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

Welcome to BTEP’s Introduction to Unix on Biowulf course series. We will meet on Tuesdays and Thursdays from 1 pm to 2 pm (starting January 24, 2023) to learn how to work in the Unix command line environment on Biowulf. We will meet for a total of seven classes, with the last class taking place on Tuesday, February 14, 2023. This course series will be held online through Webex. Following a 1-hour lesson (1 pm to 2 pm), we will host a 1-hour (2 pm to 3 pm) optional help session.

This course series is meant for those with little to no experience in working on Unix command line or Biowulf; thus, you do not need to have a Biowulf account to participate. However, you will need your government furnished computer and a connection to the NIH network either on-campus or through VPN. You will not need to install anything for this class.

The ability to work in Unix command line is important because many bioinformatics applications are designed to work on a Unix based system; therefore, the ability to work in the Unix environment is important for those wishing to pursue bioinformatics work. Biowulf is the high-performance compute cluster at NIH and runs the Unix command line environment at is core. Many applications used in bioinformatics are installed on Biowulf and together, with its computational power makes it an ideal platform for bioinformatics.

Please fill out this survey when registering to let us know if you have a Biowulf account: https://www.surveymonkey.com/r/LSWTXFJ

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 24, 2023 (Tuesday)
January 26, 2023 (Thursday)
January 31, 2023 (Tuesday)
February 2, 2023 (Thursday)
February 7, 2023 (Tuesday)
February 9, 2023 (Thursday)
February 14, 2023 (Tuesday)

Topics to be covered:

Logging into Biowulf from our local computer
Navigating the Biowulf environment
Working with files and directories
Useful Unix commands
Downloading data
Introducing Biowulf applications for working with sequencing data

Course documents:
https://btep.ccr.cancer.gov/docs/unix-on-biowulf-2023/

Meeting link:
https://cbiit.webex.com/cbiit/j.php?MTID=m933fd4cf621d4603cb70d11f05b61ae7

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.

Join Dr. Peng Liu of Iowa State University as she proposes a model-based algorithm based on Poisson hurdle models for sparse microbiome count data. Simulation results demonstrate that the proposed methods provide better clustering results than alternative methods under various settings.

In this seminar, Dr. Liu will:

• describe an expectation-maximization algorithm and a modified version using simulated annealing to conduct cluster analysis.
• provide methods for initialization and choosing the number of clusters.
• illustrate how to apply this method using her team’s developed R package, PHclust, and apply the proposed method to a microbiome data set that results in interesting biological findings.

This method can also be applied to single-cell RNA-sequencing data.

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. 

The CCR Genomics, Sequencing and Single Cell Analysis Core Facilities are pleased to host a technology seminar on single cell sequencing with Oxford Nanopore Technologies (ONT) and 10X Genomics

1:00 – 1:45 Nanopore Sequencing updates and Single Cell applications

Edward Sawicki, Jr, Regional Sequencing Specialist, Oxford Nanopore Technologies

1:45 – 2:30 Add New dimensions to your 10X single cell and spatial experiments with long read sequencing.

Bradley Toms, Science and Technology Advisor, 10X Genomics

2:30-3:00 Group Discussion and Mingle.

Webinar number: 2315 654 2799

Webinar password: JTgQbQ7H6@2 (58472774 from phones)

Join by phone  1-650-479-3207 Call-in toll number (US/Canada)

Access code: 231 565 42799

For questions about this seminar please contact any of the Facility Core heads below:

Liz Conner, CCR Genomics Core

Mike Kelly, CCR Single Cell Analysis Facility

Bao Tran, CCR Sequencing Facility

Xiaolin Wu, CCR Genomics Technology Lab

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.

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.

This class focuses on data and project management using R and RStudio. RStudio makes it possible to work on a complete research project in a more efficient, integrated, and organized manner. This intermediate-level class is designed to be relevant to participants from different disciplines. Some familiarity or experience in R and RStudio is recommended. Participants are encouraged to install R and RStudio before the class.

