Applied Bioinformatics Main Page

Applied Bioinformatics

Welcome to the Applied Bioinformatics Course Wiki.

These wiki pages are provided to coordinate information, activities and projects in the applied bioinformatics courses taught by Boris Steipe at the University of Toronto. If you are not one of my students, you can still browse this site, however only users with a login account can edit or contribute or edit material. If you are here because you are interested in general aspects of bioinformatics or computational biology, you may want to review the Wikipedia article on bioinformatics, or visit Wikiomics. Contact boris.steipe(at)utoronto.ca with any questions you may have.

The Course

BCB410H1F is the undergraduate course code and JTB2020H1S is the course code for graduate students. However the delivery and scope of the courses is very different:

• BCB410 is intended for students in the Bioinformatics and Computational Biology Specialist Program. Therefore I assume that all students are very familiar with a wide variety of computer science related topics and their practical application.
• JTB2020 is designed for students in the Collaborative PhD Program in Bioinformatics and Genome Biology. These students have a wide variety of backgrounds and prior experience. They participate in the Computational Systems Biology Course and go through a number of targeted exercises in applied bioinformatics to add as much material to their knowledge- and skill set as can reasonably be acquired in a single term.

Organization

Dates

BCB420/JTB2020 is a Winter Term course.

Lectures: Tuesdays, 16:00 to 18:00. (Classes start at 10 minutes past the hour.)

Note: there will be three open-ended collaborative planning sessions that may go well into the night. Attendance and participation is mandatory.

Final Exam: None for this course.

Events

• Tuesday, January 8 2019: Course officially begins. No class meeting. Get started on preparatory material (well in advance actually).
• Tuesday, January 15: First class meeting. Mock-quiz for preparatory material.
• Tuesday, January 22: First live quiz on preparatory material. Later: open ended session on data import
• Tuesday, February 5: Open ended session on system curation
• Tuesday, March 12: Open ended session on exploratory data analysis

Location

MS 3278 (Medical Sciences Building).

Departmental information

For BCB420 see the BCB420 Biochemistry Department Course Web page.

For JTB2020 see the JTB2020 Course Web page for general information.

Grading, Activities, Deliverables

 

For details of the deliverables, see below.

 

Activity	Weight BCB410 - (Undergraduates)	Weight JTB2020 - (Graduates)
Self-evaluation and Feedback session on preparatory material("Quiz")	20 marks	15 marks
Oral Test (March 7/8)	30 marks	30 marks
Collaborative software task and participation	20 marks	15 marks
Journal	25 marks	25 marks
Insights	5 marks	5 marks
Pull request reviews		10 marks
Total	100 marks	100 marks

 

We are covering a lot of ground in this course, and all deliverables feed into a collaborative project. Everyone's continuous, active participation is essential for making this a success: for you personally and for the class as a team.

 

Getting started

 

Everything starts with the following four units:

• Introduction to editing Wiki pages (Optional if you have taken BCH441 or BCB410.)

This should be the first learning unit you work with, since your Course Journal will be kept on a Wiki, as well as all other deliverables. This unit includes an introduction to authoring Wikitext and the structure of Wikis, in particular how different pages live in separate "Namespaces". The unit also covers the standard markup conventions - "Wikitext markup" - the same conventions that are used on Wikipedia - as well as some extensions that are specific to our Course- and Student Wiki. We also discuss page categories that help keep a Wiki organized, licensing under a Creative Commons Attribution license, and how to add licenses and other page components through template codes.

• Your Course Journal (Mandatory - your Journals will be assessed. Note that the "rules" have changed - study the unit carefully and read the evaluation rubrics.)

Keeping a journal is an essential task in a laboratory. To practice keeping a technical journal, you will document your activities as you are working through the material of the course. A significant part of your term grade will be given for this Course Journal. This unit introduces components and best practice for lab- and course journals and includes a wiki-source template to begin your own journal on the Student Wiki.

• The "Plagiarism Unit" (Mandatory - must be the first entry in your Journal.)

