Bioinformatics Training & Education Program

Join us for a one-hour talk investigating tumor signatures in the BRCA dataset by utilizing the CCBR Single-Cell RNA-seq Workflow on NIDAP. This talk will take you through an analysis of a publicly available BRCA single-cell atlas (link) to identify changes in the tumor microenvironment across breast cancer subtypes.    By the end of this session, attendees will be able to:

• • Understand the structure and capabilities of the CCBR Single-Cell RNA-seq Workflow on NIDAP, including cell filtering, batch correction, annotation, and visualization (e.g. UMAP, t-SNE, violin plot, etc.).
  • Access training resources and support for using this single-cell workflow, as well as additional NIDAP workflows (Bulk RNA-seq, Digital Spatial Profiling, and Visium) to analyze your own data

 

Who Should Attend? This session is open to all researchers interested in single-cell RNA-seq data analysis, including beginners and experienced users looking to explore NIDAP's capabilities.
Requirements: No prior experience with NIDAP is required. However, attendees are encouraged to familiarize themselves with the platform by accessing training resources or contacting CCBR Support for guidance.
Training Resources: Attendees will be introduced to key resources for further exploration:

• • Workbooks: Templates for both single-cell and bulk RNA-seq workflows.
  • Training Videos: Step-by-step tutorials covering core concepts and processes.
  • Support Contacts: Assistance for workflow implementation.

Technology Note: This talk will focus on providing an overview and does not require NIDAP access. For hands-on tutorials, consider attending our dedicated training sessions below: Sign Up for a NIDAP Hands-on Workshop
Contact for Assistance: For questions about the workflows or additional support, contact CCR Support at NCICCBRNIDAP@mail.nih.gov.

Alzheimer’s Disease (AD) presents significant challenges in prevention and treatment despite decades of research advancements. Innovative AI/ML approaches enable analysis of real-world data sources, such as electronic health records (EHRs) and longitudinal multimodal data to derive insights without the constraints of predefined selection criteria. Recent developments in integrative heterogeneous graph databases enable the synthesis of knowledge across omics relationships, facilitating the identification of molecular hypotheses linked to complex clinical phenotypes.

We performed deep phenotyping to characterize AD and sex differences in the EHR compared to a control cohort. We identified sex and AD associated with comorbidities, medication use, and lab results. We employed ML techniques to predict AD onset using clinical information and identify prioritized genes through knowledge network (e.g., APOE, ACTB, IL6) and genetic colocalization analysis (e.g., MS4A6A with osteoporosis). Our findings indicate that clinical data can effectively predict the risk of AD onset while highlighting sex-specific relationships before disease manifestation. This work has paved the way for current approaches where we leverage unsupervised learning and LLMs to elucidate AD heterogeneity further with the goal of facilitating advances in personalized prediction and interventions for AD.

Registration in advance is requested.

This three hour online training covers QIAGEN’s CLC Genomics Workbench enables researchers to analyze NGS data without the use of command line and is a powerful tool for determining differential expression. In this workshop students will explore the usage of CLC Genomics Workbench for taking raw NGS data and performing QC, processing, statistical analyses, and visualizations. The training will focus on the identifying differentially expressed genes from RNA-seq and how those results can be passed to Ingenuity Pathway Analysis (IPA) for biological interpretation. 

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

• Import FASTQ files and metadata 
• how to download references
• Map reads to a reference genome and generate gene and transcript counts and QC reports displaying % mapped reads, knee plots, etc.
• Generate visualizations of results, such as heatmaps, differential expression tables, PCA/PCOA plots, Venn diagrams and others
• Easily customize RNA-seq workflows
• Export publication-quality graphics, tables and report
• Send differential expression tables to QIAGEN Ingenuity Pathway Analysis directly from QIAGEN CLC Genomics Workbench to analyze and interpret relevant pathways

Notes on Technology

Registrants will receive an email with information and instructions to install CLC Genomics before the training. If you register the day before the training, you may not have time to download and properly install CLC Genomics. If you do not have the software installed, this training will be demo only.

NCI CCR Fellows and Young Investigators Seminar Series

AI in Medicine: Focus on Deep Learning in Medical Genomics

This series invites Principal Investigators, Senior Scientists, and Senior Clinicians  to share cutting-edge research and developments in their fields. Each session includes a 20-30 minute presentation followed by a Q&A or journal club discussion, fostering deeper insights and scholarly exchange. Pizza is provided on a first come first served basis. 

AI is already dramatically changing biomedical research and the practice of medicine. Using the field of medical genomics as an example, this talk will (relatively informally) describe how deep learning, a particularly powerful type of AI, is used and studied.

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. It provides an overview of options for working with dates, times, and options for customizing a ggplot graph.  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. In order to proceed, attendees should have the done following:  

Installed R and RStudio 

Taken Introduction to R and RStudio training. If not, here are some resources for getting started: 

• Intro to RStudio Projects: Reproducibility
• Intro to RStudio Projects: Creating a Project
• Intro to RStudio Projects: Organizing Projects

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

• Data Visualization in R:  introduction to ggplot Part 1 of 2
• Data Visualization in R: Customizations Part 2 of 2

Note on Technology

Registrants will receive an email with information and instructions to install and verify access to R, RStudio and required packages before the training. If you register the day before the training, you may not have time to download and properly install the necessary software. If you do not have the software installed, this training will be demo only.

The NCI Rising Scholars: Cancer Research Seminar Series, hosted by the Center for Cancer Training, highlights the research and important contributions made by postdoctoral scholars and early-career investigators funded via an NCI extramural fellowship or career development award or supported by the NCI Intramural Research Program.