to a Granting Agency : A formal document requesting financial support for a research project from a granting agency or organization. The proposal outlines the research questions, objectives, methodology, budget, and potential outcomes. It familiarizes learners with the process of seeking funding and strengthens their research and persuasive writing skills.
• Mathematical Problem : A mathematical problem is a task or question that requires the application of mathematical principles, formulas, or operations to find a solution. It could involve arithmetic, algebra, geometry, calculus, or other branches of mathematics, challenging individuals to solve the problem logically and accurately.
• Question : A question is a sentence or phrase used to elicit information, seek clarification, or provoke thought from someone else. Questions can be open-ended, closed-ended, or leading, depending on their purpose, and they play a crucial role in communication, problem-solving, and learning.

More Resources

CTE Teaching Tips

• Personal Response Systems
• Designing Multiple-Choice Questions
• Aligning Outcomes, Assessments, and Instruction

Other Resources

• Types of Assignments . University of Queensland.

If you would like support applying these tips to your own teaching, CTE staff members are here to help.  View the  CTE Support  page to find the most relevant staff member to contact.

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What is the difference between an Assessment and an Assignment?

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6. Assessment

6.1 assessment and evaluation.

Assessment, as defined by  www.edglossary.org , “ refers to the wide variety of methods or tools that educators use to evaluate, measure, and document the academic readiness, learning progress, skill acquisition, or educational needs of students.”   It is analogous to  evaluation, judgment, rating, appraisal, and analysis. (Great Schools Partnership, 2015)

Although the terms assessment and evaluation are often used synonymously, they are in fact distinctive and different. The intent of assessment is to measure effectiveness; evaluation adds a value component to the process.  A teacher may assess a student to ascertain how well the  individual  successfully met the learning target. If, however, the measurement is used to determine program placement, for example with a special education program, honors club, or for Individual Educational Program documentation, the assessment constitutes an evaluation.  

Goals of Assessment  

Assessment is two-fold in nature. It enables the teacher to gather information and to then determine what the learner knows or does not know and concurrently drives the planning phase. In order to meet the needs of all learners, the teacher may need to differentiate the instruction.

The teacher is then responsible for providing positive feedback in a timely manner to the student. This feedback should include specifically whether the student met the learning target, specifically what needs to be improved upon, and who and how these goals will be met.

The intent of assessment has traditionally been to determine what the learner has learned. Today, the emphasis is on authentic assessment. While the former typically employed recall methods, the latter encourages learners to demonstrate greater comprehension.  (Wiggins, 1990)

7 Keys to Effective Feedback

Methods to assess  .

Within an academic setting, assessment may include “the process of observing learning; describing, collecting, recording, scoring, and interpreting information about a student’s or one’s own learning  http://www.k12.hi.us/atr/evaluation/glossary.htm .”

It can occur by observations, interviews, tests, projects or any other information gathering method. Within the early childhood and early primary elementary grades, observations are used frequently to assess learners. Teachers may use a checklist to note areas of proficiency or readiness and may opt to use checkmarks or some other consistent means for record-keeping.

It is helpful for a teacher to include the date, day, and time. This record-keeping may result in emerging patterns. Does the learner exhibit certain behaviors or respond to learning activities because of proximity to lunchtime, or morning or afternoon? The aspect of understanding how individuals learn can be noted within the affective domain. (Kirk, N/D) This may influence how a student learns and behaves within a classroom setting. Seating, natural and artificial lighting, noise, and temperature all influence how a student feels and interacts within the environment and can have effect cognitive behaviors.

Interviews can be used on the elementary or secondary levels as an assessment tool. Like any other well- planned assessment tool, they necessitate careful planning and development of questions, positive rapport with the student, and an environment that is free from distractions, outside noise, and time constraints. Interviews may or may not be audiotaped or videotaped and scoring rubrics may be used to assess (Southerland, ND).

Tests offer yet another venue for assessment purposes. They may take the form of essay or short response, fill-in-the-blank, matching, or true or false formats. Like any of the other methods, they should be valid and reliable. Carefully thought out test questions need to be tied to learning standards and a clear and fair scoring measure needs to be in place.

Typically, assessment has been viewed as the result; the letter or point assigned at the end of an assignment; however, assessment can and should come at the beginning, end and throughout the teaching and learning process. While assessment should drive instruction, it often falls short when determining instructional decisions

Danielle Stein eagerly anticipated the upcoming parent-teacher conferences of the day. She had studied hard as a Childhood Education major and had worked diligently in her first year as a third-grade teacher at Maplewood Elementary School.  Danielle had planned interdisciplinary lessons, employed inquiry-based learning centers, and met regularly with individual students to ensure that they had mastered the skills as determined by the state standards.

Each student had a portfolio filled with dated representations of their work. Ms. Stein understood the importance of specific and timely feedback and had painstakingly provided detailed written feedback on each work sample. She meticulously arranged the portfolios along with anecdotal notes and looked forward to sharing the accomplishments of the students with their family members.

As last-minute jitters began to set in, Danielle realized that she had no grades for any of the students. Despite doing all the right things, she had no way to assign a grade to any of the work the students had done. How would she respond when guardians asked what grade their child would earn on the first report card? How would she accurately tell them how they compared with their peers in reading? In math? In social studies and science?

Danielle quickly realized she was not as prepared as she had anticipated.

Discussion Questions

How do teachers assess student work? Is there a certain number of assignments that should be graded within  a  9-week session? Are there  alternatives to  letter grades? Reflect on how you were graded as a student.   

• Foundations of Education. Authored by : SUNY Oneonta Education Department. License : CC BY: Attribution

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The Difference Between an Assessment and an Assignment

Posted 4 jun '20.

Every school has a unique method of setting work, tasks and assessing the level their students are at, but mostly these tests come in the forms of an assessment or an assignment. However, the difference between the two of these can be hard to spot - both receive task sheets, both can usually be worked on at home, they can contain some of the same content. So, how do we tell the difference and how can this help your child?

The Assignment

So, your child has come home brandishing an assignment task sheet. What does this mean exactly? An assignment is all in the name; it is the act of assigning. It is an allocation of a task or set of tasks that are marked and graded for the report card (but does not have to be). The purpose of an assignment is to give your child a more comprehensive understanding of the topic being studied and can include questions, long-form writing tasks or a more tactile and interactive activity. An assignment is usually completed at home and submitted to the school after a certain period.

The Assessment

An assessment may not come in a much different form to the assignment, but they are usually considered more important. This is because an assessment is the act of assessing the progress of your child. The assessment may be a take-home task, an exam/test, speech or something more hands-on. An assessment can be both in-class or at home. Usually, your child will get an assessment notification that is given approximately 2 weeks before the assessment is due. Particularly for Year 12s, assessments are incredibly important as they contribute to their overall internal mark.

Why It Is Important To Know The Difference

With this information, you are now able to help your child prioritise their work. Although the tasks given can look similar, knowing the weighted importance of both can help you help them to plan out when they will complete these tasks.

If you or your child require further assistance in completing schoolwork, visit www.fsedu.com.au where you can be provided personalised, one-on-one education with an experienced, dedicated teacher with an in-depth understanding of the Australian curriculum.

Written by Ben Maher - Founder and Director of Education at Full Spectrum Education

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In education, the term  assessment  refers to the wide variety of methods or tools that educators use to evaluate, measure, and document the academic readiness, learning progress, skill acquisition, or educational needs of students.

While assessments are often equated with traditional tests—especially the standardized tests  developed by testing companies and administered to large populations of students—educators use a diverse array of assessment tools and methods to measure everything from a four-year-old’s readiness for kindergarten to a twelfth-grade student’s comprehension of advanced physics. Just as academic lessons have different functions, assessments are typically designed to measure specific elements of learning—e.g., the level of knowledge a student already has about the concept or skill the teacher is planning to teach or the ability to comprehend and analyze different types of texts and readings. Assessments also are used to identify individual student weaknesses and strengths so that educators can provide specialized  academic support , educational programming, or social services. In addition, assessments are developed by a wide array of groups and individuals, including teachers, district administrators, universities, private companies, state departments of education, and groups that include a combination of these individuals and institutions.

While assessment can take a wide variety of forms in education, the following descriptions provide a representative overview of a few major forms of educational assessment.

Assessments are used for a wide variety of purposes in schools and education systems :

• High-stakes  assessments  are typically standardized tests used for the purposes of accountability—i.e., any attempt by federal, state, or local government agencies to ensure that students are enrolled in effective schools and being taught by effective teachers. In general, “high stakes” means that important decisions about students, teachers, schools, or districts are based on the scores students achieve on a high-stakes test, and either punishments (sanctions, penalties, reduced funding, negative publicity, not being promoted to the next grade, not being allowed to graduate) or accolades (awards, public celebration, positive publicity, bonuses, grade promotion, diplomas) result from those scores. For a more detailed discussion, see  high-stakes test .
• Pre-assessments  are administered before students begin a lesson, unit, course, or academic program. Students are not necessarily expected to know most, or even any, of the material evaluated by pre-assessments—they are generally used to (1) establish a baseline against which educators measure learning progress over the duration of a program, course, or instructional period, or (2) determine general academic readiness for a course, program, grade level, or new academic program that student may be transferring into.
• Formative  assessments  are in-process evaluations of student learning that are typically administered multiple times during a unit, course, or academic program. The general purpose of formative assessment is to give educators in-process feedback about what students are learning or not learning so that instructional approaches, teaching materials, and academic support can be modified accordingly. Formative assessments are usually not scored or graded, and they may take a variety of forms, from more formal quizzes and assignments to informal questioning techniques and in-class discussions with students.

Formative assessments are commonly said to be  for  learning because educators use the results to modify and improve teaching techniques during an instructional period, while summative assessments are said to be  of  learning because they evaluate academic achievement at the conclusion of an instructional period. Or as assessment expert Paul Black put it, “When the cook tastes the soup, that’s formative assessment. When the customer tastes the soup, that’s summative assessment.”

• Interim assessments   are used to evaluate where students are in their learning progress and determine whether they are on track to performing well on future assessments, such as standardized tests, end-of-course exams, and other forms of “summative” assessment. Interim assessments are usually administered periodically during a course or school year (for example, every six or eight weeks) and separately from the process of instructing students (i.e., unlike formative assessments, which are integrated into the instructional process).
• Placement assessments  are used to “place” students into a course, course level, or academic program. For example, an assessment may be used to determine whether a student is ready for Algebra I or a higher-level algebra course, such as an honors-level course. For this reason, placement assessments are administered before a course or program begins, and the basic intent is to match students with appropriate learning experiences that address their distinct learning needs.
• Screening assessments  are used to determine whether students may need specialized assistance or services, or whether they are ready to begin a course, grade level, or academic program. Screening assessments may take a wide variety of forms in educational settings, and they may be developmental, physical, cognitive, or academic. A preschool screening test, for example, may be used to determine whether a young child is physically, emotionally, socially, and intellectually ready to begin preschool, while other screening tests may be used to evaluate health, potential learning disabilities, and other student attributes.