Academic Integrity is a promise that scholars and scientists world-wide give each other, that we will uphold, protect, and promote ethical and practical standards for our work. Its most basic values are proclaimed as honesty, trust, fairness, respect, responsibility, and courage. These are simple ideas, but in order to give them meaning we need to discuss how these values get translated to the details of our everyday work. Unfortunately, this important topic is often compressed to discussing cheating and plagiarism, to managing procedures to detect dishonesty, and to threatening sanctions. It is overlooked that those are just the manifestations of much deeper problems, and focussing on those symptoms alone perpetuates a stereotyped us-versus-them mentality of educators and students alike that is much more likely to make the problem worse than to solve it. The key to counter this lies in a proper understanding of academic integrity as a relational value, and respect as its foundation.

Discussing academic integrity in the abstract is of limited use, the challenge is to put the concepts in practice, in every aspect of this course and this is not a question of behaviour, but of attitude. The attitude needs to be reflected in the choice of teaching materials, in the care in their preparation, in the attitude of impartiality and reproducibility we bring to our experiments, in mutual trust in class, in fairness in assessments, and honesty in assignments. One everyday issue is attribution and we operate a Full Disclosure Policy for attribution in this course. This means everything that is not one's own, original idea must be identified and properly attributed. Neither I nor you are already perfect in this, but I trust we can come together as a learning community to educate each other and improve.

• The "insights!" page (Mandatory - your "insights!" pages will be assessed.)

In paralell with your other work, you will maintain an insights! page on which you collect valuable insights and learning experiences of the course. Through this you ask yourself: what does this material mean - for the field, and for myself.

Once you have completed these four units, get started immediately on the Introduction-to-R units. You need time and practice, practice, practice to acquire the programming skills you need for the course. Whenever you want to take a break from studying R, continue with the other preparatory units.

 

PartI: Foundations and Data

 

Don't forget to document your work in your Journal!

 

Your level of preparedness will be assessed in a "mock quiz" in week two, after which you have one more week to fill in gaps before our Quiz in week three. With that out of the way, we will look at different data sources that are useful in systems biology, including gene-level annotations and collections of experimental data, relationship data like physical and epistatic interactions, and systems-level data like metabolic or regulatory pathways. Each of you will select one data-source in our first open-ended session and then work on the following deliverables:

• a brief summary page on the Student Wiki: the page needs to be named according to the pattern: User:<your_name>/BCB420-2019-Data_<your_data_resource> and contain the category tag: [[Category:BCH420-2019_Data_project]].
• an R package derived from rpt,
  • hosted on GitHub,
  • named according to the pattern BCB420.2019.<your_data_resource>,
  • containing an R markdown page that describes and annotates code for
    • importing the chosen data in platform-independent function calls (see the footnote for details and restrictions),
    • and cleaning it up where necessary,
    • and normalizing its identifiers to HuGO gene symbols,
  • and containing sample data for our defined reference dataset of genes,
  • and containing a report on the data statistics,
  • and containing code to validate the import process,
  • and containing the (provided) function to display the markdown file.

Required: a user needs to be able to use the information you provided to understand the semantics of the data, import the data, purify it where necessary, and associate it with HUGO IDs in an R data frame. They should be able to use the data as a feature in a machine learning protocol without further preprocessing steps.

To illustrate the requirements with a model solution, I have provided an example project page here, which links to a Github repository with the corresponding package. Studying this with some care will probably clarify many questions.

Note

• If your data refers to chromosomal coordinates in any way, you must ensure the coordinates are from GRCh38 (hg38)
• Your chosen database will not always be the best choice of data source: often you can achieve your objective faster though ensembl/biomart. See this sample annotation of BRCA2 for examples of what data is available.

 

Database choices

 

Here are the chosen (or assigned) databases. Follow the link in the "Note" column for details:

 

Name	DB	Note
Edouard Al-chami	GEO (stimulus)
Emily Ayala	Gene models
Deus Bajaj	EGGNOG
Cathy Cha	GEO (tissues)
Nada Elnour	Human Protein Atlas
Chantal Ho	GEO (diseases)
Edward Ho	Cosmic
Sapir Labes	GWAS
Judy Lee	PDB
Tina Lee	Pfam
Jian Bin Lin	GEO
Matthew Mcneil	COSMIC and GEO
Gabriela Morgenshtern	Awesome (or PANTHER)
Yoonsik Park	Reactome pathways
Alesandro Rigido	MsigDB
Fan Shen	SMART
Rachel Silverstein	Human Phenotype Ontology
Yiqiu Tang	OMIM
Denitsa Vasileva	GO annotations
Rachel Woo	Human Protein Atlas
Alison Wu	BioGRID
Yufei Yang	GTRD
Yin Yin	huMAP
Han Zhang	HitPredict
Xindi Zhang	CORUM
Yuhan Zhang	Encode
Liwen Zhuang	Human Disease Ontology

Contact me with any questions you may have.

