Bioinformatics Training & Education Program

This one-hour online training will provide a high-level overview of Python coding concepts, as well as some of the integrative development environments (IDEs, such as Jupyter notebooks) used for Python coding. Python is a programming language used for data science, specifically: data analysis, statistical analysis, and visualization of results. The training will feature the following IDEs: Google Colaboratory: Jupyter Notebook; and Anaconda’s: Spyder, Jupyter Notebook, and JupyterLab. This overview training will demonstrate how these skills can boost productivity, rigor, and transparency in reporting research findings.  

By the end of the training, attendees will be able to: 

• Recognize four freely available IDEs for python coding 

• Identify fundamental components of python code 

• Understand how and why notebooks support rigor and transparency in analysis 

Attendees are not expected to have any prior knowledge of python coding or the IDEs to be successful in this training.  

If you choose to follow along with Google Colab or Jupyter Notebooks, these IDEs should be installed and ready to go. Code will be provided during the training for this option. 

Gene set enrichment analysis (GSEA) is a popular tool for evaluating functional enrichment, especially following differential expression analysis. This session of the BTEP Coding Club will show attendees how to conduct a fast preranked gene set enrichment analysis (fGSEA) using the R package clusterProfiler.

This lesson requires attendees to have a general understanding of R programming.

This 45-minute online Lunch and Learn training will help attendees develop their own customized strategy for responsibly incorporating generative artificial intelligence (AI) tools, such as ChatGPT, into their workflows. 

By the end of this training, attendees will be able to: 

• Assess appropriate use cases for generative AI tools within their specific research/work context 
• Develop a customized generative AI usage strategy 
• Document their approach for using generative AI tools 

Attendees are not expected to have any prior knowledge of generative AI tools to be successful in this training. 

This year the Frederick Research Compute Environment (FRCE) is expanding its outreach. Our first new feature is a web-based tool that will allow you to easily run graphical applications on the FRCE cluster, including MATLAB, RStudio, VSCode, PyMOL, and more. Come see how easy it is to get connected and hear about the other ways the FRCE cluster is growing this year to support research at NCI.

Translate gene lists into biological insight using pathway enrichment tools.

This training will provide an overview the current status of pathway tools, with focus on software available to NIH community.  It will discuss the biological interoperation of mutation and expression data in the context of pathways. This workshop will cover the following tools:

• Gene Set Enrichment Analysis (GSEA): computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states
• g:Profiler: a public web server for characterizing and manipulating gene lists
• PATHVIEW: a tool set for pathway-based data integration and visualization

In this lesson, we will learn how to tidy messy data using functions from the tidyverse package, tidyr.  The primary focus will be on reshaping data from wide to long format or vice versa.  

Dr. Cleary will present their latest work developing Perturb-FISH, a method that captures the effects of genetic changes within and between cells while preserving the spatial architecture of living systems, and SOCS, an algorithm for developmental trajectory inference from time-series spatial transcriptomics data.

Join Stony Brook University’s Dr. Joel Saltz as he discusses:

• the role of pathology artificial intelligence (AI) in the creation of pathology imaging biomarkers, including tumor infiltrating lymphocytes and prediction of molecular cancer sub-classification and molecular risk of recurrence assessments.
• methods he and his group are developing to improve interpretability of AI models.
• new methods he and his group have developed for generating highly realistic, multi-scale, synthetic pathology images using novel conditional latent diffusion methods that they have also developed. You can use these methods for a variety of downstream tasks, including data augmentation for supervised and unsupervised model training, segmentation, as well as for training and education.

This one-hour and half minute online training is part one of an introductory two-part series for those who want to learn about research data management and sharing, or for those who are interested in a refresher. The series 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 part one of this training series, attendees will be able to:   

• Understand data management best practices   
• Become familiar with data management tools  
• Have a solid knowledge of the resources, enabling data sharing  

During Part 2, attendees will learn about sharing and archiving data. You must register separately for Part 2 of this training. This training is introductory, no prior knowledge required.  

This one-hour and fifteen minute online training is part two of an introductory two-part series for those who want to learn about research data management and sharing, or for those who are interested in a refresher. The series 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 part two of this training series, attendees will be able to:   

• Have a solid knowledge of the resources, enabling data sharing  
• Understand how data is archived and preserved  

Part 1 of this training covers understanding research data, how to manage research data, and how to work with data. During Part 2, attendees learn about sharing and archiving data. This training is introductory, no prior knowledge required.  

You must register separately for Part 1 of this training.  

Learn how to visualize sequencing and analysis results effectively.