Assessments are also designed in a variety of ways for different purposes:

• Standardized assessments  are designed, administered, and scored in a standard, or consistent, manner. They often use a multiple-choice format, though some include open-ended, short-answer questions. Historically, standardized tests featured rows of ovals that students filled in with a number-two pencil, but increasingly the tests are computer-based. Standardized tests can be administered to large student populations of the same age or grade level in a state, region, or country, and results can be compared across individuals and groups of students. For a more detailed discussion, see  standardized test .
• Standards-referenced or standards-based  assessments  are designed to measure how well students have mastered the specific knowledge and skills described in local, state, or national  learning standards . Standardized tests and high-stakes tests may or may not be based on specific learning standards, and individual schools and teachers may develop their own standards-referenced or standards-based assessments. For a more detailed discussion, see  proficiency-based learning .
• Common  assessments  are used in a school or district to ensure that all teachers are evaluating student performance in a more consistent, reliable, and effective manner. Common assessments are used to encourage greater consistency in teaching and assessment among teachers who are responsible for teaching the same content, e.g. within a grade level, department, or  content area . They allow educators to compare performance results across multiple classrooms, courses, schools, and/or learning experiences (which is not possible when educators teach different material and individually develop their own distinct assessments). Common assessments share the same format and are administered in consistent ways—e.g., teachers give students the same instructions and the same amount of time to complete the assessment, or they use the same scoring guides to interpret results. Common assessments may be “formative” or “summative .” For more detailed discussions, see coherent curriculum  and  rubric .
• Performance assessments  typically require students to complete a complex task, such as a writing assignment, science experiment, speech, presentation, performance, or long-term project, for example. Educators will often use collaboratively developed common assessments, scoring guides, rubrics, and other methods to evaluate whether the work produced by students shows that they have learned what they were expected to learn. Performance assessments may also be called “authentic assessments,” since they are considered by some educators to be more accurate and meaningful evaluations of learning achievement than traditional tests. For more detailed discussions, see authentic learning ,  demonstration of learning , and  exhibition .
• Portfolio-based  assessments  are collections of academic work—for example, assignments, lab results, writing samples, speeches, student-created films, or art projects—that are compiled by students and assessed by teachers in consistent ways. Portfolio-based assessments are often used to evaluate a “body of knowledge”—i.e., the acquisition of diverse knowledge and skills over a period of time. Portfolio materials can be collected in physical or digital formats, and they are often evaluated to determine whether students have met required learning standards . For a more detailed discussion, see  portfolio .

The purpose of an assessment generally drives the way it is designed, and there are many ways in which assessments can be used. A standardized assessment can be a high-stakes assessment, for example, but so can other forms of assessment that are not standardized tests. A portfolio of student work can be a used as both a “formative” and “summative” form of assessment. Teacher-created assessments, which may also be created by teams of teachers, are commonly used in a single course or grade level in a school, and these assessments are almost never “high-stakes.” Screening assessments may be produced by universities that have conducted research on a specific area of child development, such as the skills and attributes that a student should have when entering kindergarten to increase the likelihood that he or she will be successful, or the pattern of behaviors, strengths, and challenges that suggest a child has a particular learning disability. In short, assessments are usually created for highly specialized purposes.

While educational assessments and tests have been around since the days of the one-room schoolhouse, they have increasingly assumed a central role in efforts to improve the effectiveness of public schools and teaching. Standardized-test scores, for example, are arguably the dominant measure of educational achievement in the United States, and they are also the most commonly reported indicator of school, teacher, and school-system performance.

As schools become increasingly equipped with computers, tablets, and wireless internet access, a growing proportion of the assessments now administered in schools are either computer-based or online assessments—though paper-based tests and assessments are still common and widely used in schools. New technologies and software applications are also changing the nature and use of assessments in innumerable ways, given that digital-assessment systems typically offer an array of features that traditional paper-based tests and assignments cannot. For example, online-assessment systems may allow students to log in and take assessments during out-of-class time or they may make performance results available to students and teachers immediately after an assessment has been completed (historically, it might have taken hours, days, or weeks for teachers to review, score, and grade all assessments for a class). In addition, digital and online assessments typically include features, or “analytics,” that give educators more detailed information about student performance. For example, teachers may be able to see how long it took students to answer particular questions or how many times a student failed to answer a question correctly before getting the right answer. Many advocates of digital and online assessments tend to argue that such systems, if used properly, could help teachers “ personalize ” instruction—because many digital and online systems can provide far more detailed information about the academic performance of students, educators can use this information to modify educational programs, learning experiences , instructional approaches, and  academic-support strategies  in ways that address the distinct learning needs, interests, aspirations, or cultural backgrounds of individual students. In addition, many large-scale standardized tests are now administered online, though states typically allow students to take paper-based tests if computers are unavailable, if students prefer the paper-based option, or if students don’t have the technological skills and literacy required to perform well on an online assessment.

Given that assessments come in so many forms and serve so many diverse functions, a thorough discussion of the purpose and use of assessments could fill a lengthy book. The following descriptions, however, provide a brief, illustrative overview of a few of the major ways in which assessments—especially assessment results—are used in an attempt to improve schools and teaching:

• System and school accountability : Assessments, particularly standardized tests, have played an increasingly central role in efforts to hold schools, districts, and state public-school systems “accountable” for improving the academic achievement of students. The most widely discussed and far-reaching example, the 2001 federal law commonly known as the No Child Left Behind Act, strengthened federal expectations from the 1990s and required each state develop  learning standards   to govern what teachers should teach and students should learn. Under No Child Left Behind, standards are required in every grade level and  content area  from kindergarten through high school. The law also requires that students be tested annually in grades 3-8 and at least once in grades 10-12 in reading and mathematics. Since the law’s passage, standardized tests have been developed and implemented to measure how well students were meeting the standards, and scores have been reported publicly by state departments of education. The law also required that test results be tracked and reported separately for different “subgroups” of students, such as minority students, students from low-income households, students with special needs, and students with  limited proficiency in English . By publicly reporting the test scores achieved by different schools and student groups, and by tying those scores to penalties and funding, the law has aimed to close  achievement gaps  and improve schools that were deemed to be underperforming. While the No Child Left Behind Act is one of the most controversial and contentious educational policies in recent history, and the technicalities of the legislation are highly complex, it is one example of how assessment results are being used as an accountability measure.
• Teacher evaluation and compensation : In recent years, a growing number of elected officials, policy makers, and education reformers have argued that the best way to improve educational results is to ensure that students have effective teachers, and that one way to ensure effective teaching is to evaluate and compensate educators, at least in part, based on the test scores their students achieve. By basing a teacher’s income and job security on assessment results, the reasoning goes, administrators can identify and reward high-performing teachers or take steps to either help low-performing teachers improve or remove them from schools. Growing political pressure, coupled with the promise of federal grants, prompted many states to begin using student test results in teacher evaluations. This controversial and highly contentious reform strategy generally requires fairly complicated statistical techniques—known as  value-added measures   or  growth measures —to determine how much of a positive or negative effect individual teachers have on the academic achievement of their students, based primarily on student assessment results.
• Instructional improvement : Assessment results are often used as a mechanism for improving instructional quality and student achievement. Because assessments are designed to measure the acquisition of specific knowledge or skills, the design of an assessment can determine or influence what gets taught in the classroom (“teaching to the test” is a common, and often derogatory, phrase used to describe this general phenomenon). Formative assessments, for example, give teachers in-process feedback on student learning, which can help them make instructional adjustments during the teaching process, instead of having to wait until the end of a unit or course to find out how well students are learning the material. Other forms of assessment, such as standards-based assessments or common assessments, encourage educators to teach similar material and evaluate student performance in more consistent, reliable, or comparable ways.
• Learning-needs identification : Educators use a wide range of assessments and assessment methods to identify specific student learning needs, diagnose learning disabilities (such as autism, dyslexia, or nonverbal learning disabilities), evaluate language ability, or determine eligibility for specialized educational services. In recent years, the early identification of specialized learning needs and disabilities, and the proactive provision of educational support services to students, has been a major focus of numerous educational reform strategies. For a related discussion, see  academic support .

In education, there is widespread agreement that assessment is an integral part of any effective educational system or program. Educators, parents, elected officials, policy makers, employers, and the public all want to know whether students are learning successfully and progressing academically in school. The debates—many of which are a complex, wide ranging, and frequently contentious—typically center on how assessments are used, including how frequently they are being administered and whether assessments are beneficial or harmful to students and the teaching process. While a comprehensive discussion of these debates is beyond the scope of this resource, the following is a representative selection of a few major issues being debated:

• Is high-stakes testing, as an accountability measure, the best way to improve schools, teaching quality, and student achievement? Or do the potential consequences—such as teachers focusing mainly on test preparation and a narrow range of knowledge at the expense of other important skills, or increased incentives to cheat and manipulate test results—undermine the benefits of using test scores as a way to hold schools and educators more accountable and improve educational results?
• Are standardized assessments truly  objective  measures of academic achievement? Or do they reflect intrinsic biases—in their design or content—that favor some students over others, such wealthier white students from more-educated households over minority and low-income students from less-educated households? For more detailed discussions, see  measurement error and  test bias .
• Are “one-size-fits-all” standardized tests a fair way to evaluate the learning achievement of all students, given that some students may be better test-takers than others? Or should students be given a variety of assessment options and multiple opportunities to demonstrate what they have learned?
• Will more challenging and  rigorous   assessments lead to higher educational achievement for all students? Or will they end up penalizing certain students who come from disadvantaged backgrounds? And, conversely, will less-advantaged students be at an even greater disadvantage if they are not held to the same high educational standards as other students (because lowering educational standards for certain students, such as students of color, will only further disadvantage them and perpetuate the same cycle of low expectations that historically contributed to racial and socioeconomic  achievement gaps )?
• Do the costs—in money, time, and human resources—outweigh the benefits of widespread, large-scale testing? Would the funding and resources invested in testing and accountability be better spent on higher-quality educational materials, more training and support for teachers, and other resources that might improve schools and teaching more effectively? And is the pervasive use of tests providing valuable information that educators can use to improve instructional quality and student learning? Or are the tests actually taking up time that might be better spent on teaching students more knowledge and skills?
• Are technological learning applications, including digital and online assessments, improving learning experiences for students, teaching them technological skills and literacy, or generally making learning experiences more interesting and engaging? Or are digital learning applications adding to the cost of education, introducing unwanted distractions in schools, or undermining the value of teachers and the teaching process?