 

Part II: Biocuration

"Systems" are concepts and working with systems requires expert knowledge. To explore the practice of expert curation of molecular systems, each of you will select one system in our second open-ended session and report on its components, its function(s) and its architecture. To start off:

• Choose a system from the GO term table on the Student Wiki, confirm your choice with me and replace the "N.N." in the table with your name.
• Explore the term on AmiGO, and explore the linked "seed-genes" on UniProt.
• In PubMed, find recent reviews or other manuscripts that discuss the system and its context. Make sure you have not overlooked important literature, this will be part of your evaluation. If there is no suitable literature available, your GO term is not a suitable choice.
• Get an overview of your system and how it relates to the GO term you start out from.
• define the system well and define a five-letter code as a shorthand notation of the system as discussed in class.

Note

A GO term is not a system nor is the set of GOA annotated genes a complete description of the system's members. A system may overlap the component/function/process described in a GO term to a large degree, but the term is not informed or constrained by our "system" definition. We use GO terms as a first approximation to system functions, and we use GOA to define "seed" genes as a starting point that may help us build out the system description. However, a system's roles include the creation, maintenance, destruction, and potentially recycling of components, and these roles are not always included in either the literature nor the GO terms themselves.

 

Read the notes on curating a biological system.

 

 

General goal: System Architecture

A system architecture describes the system’s behaviour in terms of its subsystems and their relationships, given its context, within its boundaries.

 

Deliverables: Contents

• A structured description of the system, including its name, definition, description, associated GO terms, an initial set of computationally defined genes it contains, and references to a seed set of literature articles that will be used for curation;
• A description of concepts of importance. This includes the biological context, and background knowledge about the components.
• An enumeration of components from:
  • literature review;
  • direct annotation, i.e. genes discovered because they have been annotated with a relationship to the system, in a database such as UniProt, NCBI-Protein or any of the three GO ontologies represented in GOA (GO annotations);
  • network and pathway annotation, i.e. genes discovered in the network neighbourhood of system components, in a database like STRING or IntAct, or in pathways such as KEGG or Reactome;
  • phenotype and behaviour, i.e. genes annotated to a related phenotype in OMIM or the GWAS catalog;
  • ... each with a note on the type and quality of evidence that supports their inclusion.
• Completion of role annotation: each component has one role annotated to it (list components more than once if several distinct roles relate to the same, or overlapping entities); list roles that are expected, or required, but have no components associated with them.
• A system architecture sketch that integrates the system information;
• A formatted set of system data, ready to be imported into a system database.

Deliverables: Form

• Create a project page on the Student Wiki named according to the pattern: User:<your_name>/BCB420-2019-System_<your_system_code>;
• add the category tag: [[Category:BCH420-2019_Curation_project]];
• add the {{CC-BY}} template;
• summarize your "seed" information (follow the model for the PHALY system);
• as you are annotating your system, ensure all components have a SyRO role defined, and the evidence source and evidence code has been entered;
• the system data needs to be included in the page in a valid(!) JSON file, in an expansible section of text.

Both your data import script and your curated system model will be assessed in the Oral Test.

 

Part III: Exploration

At the end of Parts I and II we will have data available and annotated systems that induce relations on the data. Using this information, we can formulate tools for exploratory data analysis (EDA): isolating and evaluating features, looking at correlations, identifying patterns in networks, clustering data etc. Each of you will select one EDA workflow in our third open-ended session for which to build a tool in a jointly authored R package. Your deliverables are:

• a project page on the student Wiki that contains a specification of your tool;
• an implementation of your tool as part of a jointly authored R package under continuous integration;
• a Vignette in the package that describes the tool and includes sample code for which the data is also provided in the package.

Your deliverables will be evaluated together with your participation in constructing the package.

