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Learn about the NCI cancer research data ecosystem, as well as repositories and data portals for cancer research data. For more information about the webinars or to pre-register, contact Joelle Mornini at joelle.mornini@nih.gov
Check the Research Data Resources page on the Scientific Library website to learn more about research data online resources.

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As the genomic data-specific repository within NCI’s Cancer Research Data Commons (CRDC), the GDC provides cloud-based access to some of the largest and most comprehensive cancer genomic data sets as well as tools and workflows for data analysis. This monthly NCI Genomic Data Commons (GDC) webinar will give an overview of the GDC data quality strategy, the tools facilitating data quality in GDC data submission and harmonization, and the data quality metrics generated by the GDC.

During this webinar, the University of Chicago’s Dr. Bill Wysocki will:
Provide an overview of the GDC data life cycle
Review the GDC data quality strategy
Discuss GDC data quality submission and harmonization tools
Review GDC data quality metrics
Learn more about the GDC and its efforts to assure data quality by visiting https://datacommons.cancer.gov/

Presenter:
Bill Wysocki, Ph.D.
Dr. Bill Wysocki is the team lead of User Services and Outreach for the GDC at the University of Chicago.

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The goal of these workshops is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

This is very important! Please follow this link and https://rstudio-education.github.io/hopr/starting.html onto your computer before class. If you have trouble doing this please send email to ncibtep@nih.gov and we will help you.

This class is held weekly for 6 weeks. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to this training.

For more information and class updates, please see the GitHub page for the class at https://abcsfrederick.github.io/Intro-to-R-Fall2020/Schedule

Please Register to join us for a virtual workshop focused on defining the major challenges and promising new approaches for creating, curating, integrating, and querying across ultra-large chemistry databases.

This event is a Three half-days.

Overview
With the explosion of chemistry data resources capable of housing information on a billion or more molecules each, we are presented with tantalizing new opportunities and challenges for integrating and mining information across multiple ultra-large databases spanning widely divergent sets of properties.

Preliminary list of speakers:

Marc Nicklaus, Ph.D.
Head, Computer-Aided Drug Design Group
Center for Cancer Research
National Cancer Institute

Gergely Zahoranszky-Kohalmi, Ph.D.
National Center for Advancing Translational Sciences
National Institutes of Health

Eric Stahlberg, Ph.D.
Director, Biomedical Informatics and Data Science
Frederick National Laboratory

G. Sitta Sittampalam, Ph.D.
National Center for Advancing Translational Sciences
National Institutes of Health Janelle Cortner, Ph.D.
Director, Data Management Program
National Cancer Institute

Contact: Janelle Cortner (cortnerj@nih.gov) with any questions.

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In partnership with the NIH Clinical Center’s Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class geared to cover the general concepts behind common statistical tests. This two-part lecture series will help participants to better understand and prepare data, interpret results and findings, design and prepare studies, and better understand the results in published literature.
Part 1 will address considerations for the choice of statistical tests including the importance of study design and hypothesis, type of data and their distributions, and provide a brief review of important statistical features. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study.
Although you may attend only one part of this series, attending both parts will give you a better understanding of the most used statistical tests in the biomedical literature. You must register separately for Part 2 of this class series.

Instructor(s) — External Vendor:
Ninet Sinaii

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Visualizing biological data can be challenging. Heat maps are useful for showing the basic distribution of gene expression but interactivity is needed for additional, in-depth analysis. Clustergrammer2 is a visualization tool that allows users to perform more refined clustering on cell lines and genes.
In this webinar, Dr. Nicolas Fernandez, who worked on the original Clustergrammer and developed Clustergrammer2, will demonstrate how to use the latest version of this tool. Clustergrammer2, which is now available on Jupyter Notebook, can be used to explore and analyze various types of high-dimensional biological data (e.g., single-cell gene expression data) and share those results with colleagues.

Presenter:
Nicolas Fernandez, Ph.D.
Dr. Nicolas Fernandez is a senior computational biologist at https://vizgen.com/. He developed https://clustergrammer.readthedocs.io/clustergrammer2.html while at the Human Immune Monitoring Center at Mount Sinai as a computational scientist. The original https://clustergrammer.readthedocs.io/ was developed while Dr. Fernandez was a post-doctoral fellow at the Ma’ayan Laboratory at Mount Sinai.

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In partnership with the NIH Clinical Center’s Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a class geared to cover the general concepts behind common statistical tests. This two-part lecture series will help participants to better understand and prepare data, interpret results and findings, design and prepare studies, and better understand the results in published literature.
Part 2 will describe the basic concepts for using common statistical tests such as Chi-square, paired and two-sample t-tests, ANOVA, correlations, simple and multiple regression, logistic regression, and some nonparametric tests. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study.
Although you may attend only one part of this series, attending both parts will give you a better understanding of the most used statistical tests in the biomedical literature. You must register separately for Part 1 of this class series.

Instructor(s) — External Vendor:
Ninet Sinaii

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Registration is required to join this event. If you have not registered, please do so now.

It has been established that over half of all cancer types are driven by DNA Copy Number Variants (CNVs). In this presentation, we will demonstrate the use of Nexus Copy Number software for detection of CNVs from various microarray and NGS platforms and performance of cohort analysis. In particular, we will use data from The Cancer Genome Atlas (TCGA) that has been manually curated and available to NCI personnel through the NexusDB database to showcase some of the advanced statistical and visualization capabilities of Nexus Copy Number. This will include identification of recurrent aberrations in different cancer types and sub-populations, statistical comparison analysis between different sets of samples, identification of regions with high predictive power for survival analysis and many other features. Subsequent sessions will expand on integrating expression and sequence variant data into the analysis.

Register

The goal of these workshops is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

This is very important! Please follow this link and https://rstudio-education.github.io/hopr/starting.html onto your computer before class. If you have trouble doing this please send email to ncibtep@nih.gov and we will help you.

This class is held weekly for 6 weeks. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to this training.

For more information and class updates, please see the GitHub page for the class at https://abcsfrederick.github.io/Intro-to-R-Fall2020/Schedule

Register

The goal of these workshops is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

This is very important! Please follow this link and https://rstudio-education.github.io/hopr/starting.html onto your computer before class. If you have trouble doing this please send email to ncibtep@nih.gov and we will help you.

This class is held weekly for 6 weeks. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to this training.

For more information and class updates, please see the GitHub page for the class at https://abcsfrederick.github.io/Intro-to-R-Fall2020/Schedule