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Assignment vs. Assessment — What's the Difference?

Difference Between Assignment and Assessment

Table of contents, key differences, comparison chart, action vs. judgment, individual vs. group, compare with definitions, common curiosities, is every assignment followed by an assessment, what's the purpose of an assignment, how do teachers benefit from assessments, can an assignment be collaborative, what forms can assessments take, are assignments exclusive to academic settings, what is an assignment in an educational context, how does assessment differ from grading, can assessments be biased, is feedback essential after an assessment, can one forgo an assignment, do all assignments need a deadline, why are assessments integral in the learning process, how do assignments and assessments relate to real-world skills, can an assignment be both written and oral, share your discovery.

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Assignments vs Assessments: When to Use Them

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In the Instructional Design process model, the Course Design section includes consideration of assessment strategies as an integral part of the overall course concept.

There is plenty of educational research that describe different philosophical approaches to assessment and there are innumerable models of assessment that you could embrace as the basis of your instruction.

Rather than trying to cover everything that is possible in domain of assessment strategy, this chapter will provide an overview so that you can be prepared for a meaningful discussion with your ID partner that is tailored to your course’s needs.

What is an assessment strategy?

There is a difference between grading and an assessment strategy .   Before you can grade student work, you will need to determine the overarching perspective by which student work would be judged. There are a variety of ways that student work can be assessed depending on a few factors:

• The purpose or role of the course within a degree program
• The philosophy of the degree program itself (academic advancement or competency)
• The mission of the college (research or workforce readiness)

For example, a student can produce a project plan comprised of a paper, an oral presentation, and an accompanying slideshow. Overall, this work could be assessed according to:

• Standards for professional proficiency, such as indicators as described in performance statements in a set of competencies.
• The criteria set by the Program Director/Department, lead faculty, or the instructors themselves.
• A set of ideals of the embodied learner, such as in Jesuit eduction .
• The criteria created, curated, and agreed to by students .

In simplest terms, an assessment strategy reflects a decision (or a set of decisions) that identify what is important in student work worth making judgments about .

These decisions invariably reflect the values of the professional field, college, program, or instructor as well as the values that are associated with each individual discipline or area of subject matter. For example, the values and assessment criteria for social services programs may be different from project management or leadership programs.

Why does an assessment strategy matter?

When you are in a position to present the basis of assessment for your course, you are making a statement to your students about what matters in their work and, in a way, modeling what you believe matters in the community of professional or scholarly practice.

Your syllabus and assignment briefs should explain the basis of your assessment strategy so that students are certain about how their work will be judged and the perspective of your feedback.

Approaches to developing an assessment strategy

Some academic programs already include guidelines for how student work will be assessed.

If you have the liberty to design your own assessment strategy, then think about the following guiding principles:

What are the criteria that matter according to the scope of the assignment?  While there may be many possible criteria you could use, be certain that the ones you select can actually be demonstrated or indicated within the scope and expectations of the assignment .

What are reasonable levels of assessment?  For each criterion, you will need to determine how many levels there ought to be (usually no more than four or five) and what ought to be the verbiage you use to differentiate between one level and another. (See the chapter on Rubric Development).

What are the indicators associated with an exemplary assignment submission?  Some subject matter can be more complex to assess than others. For example, assessing a student’s watercolor painting can be more challenging than assessing a mathematics exercise since there is no particular “solution” in creating a work of art. This is why assessing certain kinds of subject matter may need a list of well-crafted indicator statements that can be used to seek the presence or absence of them in assessing student work.

Introduction to the Instructional Design Process Copyright © 2018 by UNH-CPS (USNH) is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Teaching excellence & educational innovation, what is the difference between formative and summative assessment, formative assessment.

The goal of formative assessment is to monitor student learning to provide ongoing feedback that can be used by instructors to improve their teaching and by students to improve their learning. More specifically, formative assessments:

• help students identify their strengths and weaknesses and target areas that need work
• help faculty recognize where students are struggling and address problems immediately

Formative assessments are generally low stakes , which means that they have low or no point value. Examples of formative assessments include asking students to:

• draw a concept map in class to represent their understanding of a topic
• submit one or two sentences identifying the main point of a lecture
• turn in a research proposal for early feedback

Summative assessment

The goal of summative assessment is to evaluate student learning at the end of an instructional unit by comparing it against some standard or benchmark.

Summative assessments are often high stakes , which means that they have a high point value. Examples of summative assessments include:

• a midterm exam
• a final project
• a senior recital

Information from summative assessments can be used formatively when students or faculty use it to guide their efforts and activities in subsequent courses.

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Assessment vs. Evaluation: What's the Difference?

Successful teachers know the difference. Do you?

Assessment vs. evaluation.

Assessment is, most likely, not a new concept for you; however, in most previous assessment situations, you were probably the one being tested. As you move into your teaching position, you will assume the responsibilities of an evaluator and an assessor. You will be required to determine how well your students are learning, gauge their performance, and measure the appropriateness of the content and the effectiveness of the methods and techniques utilized in your classroom.

Jabberwocky

When you assess your individual students, you gather information about their level of performance or achievement. Evaluation is comparing a student's achievement with other students or with a set of standards.

Effective assessment is a continuous process. It's not simply something that's done at the conclusion of a unit of study or at the end of a lesson. Effective assessment and evaluation are integrated into all aspects of the curriculum, providing both teachers and students with relevant and useful data to gauge progress and determine the effectiveness of materials and procedures.

Here are some criteria to consider for your own classroom:

Effective evaluation is a continuous, on-going process. Much more than determining the outcome of learning, it is rather a way of gauging learning over time. Learning and evaluation are never completed; they are always evolving and developing.

A variety of evaluative tools is necessary to provide the most accurate assessment of students' learning and progress. Dependence on one type of tool to the exclusion of others deprives students of valuable learning opportunities and robs you of measures that help both students and the overall program grow.

Evaluation must be a collaborative activity between teachers and students. Students must be able to assume an active role in evaluation so they can begin to develop individual responsibilities for development and self-monitoring.

Evaluation needs to be authentic. It must be based on the natural activities and processes students do both in the classroom and in their everyday lives. For example, relying solely on formalized testing procedures might send a signal to children that learning is simply a search for “right answers.”

Evaluation is intrinsically more complex than writing a test, giving it to a group of students, scoring it, and handing it back with some sort of letter grade. Indeed, it involves a combination of procedures and designs that not only gauge students' work but also help them grow in the process.

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Assessment and grading are words that are often used interchangeably—and understandably so, since they are closely related. As a result, many equate assessment with grading.

However, assessment and grading differ, starting with their goals.

The goal of grading is to evaluate individual student performance against a set of criteria for a given unit or course. Grades may or may not be an accurate measure of student learning, depending on what is being evaluated. For example, attendance, on-time assignment submission, formatting, and participation may not reveal a lot about how much a student has learned, but they can offer indicators or signals for instructor intervention.

Grades alone, while useful as a standardized measurement, don’t provide enough personalized feedback for what a student does or does not know and what they need to do to further their learning. According to Thomas Guskey, when grades are used alone, “even accurate, task-involving grades don’t lead to improved student learning. Students get no direction for improvement from a letter, number, word, phrase, or symbol attached to evidence of their learning. Only when grades are paired with individualized comments that offer guidance and direction for improvement do they enhance achievement and foster learning progress” ( Guskey, 2019) .

The goal of assessment , on the other hand, is more expansive—because it is not solely about grading and includes low-stakes formative assessments void of summative evaluations—it can further student learning by including feedback and guiding students towards next steps in learning. Assessment includes low-stakes, frequent assignments that educators give students in class or as homework, in addition to summative tests or exams. Qualitative feedback is also a component of assessment that operates as a checkpoint in the student learning journey.

Assessment does not always include grades, but grading is always a part of assessment.

Therefore, grading is a subset of assessment.

Why is this important?

Assessments are not just tests, but also low-stakes assignments and daily check-ins. They uncover more data about student learning than grades. While grades may communicate student progress in general or serve as warning indicators, assessment can identify specific learning gaps that may require teacher intervention. Grades alone don’t reveal this level of granularity.

Assessment is a critical part of teaching and learning, providing cohort-based and individual-level data insights to educators . Are students learning what we are teaching? Is there a way to increase teaching efficacy to foster better student learning outcomes? In other words, are the goals of education being met?

The above questions can be answered via assessment, which provide the following:

• Diagnostic feedback about what students do and do not know,
• Information as to what demonstrates deep comprehension of the subject,
• An opportunity to encourage student learning,
• And teacher self evaluation on what is and is not working and next steps to bridge student learning gaps .

It’s easy to see how assessment and grading are often interchanged, given their close pedagogical juxtaposition. Both grading and assessment are necessary; grading to communicate in a succinct manner student progress to inform placement and other institutions, and assessment to gain deep insights into this progress. But it’s also important to understand and acknowledge the differences as we help students navigate the educational journey.

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Iran missile and drone attack on Israel – what we know so far

Israel’s military has reported minor damage after Iran launched dozens of drones and missiles towards it late on Saturday

• Iran attack on Israel – live updates
• Full report: Iran launches drones and cruise missiles against Israel

Iran launched hundreds of drones as well as cruise missiles towards Israel , in the Islamic Republic’s first ever direct attack on the Jewish state, in response to the 1 April strike on an Iranian diplomatic building in the Syrian capital, Damascus, which killed a senior figure in Iran’s Islamic Revolutionary Guards and eight other officers.

Benny Gantz, a member of the war cabinet, said that Israel will exact a price from Iran in response to its mass missile and drone attack when the time is right. His comments came ahead of a war cabinet meeting alongside Israel’s prime minister, Benjamin Netanyahu, and the country’s defence minister, Yoav Gallant.

Tehran has warned it will strike again with greater force if Israel or the US retaliate for the Iranian strike on Israel by more 300 drones and missiles on Saturday night. The air raids , the Islamic Republic’s first ever direct attack on the Israeli state, brought a years-long shadow war into the open and threatened to draw the region into a broader conflagration as Israel said it was considering its response.

However, the attack, mostly launched from inside Iran, caused only modest damage in Israel as most were shot down with the help of the US, Britain and Jordan. An air force base in southern Israel was hit, but continued to operate as normal and a seven-year-old child was seriously hurt by shrapnel. There were no other reports of serious damage. Israeli military spokesperson Rear Adm Daniel Hagari said that 99% of the launches had been intercepted.

Most of the Iranian drones flying over Syria’s airspace during Tehran’s strikes overnight were downed by Israeli and US jets before reaching their targets in Israel, two western intelligence sources told Reuters .

The UN security council will hold an emergency meeting on Sunday at the request of Israel’s ambassador to the UN, the council’s president said in a statement.