Deliverables: Form

• On the Student Wiki -
  • Create a project page on the Student Wiki named according to the pattern: User:<your_name>/BCB420-2019-ExploratorySystemsAnalysis;
  • add the category tag: [[Category:BCH420-2019_Exploration_project]];
  • add the {{CC-BY}} template;
  • summarize the objectives of your exploration tool in terms of input, output, and interpretation;
  • write a specification for your exploration tool;
  • summarize example results.

• On GitHub -
  • Fork the project BCB420.2019.ESA;
  • Develop your code as a package function;
  • Write a vignette;
  • Make sure your changes pass without errors, warnings or notes;
  • Submit a pull request by Monday, March 25.
  • Address comments from the pull-request review before Tuesday, April 2.

The code is considered "submitted" when it passes the continuous integration checks, all pull-request reviews have been addressed, and your branch has been merged into the BCB420.2019.ESA package.

 

Extensions for term work

 

Extensions for term work in this course are subject to Faculty regulations and will only be considered within the framework determined by the Faculty policies.

• Regular Submissions

It is Faculty policy to require assessments to be "fair, equitable and reasonable". In order to be equitable, granting extensions requires the student to demonstrate that the need for the extension is due to unavoidable circumstances that go significantly beyond what was expected of the rest of the class. In general "official" documentation will be required: UofT Verification of Illness or Injury Form, Student Health or Disability Related Certificate, a College Registrar’s Letter, and an Accessibility Services Letter.

• Signing up for the oral tests.

The dates for the Oral Test have been announced at the beginning of the term on this syllabus. If you fail to sign up for a slot, or if you fail to show up at the scheduled time, we apply the Faculty policy for a missed Midterm Test: "if the reasons for missing your test are acceptable to the instructor, a make-up opportunity should be offered to the student where practicable. "Acceptable" reasons will be considered

• if they are justified,
• if the consideration is "fair, equitable and reasonable", and
• if the reason is documented through one of the four types of "official" documentation: UofT Verification of Illness or Injury Form, Student Health or Disability Related Certificate, a College Registrar’s Letter, and an Accessibility Services Letter.

Scope for a "practicable" make-up opportunity for the Oral Test will be limited.

• Submissions due on the last day to submit course work in the Spring term (Tuesday, April 2 2019).

Since the course does not have a final exam, the Faculty requires grades to be marked, collated and submitted a few days after the last day to submit course work in the Spring term (Tuesday, April 2 2019). Therefore I cannot normally grant extensions beyond this date. The Faculty allows so called informal extensions to be granted "in extraordinary circumstances"; in those cases too, the requirement to be "fair, equitable and reasonable" will apply, i.e. you would need to demonstrate that the need for the extension was due to unavoidable circumstances that go significantly beyond what was expected of the rest of the class, and submit "official" documentation to me. In that case, (i) we would determine an adjusted submission date, (ii) I will initially submit a mark of 0 for the missing submissions, and (iii) I will submit an amended mark, after that date, if appropriate. Note that the Faculty requires that such extensions don't go beyond a few days after the end of the Final Examination Period. If you require an extension beyond that date you need to submit a formal petition through your College Registrar.

 

Late penalties

 

Late penalties will be applied according to the following formula: (marks achieved) * 0.5^(fractional days late). However material submitted more than 3.0 days late (72 hours or more) will be marked zero. Note: this does not apply to material due before the Oral Test (see there).

 

Copyright and Licensing

 

We follow [FOSS] principles in this course. You automatically own copyright to all material you prepare. All material must be licensed for free re-use, under the condition of fair attribution. In practice:

All pages that you place on the Student Wiki must include a {{CC-BY}} tag. All documentation within GitHub pages that you prepare for this course must include a Creative Commons License - Attribution (CC-BY), v. 4.0 or later. All code submitted for this course must be licensed under the MIT software license. Unlicensed submissions will have marks deducted and may be removed from the Wiki.

 

Academic integrity

Our rules on Plagiarism and Academic Misconduct are clearly spelled out in this learning unit. This unit is part of our course prerequisites, and everyone documents in their course journal that they have worked through the unit and understood it. Consequences of having to report to the Office of Student Academic Integrity (OSAI) for plagiarism, misrepresentation or falsification include an indelible failing mark on the transcript, a delay in graduation, or not being able to complete your POSt. Please take extra time to clearly understand the requirements, and define for yourself what they mean for every aspect of your work.

