Reference
Last updated on 2026-09-08 | Edit this page
Additional Reading
Hao, Y. et al. (2024). Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nature Biotechnology, 42, 293-304. doi:10.1038/s41587-023-01767-y - The Seurat v5 paper describing the dictionary learning framework and layer-based architecture used in this workshop.
Luecken, M.D. & Theis, F.J. (2019). Current best practices in single-cell RNA-seq analysis: a tutorial. Molecular Systems Biology, 15(6), e8746. doi:10.15252/msb.20188746 - A comprehensive overview of best practices for scRNA-seq analysis, covering QC, normalization, dimensionality reduction, clustering, and more.
Kaminow, B., Yunusov, D., & Dobin, A. (2021). STARsolo: accurate, fast and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data. bioRxiv. doi:10.1101/2021.05.05.442755 - The STARsolo paper describing the open-source alternative to Cell Ranger used in Episode 2.
Aran, D. et al. (2019). Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nature Immunology, 20, 163-172. doi:10.1038/s41590-018-0276-y - The SingleR paper describing the reference-based automated annotation method used in Episode 6.
Glossary
- Barcode
- A short DNA sequence (typically 16 nucleotides in 10x Genomics) that uniquely identifies a cell in a droplet-based scRNA-seq experiment.
- Batch effect
- Systematic technical differences between samples processed at different times, on different lanes, or with different reagents that are unrelated to the biology of interest.
- CCA (Canonical Correlation Analysis)
- A statistical method used by Seurat for data integration that identifies shared sources of variation between two datasets.
- Cell Ranger
- A set of analysis pipelines from 10x Genomics for processing Chromium single-cell data, including alignment, barcode counting, and gene expression quantification.
- Cluster
- A group of cells that share similar gene expression profiles, typically identified through graph-based community detection algorithms.
- Count matrix
- A genes-by-cells matrix where each entry represents the number of UMI counts detected for a given gene in a given cell.
- Differential expression (DE)
- Statistical testing to identify genes whose expression levels differ significantly between two or more groups of cells.
- Dimensionality reduction
- Mathematical techniques (PCA, UMAP, t-SNE) that reduce high-dimensional gene expression data to a lower-dimensional representation for visualization and analysis.
- Doublet
- An artifact where two cells are captured in the same droplet and assigned the same barcode, producing a hybrid expression profile.
- Feature
- In scRNA-seq, typically refers to a gene. Feature selection identifies genes with the most variation across cells.
- GEM (Gel Bead-in-Emulsion)
- A droplet in the 10x Chromium system containing a single gel bead (with barcode oligonucleotides) and ideally a single cell.
- Highly variable genes (HVGs)
- Genes that show the most variation in expression across cells, selected as informative features for dimensionality reduction and clustering.
- Leiden algorithm
- A community detection algorithm used for clustering cells in a shared nearest neighbor graph. An improvement over the Louvain algorithm.
- LogNormalize
- A normalization method that divides gene counts by total counts per cell, multiplies by a scale factor (10,000), and log-transforms the result.
- Marker gene
- A gene whose expression is specific to or enriched in a particular cell type, used for cell type annotation.
- PCA (Principal Component Analysis)
- A linear dimensionality reduction technique that identifies the axes of greatest variance in the data.
- Pseudobulk
- An approach that aggregates single-cell expression profiles by sample and cell type, creating pseudo-bulk samples for statistically valid differential expression analysis.
- QC (Quality Control)
- The process of identifying and removing low-quality cells based on metrics such as total counts, number of genes detected, and mitochondrial gene percentage.
- SCTransform
- A normalization method that uses regularized negative binomial regression to stabilize variance across genes with different expression levels.
- Seurat object
- The primary data structure in Seurat that stores expression data, cell metadata, dimensionality reductions, and analysis results.
- SingleR
- An R package for automated cell type annotation that assigns labels by comparing expression profiles to labeled reference datasets.
- SNN (Shared Nearest Neighbor) graph
- A graph where cells are nodes and edges connect cells that share nearest neighbors in PCA space, used as input for clustering algorithms.
- STARsolo
- An open-source tool integrated into the STAR aligner for processing droplet-based scRNA-seq data, serving as a fast alternative to Cell Ranger.
- UMAP (Uniform Manifold Approximation and Projection)
- A nonlinear dimensionality reduction technique used primarily for 2D visualization of single-cell data.
- UMI (Unique Molecular Identifier)
- A short random DNA sequence (typically 12 nucleotides in 10x Genomics) attached to each mRNA molecule before amplification, used to count unique transcripts and remove PCR duplicates.