BTEP Single-Cell and Spatial Omics Seminar Series

This session will introduce major concepts and practical workflows for single-cell multi-omics, with emphasis on paired same-cell measurements such as CITE-seq and single-cell RNA+ATAC multiome, as well as integration of unpaired and spatial modalities. We will discuss how transcriptomic, protein, chromatin-accessibility, genotype/repertoire, perturbation, and spatial readouts can be combined to improve cell-state annotation and biological interpretation, with practical examples comparing Seurat/Signac and scverse-oriented workflows.

This presentation examines spatial transcriptomic analysis workflows and demonstrates how geometric modeling, graph-based approaches, and statistical inference can be integrated to characterize tissue architecture and gene expression patterns. The underlying workflow within this presentation is based on the computational framework used in the study: Multimodal spatial transcriptomic characterization of mouse kidney injury and repair (https://doi.org/10.1038/s41467-025-62599-9).

Single cell RNASeq experiments will often include multiple samples with variable conditions. In this seminar, we will explore some of the common practices associated with combining samples to minimize batch effects, identifying cell types present in the data, and characterizing differentially expressed genes that can drive further cell type annotation and analysis for deeper exploration.

This seminar will introduce the major steps in a standard single-cell RNA-seq analysis workflow, from quality control and filtering through normalization, dimensionality reduction, and clustering. The talk will focus on the reasoning behind each step, common choices researchers face, and how these early analytical decisions shape downstream interpretation of cell populations and biological results.

This session introduces the major ecosystems for single-cell analysis (Seurat, scverse, and Bioconductor), comparing their core data objects and highlighting key learning resources. The session then transitions into a practical walkthrough of data import and the fundamentals of the Seurat object, setting the stage for hands-on analysis in the sessions ahead.

This seminar spotlights the initial data processing steps at SCSC (CCR SCAF) as well as the early quality assessment of single-cell and spatial transcriptomics data, providing insights into dataset quality before moving on to downstream filtering and quality control in Seurat or other analysis workflows.

This session will introduce the Single Cell and Spatial Core (SCSC), formerly known as CCR Single Cell Analysis Facility (CCR SCAF), offering an overview of the single-cell and spatial genomics technologies available to NCI CCR investigators. We’ll highlight core resources and support for project planning, experimental design, sequencing, and downstream data analysis, with a particular focus on how these methods can advance cancer research.