 

Marks adjustments

I do not adjust marks towards a target mean and variance (i.e. there will be no "belling" of grades). I feel strongly that such "normalization" detracts from a collaborative and mutually supportive learning environment. If your classmate gets a great mark because you helped them with a difficult concept, this should never have the effect that it brings down your mark through class average adjustments. Collaborate as much as possible, it is a great way to learn. But do keep it honest and carefully consider our rules on Plagiarism and Academic Misconduct.

 

Timetable and contents details

Note: The general outline of the course as described above is current for the 2019 Winter Term. Filling in the activity details below is still in progress.

 

Note: Click on the "▽" - symbol to see details for each week's activities.

 

Part I: Foundations

 

Week	In class: Tuesday, January 8 2019	This week's activities
1	No class meeting this day!	To prepare before next meeting ... study or review ABC learning unit material start or update your User page on the Student Wiki start your course journal   Details ...  ▽△ You are not submitting learning units for credit, thus you should be able to progress quickly through the material up to the   Milestone units  . But do not skip units. If you have worked with the ABC-units RStudio project before, you need to pull the most recent version from the GitHub repository. Update it from time to time, code will change. If you have not worked with this RStudio project before, make sure you work through the "Introduction to R" units in detail and with great care. Your course journal must contain the following category tag: [[Category:BCB420-2019_Journal]]. Your User page must contain the following category tag: [[Category:BCB420-2019]].

 

Week	In class: Tuesday, January 15 2019	This week's activities
2	First class meeting Review of preparatory materials (you should have worked through all of the materials in preparation for class). Practice quiz on preparations (not for credit) Course overview and Q&A	Follow up from class meeting ... Patch any gaps in your preparation until next Tuesday (Live Quiz!) Carefully study the the Systems Concepts unit To prepare before next meeting ... Get an overview of the the rpt package so you can ask questions next week. Review data sources, you will need to choose one to work on. Review requirements for your data source deliverable. Make sure you can work from it and discuss it in class.   Details ...  ▽△ In progress ... You need a GitHub account and you need to have your RStudio client set up to pull from and push to Github hosted projects. See the rpt package for details. Data: our goal is to make data available that can be used for the annotation of curated biological systems. Data types that interest us in principle include: Component annotations: sequence, structure, function (GO), localization ... Component dynamics (time, space, virtual dimensions): expression profiles, modification dynamics, ... Relationships: protein-protein interaction data, metabolic and regulatory pathways, functional associations (STRING), ... Perturbations: cancer genomes, epistatic effects, ... Phenotypes: OMIM, Navigome ... Expert curated sets: MSigDB ... To be well prepared, you need to understand the various categories of data that are available and have narrowed your choice to two or three datasets for which you know that they fulfill the requirements. Read: Grabowski & Rappsilber (2019) A Primer on Data Analytics in Functional Genomics: How to Move from Data to Insight?. Trends Biochem Sci 44:21-32. (pmid: 30522862) [ PubMed ] [ DOI ] High-throughput methodologies and machine learning have been central in developing systems-level perspectives in molecular biology. Unfortunately, performing such integrative analyses has traditionally been reserved for bioinformaticians. This is now changing with the appearance of resources to help bench-side biologists become skilled at computational data analysis and handling large omics data sets. Here, we show an entry route into the field of omics data analytics. We provide information about easily accessible data sources and suggest some first steps for aspiring computational data analysts. Moreover, we highlight how machine learning is transforming the field and how it can help make sense of biological data. Finally, we suggest good starting points for self-learning and hope to convince readers that computational data analysis and programming are not intimidating.