Iran informed Turkey in advance of its planned operation against Israel, a Turkish diplomatic source has told Reuters . The source also said that the US conveyed to Iran via Ankara that its operation must be “within certain limits”. These reports come after Iran’s foreign minister, Hossein Amirabdollahian , said in a meeting with foreign ambassadors in Tehran that Iran had informed the US that its attacks against Israel will be “limited” and for self-defence only.

John Kirby, the White House’s top national security spokesperson, told ABC’s This Week programme on Sunday that the US will continue to help Israel defend itself, but does not want war with Iran. “We don’t seek escalated tensions in the region. We don’t seek a wider conflict,” Kirby said. News outlet Axios reported that Joe Biden , the US president, had told Netanyahu that he would oppose an Israeli counterattack against Iran and that the prime minister should “take the win”.

UK Royal Air Force fighter jets and refuelling aircraft were also involved in Israel’s defence, taking off from bases in Cyprus. Their role, according to the UK Ministry of Defence, was to fill in for the US air force in the sorties against Islamic State normally carried out over Iraq and north-eastern Syria, but also to intercept Iranian drones if they came into the UK area of operations.

World leaders have condemned Iran’s attack, with regional powers including Saudi Arabia and Egypt calling for restraint. The UN secretary general, António Guterres, said: “I am deeply alarmed about the very real danger of a devastating region-wide escalation. I urge all parties to exercise maximum restraint to avoid any action that could lead to major military confrontations on multiple fronts in the Middle East.”

Jordan’s prime minister, Bisher Khasawneh , warned that any escalation in the region would lead to “dangerous paths”, joining a chorus of condemnation from world leaders to the attack. Other countries including the UK, Spain, the US, Egypt, Saudi Arabia and China, have called for restraint amid fears of a regional escalation of conflict across the Middle East. Iran’s foreign ministry has summoned the ambassadors of the UK , France , and Germany to question what it referred to as their “irresponsible stance” regarding Tehran’s retaliatory strikes on Israel, the semi-official Iranian Labour news agency reported .

Major airlines across the Middle East, including Emirates Airlines and Qatar Airways , announced they would resume some of their operations in the region after cancelling or rerouting some flights in response to Iran’s attack on Israel. Israel said it had reopened its airspace as of 7:30am local time on Sunday morning, with Beirut airport also reopening this morning. Several Iranian airports, including Tehran’s Imam Khomeini International, however, have cancelled flights until Monday.

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• Published: 15 April 2024

Demuxafy : improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods

• Drew Neavin 1 ,
• Anne Senabouth 1 ,
• Himanshi Arora 1 , 2 ,
• Jimmy Tsz Hang Lee 3 ,
• Aida Ripoll-Cladellas 4 ,
• sc-eQTLGen Consortium ,
• Lude Franke 5 ,
• Shyam Prabhakar 6 , 7 , 8 ,
• Chun Jimmie Ye 9 , 10 , 11 , 12 ,
• Davis J. McCarthy 13 , 14 ,
• Marta Melé 4 ,
• Martin Hemberg 15 &
• Joseph E. Powell   ORCID: orcid.org/0000-0002-5070-4124 1 , 16  

Genome Biology volume  25 , Article number:  94 ( 2024 ) Cite this article

133 Accesses

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Recent innovations in single-cell RNA-sequencing (scRNA-seq) provide the technology to investigate biological questions at cellular resolution. Pooling cells from multiple individuals has become a common strategy, and droplets can subsequently be assigned to a specific individual by leveraging their inherent genetic differences. An implicit challenge with scRNA-seq is the occurrence of doublets—droplets containing two or more cells. We develop Demuxafy, a framework to enhance donor assignment and doublet removal through the consensus intersection of multiple demultiplexing and doublet detecting methods. Demuxafy significantly improves droplet assignment by separating singlets from doublets and classifying the correct individual.

Droplet-based single-cell RNA sequencing (scRNA-seq) technologies have provided the tools to profile tens of thousands of single-cell transcriptomes simultaneously [ 1 ]. With these technological advances, combining cells from multiple samples in a single capture is common, increasing the sample size while simultaneously reducing batch effects, cost, and time. In addition, following cell capture and sequencing, the droplets can be demultiplexed—each droplet accurately assigned to each individual in the pool [ 2 , 3 , 4 , 5 , 6 , 7 ].

Many scRNA-seq experiments now capture upwards of 20,000 droplets, resulting in ~16% (3,200) doublets [ 8 ]. Current demultiplexing methods can also identify doublets—droplets containing two or more cells—from different individuals (heterogenic doublets). These doublets can significantly alter scientific conclusions if they are not effectively removed. Therefore, it is essential to remove doublets from droplet-based single-cell captures.

However, demultiplexing methods cannot identify droplets containing multiple cells from the same individual (homogenic doublets) and, therefore, cannot identify all doublets in a single capture. If left in the dataset, those doublets could appear as transitional cells between two distinct cell types or a completely new cell type. Accordingly, additional methods have been developed to identify heterotypic doublets (droplets that contain two cells from different cell types) by comparing the transcriptional profile of each droplet to doublets simulated from the dataset [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. It is important to recognise that demultiplexing methods achieve two functions—segregation of cells from different donors and separation of singlets from doublets—while doublet detecting methods solely classify singlets versus doublets.

Therefore, demultiplexing and transcription-based doublet detecting methods provide complementary information to improve doublet detection, providing a cleaner dataset and more robust scientific results. There are currently five genetic-based demultiplexing [ 2 , 3 , 4 , 5 , 6 , 7 , 16 ] and seven transcription-based doublet-detecting methods implemented in various languages [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Under different scenarios, each method is subject to varying performance and, in some instances, biases in their ability to accurately assign cells or detect doublets from certain conditions. The best combination of methods is currently unclear but will undoubtedly depend on the dataset and research question.

Therefore, we set out to identify the best combination of genetic-based demultiplexing and transcription-based doublet-detecting methods to remove doublets and partition singlets from different donors correctly. In addition, we have developed a software platform ( Demuxafy ) that performs these intersectional methods and provides additional commands to simplify the execution and interpretation of results for each method (Fig. 1 a).

Study design and qualitative method classifications. a  Demuxafy is a platform to perform demultiplexing and doublet detecting with consistent documentation. Demuxafy also provides wrapper scripts to quickly summarize the results from each method and assign clusters to each individual with reference genotypes when a reference-free demultiplexing method is used. Finally, Demuxafy provides a script to easily combine the results from multiple different methods into a single data frame and it provides a final assignment for each droplet based on the combination of multiple methods. In addition, Demuxafy provides summaries of the number of droplets classified as singlets or doublets by each method and a summary of the number of droplets assigned to each individual by each of the demultiplexing methods. b  Two datasets are included in this analysis - a PBMC dataset and a fibroblast dataset. The PBMC dataset contains 74 pools that captured approximately 20,000 droplets each with 12-16 donor cells multiplexed per pool. The fibroblast dataset contains 11 pools of roughly 7,000 droplets per pool with sizes ranging from six to eight donors per pool. All pools were processed by all demultiplexing and doublet detecting methods and the droplet and donor classifications were compared between the methods and between the PBMCs and fibroblasts. Then the PBMC droplets that were classified as singlets by all methods were taken as ‘true singlets’ and used to generate new pools in silico. Those pools were then processed by each of the demultiplexing and doublet detecting methods and intersectional combinations of demultiplexing and doublet detecting methods were tested for different experimental designs

To compare the demultiplexing and doublet detecting methods, we utilised two large, multiplexed datasets—one that contained ~1.4 million peripheral blood mononuclear cells (PBMCs) from 1,034 donors [ 17 ] and one with ~94,000 fibroblasts from 81 donors [ 18 ]. We used the true singlets from the PBMC dataset to generate new in silico pools to assess the performance of each method and the multi-method intersectional combinations (Fig. 1 b).

Here, we compare 14 demultiplexing and doublet detecting methods with different methodological approaches, capabilities, and intersectional combinations. Seven of those are demultiplexing methods ( Demuxalot [ 6 ], Demuxlet [ 3 ], Dropulation [ 5 ], Freemuxlet [ 16 ], ScSplit [ 7 ], Souporcell [ 4 ], and Vireo [ 2 ]) which leverage the common genetic variation between individuals to identify cells that came from each individual and to identify heterogenic doublets. The seven remaining methods ( DoubletDecon [ 9 ], DoubletDetection [ 14 ], DoubletFinder [ 10 ], ScDblFinder [ 11 ], Scds [ 12 ], Scrublet [ 13 ], and Solo [ 15 ]) identify doublets based on their similarity to simulated doublets generated by adding the transcriptional profiles of two randomly selected droplets in the dataset. These methods assume that the proportion of real doublets in the dataset is low, so combining any two droplets will likely represent the combination of two singlets.

We identify critical differences in the performance of demultiplexing and doublet detecting methods to classify droplets correctly. In the case of the demultiplexing techniques, their performance depends on their ability to identify singlets from doublets and assign a singlet to the correct individual. For doublet detecting methods, the performance is based solely on their ability to differentiate a singlet from a doublet. We identify limitations in identifying specific doublet types and cell types by some methods. In addition, we compare the intersectional combinations of these methods for multiple experimental designs and demonstrate that intersectional approaches significantly outperform all individual techniques. Thus, the intersectional methods provide enhanced singlet classification and doublet removal—a critical but often under-valued step of droplet-based scRNA-seq processing. Our results demonstrate that intersectional combinations of demultiplexing and doublet detecting software provide significant advantages in droplet-based scRNA-seq preprocessing that can alter results and conclusions drawn from the data. Finally, to provide easy implementation of our intersectional approach, we provide Demuxafy ( https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/index.html ) a complete platform to perform demultiplexing and doublet detecting intersectional methods (Fig. 1 a).

Study design

To evaluate demultiplexing and doublet detecting methods, we developed an experimental design that applies the different techniques to empirical pools and pools generated in silico from the combination of true singlets—droplets identified as singlets by every method (Fig. 1 a). For the first phase of this study, we used two empirical multiplexed datasets—the peripheral blood mononuclear cell (PBMC) dataset containing ~1.4 million cells from 1034 donors and a fibroblast dataset of ~94,000 cells from 81 individuals (Additional file 1 : Table S1). We chose these two cell systems to assess the methods in heterogeneous (PBMC) and homogeneous (fibroblast) cell types.

