Format: Webinar

This class provides a basic overview of the methods used to visualize the association among two or more quantitative variables. This class will focus on scatterplots, scatterplot matrix, and visualizing paired data. Participants are expected to have taken the Introduction to Data Visualization in R: ggplot class. Participants are encouraged to install R, RStudio, and the tidyverse package, before the webinar so that they can follow along with the instructor.

Over the last two decades three major technologies have developed as the bed rock of how we understand living systems; genomics, including nucleic acid based molecular biology readouts, imaging from near-atomic resolution to whole organism structures and machine learning including the very large non-linear architectures of artificial intelligence (AI). Dr. Ewan Birney will provide a brief tour of this technology development, using examples from the European Molecular Biology Laboratory (EMBL) and touching on both fundamental biological discoveries through to clinical applications. He will end by providing a perspective for the future of this triad with their impressive opportunities, the landscape of data and skills needed to actualize them and some potential pitfalls to avoid.

Speaker:

Ewan Birney, Ph.D.

Deputy Director General of the European Molecular Biology Laboratory (EMBL)

Director of EMBL’s European Bioinformatics Institute

This seminar will be held in-person in Lipsett Amphitheater, NIH Clinical Center and via Zoom

Join us for an introduction to bioinformatics resources for NCI CCR researchers. We will look at available information from:

• NIH Bioinformatics Calendar
• Training opportunities
• Upcoming events
• New website resources
• Biowulf
• Next-Gen Seq Analysis tools (Partek,Qlucore, Qiagen)
• Workflows
• Cloud resources
• NCI sequencing cores
• NIH resources

Python is a programming language used for data science, specifically: data analysis, statistical analysis, and visualization of results. This class will demonstrate integrated development and learning (IDE) platforms for learning Python, the fundamentals of Python coding, and why it is advantageous to develop these skills.  The session will feature the following IDEs: Google Colaboratory: Jupyter Notebook; and Anaconda’s: Spyder, Jupyter Notebook, and JupyterLab.  It will also provide an overview of programming constructs needed to learn Python. Finally, this class will demonstrate why these skills can boost productivity, rigor, and transparency in reporting research findings.

This is the first class in the NIH Library Introduction to R Series. This class provides a basic overview of the functionality of R programming language and RStudio. 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 and Bioinformatics programs 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, students should be able to: list reasons for using R; describe the purpose of the RStudio Script, Console, Environment, and Plots panes; describe the various methods for finding help on R and RStudio; organize files and directories for a set of analyses as an R Project; define the following terms as they relate to R: object, assign, comment, call, function, and arguments; and assign values to objects in R.

Students are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Please bring your laptop with R and RStudio installed.

The National Cancer Institute (NCI) has launched a new virtual seminar series titled NCI Rising Scholars: Cancer Research Seminar Series. This monthly seminar series is an opportunity to highlight the research and the important contributions made by NCI-supported postdoctoral fellows and early career investigators at NCI laboratories and NCI-funded institutions nationwide.

Speaker:

Ajit Johnson Nirmal, Ph.D.

Instructor in Medicine

Harvard Medical School and Dana Farber Cancer Institute

In this seminar, NCI K99/R00 Awardee Dr. Ajit Johnson Nirmal from Harvard Medical School and Dana Farber Cancer Institute will discuss his research from the following publication: Spatial landscape of progression and immunoediting in primary melanoma at single cell resolution.

Melanoma is a type of skin cancer that can be cured if caught early but can be life-threatening if it spreads. In this study, Dr. Nirmal and his team used a combination of imaging and sequencing technology to study how melanoma interacts with its microenvironment. They found that the organization of cancer cells, immune cells, and other cells in the body changes as melanoma progresses. In early stages, there are signs that the immune system is being suppressed. When melanoma becomes invasive, specific areas form where the immune system is suppressed, and cancer cells can grow and spread. However, a short distance away, there are also areas where the immune system fights cancer. This shows that cancer and the immune system can coexist and evolve together. This type of study helps understand how cancer can avoid being destroyed by the immune system.

This course will include a series of  8 lessons for scientists new to R or with limited R experience. The purpose of this course is to introduce the foundational skills necessary to begin to analyze and visualize data in R. Lessons will be on Mondays and Wednesdays from 1 pm to 2:15 pm and will be followed by a 45 minute optional help session.

To participate in this class you will need your government-issued computer and a reliable internet connection. You do not need to download or install any software to participate in the class. However, at the end of the class, we will provide instruction on installing R and R Studio on your local machine.

This class will be taught on the DNAnexus platform. Every learner will need to create an account at https://dnanexus.com.

After you have created your DNAnexus account, please complete this form. If you fail to complete the form, we will not be able to give you access to the course on DNAnexus.

Class materials will be accessible online at https://btep.ccr.cancer.gov/docs/rintro/.

By registering for this class you are registering for ALL sessions of the class. You do not need to register separately for every session.

Course dates:

January 23rd – February 15th

Topics to be covered:

Getting started with R and RStudio

R basics

Working with tabular data

Introduction to data wrangling with the tidyverse

Introduction to data visualization with ggplot2

Bioconductor and report generating

Meeting link:

https://cbiit.webex.com/cbiit/j.php?MTID=m46a79d660bad841acb4e7a088e8de741

Bulk RNA-Seq data analysis – learn all about expression counts (raw counts, FPKM, RPKM, TMM, TPM, CPM).

Those of you, who are hands-on with RNA-seq, or even simply reading publications on this know there are different types of expression counts – raw counts, FPKM, RPKM, TMM, TPM, and CPM.

Some of you get a master matrix from your sequencing core with several types of counts calculated for each dataset. We will go over them to help you make better decisions with your data.