 

Week	In class: Tuesday, January 22 2019	This week's activities
3	Open ended session: Preparations review Q & A Quiz Choosing a dataset to define an import workflow ...	Follow up from class meeting ... Data import Analyze your datasource Define cleanup and normalization needs To prepare before next meeting ... create a project page on the Student Wiki study your database and figure out how the information it provides is related to the system data model define your requirements create a package based on rpt begin writing your workflow as a "literate programming" document   Details ...  ▽△ Understand the context: What data is available? Explore your database and be sure to understand the semantics of the data. How is your data going to support systems annotations? Study the systems data model in the resources project How are you going to present your data? The rpt package: read the README and understand how this supports you to construct your own R package. Markdown: work through the Literate Programming unit to get an idea in principle, but note the difference between .Rmd and .md documents (We are doing .md here, this is simpler.) Study the sample solution well. Understand what parts of this are relevant for your project, which ones are not, and what parts you may need that are not in the sample solution. Get started: Define your requirements. Define how you are going to download the source data, what the results data should look like, and how you are going to construct the results. Identify ambiguities, cleanup needs, possibilities for validation. Start a project page on the Student Wiki, write your requirements in point form Start building your package. Follow the instructions in the rpt package. Push the result to GitHub. Link to your package from your project page. Draft an outline of your workflow in your README.md document. Commit and push to GitHub. Communicate: whenever questions come up, post on the list.   Don't forget your Journal!

 

Week	In class: Tuesday, January 29 2019	This week's activities
4	Normalizing gene names Validating datasets Scaling transformations Intro of test dataset Reproducible research aspects	Follow up from class meeting ... solve any normalization issues your dataset may have Get your ORCID IDs Prepare your data that relates to the test set Include scaling code, where indicated To prepare before next meeting ... work through literate programming finalize package validate correctness document "Release" your package before Tuesday, February 5 2019 at 16:00. Review systems theory Intro to BioCuration   Details ...  ▽△ TBD ... ...

 

Part II: Curation

 

Week	In class: Tuesday, February 5 2019	This week's activities
5	Open ended session: Systems concepts A systems ontology A systems data model Biocuration Choosing your system for a systems curation project ...	Follow up from class meeting ... Choose your system To prepare before next meeting ... Begin your project page Define observables Begin exploring your system Start drafting a systems architecture   Details ...  ▽△ draft a hand drawn sketch of the system architecture (cf. "Systems Concepts" (this is the file that was assigned as required reading in Week 2); write down a list of observables for your system, the relationship of the data we explored in Phase I to the system: What features do you expect to find for a gene that occurs in the system? (Annotation-type data) What features do you expect to be shared by two genes that occur in your system? (Network-type data) What features do you expect to be enriched for all genes in your system, or a defined subset? (Set/enrichment-type data)   Don't forget to write your Journal as you explore your system!

 

Week	In class: Tuesday, February 12 2019	This week's activities
6	Class was canceled due to an ice storm	Follow up from class meeting ... ... ... To prepare before next meeting ... ... ...   Details ...  ▽△ TBD ... ...

 

Week	In class: Tuesday, February 19 2019	This week's activities
–	No class meeting - Reading Week	To prepare during reading week ... Start your project page on the Student Wiki; draft a hand drawn sketch of the system architecture; draft a list of system observables; For details see the "Biocuration" deliverables (above).

 

Week	In class: Tuesday, February 26 2019	This week's activities
7	Milestone report: (major progress: you should be nearly done)	Follow up from class meeting ... ... ... To prepare before next meeting ... ... ...   Details ...  ▽△ TBD ... ...

 

Week	In class: Tuesday, March 5 2019	This week's activities
8	Milestone III: report (final) A brief overview of Exploratory Data Analysis (EDA) for Systems Biology (overview of materials and outline how to study) Data model of systems data for a shared package	Follow up from class meeting ... Finalize curation report Validate To prepare before next meeting ... Curation project deadline Prepare for Oral Tests: March 7/8 Study introduction to EDA materials   Details ...  ▽△ TBD ... ...

 

Part III: Exploration

 

Week	In class: Tuesday, March 12 2019	This week's activities
9	Open ended session: Exploratory Data Analysis of Systems data rptPlus and rptTeam Contributing to a team-authored package on GitHub: forks, branches, pull-requests and Continuous Integration Choose your workflow for a team-authored systems EDA package ...	Follow up from class meeting ... Study rptPlus and rptTeam documentation ... To prepare before next meeting ... ... ...   Details ...  ▽△ TBD ... ...

 

Week	In class: Tuesday, March 19 2019	This week's activities
10	Vignettes ...	Follow up from class meeting ... ... ... To prepare before next meeting ... ... ...   Details ...  ▽△ TBD ... ...

