Classes & Events

This one-hour online training, provided by SAS, will review multiple ways to combine SAS data sets. 

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

• Utilize Concatenation on SAS data sets (SET Statement, PROC SQL, PROC APPEND) 

• Use Interleaving on SAS data sets (SET Statement with BY Statement) 

• Merge SAS data sets (MERGE Statement, PROC SQL, etc.) 

• Update SAS data sets (UPDATE, MODIFY Statements, etc.) 

Attendees are expected to have some working experience with SAS 9.4 or to have attended an introductory SAS class, such as SAS® Programming 1: Essentials.   

This is the fourth and final part of a four-part workshop series on Parallel Machine Learning Model Training, presented by AIIG co-chair Samar Samarjeet, PhD (NHLBI). Over the course of the workshop, Samar will cover topics on data and model parallelism including pipeline and looping parallelism, profiling, data sharding, using Jax and PyTorch, and specific tools like DeepSpeed and PyTorch-lightning.

In this lesson, attendees will learn the basics of ggplot2 to create simple, pretty, and effective figures with R. 

This one-hour online training equips participants with powerful data wrangling techniques using R and the tidyverse ecosystem. The tidyverse is a cohesive ecosystem of R packages designed to make data science workflows more intuitive and efficient through consistent syntax and design principles. Designed for both beginners and those looking to refine their skills, this training tackles the challenges of messy datasets.  

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

• Demonstrate how to clean messy clinical data using R 

• Implement methods for standardizing text, dates, and numerical values 

• Discuss the different ways to automate data transformations and aggregations using tidyverse functions 

• Transform and organize data using the dplyr and tidyr packages 

• Reshape data, handle missing values, create calculated fields, and prepare clean datadsets ready for visualization and analysis 

Requirements 

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.

This one-hour online training introduces attendees to modeling and simulation of biological systems using MATLAB’s SimBiology and BioPipeline Designer toolboxes. SimBiology is a versatile toolbox for modeling, simulating, and analyzing dynamic biological systems such as metabolic pathways, signaling cascades, and pharmacokinetics/pharmacodynamics (PK/PD) models. BioPipeline Designer complements this by streamlining workflows for integrating biological data and automating computational analyses. 

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

• Describe the capabilities and applications of SimBiology and BioPipeline Designer for modeling and analyzing biological systems. 

• Construct and parameterize basic models of biological processes using SimBiology’s graphical and programmatic interfaces. 

• Simulate dynamic behaviors of biological systems, such as time-course analyses, and interpret simulation results. 

• Automate and streamline data integration workflows using BioPipeline Designer to enhance reproducibility and efficiency. 

• Access and utilize resources for further learning, including tutorials, user guides, and MATLAB community forums 

Attendees are expected to be familiar with the basic functions of the MATLAB to be successful in this training. 

In this lesson, attendees will continue learning how to create publishable figures with ggplot2. Topics will include statistical transformations, coordinate systems, and themes. 

In this talk, Dr. Carey will describe how Bioconductor approaches new challenges in supporting open method development and reproducible
analyses in genomic data science. He will discuss aspects of the project that bear on education in cancer epidemiology and
computational cancer genomics, and on emerging topics in software and data engineering for scalable omics analyses.

In this lesson, attendees and instructor will work together to craft a publishable volcano plot using the skills previously learned. 

AI-Driven Spatial Transcriptomics Unlocks Large-Scale Breast Cancer Biomarker Discovery from Histopathology

This lesson introduces general recommendations and tips to consider when creating effective and reproducible visualizations. Additional topics to be discussed include multi-figure panels, complementary or related R packages, and the use of ggplot2 in functions. 

This one-hour online training will cover the fundamentals, applications, and ethical considerations of Artificial Intelligence (AI). Attendees will explore key topics such as machine learning, deep learning, data handling, and real-world AI applications across various industries. The session will also delve into the ethical implications of AI and provide insights on becoming AI literate. Whether you're a seasoned professional or just starting your AI journey, this session will equip you with essential knowledge to navigate the AI landscape effectively and make informed decisions in our data-driven world.

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

• Understand the core concepts of AI 
• Recognize the significance of ethical considerations in AI 
• Begin the journey toward AI literacy

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

Qiagen CLC Genomics Workbench is a point-and-click software that runs on a personal computer and enables bulk RNA sequencing, ChIP sequencing, long reads, and variant analysis that is available to NCI scientists. Submit a ticket with https://service.cancer.gov/ncisp to get it installed on personal computer. This Qiagen scientist led training will show participants how analyze bulk RNA sequencing data starting from FASTQ files and ending with differential expression analysis as well as constructing of visualizations (i.e. PCA and heatmap). Experience using or installation of CLC Genomics Workbench is not required for participation. This session is a demonstration and not hands-on. Attendance is restricted to NIH staff.