This session is designed for biologists who want to learn more about RNA-seq but have no formal training in bioinformatics.

(Public RNA-seq datasets will be used, and the live demo part will be done in Qlucore Omics Explorer.)

This seminar series showcases research from different Cancer Moonshot initiatives that support the 10 recommendations of the Blue Ribbon Panel Report. These presentations will inform the community about the progress of Cancer Moonshot–funded projects, provide outreach related to Cancer Moonshot projects, enhance discussions and collaborations related to Cancer Moonshot research, and promote the sharing of data from Cancer Moonshot initiatives.

Speaker: Heidi Hanson, Ph.D., Oak Ridge National Laboratory

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.

Are you interested in accessing cancer data from genomics studies? If so, register for this NCI webinar to learn more about the Genomic Data Commons (GDC)!

University of Chicago’s Dr. Bill Wysocki will discuss how users can better understand the structure and content of data in the GDC, helping them access, submit, or analyze available data.

Dr. Wysocki will review the GDC Data Model, a graph-based data model that maintains a relationship between a case, biospecimen, clinical, and submitted data files. He’ll also review the GDC Data Dictionary, a resource that describes the clinical, biospecimen, administrative, and genomic metadata that can be used with the genomic data generated by the GDC. Topics of interest include:

• recent structural updates supporting sample property standardization.
• new fields supporting NCI programs such as The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET).

As part of the NCI Cancer Research Data Commons, the GDC supports the submission of data from cancer genomics studies and makes submitted data available to the research community through data access tools.

This course will include a series of  8 lessons for scientists new to R or with limited R experience. The purpose of this course is to introduce the foundational skills necessary to begin to analyze and visualize data in R. Lessons will be on Mondays and Wednesdays from 1 pm to 2:15 pm and will be followed by a 45 minute optional help session.

To participate in this class you will need your government-issued computer and a reliable internet connection. You do not need to download or install any software to participate in the class. However, at the end of the class, we will provide instruction on installing R and R Studio on your local machine.

This class will be taught on the DNAnexus platform. Every learner will need to create an account at https://dnanexus.com.

After you have created your DNAnexus account, please complete this form. If you fail to complete the form, we will not be able to give you access to the course on DNAnexus.

Class materials will be accessible online at https://btep.ccr.cancer.gov/docs/rintro/.

By registering for this class you are registering for ALL sessions of the class. You do not need to register separately for every session.

Course dates:

January 23rd – February 15th

Topics to be covered:

Getting started with R and RStudio

R basics

Working with tabular data

Introduction to data wrangling with the tidyverse

Introduction to data visualization with ggplot2

Bioconductor and report generating

Meeting link:

https://cbiit.webex.com/cbiit/j.php?MTID=m46a79d660bad841acb4e7a088e8de741

This introductory course teaches the basics of deep learning and of different types of deep learning networks through a set of hands-on biological examples implemented in Keras, one example per class. Class #6 will focus on on Graph Convolutional Networks, handling imbalanced data and their application to classification of cancer types.

Expected knowledge: Basic Python, Basic Linux/Unix.

This class is part of a series, but each class is stand-alone. The class will be webcast and recorded. Handouts will be made available one day prior to the class.

Instructor: Gennady Denisov (NIH HPC staff)

For inquiries email staff@hpc.nih.gov

The National Cancer Institute (NCI) is holding a virtual meeting on February 7-8, 2023 from 12 – 4 PM ET, Variation to Biology: Optimizing Functional Analysis of Cancer Risk Variants, to identify and discuss how best to address scientific challenges and opportunities for understanding the path from genetic variation to cancer phenotype. This meeting is open to the public and free to attend, but registration is required to access the virtual event.

Research conducted over the past 15+ years has identified thousands of common genetic variants that associate with risk for cancer. Because most of these variants have small effect sizes and are located in non-protein coding regions of the genome, understanding how they impact molecular mechanisms and the biology underlying cancer risk is challenging.

The main goal for this meeting is to convene participants to identify and discuss challenges to using genetic association data to discover and understand the mechanisms underlying cancer risk and how they ultimately lead to cancer, and consider ways that NCI could help optimize progress in this area. Register for our meeting and stay tuned for more details about the agenda!

For more information, please contact nciepicommunications@mail.nih.gov.

NCI announces availability of the institute-wide site license for MATLAB that provides access to MATLAB, Simulink, and additional products on government-furnished equipment to all NCI staff, including fellows, contractors, and trainees. These powerful tools and accompanying training courses will soon be made available to advance scientific research. 

Who Should Attend

Contact the NCI MATLAB Team at NCIMatLabTeam@mail.nih.gov for more information. 

Data Wrangling is another class in the NIH Library Introduction to R and RStudio Series. A basic understanding of using R and RStudio to manage data is expected. This one-hour class will build on the introduction provided in Data Wrangling Part 1 to further explore the R tidyverse package and how to use it to manipulate, analyze and export data. This class will explore options for using the tidyverse functions group_by, summarize, count, arrange, pivot_wider, pivot_longer, and export. Learning these functions will enable users to have well formatted data frames for future clean and consistent analysis and visualizations.

The upcoming presentations will showcase research from different Cancer Moonshot initiatives that support the 10 recommendations of the Blue Ribbon Panel report. They will inform the community about the progress of Cancer Moonshot-funded projects, provide outreach related to Cancer Moonshot projects, enhance discussions and collaborations related to Cancer Moonshot research, and promote the sharing of data from Cancer Moonshot initiatives.

Speaker: Sandro Santagata, M.D., Ph.D., Brigham and Women’s Hospital