This session describes the application of the web-based interactive OmicCircos in R Shiny to construct circular plots with desired biological features. Example data from human and mouse genomes will be used to demonstrate over thirty plot functions along with the color selection, annotation, labeling, and zoom capabilities. User-guide, take-home video and sample plots from publications will be provided.

In this hour and half online training, attendees will learn about how to call MATLAB from Python and how to call Python libraries from MATLAB. Attendees will use MATLAB’s Python integration to improve the compatibility and usability of their code.   

By the end of this training, attendees will be able to:  

• Call Python libraries  

• Call user-defined Python commands, scripts, and modules  

• Package MATLAB algorithms to be called from Python  

Attendees are expected to have some prior knowledge of Python Libraries and/or MATLAB. This training is introductory taught by MathWorks. Installation for MATLAB is not needed. 

This lesson will introduce the tidyverse package, dplyr. Attendees will primarily learn how to filter rows and select columns from data frames.

This 45-minute online training provides a high-level overview of recent developments in artificial intelligence (AI). Each session highlights emerging trends, tools, and use cases in the evolving AI landscape, with an emphasis on practical relevance and responsible use. Whether you're just getting started or looking to stay current, this training offers timely insights in a concise format.  

By the end of this training, attendees will be able to:   

• Summarize key trends and developments in AI 

• Identify new tools, capabilities, or applications relevant to their work 

• Describe considerations for ethical and responsible use of AI technologies 

Attendees are not expected to have any prior knowledge to be successful in this training.

This hour-and-a-half online training will examine how humans process and encode visual information and how visual attributes can be utilized to create effective visualizations. This will focus on enhancing graphic literacy, exploring methods for making better visualizations, and using stakeholder needs to guide your design choices.

By the end of this training, attendees will be able to:

• Discuss the value of data visualization and key visualization goals
• Provide an introduction to human perception and its role in visualization
• Describe the principles of visual encoding.
• Provide an overview of core visualization techniques
• Outline the steps for effectively presenting your visualizations to different audiences.

This lesson will introduce the "split-apply-combine" approach to data analysis and the key players in the dplyr package used to implement this type of workflow.  

This is the final lesson in the course Introductory R for Novices: Introduction to Data Wrangling. This lesson will show attendees how to join multiple data frames and transform and create new variables using dplyr.

NIDDK Biostats Seminar Series: From Research Study Design to Collecting, Managing, and Analyzing Data.

Learning Objectives

1. The learner should know the difference between observational studies, clinical trials (drug and non-drug studies), and secondary data (new data from stored samples, existing data) as defined for the NIH Clinical Center and how study development differs for each.

2. The learner should understand the development process, know the timeline, and know the resources available for successful protocol development.

3. The learner should understand the purpose and scope of ClinicalTrials.gov.

4. The learner should be able to identify and understand key data elements and each step of trial registration and reporting.

5. The learner should be able to understand the differences between a scientific hypothesis and a statistical hypothesis.

6. The learner should be able to translate scientific hypotheses into statistical design elements: study design, primary outcomes, statistical hypotheses, sample size calculation, and statistical analysis plan.

Tentative Webinar Outline:

2:30-3:00pm – Dr. Paige Studlack(Clinical Protocol Coordinator, NIDDK)

Research study types, timelines, and process for successful protocol development, IRB approval, and study initiation at the NIH, with particular emphasis on NIDDK resources and processes.

3:00– 3:30pm – Dr. Elizabeth Wright (Mathematical Statistician, Biostatistics Program Office, NIDDK)

Understanding ClinicalTrial.gov elements and how they are used in trial registration and reporting for studies at the NIH.

3:30-4:00pm – Dr. Sungyoung Auh (Mathematical Statistician, Biostatistics Program Office, NIDDK)

Translating scientific questions to needed statistical design elements for research study planning, documentation, completion, and reporting.

This one and a half-hour online training covers the basic principles of FAIR (Findable, Accessible, Interoperable, Reusable) data and why it is important to make your data FAIR.  This is an introductory level training.

•  By the end of this training, attendees will be able to:  
• Define FAIR data   
• Explain what purpose FAIR data serves 
• Apply FAIR data principles to make data findable, accessible, interoperable, and reusable 

The "Data Visualization in R" series focuses on using ggplot and the broader tidyverse ecosystem to create insightful and customizable visualizations. It covers key principles of data visualization, from basic plots to advanced techniques, emphasizing the flexibility and power of ggplot within a tidy data workflow. By the end of the series, participants will be proficient in building plots using the tidyverse ecosystem. 