This one-hour and thirty 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.  

• Intro to STRIDES and Cloud Lab
• Tour the tutorial libraries: Overview of STRIDES Cloud Lab GitHub (AWS/GCP/Azure notebooks) and the NIGMS GitHub.
• Cloud demo: Build a chatbot with grounding using a Snakemake datastore. Configure datastore, query through the chatbot, and show responses based on the indexed sources.

The Buenrostro lab is broadly dedicated to advancing our knowledge of gene regulation and the downstream consequences on cell fate decisions. To do this, the Buenrostro lab develops new technologies utilizing molecular biology, microscopy and bioinformatics approaches and applies these tools to study stem cells in normal, ageing and cancer tissues in effort to discover chromatin regulators and their contribution to disease.

ATAC-seq was first developed by Buenrostro and colleagues in 2013 and its use is now ubiquitous in genomics — for example in major efforts like the Human Cell Atlas project to understand and map genome function. As a reflection of the impact of this work, MIT’s Technology Review named Buenrostro as one of its 2019 "Innovators under 35". 

This one-hour online training provides researchers with an overview of online resources for locating research datasets, data repositories, and data publications for data sharing and re-use. Participants will learn search strategies for locating datasets through federated data search portals and generalist data repositories, including directories for locating discipline-specific and institutional data repositories. An overview of key issues to consider when re-using datasets or when locating a data repository for sharing and preservation purposes will be discussed. 

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

• Locate different types of data repositories and datasets 

• Identify issues to consider with data repositories 

• Discuss how data repositories can improve reproducibility

• Identify issues to consider when re-using datasets 

• Describe guidelines and resources for citing datasets 

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

This one hour and half hour online training will equip attendees with essential knowledge and skills for effective interactions with Large Language Model (LLM) AI chatbots. Explore the intricacies of prompt engineering and its pivotal role in optimizing the conversational capabilities of LLMs. Emphasizing best practices and practical applications, this training features live demonstrations and provides valuable skills for the effective use of LLMs. 

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

• Define LLMs, prompt patterns, and prompt engineering
• Identify potential uses and issues to consider when using LLMs in the biomedical research field
• Use a selection of prompt patterns to improve generated output from LLMs
• Identify resources for learning more about prompt engineering in LLMs 

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

Qiagen Ingenuity Pathway Analysis (IPA) is a point-and-click software that enables scientists to discern how genomic, transcriptomic, proteomic, and metabolomic changes influence molecular biology pathways and networks. This software is available to NCI investigators. Submit a ticket with NCI computing help desk (https://service.cancer.gov/ncisp) to get it installed on personal computer. In this Qiagen scientist led training, participants will learn conduct path analysis from bulk RNA sequencing differential expression results using this software. Experience using or installation of IPA is not required for participation. This class is a demonstration and not hands-on. Attendance is restricted to NIH staff.

Investigating the Impact of Silencers on Disease using Deep Learning

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. 

This talk delves into the innovative utilization of generative AI in propelling biomedical research forward. By harnessing single-cell sequencing data, we developed scGPT, a foundational model that extracts biological insights from an extensive dataset of over 33 million cells. Analogous to how words form text, genes define cells, effectively bridging the technological and biological realms. The strategic application of scGPT via transfer learning significantly boosts its efficacy in diverse applications such as cell-type annotation, multi-batch integration, and gene network inference.

Additionally, the talk will spotlight MedSAM, a state-of-the-art segmentation foundational model. Designed for universal application, MedSAM excels across various medical imaging tasks and modalities. It showcased unprecedented advancements in 30 segmentation tasks, outperforming existing models considerably. Notably, MedSAM possesses the unique ability for zero-shot and few-shot segmentation, enabling it to identify previously unseen tumor types and swiftly adapt to novel imaging modalities. Collectively, these breakthroughs emphasize the importance of developing versatile and efficient foundational models. These models are poised to address the expanding needs of imaging and omics data, thus driving continuous innovation in biomedical analysis.

Qlucore Omics Explorer is a point-and-click software that enables analysis of RNA sequencing (bulk and single cell), proteomics and metabolomics data. It’s machine learning capabilities allow for cell type classification. This software is available to NCI CCR scientists. Submit a ticket at https://service.cancer.gov/ncisp to get it installed. This session covering bulk RNA sequencing introduces participants to experimental design, data import, normalization, differential expression analysis, visualizations, and biological interpretation (i.e. GSEA, pathway visualization, biological networks, GO enrichment). Experience using or installation of this software is not required for attendance. This class is a demonstration and not hands-on. Participation is restricted to NIH staff. Meeting link will be provided upon approval of registration.

