Classes & Events

Gil Kanfer, PhD, of the NCI CCR High-Throughput Imaging Facility (HiTIF), in the Laboratory of Receptor Biology and Gene Expression (LRBGE), will present the spatial biology analysis stack HiTIF is building to support Center for Cancer Research (CCR) researchers, with a focus on high-resolution spatial transcriptomics and multiplex protein imaging platforms existing in CCR Cores (e.g., Visium HD, Xenium-5k, CODEX) and how they can be turned into robust, reusable analysis workflows.

Using recent liver cancer and melanoma projects run by CCR investigators with the NCI CCR Single Cell Analysis Facility (SCAF) and Spatial Imaging Technology Resource (SpITR) core facilities in collaboration with HiTIF as examples, he will show how in-house algorithms—such as a zonation prediction model for mapping periportal vs pericentral regions and custom methods for collagen-based proximity and niche analysis—are combined with open-source tools to align images, integrate RNA and protein data, quantify cell-type composition and spatial organization, and systematically screen ligand–receptor interactions across conditions and time points. The talk will emphasize generalizable, technology-driven pipelines that take core-generated images all the way to quantitative, biologically interpretable spatial-omics readouts for CCR labs.

Attendance at this event is limited to NCI CCR personnel.

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. 

The “NIH Data Science Trainers Group” invites you to join us. We meet monthly (3rd Thursday, 3-4 PM) on topics of interest within the bioinformatics and data science training community. These meetings are not restricted to any institute or division, everyone involved in training within NIH are welcomed.

(However, If you are looking to learn how to perform bioinformatics and data science analysis, this is not the meeting for you, instead please see the NIH Bioinformatics Training Calendar at https://bioinformatics.ccr.cancer.gov/btep)

Past presentations have included the following topics: NIAID BioViz Lab, NIGMS Bioinformatics Sandbox, Coursera Online Learning Platform, Foundation for Advanced Education in the Sciences (FAES), NIH High Performance Compute (HPC) Cluster, Office of Data Science Strategy (ODSS), NCBI, and the Bioinformatics Training and Education Program (BTEP).

This is our RECRUITMENT meeting for new members to meet current members and join the group.

This one-hour online training introduces applying data science and artificial intelligence (AI) techniques to signals and time-series datasets using MATLAB. The training will cover the entire AI pipeline, from signal exploration to deployment. Participants will explore the fundamentals of processing, analyzing, and visualizing signal data, as well as implementing machine learning and AI algorithms tailored for time-series datasets. This training is designed for researchers, engineers, and data scientists who work with signals or temporal data and seek to enhance their analytical capabilities through MATLAB's data science and AI functionalities. 

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

• Understand the unique challenges and opportunities in analyzing signals and time-series data. 

• Import, preprocess, and visualize signal and time-series datasets in MATLAB. 

• Apply machine learning techniques, including supervised and unsupervised algorithms, to create predictive models for time-series data. 

• Explore deep learning approaches, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, for advanced time-series analysis. 

• Deploy trained AI models and automate workflows to integrate insights into research or operational pipelines. 

• Utilize MATLAB’s documentation, online resources, and toolboxes to extend their data science and AI capabilities. 

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

Part of the Childhood Cancer Data Initiative (CCDI) Webinar Series

During this webinar, experts from the USC Norris Comprehensive Cancer Center (NCCC) and Children's Hospital Los Angeles (CHLA) will present:

• An overview of the CHLA population, the NCCC, and work done under the prior CCDI P30 supplement award (dbGaP accession: phs002518)
• Methylation sequencing of pediatric central nervous system tumors (technical approaches and results)
• Whole-slide imaging in the analysis of pediatric solid tumors
• AI-driven multimodal analysis and integrated reporting

Alzheimer’s disease and related dementias (ADRD) remain a major health crisis with profound social and economic burdens. Innovative strategies are needed to identify genetic risk and protective factors, model disease mechanisms, and accelerate therapeutic discovery. Advances in AI and informatics now enable the integration of multimodal genetics, omics, imaging, and outcome data from large biobanks, creating powerful opportunities for biomarker and gene discovery beyond categorical diagnoses. At the same time, generative AI and large language models (LLMs) extend these capabilities to text-rich sources such as scientific literature, clinical notes, and caregiver narratives. When integrated with knowledge graphs, LLMs can dynamically retrieve and synthesize domain-specific knowledge, improving interpretability and advancing biomarker and drug discovery. Equally important, AI applied to conversational datasets and social media can help uncover caregiver needs and power novel mental health support tools. This talk will highlight how these approaches can advance both the science of ADRD and the care of older adults and their caregivers.

