Classes & Events

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

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

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. 

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.