List of software packages for single-cell data analysis, including RNA-seq, ATAC-seq, etc.
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List of software packages (and the people developing these methods) for single-cell data analysis, including RNA-seq, ATAC-seq, etc. Contributions welcome...

Software packages


  • anchor - [Python] - ⚓ Find bimodal, unimodal, and multimodal features in your data
  • BackSPIN - [Python] - Biclustering algorithm developed taking into account intrinsic features of single-cell RNA-seq experiments.
  • BASiCs - [R] - Bayesian Analysis of single-cell RNA-seq data. Estimates cell-specific normalization constants. Technical variability is quantified based on spike-in genes. The total variability of the expression counts is decomposed into technical and biological components.
  • bonvoyage - [Python] - 📐 Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction.
  • BPSC - [R] - Beta-Poisson model for single-cell RNA-seq data analyses
  • Cellity - [R] - Classification of low quality cells in scRNA-seq data using R
  • cellTree - [R] - Cell population analysis and visualization from single cell RNA-seq data using a Latent Dirichlet Allocation model.
  • clusterExperiment - [R] - Functions for running and comparing many different clusterings of single-cell sequencing data. Meant to work with SCONE and slingshot.
  • ECLAIR - [python] - ECLAIR stands for Ensemble Clustering for Lineage Analysis, Inference and Robustness. Robust and scalable inference of cell lineages from gene expression data.
  • Falco - [AWS cloud] - Falco: A quick and flexible single-cell RNA-seq processing framework on the cloud.
  • flotilla - [Python] Reproducible machine learning analysis of gene expression and alternative splicing data
  • HocusPocus - [R] - Basic PCA-based workflow for analysis and plotting of single cell RNA-seq data.
  • MAST - [R] - Model-based Analysis of Single-cell Transcriptomics (MAST) fits a two-part, generalized linear models that are specially adapted for bimodal and/or zero-inflated single cell gene expression data.
  • Monocle - [R] - Differential expression and time-series analysis for single-cell RNA-Seq.
  • OEFinder - [R] - Identify ordering effect genes in single cell RNA-seq data. OEFinder shiny impelemention depends on packages shiny, shinyFiles, gdata, and EBSeq.
  • Ouija - [R] - Incorporate prior information into single-cell trajectory (pseudotime) analyses using Bayesian nonlinear factor analysis.
  • outrigger - [Python] - Outrigger is a program to calculate alternative splicing scores of RNA-Seq data based on junction reads and a de novo, custom annotation created with a graph database, especially made for single-cell analyses.
  • SC3 - [R] - SC3 is an interactive tool for the unsupervised clustering of cells from single cell RNA-Seq experiments.
  • scater - [R] - Scater places an emphasis on tools for quality control, visualisation and pre-processing of data before further downstream analysis, filling a useful niche between raw RNA-sequencing count or transcripts-per-million data and more focused downstream modelling tools such as monocle, scLVM, SCDE, edgeR, limma and so on.
  • scDD - [R] - scDD (Single-Cell Differential Distributions) is a framework to identify genes with different expression patterns between biological groups of interest. In addition to traditional differential expression, it can detect differences that are more complex and subtle than a mean shift.
  • SCDE - [R] - Differential expression using error models and overdispersion-based identification of important gene sets.
  • SCell - [matlab] - SCell is an integrated software tool for quality filtering, normalization, feature selection, iterative dimensionality reduction, clustering and the estimation of gene-expression gradients from large ensembles of single-cell RNA-seq datasets. SCell is open source, and implemented with an intuitive graphical interface.
  • scLVM - [R] - scLVM is a modelling framework for single-cell RNA-seq data that can be used to dissect the observed heterogeneity into different sources, thereby allowing for the correction of confounding sources of variation. scLVM was primarily designed to account for cell-cycle induced variations in single-cell RNA-seq data where cell cycle is the primary soure of variability.
  • SCONE - [R] - SCONE (Single-Cell Overview of Normalized Expression), a package for single-cell RNA-seq data quality control (QC) and normalization. This data-driven framework uses summaries of expression data to assess the efficacy of normalization workflows.
  • SCOUP - [C++] - Uses probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation.
  • scran - [R] - This package implements a variety of low-level analyses of single-cell RNA-seq data. Methods are provided for normalization of cell-specific biases, pool-based norms to estimate size factors, assignment of cell cycle phase, and detection of highly variable and significantly correlated genes.
  • SCRAT - [R] - SCRAT provides essential tools for users to read in single-cell regolome data (ChIP-seq, ATAC-seq, DNase-seq) and summarize into different types of features. It also allows users to visualize the features, cluster samples and identify key features.
  • SEPA - [R] - SEPA provides convenient functions for users to assign genes into different gene expression patterns such as constant, monotone increasing and increasing then decreasing. SEPA then performs GO enrichment analysis to analysis the functional roles of genes with same or similar patterns.
  • scTCRseq - [python] - Map T-cell receptor (TCR) repertoires from single cell RNAseq.
  • Seurat - [R] - It contains easy-to-use implementations of commonly used analytical techniques, including the identification of highly variable genes, dimensionality reduction (PCA, ICA, t-SNE), standard unsupervised clustering algorithms (density clustering, hierarchical clustering, k-means), and the discovery of differentially expressed genes and markers.
  • sincell - [R] - Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework.
  • sincera - [R] - R-based pipeline for single-cell analysis including clustering and visualization.
  • SinQC - [R] - A Method and Tool to Control Single-cell RNA-seq Data Quality.
  • SLICER - [R] - Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories.
  • slingshot - [R] - Functions for identifying and characterizing continuous developmental trajectories in single-cell sequencing data.
  • SPADE - [R] - Visualization and cellular hierarchy inference of single-cell data using SPADE.
  • switchde - [R] - Differential expression analysis across pseudotime. Identify genes that exhibit switch-like up or down regulation along single-cell trajectories along with where in the trajectory the regulation occurs.
  • TraCeR - [python] - Reconstruction of T-Cell receptor sequences from single-cell RNA-seq data.
  • TSCAN - [R] - Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.

Copy number analysis

  • Gingko - [R, C] - Gingko is a cloud-based web app single-cell copy-number variation analysis tool.

Tutorials and workflows

Web portals and apps

Journal articles of general interest

Experimental design

Methods comparisons

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