Demultiplexing and doublet detecting methods perform similarly for heterogeneous and homogeneous cell types

We applied the demultiplexing methods ( Demuxalot , Demuxlet , Dropulation , Freemuxlet , ScSplit , Souporcell , and Vireo ) and doublet detecting methods ( DoubletDecon , DoubletDetection , DoubletFinder , ScDblFinder , Scds , Scrublet , and Solo ) to the two datasets and assessed the results from each method. We first compared the droplet assignments by identifying the number of singlets and doublets identified by a given method that were consistently annotated by all methods (Fig. 2 a–d). We also identified the percentage of droplets that were annotated consistently between pairs of methods (Additional file 2 : Fig S1). In the cases where two demultiplexing methods were compared to one another, both the droplet type (singlet or doublet) and the assignment of the droplet to an individual had to match to be considered in agreement. In all other comparisons (i.e. demultiplexing versus doublet detecting and doublet detecting versus doublet detecting), only the droplet type (singlet or doublet) was considered for agreement since doublet detecting methods cannot annotate donor assignment. We found that the two method types were more similar to other methods of the same type (i.e., demultiplexing versus demultiplexing and doublet detecting versus doublet detecting) than they were to methods from a different type (demultiplexing methods versus doublet detecting methods; Supplementary Fig 1). We found that the similarity of the demultiplexing and doublet detecting methods was consistent in the PBMC and fibroblast datasets (Pearson correlation R = 0.78, P -value < 2×10 −16 ; Fig S1a-c). In addition, demultiplexing methods were more similar than doublet detecting methods for both the PBMC and fibroblast datasets (Wilcoxon rank-sum test: P < 0.01; Fig. 2 a–b and Additional file 2 : Fig S1).

Demultiplexing and Doublet Detecting Method Performance Comparison. a  The proportion of droplets classified as singlets and doublets by each method in the PBMCs. b  The number of other methods that classified the singlets and doublets identified by each method in the PBMCs. c  The proportion of droplets classified as singlets and doublets by each method in the fibroblasts. d The number of other methods that classified the singlets and doublets identified by each method in the fibroblasts. e - f The performance of each method when the majority classification of each droplet is considered the correct annotation in the PBMCs ( e ) and fibroblasts ( f ). g - h  The number of droplets classified as singlets (box plots) and doublets (bar plots) by all methods in the PBMC ( g ) and fibroblast ( h ) pools. i - j  The number of donors that were not identified by each method in each pool for PBMCs ( i ) and fibroblasts ( j ). PBMC: peripheral blood mononuclear cell. MCC: Matthew’s correlationcoefficient

The number of unique molecular identifiers (UMIs) and genes decreased in droplets that were classified as singlets by a larger number of methods while the mitochondrial percentage increased in both PBMCs and fibroblasts (Additional file 2 : Fig S2).

We next interrogated the performance of each method using the Matthew’s correlation coefficient (MCC) to calculate the consistency between Demuxify and true droplet classification. We identified consistent trends in the MCC scores for each method between the PBMCs (Fig. 2 e) and fibroblasts (Fig. 2 f). These data indicate that the methods behave similarly, relative to one another, for heterogeneous and homogeneous datasets.

Next, we sought to identify the droplets concordantly classified by all demultiplexing and doublet detecting methods in the PBMC and fibroblast datasets. On average, 732 singlets were identified for each individual by all the methods in the PBMC dataset. Likewise, 494 droplets were identified as singlets for each individual by all the methods in the fibroblast pools. However, the concordance of doublets identified by all methods was very low for both datasets (Fig. 2 e–f). Notably, the consistency of classifying a droplet as a doublet by all methods was relatively low (Fig. 2 b,d,g, and h). This suggests that doublet identification is not consistent between all the methods. Therefore, further investigation is required to identify the reasons for these inconsistencies between methods. It also suggests that combining multiple methods for doublet classification may be necessary for more complete doublet removal. Further, some methods could not identify all the individuals in each pool (Fig. 2 i–j). The non-concordance between different methods demonstrates the need to effectively test each method on a dataset where the droplet types are known.

Computational resources vary for demultiplexing and doublet detecting methods

We recorded each method’s computational resources for the PBMC pools, with ~20,000 cells captured per pool (Additional file 1 : Table S1). Of the demultiplexing methods, ScSplit took the most time (multiple days) and required the most steps, but Demuxalot , Demuxlet , and Freemuxlet used the most memory. Solo took the longest time (median 13 h) and most memory to run for the doublet detecting methods but is the only method built to be run directly from the command line, making it easy to implement (Additional file 2 : Fig S3).

Generate pools with known singlets and doublets

However, there is no gold standard to identify which droplets are singlets or doublets. Therefore, in the second phase of our experimental design (Fig. 1 b), we used the PBMC droplets classified as singlets by all methods to generate new pools in silico. We chose to use the PBMC dataset since our first analyses indicated that method performance is similar for homogeneous (fibroblast) and heterogeneous (PBMC) cell types (Fig. 2 and Additional file 2 : Fig S1) and because we had many more individuals available to generate in silico pools from the PBMC dataset (Additional file 1 : Table S1).

We generated 70 pools—10 each of pools that included 2, 4, 8, 16, 32, 64, or 128 individuals (Additional file 1 : Table S2). We assume a maximum 20% doublet rate as it is unlikely researchers would use a technology that has a higher doublet rate (Fig. 3 a).

In silico Pool Doublet Annotation and Method Performance. a  The percent of singlets and doublets in the in -silico pools - separated by the number of multiplexed individuals per pool. b  The percentage and number of doublets that are heterogenic (detectable by demultiplexing methods), heterotypic (detectable by doublet detecting methods), both (detectable by either method category) and neither (not detectable with current methods) for each multiplexed pool size. c  Percent of droplets that each of the demultiplexing and doublet detecting methods classified correctly for singlets and doublet subtypes for different multiplexed pool sizes. d  Matthew’s Correlation Coefficient (MCC) for each of the methods for each of the multiplexed pool sizes. e  Balanced accuracy for each of the methods for each of the multiplexed pool sizes

We used azimuth to classify the PBMC cell types for each droplet used to generate the in silico pools [ 19 ] (Additional file 2 : Fig S4). As these pools have been generated in silico using empirical singlets that have been well annotated, we next identified the proportion of doublets in each pool that were heterogenic, heterotypic, both, and neither. This approach demonstrates that a significant percentage of doublets are only detectable by doublet detecting methods (homogenic and heterotypic) for pools with 16 or fewer donors multiplexed (Fig. 3 b).

While the total number of doublets that would be missed if only using demultiplexing methods appears small for fewer multiplexed individuals (Fig. 3 b), it is important to recognise that this is partly a function of the ~732 singlet cells per individual used to generate these pools. Hence, the in silico pools with fewer individuals also have fewer cells. Therefore, to obtain numbers of doublets that are directly comparable to one another, we calculated the number of each doublet type that would be expected to be captured with 20,000 cells when 2, 4, 8, 16, or 32 individuals were multiplexed (Additional file 2 : Fig S5). These results demonstrate that many doublets would be falsely classified as singlets since they are homogenic when just using demultiplexing methods for a pool of 20,000 cells captured with a 16% doublet rate (Additional file 2 : Fig S5). However, as more individuals are multiplexed, the number of droplets that would not be detectable by demultiplexing methods (homogenic) decreases. This suggests that typical workflows that use only one demultiplexing method to remove doublets from pools that capture 20,000 droplets with 16 or fewer multiplexed individuals fail to adequately remove between 173 (16 multiplexed individuals) and 1,325 (2 multiplexed individuals) doublets that are homogenic and heterotypic which could be detected by doublet detecting methods (Additional file 2 : Fig S5). Therefore, a technique that uses both demultiplexing and doublet detecting methods in parallel will complement more complete doublet removal methods. Consequently, we next set out to identify the demultiplexing and doublet detecting methods that perform the best on their own and in concert with other methods.

Doublet and singlet droplet classification effectiveness varies for demultiplexing and doublet detecting methods

Demultiplexing methods fail to classify homogenic doublets.

We next investigated the percentage of the droplets that were correctly classified by each demultiplexing and doublet detecting method. In addition to the seven demultiplexing methods, we also included Demuxalot with the additional steps to refine the genotypes that can then be used for demultiplexing— Demuxalot (refined). Demultiplexing methods correctly classify a large portion of the singlets and heterogenic doublets (Fig. 3 c). This pattern is highly consistent across different cell types, with the notable exceptions being decreased correct classifications for erythrocytes and platelets when greater than 16 individuals are multiplexed (Additional file 2 : Fig S6).

However, Demuxalot consistently demonstrates the highest correct heterogenic doublet classification. Further, the percentage of the heterogenic doublets classified correctly by Souporcell decreases when large numbers of donors are multiplexed. ScSplit is not as effective as the other demultiplexing methods at classifying heterogenic doublets, partly due to the unique doublet classification method, which assumes that the doublets will generate a single cluster separate from the donors (Table 1 ). Importantly, the demultiplexing methods identify almost none of the homogenic doublets for any multiplexed pool size—demonstrating the need to include doublet detecting methods to supplement the demultiplexing method doublet detection.

Doublet detecting method classification performances vary greatly

In addition to assessing each of the methods with default settings, we also evaluated ScDblFinder with ‘known doublets’ provided. This method can take already known doublets and use them when detecting doublets. For these cases, we used the droplets that were classified as doublets by all the demultiplexing methods as ‘known doublets’.

Most of the methods classified a similarly high percentage of singlets correctly, with the exceptions of DoubletDecon and DoubletFinder for all pool sizes (Fig. 3 c). However, unlike the demultiplexing methods, there are explicit cell-type-specific biases for many of the doublet detecting methods (Additional file 2 : Fig S7). These differences are most notable for cell types with fewer cells (i.e. ASDC and cDC2) and proliferating cells (i.e. CD4 Proliferating, CD8 Proliferating, and NK Proliferating). Further, all of the softwares demonstrate high correct percentages for some cell types including CD4 Naïve and CD8 Naïve (Additional file 2 : Fig S7).

As expected, all doublet detecting methods identified heterotypic doublets more effectively than homotypic doublets (Fig. 3 c). However, ScDblFinder and Scrublet classified the most doublets correctly across all doublet types for pools containing 16 individuals or fewer. Solo was more effective at identifying doublets than Scds for pools containing more than 16 individuals. It is also important to note that it was not feasible to run DoubletDecon for the largest pools containing 128 multiplexed individuals and an average of 115,802 droplets (range: 113,594–119,126 droplets). ScDblFinder performed similarly when executed with and without known doublets (Pearson correlation P = 2.5 × 10 -40 ). This suggests that providing known doublets to ScDblFinder does not offer an added benefit.

Performances vary between demultiplexing and doublet detecting method and across the number of multiplexed individuals

We assessed the overall performance of each method with two metrics: the balanced accuracy and the MCC. We chose to use balanced accuracy since, with unbalanced group sizes, it is a better measure of performance than accuracy itself. Further, the MCC has been demonstrated as a more reliable statistical measure of performance since it considers all possible categories—true singlets (true positives), false singlets (false positives), true doublets (true negatives), and false doublets (false negatives). Therefore, a high score on the MCC scale indicates high performance in each metric. However, we provide additional performance metrics for each method (Additional file 1 : Table S3). For demultiplexing methods, both the droplet type (singlet or doublet) and the individual assignment were required to be considered a ‘true singlet’. In contrast, only the droplet type (singlet or doublet) was needed for doublet detection methods.

