Work-in-progress list of my favorite tools for scRNA-seq and scATAC-seq data processing.
Table of Contents
scRNA-seq preprocessing
- Cell Ranger: 10x alignment & quantification
- STARsolo: fast droplet-based alignment
scRNA-seq processing
- Seurat: R ecosystem
- Scanpy: Python ecosystem
scRNA-seq batch correction
scRNA-seq doublet detection
Trajectory / RNA Velocity
Cell type proportions
- Speckle/Propeller
- scCoda: identifies compositional changes (i.e. cell type proportions changes) in scRNA-seq datasets.
- tascCoda: extension of scCoda accounting a tree structure, such a cell lineage hierarchy or a taxonomic tree.
- Milo: differential abundance analysis using KNN.
scATAC-seq preprocessing
scATAC-seq processing
- Signac: Seurat companion for ATAC
- SnapATAC2
- ArchR
Multimodal integration
- WNN (Seurat v4/v5)
- MOFA+
- MultiVI
- scGlue
- Cobolt
Multimodal analysis
- SCENIC+: inference of enhancers, TFs and target to reconstruct gene regulatory networks (GRN)
- monaLisa: enrichment of TF motifs
GWAS analysis
- S-LDSC: Tests whether SNPs within functional genomic annotations are disproportionately enriched for trait heritability.
- MAGMA: Gene-based tool that aggregates SNP-level GWAS signals up to the gene level to identify biological processes enriched for genetic associations.
scWGS analysis
- SCAN2: Genotyper for somatic SNVs in scWGS
Visualization
- scverse: includes anndata, scanpy, SnapATAC2, and scvi-tools among others.
- scvi-tools: Python library for probabilistic modeling of single-cell data. Includes scVI and MultiVI.