This class focuses on using Git and GitHub, with RStudio.  Using integrated RStudio tools, learners will have a chance to experiment with this integration and understand its advantages for collaboration and version control. This intermediate-level class is designed to be relevant to participants from different disciplines. Some familiarity or experience in R and RStudio is recommended. Participants are encouraged to install R and RStudio before the class.  Participants will need to download the class data before the class.

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.

Learn about a wide range of capabilities for image processing and computer vision including machine and deep learning using deep convolutional neural networks (CNNs). Transitioning image models from pixel-based to feature-based allows us to extract information from images and video at a high level, to detect, classify, and track objects, co-register images, or understand a real-world scene. Using collections of features, we can train computers to recognize objects, with user-specified or automatically determined features. This is an introductory class, but familiarity with MATLAB or image processing is recommended.

This class is designed for those who want to learn the basic of R Markdown and apply those skills in Quarto. Quarto is an open-source scientific and technical publishing system that offers multilingual programming language support to create dynamic and static documents, books, presentations, blogs, and other online resources.  In this intermediate-level class participants will learn the basics of creating markdown documents. Some familiarity or experience in R and RStudio is recommended. Participants are encouraged to install R and RStudio before the class.

In partnership with the NIH Clinical Center’s Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering classes geared to cover general concepts behind statistics and epidemiology. This five-part lecture series will help participants better understand statistical and epidemiological features in biomedical research, interpret results and findings, design and prepare studies, and understand/critically review the results in published literature.

Part 1 will address fundamental statistical concepts including hypothesis testing, p-values and confidence intervals, types of data and their distributional importance, and bias and confounding. 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 any part of this series by itself, attending all five parts will provide a more comprehensive review of important statistical and epidemiological considerations that build on each other. You must register separately for each part of this class series.

This class is designed for those who want to extend the basics of R Markdown and apply those skills in Quarto. Quarto is an open-source scientific and technical publishing system that offers multilingual programming language support to create dynamic and static documents, books, presentations, blogs, and other online resources. In this intermediate-level class, participants will learn how to render documents in multiple format, and format citations and bibliographies using Zotero. Zotero is a free, easy-to-use tool to help collect, organize, annotate, cite, and share research. Some familiarity or experience in R and RStudio is recommended. Participants are encouraged to install R and RStudio before the class.

In partnership with the NIH Clinical Center’s Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering classes geared to cover general concepts behind statistics and epidemiology. This five-part lecture series will help participants better understand statistical and epidemiological features in biomedical research, interpret results and findings, design and prepare studies, and understand/critically review the results in published literature.

Part 3 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 survival analysis. 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 any part of this series by itself, attending all five parts will provide a more comprehensive review of important statistical and epidemiological considerations that build on each other. You must register separately for each part of this class series.

This class provides a basic overview of creating plots using ggplot. ggplot is a part of the Tidyverse, a collection of R packages designed for data science. This class will focus on identifying the appropriate packages for plotting, defining plot aesthetics, and demonstrating how to add layers to ggplot graphs. Participants are encouraged to install R, RStudio, and the tidyverse package, before the class.

In partnership with the NIH Clinical Center’s Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering classes geared to cover general concepts behind statistics and epidemiology. This five-part lecture series will help participants better understand statistical and epidemiological features in biomedical research, interpret results and findings, design and prepare studies, and understand/critically review the results in published literature.

Part 4 will provide a brief review of the principles of epidemiology, outbreak investigations, implications in public health, key concepts and terms, and commonly used statistics in epidemiology (e.g., morbidity and mortality rates; incidence and prevalence; relative risk; odds ratio; sensitivity and specificity). The instructor will present a set of exercises to work through during the lesson (a calculator will be needed).

Although you may attend any part of this series by itself, attending all five parts will provide a more comprehensive review of important statistical and epidemiological considerations that build on each other. You must register separately for each part of this class series.

This advanced class provides an overview of options for customizing a ggplot graph. This class will focus on methods for creating small multiples, options for customizing a graph, and how to apply ggplot themes. Participants are encouraged to install R, RStudio, and the tidyverse package, before the class.

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