The Cancer Genomics Cloud (CGC)(link is external), powered by Seven Bridges, is an NCI-funded resource that provides a unified platform for cancer data analysis by co-localizing three components within the cloud: 1) large cancer datasets from TCGA, CPTAC and several others; 2) more than 500 bioinformatics tools and best-practice workflows for analyzing multi-omics data; and 3) the computational capabilities to do large-scale analyses. This hands-on session of CGC allows the participants to browse, query, and filter datasets of interest and bring their own data for collaborative analysis. The CGC also provides the flexibility to use private tools and the ability to complete reproducible and interactive data analyses (e.g., RStudio, Jupyter notebook). Currently, data analysis is supported in both Google and Amazon cloud environments. Altogether, the CGC is a network of findable, accessible, interoperable, and reusable (FAIR) datasets, workflows, and services which make cancer data analysis faster and more easily available for all.

Please join us for a discussion followed by question and answer session on the topic of “R or Python, Which Should I Learn?”.

We will be joined by Andrew Weisman of the Frederick National Laboratory for Cancer Research, Strategic Data Science Initiative (FNLCR, SDSI) and new BTEP Trainer Joe Wu to discuss the utility of R and Python for bioinformatics and data science analyses. You will gain insight into which of these 2 programming languages might be more appropriate for the types of analyses you want to perform. For example:

• Are there bioinformatics or genomics specialized tools in R? (Bioconductor). In Python? (BioPython)
• What kinds of data visualization can I do in R and how do I do them?
• Should I use Python for Machine Learning?
• Can I run Python and R on my local machine?
• Should I use Python or R on NIH High Performance Unix Cluster Biowulf?
• What are Integrated Data Environments (IDE) for R and Python? Why should I use them and how do I access them?
• How do I upload my data into Python? Into R?
• Where should I go next to learn more about R and Python?

FireCloud (Powered by Terra(link is external)) is a data analysis platform that contains a system of workspace functionalities centered on data sharing and analysis. Researchers can use FireCloud to perform a variety of analyses through two main mechanisms: batch workflow execution and interactive analysis (including data visualization). Workflow execution is achieved through a workflow management system called Cromwell, which is designed to be highly portable, capable of connecting to multiple computing platforms, and horizontally scalable. Interactive analysis is achieved through Jupyter notebooks or RStudio, which can be associated with a range of configurable runtime environments and incorporate various kernels and packages to support exploratory analyses of arbitrary scale and complexity.

In this hands-on session, we will introduce FireCloud to all attendees as a part of the NCI Cloud Resources. The participants will learn how to access key data sets such as TCGA and CPTAC and bring this data (or their own data) into a secure FireCloud workspace. Participants will also learn how to configure and launch workflows from their workspace and perform interactive analysis with applications such as Jupyter Notebooks, RStudio, and more. This session will also cover how billing operates in FireCloud and how to securely share the personal FireCloud workspace with collaborators.

This is the second class in the NIH Library Introduction to R Series. A basic understanding of R and RStudio is expected. This class provides a basic overview of R data types, data frames, and factors. Additionally, this class will cover indexing and subsetting data frames, and dealing with missing data. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services program that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons.

By the end of this class, participants should be able to: define R data frames; characterize how to inspect data frames; list the major methods for describing the content and structure of data frames; illustrate how to index and subset a data frame; describe how to use comparison operators on a data frame; discuss R factors; describe how to convert R factors; describe how to rename factors; discuss options for dealing with missing data in R; and describe how to save data in R.

Participants are encouraged to install R(link is external) and RStudio(link is external) before the class so that they can follow along with the instructor. Attendees will need to download the class data before the class.

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Faculty: Joao Xavier, PhD – Memorial Sloan Kettering Cancer Center; NCI CSBC

Moderator: Matthew J. Reilley, MD – University of Virginia

Target Audience
This series will serve as an excellent resource for all stakeholders interested in expanding their knowledge in computation immune-oncology. Specifically, early career scientists who want to further their training in computational immuno-oncology, as well as more senior career individuals who want to implement these techniques for the first time will greatly benefit from the series.

Learning Objectives
A key goal of this training program is to ensure participants remain on the cutting edge of computational immuno-oncology, to increase the participants’ awareness of the NCI-supported Cancer Moonshot Immunotherapy Networks, to enhance scientific engagements between the Cancer Moonshot(SM) Immunotherapy Networks and the broader cancer immunotherapy community, and to fulfill the Blue Ribbon Panel goal of acceleration of progress in cancer research.

Series Organizers
Kellie N. Smith, PhD – Johns Hopkins School of Medicine
Big Data and Data Sharing Committee, Chair
Song Liu, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Co-Chair
Alan Hutson, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Immediate Past Chair
Carsten Krieg, PhD – Medical University of South Carolina
Big Data and Data Sharing Committee, Member

This course provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation. By the end of this course, participants will have an understanding of data management best practices, data management tools, and resources that enable data sharing.

This is an introductory two-part course for those who want to learn about research data management and sharing, or for those who are interested in a refresher. Part 1 of this training will cover understanding research data, how to manage research data, and how to work with data. Audience: Researchers, fellows, post-docs, and trainees. You must register separately for Part 2 of this class.

This course provides detailed information on managing and sharing data from the first data planning stage, through the data life cycle, to data archiving, and finally to selecting an appropriate repository for data preservation. By the end of this course, participants will have an understanding of data management best practices, data management tools, and resources that enable data sharing.

This is an introductory two-part course for those who want to learn about research data management and sharing, or for those who are interested in a refresher. During Part 2 of this training, participants will learn about sharing and archiving data. Audience: Researchers, fellows, post-docs, and trainees. You must register separately for Part 1 of this class.

Register Now

Faculty: Yi Xing, PhD – Children’s Hospital of Pennsylvania/University of Pennsylvania; NCI Cancer Moonshot IOTN

Moderator: Kellie N. Smith, PhD – Johns Hopkins School of Medicine

Target Audience
This series will serve as an excellent resource for all stakeholders interested in expanding their knowledge in computation immune-oncology. Specifically, early career scientists who want to further their training in computational immuno-oncology, as well as more senior career individuals who want to implement these techniques for the first time will greatly benefit from the series.

Learning Objectives
A key goal of this training program is to ensure participants remain on the cutting edge of computational immuno-oncology, to increase the participants’ awareness of the NCI-supported Cancer Moonshot Immunotherapy Networks, to enhance scientific engagements between the Cancer Moonshot(SM) Immunotherapy Networks and the broader cancer immunotherapy community, and to fulfill the Blue Ribbon Panel goal of acceleration of progress in cancer research.

Series Organizers
Kellie N. Smith, PhD – Johns Hopkins School of Medicine
Big Data and Data Sharing Committee, Chair
Song Liu, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Co-Chair
Alan Hutson, PhD – Roswell Park Comprehensive Cancer Center, NCI Cancer Moonshot IOTN & DRSN
Big Data and Data Sharing Committee, Immediate Past Chair
Carsten Krieg, PhD – Medical University of South Carolina
Big Data and Data Sharing Committee, Member