The MCC and balanced accuracy metrics are similar (Spearman’s ⍴ = 0.87; P < 2.2 × 10 -308 ). Further, the performance of Souporcell decreases for pools with more than 32 individuals multiplexed for both metrics (Student’s t -test for MCC: P < 1.1 × 10 -9 and balanced accuracy: P < 8.1 × 10 -11 ). Scds , ScDblFinder , and Scrublet are among the top-performing doublet detecting methods Fig. 3 d–e).

Overall, between 0.4 and 78.8% of droplets were incorrectly classified by the demultiplexing or doublet detecting methods depending on the technique and the multiplexed pool size (Additional file 2 : Fig S8). Demuxalot (refined) and DoubletDetection demonstrated the lowest percentage of incorrect droplets with about 1% wrong in the smaller pools (two multiplexed individuals) and about 3% incorrect in pools with at least 16 multiplexed individuals. Since some transitional states and cell types are present in low percentages in total cell populations (i.e. ASDCs at 0.02%), incorrect classification of droplets could alter scientific interpretations of the data, and it is, therefore, ideal for decreasing the number of erroneous assignments as much as possible.

False singlets and doublets demonstrate different metrics than correctly classified droplets

We next asked whether specific cell metrics might contribute to false singlet and doublet classifications for different methods. Therefore, we compared the number of genes, number of UMIs, mitochondrial percentage and ribosomal percentage of the false singlets and doublets to equal numbers of correctly classified cells for each demultiplexing and doublet detecting method.

The number of UMIs (Additional file 2 : Fig S9 and Additional file 1 : Table S4) and genes (Additional file 2 : Fig S10 and Additional file 1 : Table S5) demonstrated very similar distributions for all comparisons and all methods (Spearman ⍴ = 0.99, P < 2.2 × 10 -308 ). The number of UMIs and genes were consistently higher in false singlets and lower in false doublets for most demultiplexing methods except some smaller pool sizes (Additional file 2 : Fig S9a and Additional file 2 : Fig S10a; Additional file 1 : Table S4 and Additional file 1 : Table S5). The number of UMIs and genes was consistently higher in droplets falsely classified as singlets by the doublet detecting methods than the correctly identified droplets (Additional file 2 : Fig S9b and Additional file 2 : Fig S10b; Additional file 1 : Table S4 and Additional file 1 : Table S5). However, there was less consistency in the number of UMIs and genes detected in false singlets than correctly classified droplets between the different doublet detecting methods (Additional file 2 : Fig S9b and Additional file 2 : Fig S10b; Additional file 1 : Table S4 and Additional file 1 : Table S5).

The ribosomal percentage of the droplets falsely classified as singlets or doublets is similar to the correctly classified droplets for most methods—although they are statistically different for larger pool sizes (Additional file 2 : Fig S11a and Additional file 1 : Table S6). However, the false doublets classified by some demultiplexing methods ( Demuxalot , Demuxalot (refined), Demuxlet , ScSplit , Souporcell , and Vireo ) demonstrated higher ribosomal percentages. Some doublet detecting methods ( ScDblFinder , ScDblFinder with known doublets and Solo) demonstrated higher ribosomal percentages for the false doublets while other demonstrated lower ribosomal percentages ( DoubletDecon , DoubletDetection , and DoubletFinder ; Additional file 2 : Fig S11b and Additional file 1 : Table S6).

Like the ribosomal percentage, the mitochondrial percentage in false singlets is also relatively similar to correctly classified droplets for both demultiplexing (Additional file 2 : Fig S12a and Additional file 1 : Table S7) and doublet detecting methods (Additional file 2 : Fig S12b). The mitochondrial percentage for false doublets is statistically lower than the correctly classified droplets for a few larger pools for Freemuxlet , ScSplit , and Souporcell . The doublet detecting method Solo also demonstrates a small but significant decrease in mitochondrial percentage in the false doublets compared to the correctly annotated droplets. However, other doublet detecting methods including DoubletFinder and the larger pools of most other methods demonstrated a significant increase in mitochondrial percent in the false doublets compared to the correctly annotated droplets (Additional file 2 : Fig S12b).

Overall, these results demonstrate a strong relationship between the number of genes and UMIs and limited influence of ribosomal or mitochondrial percentage in a droplet and false classification, suggesting that the number of genes and UMIs can significantly bias singlet and doublet classification by demultiplexing and doublet detecting methods.

Ambient RNA, number of reads per cell, and uneven pooling impact method performance

To further quantify the variables that impact the performance of each method, we simulated four conditions that could occur with single-cell RNA-seq experiments: (1) decreased number of reads (reduced 50%), (2) increased ambient RNA (10%, 20% and 50%), (3) increased mitochondrial RNA (5%, 10% and 25%) and 4) uneven donor pooling from single donor spiking (0.5 or 0.75 proportion of pool from one donor). We chose these scenarios because they are common technical effects that can occur.

We observed a consistent decrease in the demultiplexing method performance when the number of reads were decreased by 50% but the degree of the effect varied for each method and was larger in pools containing more multiplexed donors (Additional file 2 : Fig S13a and Additional file 1 : Table S8). Decreasing the number of reads did not have a detectable impact on the performance of the doublet detecting methods.

Simulating additional ambient RNA (10%, 20%, or 50%) decreased the performance of all the demultiplexing methods (Additional file 2 : Fig S13b and Additional file 1 : Table S9) but some were unimpacted in pools that had 16 or fewer individuals multiplexed ( Souporcell and Vireo ). The performance of some of the doublet detecting methods were impacted by the ambient RNA but the performance of most methods did not decrease. Scrublet and ScDblFinder were the doublet detecting methods most impacted by ambient RNA but only in pools with at least 32 multiplexed donors (Additional file 2 : Fig S13b and Additional file 1 : Table S9).

Increased mitochondrial percent did not impact the performance of demultiplexing or doublet detecting methods (Additional file 2 : Fig S13c and Additional file 1 : Table S10).

We also tested whether experimental designs that pooling uneven proportions of donors would alter performance. We tested scenarios where either half the pool was composed of a single donor (0.5 spiked donor proportion) or where three quarters of the pool was composed of a single donor. This experimental design significantly reduced the demultiplexing method performance (Additional file 2 : Fig S13d and Additional file 1 : Table S11) with the smallest influence on Freemuxlet . The performance of most of the doublet detecting methods were unimpacted except for DoubletDetection that demonstrated significant decreases in performance in pools where at least 16 donors were multiplexed. Intriguingly, the performance of Solo increased with the spiked donor pools when the pools consisted of 16 donors or less.

Our results demonstrate significant differences in overall performance between different demultiplexing and doublet detecting methods. We further noticed some differences in the use of the methods. Therefore, we have accumulated these results and each method’s unique characteristics and benefits in a heatmap for visual interpretation (Fig. 4 ).

Assessment of each of the demultiplexing and doublet detecting methods. Assessments of a variety of metrics for each of the demultiplexing (top) and doublet detecting (bottom) methods

Framework for improving singlet classifications via method combinations

After identifying the demultiplexing and doublet detecting methods that performed well individually, we next sought to test whether using intersectional combinations of multiple methods would enhance droplet classifications and provide a software platform— Demuxafy —capable of supporting the execution of these intersectional combinations.

We recognise that different experimental designs will be required for each project. As such, we considered this when testing combinations of methods. We considered multiple experiment designs and two different intersectional methods: (1) more than half had to classify a droplet as a singlet to be called a singlet and (2) at least half of the methods had to classify a droplet as a singlet to be called a singlet. Significantly, these two intersectional methods only differ when an even number of methods are being considered. For combinations that include demultiplexing methods, the individual called by the majority of the methods is the individual used for that droplet. When ties occur, the individual is considered ‘unassigned’.

Combining multiple doublet detecting methods improve doublet removal for non-multiplexed experimental designs

For the non-multiplexed experimental design, we considered all possible method combinations (Additional file 1 : Table S12). We identified important differences depending on the number of droplets captured and have provided recommendations accordingly. We identified that DoubletFinder , Scrublet , ScDblFinder and Scds is the ideal combination for balanced droplet calling when less than 2,000 droplets are captured. Scds and ScDblFinder or Scrublet , Scds and ScDblFinder is the best combination when 2,000–10,000 droplets are captured. Scds , Scrublet, ScDblFinder and DoubletDetection is the best combination when 10,000–20,000 droplets are captured and Scrublet , Scds , DoubletDetection and ScDblFinder . It is important to note that even a slight increase in the MCC significantly impacts the number of true singlets and true doublets classified with the degree of benefit highly dependent on the original method performance. The combined method increases the MCC compared to individual doublet detecting methods on average by 0.11 and up to 0.33—a significant improvement in the MCC ( t -test FDR < 0.05 for 95% of comparisons). For all combinations, the intersectional droplet method requires more than half of the methods to consider the droplet a singlet to classify it as a singlet (Fig. 5 ).

Recommended Method Combinations Dependent on Experimental Design. Method combinations are provided for different experimental designs, including those that are not multiplexed (left) and multiplexed (right), including experiments that have reference SNP genotypes available vs those that do not and finally, multiplexed experiments with different numbers of individuals multiplexed. The each bar represents either a single method (shown with the coloured icon above the bar) or a combination of methods (shown with the addition of the methods and an arrow indicating the bar). The proportion of true singlets, true doublets, false singlets and false doublets for each method or combination of methods is shown with the filled barplot and the MCC is shown with the black points overlaid on the barplot. MCC: Matthew’s Correlation Coefficient

Demuxafy performs better than Chord

Chord is an ensemble machine learning doublet detecting method that uses Scds and DoubletFinder to identify doublets. We compared Demuxafy using Scds and DoubletFinder to Chord and identified that Demuxafy outperformed Chord in pools that contained at least eight donors and was equivalent in pools that contained less than eight donors (Additional file 2 : Fig S14). This is because Chord classifies more droplets as false singlets and false doublets than Demuxafy . In addition, Chord failed to complete for two of the pools that contained 128 multiplexed donors.