 

Week	In class: Tuesday, March 26 2019	This week's activities
11	...	Follow up from class meeting ... ... ... To prepare before next meeting ... ... ...   Details ...  ▽△ TBD ... ...

 

Week	In class: Tuesday, April 2 2019	This week's activities
12	No class meeting this day Deadline for computational tasks to be documented in journal Deadline for all remaining course deliverables	NA   Details ...  ▽△ TBD ... ...

 

Resources

Course related

• Student Wiki
• The Course Google Group.
• Netiquette for the Group mailing list

 

Miller et al. (2011) Strategies for aggregating gene expression data: the collapseRows R function. BMC Bioinformatics 12:322. (pmid: 21816037)

[ PubMed ] [ DOI ] BACKGROUND: Genomic and other high dimensional analyses often require one to summarize multiple related variables by a single representative. This task is also variously referred to as collapsing, combining, reducing, or aggregating variables. Examples include summarizing several probe measurements corresponding to a single gene, representing the expression profiles of a co-expression module by a single expression profile, and aggregating cell-type marker information to de-convolute expression data. Several standard statistical summary techniques can be used, but network methods also provide useful alternative methods to find representatives. Currently few collapsing functions are developed and widely applied. RESULTS: We introduce the R function collapseRows that implements several collapsing methods and evaluate its performance in three applications. First, we study a crucial step of the meta-analysis of microarray data: the merging of independent gene expression data sets, which may have been measured on different platforms. Toward this end, we collapse multiple microarray probes for a single gene and then merge the data by gene identifier. We find that choosing the probe with the highest average expression leads to best between-study consistency. Second, we study methods for summarizing the gene expression profiles of a co-expression module. Several gene co-expression network analysis applications show that the optimal collapsing strategy depends on the analysis goal. Third, we study aggregating the information of cell type marker genes when the aim is to predict the abundance of cell types in a tissue sample based on gene expression data ("expression deconvolution"). We apply different collapsing methods to predict cell type abundances in peripheral human blood and in mixtures of blood cell lines. Interestingly, the most accurate prediction method involves choosing the most highly connected "hub" marker gene. Finally, to facilitate biological interpretation of collapsed gene lists, we introduce the function userListEnrichment, which assesses the enrichment of gene lists for known brain and blood cell type markers, and for other published biological pathways. CONCLUSIONS: The R function collapseRows implements several standard and network-based collapsing methods. In various genomic applications we provide evidence that both types of methods are robust and biologically relevant tools.

Chang et al. (2013) Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline. BMC Bioinformatics 14:368. (pmid: 24359104)

[ PubMed ] [ DOI ] BACKGROUND: As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations. RESULTS: We performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HS(A): DE genes with non-zero effect sizes in all studies, (2) HS(B): DE genes with non-zero effect sizes in one or more studies and (3) HS(r): DE gene with non-zero effect in "majority" of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively. CONCLUSIONS: The aggregated results from the simulation study categorized the 12 methods into three hypothesis settings (HS(A), HS(B), and HS(r)). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author's publication website.

Thompson et al. (2016) Cross-platform normalization of microarray and RNA-seq data for machine learning applications. PeerJ 4:e1621. (pmid: 26844019)

[ PubMed ] [ DOI ] Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training datasets can be created and validation can be performed on newly generated data. We developed Training Distribution Matching (TDM), which transforms RNA-seq data for use with models constructed from legacy platforms. We evaluated TDM, as well as quantile normalization, nonparanormal transformation, and a simple log 2 transformation, on both simulated and biological datasets of gene expression. Our evaluation included both supervised and unsupervised machine learning approaches. We found that TDM exhibited consistently strong performance across settings and that quantile normalization also performed well in many circumstances. We also provide a TDM package for the R programming language.

 

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Topics and exercises

• GPU and Cloud computing

 

Resources

Course related

• The Course Web site.
• The Course Google Group.
• Netiquette for the Group mailing list
• Previous Exam_questions
• 2007 course feedback
• 2008 course feedback

 

Contents related

• The VMD tutorial
• A Stereo Vision tutorial
• MetaDatabase
• NAR January-2008 Database issue
• NAR July-2008 Web server issue

 

 

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