Resources for single-cell RNA-seq analysis
This list provides some links to resources on single-cell RNA-seq analysis methods that may be useful to you as you develop your own single-cell RNA-seq analysis skills and practices. Please note, this is not an exhaustive list. It includes multiple types of resources for various topics in single-cell RNA-seq analysis, but does not represent the complete breadth of analysis topics. Resources are listed by topic and in alphabetical order, not in order of recommendation.
Table of Contents
- General Single-cell resources
- Alignment and quantification of gene expression
- Filtering and normalization
- Dimensionality reduction and clustering
- Cell type annotation
- CITE-seq
- Integrating scRNA-seq samples
- Differential expression analysis
- Differential abundance
General Single-cell resources
- An introduction to the SingleCellExperiment class - Bioconductor
- Analysis of single cell RNA-seq data - Hemburg Lab
- Current best practices in single-cell RNA-seq analysis: a tutorial - Luecken and Theis (2019)
- Orchestraing Single-cell Analysis with Bioconductor - Bioconductor
Alignment and quantification of gene expression
- A like-for-like comparison of lightweight-mapping pipelines for single-cell RNA-seq data pre-processing - Zakeri et al. (2021)
- Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq data - He et al. (2022)
- Cell Ranger Overview - 10X Genomics
- Comparative analysis of common alignment tools for single-cell RNA sequencing - Bruning et al. (2022)
Filtering and normalization
- EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data - Lun et al. (2019)
- miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data - Hippen et al. (2021)
- OSCA Basics
- Utilities for handling droplet-based single-cell RNA-seq data: Detecting empty droplets
Dimensionality reduction and clustering
- OSCA chapter on clustering
- OSCA chapter on clustering metrics
- OSCA chapter on dimensionality reduction
- PCA - Principal Component Analysis
- Principal Component Analysis - A Brief Introduction
Cell type annotation
- AUCell: Identifying cells with active gene sets
- Azimuth
- Identifying cell types to interpret scRNA-seq data: how, why and more possibilities - Wang et al. (2020)
- OSCA chapter on cell type annotation
- scType
- The SingleR Book - Bioconductor
- Web resources for cell type annotation - 10X Genomics
CITE-seq
- OSCA chapter on integrating with protein abundance
- Simultaneous epitope and transcriptome measurement in single cells - Stoeckius et al. (2017)
Integrating scRNA-seq samples
- A description of the theory behind the
fastMNNalgorithm - Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors - Haghverdi et al. (2018)
- Benchmarking atlas-level data integration in single-cell genomics - Luecken et al. (2021)
- Fast, sensitive and accurate integration of single-cell data with Harmony - Korsunsky et al. (2019)
- OSCA multi-sample
Differential expression analysis
- Batch effects and the effective design of single-cell gene expression studies - Tung et al. (2017)
- Differential gene expression analyses in scRNA-seq data between conditions with biological replicates - 10X Genomics
- Harvard-Chan Bioinformatics Core tutorial on differential expression analysis with DESeq2
- OSCA chapter on DE analyses between conditions