Combining multiple demultiplexing and doublet detecting methods improve doublet removal for multiplexed experimental designs

For experiments where 16 or fewer individuals are multiplexed with reference SNP genotypes available, we considered all possible combinations between the demultiplexing and doublet detecting methods except ScDblFinder with known doublets due to its highly similar performance to ScDblFinder (Fig 3 ; Additional file 1 : Table S13). The best combinations are DoubletFinder , Scds , ScDblFinder , Vireo and Demuxalot (refined) (<~5 donors) and Scrublet , ScDblFinder , DoubletDetection , Dropulation and Demuxalot (refined) (Fig. 5 ). These intersectional methods increase the MCC compared to the individual methods ( t -test FDR < 0.05), generally resulting in increased true singlets and doublets compared to the individual methods. The improvement in MCC depends on every single method’s performance but, on average, increases by 0.22 and up to 0.71. For experiments where the reference SNP genotypes are unknown, the individuals multiplexed in the pool with 16 or fewer individuals multiplexed, DoubletFinder , ScDblFinder, Souporcell and Vireo (<~5 donors) and Scds , ScDblFinder , DoubletDetection , Souporcell and Vireo are the ideal methods (Fig. 5 ). These intersectional methods again significantly increase the MCC up to 0.87 compared to any of the individual techniques that could be used for this experimental design ( t -test FDR < 0.05 for 94.2% of comparisons). In both cases, singlets should only be called if more than half of the methods in the combination classify the droplet as a singlet.

Combining multiple demultiplexing methods improves doublet removal for large multiplexed experimental designs

For experiments that multiplex more than 16 individuals, we considered the combinations between all demultiplexing methods (Additional file 1 : Table S14) since only a small proportion of the doublets would be undetectable by demultiplexing methods (droplets that are homogenic; Fig 3 b). To balance doublet removal and maintain true singlets, we recommend the combination of Demuxalot (refined) and Dropulation . These method combinations significantly increase the MCC by, on average, 0.09 compared to all the individual methods ( t -test FDR < 0.05). This substantially increases true singlets and true doublets relative to the individual methods. If reference SNP genotypes are not available for the individuals multiplexed in the pools, Vireo performs the best (≥ 16 multiplexed individuals; Fig. 5 ). This is the only scenario in which executing a single method is advantageous to a combination of methods. This is likely due to the fact that most of the methods perform poorly for larger pool sizes (Fig. 3 c).

These results collectively demonstrate that, regardless of the experimental design, demultiplexing and doublet detecting approaches that intersect multiple methods significantly enhance droplet classification. This is consistent across different pool sizes and will improve singlet annotation.

Demuxafy improves doublet removal and improves usability

To make our intersectional approaches accessible to other researchers, we have developed Demuxafy ( https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/index.html ) - an easy-to-use software platform powered by Singularity. This platform provides the requirements and instructions to execute each demultiplexing and doublet detecting methods. In addition, Demuxafy provides wrapper scripts that simplify method execution and effectively summarise results. We also offer tools that help estimate expected numbers of doublets and provide method combination recommendations based on scRNA-seq pool characteristics. Demuxafy also combines the results from multiple different methods, provides classification combination summaries, and provides final integrated combination classifications based on the intersectional techniques selected by the user. The significant advantages of Demuxafy include a centralised location to execute each of these methods, simplified ways to combine methods with an intersectional approach, and summary tables and figures that enable practical interpretation of multiplexed datasets (Fig. 1 a).

Demultiplexing and doublet detecting methods have made large-scale scRNA-seq experiments achievable. However, many demultiplexing and doublet detecting methods have been developed in the recent past, and it is unclear how their performances compare. Further, the demultiplexing techniques best detect heterogenic doublets while doublet detecting methods identify heterotypic doublets. Therefore, we hypothesised that demultiplexing and doublet detecting methods would be complementary and be more effective at removing doublets than demultiplexing methods alone.

Indeed, we demonstrated the benefit of utilising a combination of demultiplexing and doublet detecting methods. The optimal intersectional combination of methods depends on the experimental design and capture characteristics. Our results suggest super loaded captures—where a high percentage of doublets is expected—will benefit from multiplexing. Further, when many donors are multiplexed (>16), doublet detecting is not required as there are few doublets that are homogenic and heterotypic.

We have provided different method combination recommendations based on the experimental design. This decision is highly dependent on the research question.

Conclusions

Overall, our results provide researchers with important demultiplexing and doublet detecting performance assessments and combinatorial recommendations. Our software platform, Demuxafy ( https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/index.html ), provides a simple implementation of our methods in any research lab around the world, providing cleaner scRNA-seq datasets and enhancing interpretation of results.

PBMC scRNA-seq data

Blood samples were collected and processed as described previously [ 17 ]. Briefly, mononuclear cells were isolated from whole blood samples and stored in liquid nitrogen until thawed for scRNA-seq capture. Equal numbers of cells from 12 to 16 samples were multiplexed per pool and single-cell suspensions were super loaded on a Chromium Single Cell Chip A (10x Genomics) to capture 20,000 droplets per pool. Single-cell libraries were processed per manufacturer instructions and the 10× Genomics Cell Ranger Single Cell Software Suite (v 2.2.0) was used to process the data and map it to GRCh38. Cellbender v0.1.0 was used to identify empty droplets. Almost all droplets reported by Cell Ranger were identified to contain cells by Cellbender (mean: 99.97%). The quality control metrics of each pool are demonstrated in Additional file 2 : Fig S15.

PBMC DNA SNP genotyping

SNP genotype data were prepared as described previously [ 17 ]. Briefly, DNA was extracted from blood with the QIAamp Blood Mini kit and genotyped on the Illumina Infinium Global Screening Array. SNP genotypes were processed with Plink and GCTA before imputing on the Michigan Imputation Server using Eagle v2.3 for phasing and Minimac3 for imputation based on the Haplotype Reference Consortium panel (HRCr1.1). SNP genotypes were then lifted to hg38 and filtered for > 1% minor allele frequency (MAF) and an R 2 > 0.3.

Fibroblast scRNA-seq data

The fibroblast scRNA-seq data has been described previously [ 18 ]. Briefly, human skin punch biopsies from donors over the age of 18 were cultured in DMEM high glucose supplemented with 10% fetal bovine serum (FBS), L-glutamine, 100 U/mL penicillin and 100 μg/mL (Thermo Fisher Scientific, USA).

For scRNA-seq, viable cells were flow sorted and single cell suspensions were loaded onto a 10× Genomics Single Cell 3’ Chip and were processed per 10× instructions and the Cell Ranger Single Cell Software Suite from 10× Genomics was used to process the sequencing data into transcript count tables as previously described [ 18 ]. Cellbender v0.1.0 was used to identify empty droplets. Almost all droplets reported by Cell Ranger were identified to contain cells by Cellbender (mean: 99.65%). The quality control metrics of each pool are demonstrated in Additional file 2 : Fig S16.

Fibroblast DNA SNP genotyping

The DNA SNP genotyping for fibroblast samples has been described previously [ 18 ]. Briefly, DNA from each donor was genotyped on an Infinium HumanCore-24 v1.1 BeadChip (Illumina). GenomeStudioTM V2.0 (Illumina), Plink and GenomeStudio were used to process the SNP genotypes. Eagle V2.3.5 was used to phase the SNPs and it was imputed with the Michigan Imputation server using minimac3 and the 1000 genome phase 3 reference panel as described previously [ 18 ].

Demultiplexing methods

All the demultiplexing methods were built and run from a singularity image.

Demuxalot [ 6 ] is a genotype reference-based single cell demultiplexing method. Demualot v0.2.0 was used in python v3.8.5 to annotate droplets. The likelihoods, posterior probabilities and most likely donor for each droplet were estimated using the Demuxalot Demultiplexer.predict_posteriors function. We also used Demuxalot Demultiplexer.learn_genotypes function to refine the genotypes before estimating the likelihoods, posterior probabilities and likely donor of each droplet with the refined genotypes as well.

The Popscle v0.1-beta suite [ 16 ] for population genomics in single cell data was used for Demuxlet and Freemuxlet demultiplexing methods. The popscle dsc-pileup function was used to create a pileup of variant calls at known genomic locations from aligned sequence reads in each droplet with default arguments.

Demuxlet [ 3 ] is a SNP genotype reference-based single cell demultiplexing method. Demuxlet was run with a genotype error coefficient of 1 and genotype error offset rate of 0.05 and the other default parameters using the popscle demuxlet command from Popscle (v0.1-beta).

Freemuxlet [ 16 ] is a SNP genotype reference-free single cell demultiplexing method. Freemuxlet was run with default parameters including the number of samples included in the pool using the popscle freemuxlet command from Popscle (v0.1-beta).

Dropulation

Dropulation [ 5 ] is a SNP genotype reference-based single cell demultiplexing method that is part of the Drop-seq software. Dropulation from Drop-seq v2.5.1 was implemented for this manuscript. In addition, the method for calling singlets and doublets was provided by the Dropulation developer and implemented in a custom R script available on Github and Zenodo (see “Availability of data and materials”).

ScSplit v1.0.7 [ 7 ] was downloaded from the ScSplit github and the recommended steps for data filtering quality control prior to running ScSplit were followed. Briefly, reads that had read quality lower than 10, were unmapped, were secondary alignments, did not pass filters, were optical PCR duplicates or were duplicate reads were removed. The resulting bam file was then sorted and indexed followed by freebayes to identify single nucleotide variants (SNVs) in the dataset. The resulting SNVs were filtered for quality scores greater than 30 and for variants present in the reference SNP genotype vcf. The resulting filtered bam and vcf files were used as input for the s cSplit count command with default settings to count the number of reference and alternative alleles in each droplet. Next the allele matrices were used to demultiplex the pool and assign cells to different clusters using the scSplit run command including the number of individuals ( -n ) option and all other options set to default. Finally, the individual genotypes were predicted for each cluster using the scSplit genotype command with default parameters.

Souporcell [ 4 ] is a SNP genotype reference-free single cell demultiplexing method. The Souporcell v1.0 singularity image was downloaded via instructions from the gihtub page. The Souporcell pipeline was run using the souporcell_pipeline.py script with default options and the option to include known variant locations ( --common_variants ).

Vireo [ 2 ] is a single cell demultiplexing method that can be used with reference SNP genotypes or without them. For this assessment, Vireo was used with reference SNP genotypes. Per Vireo recommendations, we used model 1 of the cellSNP [ 20 ] version 0.3.2 to make a pileup of SNPs for each droplet with the recommended options using the genotyped reference genotype file as the list of common known SNP and filtered with SNP locations that were covered by at least 20 UMIs and had at least 10% minor allele frequency across all droplets. Vireo version 0.4.2 was then used to demultiplex using reference SNP genotypes and indicating the number of individuals in the pools.

Doublet detecting methods

All doublet detecting methods were built and run from a singularity image.