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.   

Attend this webinar to learn more about XNAT Scout—a new extension of the XNAT imaging informatics platform that’s designed to close the gap between artificial intelligence (AI) model development and clinical deployment.

Washington University’s Dr. Daniel Marcus will introduce XNAT Scout’s architecture, key capabilities, and early deployment experiences. XNAT Scout provides structured tools for assembling training cohorts, managing annotations, benchmarking models, and monitoring performance over time. Integrated with XNAT’s mature imaging workflows and governance frameworks, it enables reproducible validation, multi-site collaboration, and deployment pathways aligned with clinical interoperability and security requirements. By unifying data curation, evaluation, and operationalization in one platform, XNAT Scout accelerates translation and supports health systems in safely adopting AI at scale.

XNAT is a a globally used, open-source imaging informatics platform funded by the NCI Informatics Technology for Cancer Research (ITCR) program.

Join the NCI Cohort Consortium for a webinar on innovative approaches to improving data interoperability across cohort studies. The session will highlight efforts to apply the Observational Medical Outcomes Partnership Common Data Model to survey data and introduce tools, including Code Map capability, that enable mapping across data models based on Common Data Element semantic concepts. These approaches are designed to enhance data integration and support collaborative cancer cohort research.

Learn about Data Science from Dr. Mark Jensen, Director of Data Science, Center for Operations and Technical Support (CTOS), Bioinformatics and Computational Science (BACS), Leidos Biomedical Research, Inc.

Navigating Risk in Sharing Cancer Data: Sharing cancer data accelerates scientific discovery and promotes innovation across disciplines, which brings complex ethical, legal, and procedural challenges. This panel will share practical experiences; current frameworks and examples; and emerging tools for ethical, secured and FAIR cancer data sharing to help shape the standards that will define responsible data sharing for years to come.

Confronting Challenges to Sharing Cancer Data: Discussion of resources, incentives, obstacles and solutions to confront barriers to effective, efficient and equitable data sharing, including difficult to share data and less common cancer data types.

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 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:

• Analyze how different visual encodings affect the accuracy of data interpretation.
• Use Gestalt principles and preattentive attributes to design visualizations that improve clarity, grouping, and rapid perception.
• Evaluate the appropriateness of color scales.
• Identify and correct common visualization pitfalls.

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 hour and half 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. 

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering several trainings that cover general concepts behind statistics and epidemiology. These trainings will help participants  better understand and prepare data, interpret results and findings, design and prepare studies, and understand the results in published literature. 

This four-hour online training will address fundamental statistical concepts including hypothesis testing, p-values and confidence intervals, types of data and their distributional importance, and bias and confounding. Time will be devoted to questions from attendees and references will be provided for in-depth self-study.    

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

• Describe key concepts in statistical procedures

• Understand the steps involved in hypothesis testing 

• Define p-values and be familiar with their appropriate uses 

• Describe confidence intervals and their uses

• Understand differences in types of data and how to summarize them 

• Describe bias and confounding

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. 

This one and a half 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 addresses the challenges posed by messy datasets.  

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

• Diagnose and address common data quality issues in clinical datasets.
• Apply systematic approaches to clean and standardize text, dates, and numerical values.
• Transform messy data and handle missing values using tidyverse functions, including appropriate imputation strategies.
• Design reproducible, automated data-cleaning workflows with tidyverse tools for transformation and aggregation.

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 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.

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.

The ability to measure gene expression levels for individual cells (vs. pools of cells) and with spatial resolution is crucial to address many important biological and medical questions, such as the study of stem cell differentiation, the discovery of cellular subtypes in the brain, and cancer diagnosis and treatment. Single-cell transcriptome sequencing (RNA-Seq) allows the high-throughput measurement of gene expression levels for entire genomes at the resolution of single cells. Spatially-resolved transcriptomics further allows the measurement of gene expression levels along with the location of the RNA molecules within a tissue. Transcriptomics exemplifies the range of issues one encounters in a data science workflow, where the data are complex in a variety of ways, questions are not always clearly formulated, there are multiple analysis steps, and drawing on rigorous statistical principles and methods is essential to derive meaningful and reliable biological results. 

In this talk, Dr. Dudoit will provide a survey of statistical questions related to the analysis of single-cell transcriptome sequencing data to investigate the differentiation of stem cells in the brain, including, exploratory data analysis, expression quantitation, cluster analysis, and the inference of cellular lineages. She will also address differential expression analysis in spatial transcriptomics.