Our Courses
About our Courses
Our courses are hosted online to enable learners to obtain certification when they successfully complete a course! Our courses are published on Leanpub and Coursera!
You can also find the material for our courses on the Bookdown websites. These are nice for referring back to the material. They also do not require making an account like Leanpub and Coursera.
Our courses are open source, so you can find all the source material on GitHub through our source material links.
Check out our repositories if you want to see details about how we created our particular course, reuse our content (please give us attribution), or send us an issue or pull request. All content is licensed with CC-BY 4.0. You can find all of our repositories here.
Our courses aim to:
Prepare users of ITCR tools with fundamental knowledge
Spread awareness about ITCR tools
Help ITCR tool developers make their tools more accessible
CurRent Courses
LeaderShip For Cancer Informatics Research
This course covers the pitfalls of informatics research and discusses best practices and tools to overcome the challenges of working with and managing multidisciplinary teams. It also covers guidelines to promote diversity and inclusion in your lab and research. Click the arrows that appear when you hover over the image to see more about the course.
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Documentation and Usability
This course covers the basics of creating documentation and tutorials to maximize the usability of informatics tools. It is meant for individuals developing tools for informatics. Click the arrows that appear when you hover over the image to see more about the course.
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Introduction to Reproducibility in Cancer Informatics
This course introduces the concepts of reproducibility and replicability in the context of cancer informatics. It is the first course in a two part course on reproducibility. It uses hands-on exercises to demonstrate in practical terms how to increase the reproducibility of data analyses. Click the arrows that appear when you hover over the image to see more about the course.
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Advanced Reproducibility in Cancer Informatics
This course introduces more advanced tools to increase the reproducibility of data analyses; building upon the Intro to Reproducibility course. GitHub, Docker, Code Review, and GitHub actions are discussed. Click the arrows that appear when you hover over the image to see more about the course.
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Computing for Cancer Informatics
This course is designed to help investigators understand more about computing basics, as well as familiarize researchers with various computing platform options. Click the arrows that appear when you hover over the image to see more about the course.
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Introduction to LaTeX and Overleaf for Scientific Articles
This course is designed to help researchers and trainees write scientific articles using LaTeX and Overleaf. Click the arrows that appear when you hover over the image to see more about the course.
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Coursera version coming soon!
Choosing Genomics Tools
Based on their genomic data types and goals, this course will help learners find educational resources and tools to help them process and interpret data. Click the arrows that appear when you hover over the image to see more about the course.
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Coursera version coming soon!
Ethical Data Handling for Cancer Research
This course is designed to help researchers and investigators understand the key principles of data management from an ethics, privacy, security, usability and discoverability perspective.
Click the arrows that appear when you hover over the image to see more about the course.
Links:
Coursera version coming soon!
Additional Course Materials
AI for EFficient programming Harnessing the Power of Large Language Models
This course on AI for software development explores the use of AI large language models such as ChatGPT, Bard, and others and their potential benefits and challenges. Through examples and hands-on activities, students will develop an understanding of the ways in which AI can speed up software development tasks and free up time for more creative and strategic work. By the end of the course, students will be equipped to navigate the rapidly changing landscape of software development and use AI chatbots in a way that maximizes benefits and efficiency while limiting harm as much as possible.
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NIH Data Management and Sharing Policy
The National Institutes of Health (NIH) is rolling out a new policy for the management and sharing of scientific data starting for (most) grants submitted January 25, 2023 or later. The main requirement of this new policy is that researchers include a Data Management and Sharing (DMS) plan with their proposal. Not all research will require data sharing. However, everyone must provide a justification if they can’t share their data. Once a DMS plan is accepted by the funding agency, the researchers will be required to carry out the plan. In this course, we describe what the new policy requires, places where you might want to share your particular kind of data, and how to deal with possible challenges associated with the policy.
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Affiliated ITCR course Materials
CoGAPS on SciServer
This introductory course will provide a quick overview of how the Bayesian NMF algorithm, CoGAPS (Coordinated Gene Activity across Pattern Subsets), can provide new insights into single cell datasets. Through the exercises you will analyze a real dataset using the SciServer compute platform.
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Introduction to pVactools
The course is intended for anyone seeking a better understanding of current best practices in neoantigen identification and prioritization using pVACtools. It assumes that the learner is familiar with basic biology, genetics and immunology concepts. This course will teach learners to:
Understand key concepts of immunogenomics and neoantigen identification
How to run the pVACtools software suite
How to visualize and prioritize neoantigen candidates with pVACview
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Introduction to Immuno on Terra
This course is an introduction to the immuno workflow on Terra, which leverages a collection of modular WDL workflows for analysis of genomic sequencing data. These enable primary analysis of DNA and RNA-seq data, including for quality control, somatic and germline variant calling, expression analysis, fusion detection, HLA typing, somatic variant allele expression, and immunogenomics approaches for cancer vaccine development (including neoantigen prediction with pVACtools).
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Introduction to Clinical Interpretation of Somatic Cancer Variants
This course will introduce background concepts, tools, resources, and relevant standard operating procedures and guidelines for the clinical interpretation of somatic cancer variants.
The course will teach learners to:
Understand key concepts of somatic cancer variant interpretation
Introduce key SOPs and guidelines for classifying the clinical relevance and oncogenicity of somatic cancer variants
Introduce ClinGen Somatic Cancer efforts
Describe somatic variant knowledgebases
Introduce CIViC as a curation platform for somatic variant interpretation
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Future ITN Course Materials
Cancer Imaging Informatics
Source Materials: All the materials used to create the course.
The course will show reproducible pipelines and analyses that can be done with medical imaging and pathological images, from raw data to statistical analysis.
Cancer Clinical Informatics
Source Materials: All the materials used to create the course.
It can be difficult to organize and keep track of all the various clinical data that a researcher may collect or attempt to explore. Learning how to automate data extraction from clinical documents and how to summarize across documents can save valuable time and decrease the chance for errors when transcribing data from one format to another.
Cancer Informatics Data Visualization
Source Materials: All the materials used to create the course.
This course will cover the key concepts behind data visualization. It will also cover how to create effective visualizations for exploration, exposition, and validation. Finally it will cover how to use specific tools to make visualizations for omics, imaging, and clinical data.
Machine Learning for Cancer Informatics
Source Materials: All the materials used to create the course.
As the size and scope of data sets dramatically increases the opportunity to use these data sets to develop prognostic and predictive algorithms in cancer research also grows. This course will cover the key concepts and applications of machine learning in cancer research.
Dissemination and Engagement
Source Materials: All the materials used to create the course.
Even the most useful and best developed tools need to be put into the hands of relevant users to have an impact. There are a variety of modern approaches for disseminating cancer informatics software including Github, Social Media, workflow tools, and workflow publications. There is also a growing ecosystem for engaging users of software in everything from Github issues, to massive online open courses, to social media discussions. This course is designed to introduce effective ways of disseminating software and engaging with users to maximize impact.