DoubletDecon

DoubletDecon [ 9 ] is a transcription-based deconvolution method for identifying doublets. DoubletDecon version 1.1.6 analysis was run in R version 3.6.3. SCTransform [ 21 ] from Seurat [ 22 ] version 3.2.2 was used to preprocess the scRNA-seq data and then the Improved_Seurat_Pre_Process function was used to process the SCTransformed scRNA-seq data. Clusters were identified using Seurat function FindClusters with resolution 0.2 and 30 principal components (PCs). Then the Main_Doublet_Decon function was used to deconvolute doublets from singlets for six different rhops—0.6, 0.7, 0.8, 0.9, 1.0 and 1.1. We used a range of rhop values since the doublet annotation by DoubletDecon is dependent on the rhop parameter which is selected by the user. The rhop that resulted in the closest number of doublets to the expected number of doublets was selected on a per-pool basis and used for all subsequent analysis. Expected number of doublets were estimated with the following equation:

where N is the number of droplets captured and D is the number of expected doublets.

DoubletDetection

DoubletDetection [ 14 ] is a transcription-based method for identifying doublets. DoubletDetection version 2.5.2 analysis was run in python version 3.6.8. Droplets without any UMIs were removed before analysis with DoubletDetection . Then the doubletdetection.BoostClassifier function was run with 50 iterations with use_phenograph set to False and standard_scaling set to True. The predicted number of doublets per iteration was visualised across all iterations and any pool that did not converge after 50 iterations, it was run again with increasing numbers of iterations until they reached convergence.

DoubletFinder

DoubletFinder [ 10 ] is a transcription-based doublet detecting method. DoubletFinder version 2.0.3 was implemented in R version 3.6.3. First, droplets that were more than 3 median absolute deviations (mad) away from the median for mitochondrial per cent, ribosomal per cent, number of UMIs or number of genes were removed per developer recommendations. Then the data was normalised with SCTransform followed by cluster identification using FindClusters with resolution 0.3 and 30 principal components (PCs). Then, pKs were selected by the pK that resulted in the largest BC MVN as recommended by DoubletFinder. The pK vs BC MVN relationship was visually inspected for each pool to ensure an effective BC MVN was selected for each pool. Finally, the homotypic doublet proportions were calculated and the number of expected doublets with the highest doublet proportion were classified as doublets per the following equation:

ScDblFinder

ScDblFinder [ 11 ] is a transcription-based method for detecting doublets from scRNA-seq data. ScDblFinder 1.3.25 was implemented in R version 4.0.3. ScDblFinder was implemented with two sets of options. The first included implementation with the expected doublet rate as calculated by:

where N is the number of droplets captured and R is the expected doublet rate. The second condition included the same expected number of doublets and included the doublets that had already been identified by all the demultiplexing methods.

Scds [ 12 ] is a transcription-based doublet detecting method. Scds version 1.1.2 analysis was completed in R version 3.6.3. Scds was implemented with the cxds function and bcds functions with default options followed by the cxds_bcds_hybrid with estNdbl set to TRUE so that doublets will be estimated based on the values from the cxds and bcds functions.

Scrublet [ 13 ] is a transcription-based doublet detecting method for single-cell RNA-seq data. Scrublet was implemented in python version 3.6.3. Scrublet was implemented per developer recommendations with at least 3 counts per droplet, 3 cells expressing a given gene, 30 PCs and a doublet rate based on the following equation:

where N is the number of droplets captured and R is the expected doublet rate. Four different minimum number of variable gene percentiles: 80, 85, 90 and 95. Then, the best variable gene percentile was selected based on the distribution of the simulated doublet scores and the location of the doublet threshold selection. In the case that the selected threshold does not fall between a bimodal distribution, those pools were run again with a manual threshold set.

Solo [ 15 ] is a transcription-based method for detecting doublets in scRNA-seq data. Solo was implemented with default parameters and an expected number of doublets based on the following equation:

where N is the number of droplets captured and D is the number of expected doublets. Solo was additionally implemented in a second run for each pool with the doublets that were identified by all the demultiplexing methods as known doublets to initialize the model.

In silico pool generation

Cells that were identified as singlets by all methods were used to simulate pools. Ten pools containing 2, 4, 8, 16, 32, 64 and 128 individuals were simulated assuming a maximum 20% doublet rate as it is unlikely researchers would use a technology that has a higher doublet rate. The donors for each simulated pool were randomly selected using a custom R script which is available on Github and Zenodo (see ‘Availability of data and materials’). A separate bam for the cell barcodes for each donor was generated using the filterbarcodes function from the sinto package (v0.8.4). Then, the GenerateSyntheticDoublets function provided by the Drop-seq [ 5 ] package was used to simulate new pools containing droplets with known singlets and doublets.

Twenty-one total pools—three pools from each of the different simulated pool sizes (2, 4, 8, 16, 32, 64 and 128 individuals) —were used to simulate different experimental scenarios that may be more challenging for demultiplexing and doublet detecting methods. These include simulating higher ambient RNA, higher mitochondrial percent, decreased read coverage and imbalanced donor proportions as described subsequently.

High ambient RNA simulations

Ambient RNA was simulated by changing the barcodes and UMIs on a random selection of reads for 10, 20 or 50% of the total UMIs. This was executed with a custom R script that is available in Github and Zenodo (see ‘Availability of data and materials’).

High mitochondrial percent simulations

High mitochondrial percent simulations were produced by replacing reads in 5, 10 or 25% of the randomly selected cells with mitochondrial reads. The number of reads to replace was derived from a normal distribution with an average of 30 and a standard deviation of 3. This was executed with a custom R script available in Github and Zenodo (see ‘Availability of data and materials’).

Imbalanced donor simulations

We simulated pools that contained uneven proportions of the donors in the pools to identify if some methods are better at demultiplexing pools containing uneven proportions of each donor in the pool. We simulated pools where 50, 75 or 95% of the pool contained cells from a single donor and the remainder of the pool was even proportions of the remaining donors in the pool. This was executed with a custom R script available in Github and Zenodo (see ‘Availability of data and materials’).

Decrease read coverage simulations

Decreased read coverage of pools was simulated by down-sampling the reads by two-thirds of the original coverage.

Classification annotation

Demultiplexing methods classifications were considered correct if the droplet annotation (singlet or doublet) and the individual annotation was correct. If the droplet type was correct but the individual annotation was incorrect (i.e. classified as a singlet but annotated as the wrong individual), then the droplet was incorrectly classified.

Doublet detecting methods were considered to have correct classifications if the droplet annotation matched the known droplet type.

All downstream analyses were completed in R version 4.0.2.

Availability of data and materials

All data used in this manuscript is publicly available. The PBMC data is available on GEO (Accession: GSE196830) [ 23 ] as originally described in [ 17 ]. The fibroblast data is available on ArrayExpress (Accession Number: E-MTAB-10060) [ 24 ] and as originally described in [ 18 ]. The code used for the analyses in this manuscript are provided on Github ( https://github.com/powellgenomicslab/Demuxafy_manuscript/tree/v4 ) and Zenodo ( https://zenodo.org/records/10813452 ) under an MIT Open Source License [ 25 , 26 ]. Demuxafy is provided as a package with source code available on Github ( https://github.com/drneavin/Demultiplexing_Doublet_Detecting_Docs ) and instructions on ReadTheDocs ( https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/ ) under an MIT Open Source License [ 27 ]. Demuxafy is also available on Zenodo with the link https://zenodo.org/records/10870989 [ 28 ].

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Authors’ Twitter handles

Twitter handles: @drneavin (Drew Neavin), @thjimmylee (Jimmy Tsz Hang Lee), @marta_mele_m (Marta Melé)

Peer review information

Wenjing She was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Review history

The review history is available as Additional file 3 .

This work was funded by the National Health and Medical Research Council (NHMRC) Investigator grant (1175781), and funding from the Goodridge foundation. J.E.P is also supported by a fellowship from the Fok Foundation.

Author information

Authors and affiliations.

Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute for Medical Research, Darlinghurst, NSW, Australia

Drew Neavin, Anne Senabouth, Himanshi Arora & Joseph E. Powell

Present address: Statewide Genomics at NSW Health Pathology, Sydney, NSW, Australia

Himanshi Arora

Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK

Jimmy Tsz Hang Lee

Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Catalonia, Spain

Aida Ripoll-Cladellas & Marta Melé

Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

Lude Franke

Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore

Shyam Prabhakar

Population and Global Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Republic of Singapore

Cancer Science Institute of Singapore, National University of Singapore, Singapore, Republic of Singapore

Bakar Institute for Computational Health Sciences, University of California, San Francisco, CA, USA

Chun Jimmie Ye

Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA

Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

Chan Zuckerberg Biohub, San Francisco, CA, USA

Bioinformatics and Cellular Genomics, St Vincent’s Institute of Medical Research, Fitzroy, Australia

Davis J. McCarthy

Melbourne Integrative Genomics, School of BioSciences–School of Mathematics & Statistics, Faculty of Science, University of Melbourne, Melbourne, Australia

Present address: The Gene Lay Institute of Immunology and Inflammation, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

Martin Hemberg

UNSW Cellular Genomics Futures Institute, University of New South Wales, Kensington, NSW, Australia

Joseph E. Powell

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Contributions.

DRN and JEP conceived the project idea and study design. JTHL, AR, LF, SP, CJY, DJM, MM and MH provided feedback on experimental design. DRN carried out analyses with support on coding from AS. JTHL and AR tested Demuxafy and provided feedback. DRN and JEP wrote the manuscript. All authors reviewed and provided feedback on the manuscript.

Corresponding authors

Correspondence to Drew Neavin or Joseph E. Powell .

Ethics declarations

Ethics approval and consent to participate.

Briefly, all work was approved by the Royal Hobart Hospital, the Hobart Eye Surgeons Clinic, Human Research Ethics Committees of the Royal Victorian Eye and Ear Hospital (11/1031), University of Melbourne (1545394) and University of Tasmania (H0014124) in accordance with the requirements of the National Health & Medical Research Council of Australia (NHMRC) and conformed with the Declaration of Helsinki [ 29 ].

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Competing interests

C.J.Y. is founder for and holds equity in DropPrint Genomics (now ImmunAI) and Survey Genomics, a Scientific Advisory Board member for and holds equity in Related Sciences and ImmunAI, a consultant for and holds equity in Maze Therapeutics, and a consultant for TReX Bio, HiBio, ImYoo, and Santa Ana. Additionally, C.J.Y is also newly an Innovation Investigator for the Arc Institute. C.J.Y. has received research support from Chan Zuckerberg Initiative, Chan Zuckerberg Biohub, Genentech, BioLegend, ScaleBio and Illumina.

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Neavin, D., Senabouth, A., Arora, H. et al. Demuxafy : improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods. Genome Biol 25 , 94 (2024). https://doi.org/10.1186/s13059-024-03224-8

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Received : 07 March 2023

Accepted : 25 March 2024

Published : 15 April 2024

DOI : https://doi.org/10.1186/s13059-024-03224-8

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