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. 

Replicability in Biomedicine: Challenges, Causes and Corrections

In this hour and a half online training, attendees will be presented with simple ways to improve and optimize their code that can boost execution speed by orders of magnitude. Attendees will also learn about common pitfalls in writing MATLAB code, explore the use of the MATLAB Profiler to find bottlenecks, and will be introduced to the use of Parallel Computing Toolbox and MATLAB Parallel Server to solve computationally and data-intensive problems on GPUs, multicore computers, clusters and cloud platforms (e.g. AWS, Azure, etc).

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

• Understanding vectorization and best coding practices in MATLAB
• Addressing bottlenecks in your programs
• Incorporating compiled languages, such as C, into your MATLAB applications
• Utilizing additional hardware, including multicore processors and GPUS, to improve performance
• Scaling up to a computer cluster, grid environment or cloud

Attendees are expected to have some prior knowledge of MATLAB. This training is taught by MathWorks. Installation for MATLAB is not needed.  

This 30-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. 

Simple Questions Your RAG System Can't Answer

Qlucore Omics Explorer is a point-and-click package available to NCI CCR scientists that enables visualization-based analysis of multi-omics data including RNA-seq, scRNA-seq, proteomics, metabolomics, as well as enabling machine learning classification of cell types. Submit a ticket at https://service.cancer.gov/ncisp to get it installed. In this session, participants will learn to apply regression approaches to identify correlation between bulk RNA and protein expression using this software. Experience using or installation of Qlucore Omics Explorer is not needed to attend. Attendance is restricted to NIH staff. Meeting link will be provided upon approval of registration.

This one-hour online training offers an overview of the NIH-sponsored Generalist Repository Ecosystem Initiative (GREI) (Dataverse, Dryad, Figshare, Mendeley Data, Open Science Framework, Vivli, and Zenodo), and the role of participating in these repositories in the NIH data repository landscape for intramural researchers. The session will highlight how these repositories support compliance with the NIH Data Management and Sharing Policy.

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

• Describe how generalist repositories fit into the NIH data repository landscape for intramural researchers.
• Understand how these repositories support compliance with the NIH Data Management and Sharing Policy
• Learn about the resources developed by GREI repositories to support data sharing workflows, including a generalist repository comparison chart, a generalist repository selection flowchart, a data submission checklist, and a data management and sharing plan guide.
• Gain practical insights from real-world examples, demonstrating  how researchers use generalist repositories for data sharing and reuse, and how these efforts contribute to the broader NIH data sharing ecosystem.

Attendees are not expected to have any prior knowledge of the NIH Data Repository Landscape.    

Partek Flow enables scientists to construct analysis workflows for multi-omics sequencing data including DNA, bulk and single cell RNA, spatial transcriptomics, ATAC and ChIP. It is a point-and-click software suitable for those who wish to avoid the steep learning curve involved with analyzing sequencing data through coding. This class focuses on bulk RNA sequencing analysis where Partek scientist will teach participants how to start from FASTQ files and obtain differential expression analysis results, create visualizations, and extract biological insight through pathway analysis. This class is a demonstration and not hands-on. Experience using or access to Partek Flow is not required to participate. Attendance is restricted to NIH staff.

The role of computational science in biomedical research has typically been downstream of experiments, where it plays important roles in signal processing, data integration, pattern detection, and hypothesis testing. But this is changing, and predictive models are now being used to generate and test hypotheses in silico. In this talk, Dr. Pollard will share examples from human genetics, where they have built deep learning models of 3D chromatin interactions that take only sequence as input and then used them to interpret disease variants. This strategy leads to causal hypotheses and enables them to prioritize variants with predicted functional effects. Experiments designed using model outputs are accelerating the rate of discoveries, shedding light on genetic mechanisms in cancer and developmental disorders. This prediction-first strategy exemplifies Dr. Pollard's vision for a more proactive, rather than reactive, role for computational science in biomedical research.

Partek Flow enables scientists to construct analysis workflows for multi-omics sequencing data including DNA, bulk and single cell RNA, spatial transcriptomics, ATAC and ChIP. It is a point-and-click software suitable for those who wish to avoid the steep learning curve involved with analyzing sequencing data through coding. In this class, taught by Partek scientist, participants will learn about conducting QA/QC, performing cell type classification, obtaining differential analysis results, performing pathway analysis, and creating visualizations for single cell RNA sequencing data. This session is a demonstration and not hands-on. Experience using or access to Partek Flow is not needed for participation. Attendance is restricted to NIH staff.