This hour and half online training will explore the topics of perception and cognition, and how these apply to data visualization. This training will also teach you how to visualize your data using ggplot2. We will start by creating a simple scatterplot and use that to introduce aesthetic mappings and geometric objects, the fundamental building blocks of ggplot2. You must have taken Introduction to R and RStudio training to be successful in this training. 

You can register for the other training in this series via the link below.

• Data Visualization in R: Customizations Part 2 of 2

By the end of this training, participants should be able to: 

• Describe how perception and cognition inform visualizations.
• Distinguish between aesthetic mappings and geometric objects, the fundamental building blocks of ggplot.
• Create a simple scatterplot.
• Create a plot and save it in a high-resolution format.
• Attendees are expected to have a basic understanding of R and RStudio. To proceed, attendees should have done the following:
• Installed R and RStudio.
• Have a basic understanding of R and RStudio.
• Reviewed our R basics training on the NIH Data Services: On Demand Content YouTube Playlist, if you are new to R.

NIDDK Biostats Seminar Series: From Research Study Design to Collecting, Managing, and Analyzing Data.

Learning Objectives:

1.         To delineate features of REDCap to support project management for research studies.

2.         To outline steps to create detailed data collection plans which fulfill regulatory requirements. 

3.         To identify principled approaches to data collection and management. 

4.         To explain the connections between research rigor and reproducibility.

Outline:

2:30-3:00pm – Matthew Breymaier (Informatics Specialist, Office of the Clinical Director, NIDDK), Sai Theja (Senior Data Analyst, Office of the Clinical Director, NIDDK)

RedCap – functionality and basics of setup and how different types of studies can be designed in RedCap (longitudinal vs cross-sectional etc), with emphasis on NIDDK RedCap.  

3:00– 4:00pm – Dr. Kenneth Wilkins (Mathematical Statistician, Biostatistics Program Office, NIDDK)

Document organization and access as part of study planning: regulatory, clinical, and case report forms 

Data Management and Sharing Plans

Data Management for Reproducibility

The "Data Visualization in R" series focuses on using ggplot and the broader tidyverse ecosystem to create insightful and customizable visualizations. It covers key principles of data visualization, from basic plots to advanced techniques, emphasizing the flexibility and power of ggplot within a tidy data workflow. By the end of the series, participants will be proficient in building plots using the tidyverse ecosystem. 

This one hour and half online training builds on the topics covered in the Data Visualization in ggplot training. This training emphasizes advanced customization techniques in ggplot, to create effective and clear visualizations. Participants will build on the foundational skills learned in Part 1 of the series and apply various customization options, such as faceting, labeling, themes, and color scales.  You must have taken Data Visualization in R: Introduction to ggplot: Part 1 of 2 training to be successful in this training.  

By the end of this training, attendees should be able to:  

• Create a scatterplot in ggplot 
• Learn how to facet a plot 
• Demonstrate options for customizing the title and axis 
• Apply different ggplot themes Attendees are expected to have a basic understanding of R and RStudio. To proceed, attendees should have done the following:
• Installed R and RStudio.
• Have a basic understanding of R and RStudio.
• Reviewed our R basics training on the NIH Data Services: On Demand Content YouTube Playlist, if you are new to R.
• You can register for the training in this series via the link below:  
• Data Visualization in R:  introduction to ggplot Part 1 of 2 

NIDDK Biostats Seminar Series: From Research Study Design to Collecting, Managing, and Analyzing Data.

Learning Objectives:

Be able to identify, load, and use R resources/packages based upon needs and experience level with R.  

1. For beginners, know how to load R Commander, import data, and navigate the GUI.  

2. For those interested in learning more about coding/functions, how to use R Swirl to learn foundations for functions and coding higher level operations (loops, combining functions, and building new functions).  

3. For regular users of R, how to use tidyverse for data manipulation, organization, and preparation for analysis.  

4. For those using R for research work, how to utilize R Markdown for appropriate and thorough project documentation and management.  

Outline:

2:30-3:00pm –Beginner level (Dr. Wilkins, Mathematical Statistician, Biostatistics Program Office, NIDDK)

How to get the basics accomplished: load data, navigate RCommander GUI, and export data.

3:00– 3:30pm – Intermediate level (Dr. Leary, Chief, Biostatistics Program Office, NIDDK)

Data manipulation and organization for analysis with focus on tools for more complex coding and functionality.

3:30-4:00pm – Advanced topics (Dr. Leary)

Leveraging R Markdown and other resources for project management, documentation, and archiving.