sc-Epigenomics software

BEAT R BS-Seq Epimutation Analysis Toolkit, Model-based analysis of single-cell methylation data
DeepCpG Python Accurate prediction of single-cell DNA methylation states using deep learning
epiScanpy Python Computational framework for the analysis of single-cell DNA methylation and single-cell ATAC-seq data
BrockmanRBrockman Representation Of Chromatin by K-mers in Mark-Associated Nucleotides,  tool for exploratory data analysis for highdimensional or single cell epigenomics
ChromVARRAnalysis of sparse chromatin accessibility data from single cell or bulk ATAC or DNAse-seq data
CiceroRTools for analyzing single-cell chromatin accessibility experiments
cisTopicRSimultaneously identify cell states and cis-regulatory topics from single cell epigenomics data
DestinRToolkit for single-cell analysis of chromatin accessibility, scATAC-seq processing and cell clustering pipeline
Dr.seq2R, PythonQuality control and analysis pipeline for parallel single cell transcriptome and epigenome data (including scATAC-seq and Drop-ChIP data)
EpiclomalPythonProbabilistic clustering of sparse single-cell DNA methylation data
MelissaRMEthyLation Inference for Single cell Analysis, Bayesian Clustering and Imputation of Single Cell Methylomes
PDclustRAnalytical strategy to define single-cell DNA methylation states through pairwise comparisons of single-CpG methylation measurements
plate_scATAC-seqsnakemakeRapid and robust plate-based single cell ATAC-seq method, repository contains codes for processing and analysing scATAC-seq data
scABCRUnsupervised clustering and analysis of scATAC-seq data
SCRATRSingle-Cell Regulome Analysis Tool (ATAC-seq, DNase-seq, ChIP-seq, )
SCREWR, ShellSingle Cell Reproducible Epigenomics Workflow, pipeline and docker image for performing standard single-cell DNA methylation analyses
SnapATACR, PythonSingle Nucleus Analysis Pipeline for ATAC-seq

sc-Transcriptomics software

cardelinoRIntegrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants
cycloneRComputational assignment of cell-cycle stage from single-cell transcriptome data
DCAPythonDeep Count Autoencoder, denoising of scRNA-seq datasets
destinyRDiffusion maps for high-dimensional single-cell analysis of differentiation data
DistMapRSpatial mapping of single cell RNA sequencing data by using an existing reference database of in situs
FateIDRAlgorithm for the inference of cell fate bias in multipotent progenitors from singe-cell RNA-seq data
kBETRk-nearest neighbour Batch Effect Test, R package to test for batch effects in high-dimensional single-cell RNA sequencing data
MCARMultiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data
netSmoothRR/Bioconductor package for network smoothing of single cell RNA sequencing data
novoSpaRcPythonde novo Spatial Reconstruction of Single-Cell Gene Expression
PAGAJupyter NotebookGraph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (available within Scanpy)
PiGx scRNAseqsnakemakePipeline for analysis of Dropseq single cell RNA-seq data
powsimRRPower analysis for bulk and single cell RNA-seq experiments
RaceID3RAlgorithm for rare cell type identification from single-cell RNA-seq data
ScanpyPythonSingle-Cell Analysis in Python, scalable toolkit for analyzing single-cell gene expression data including preprocessing, visualization, clustering, pseudotime and trajectory inference and differential expression testing
scGenPythonGenerative model to predict single-cell perturbation response across cell types, studies and species
sc-LVMR, PythonModelling framework for single-cell RNA-seq data
SCRATRSingle Cell R Analysis Toolkit
slalomR, PythonScalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity
StemID2RAlgorithm for the inference of differentiation trajectories and the stem cell identity from single-cell RNA-seq data
stochprofMLRTool to infer single-cell regulatory states from expression measurements taken from small groups of cells (averaging-and-deconvolution approach)
zUMIsRFast and flexible pipeline to process (single-cell) RNA sequencing data with UMIs

Selection of popular software:

BackSPINPythonBiclustering algorithm developed taking into account intrinsic features of single-cell RNA-seq experiments
BASiCSRBayesian Analysis of single-cell RNA-seq data, estimates cell-specific normalization constants, total variability of the expression counts is decomposed into technical and biological components
CellityRClassification of low quality cells in scRNA-seq data using R
CellRangerR, PythonSet of analysis pipelines that process Chromium single cell 3’ RNA-seq output to align reads, generate gene-cell matrices and perform clustering and gene expression analysis
inferCNVRInfer Copy Number Variation using single-cell RNA-seq expression data
MAGICR, PythonMarkov Affinity-based Graph Imputation of Cells
MASTRModel-based Analysis of Single-cell Transcriptomics, fits a two-part, generalized linear models that are specially adapted for bimodal and/or zero-inflated single cell gene expression data
MIMOSCAPythonMultiple Input Multiple Output Single Cell Analysis
MonocleRDifferential expression and time-series analysis for single-cell RNA-seq
SC3RInteractive tool for the unsupervised clustering of cells from single cell RNA-seq experiments
SCDERDifferential expression using error models and overdispersion-based identification of important gene sets
SCENICR, PythonTool to infer gene regulatory networks and cell types from single-cell RNA-seq data
scranRPackage that implements a variety of low-level analyses of single-cell RNA-seq data (methods 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)
SeuratREasy-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
SIMLRR, PythonSingle-cell Interpretation via Multi-kernel LeaRning which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization
SPADERVisualization and cellular hierarchy inference of single-cell data
TraCeRPythonReconstruction of T cell receptor sequences from single-cell RNA-seq data
TSCANRPseudo-time reconstruction and evaluation in single-cell RNA-seq analysis
ZIFAPythonZero-inflated dimensionality reduction algorithm for single-cell data

Other sc-Software

BaSiCTool for background and shading correction of optical microscopy images, improves continuous single-cell quantification through correction of temporal drift in time-lapse microscopy data
DeepFlowPythonData analysis workflow for imaging flow cytometry that combines deep convolutional neural networks with non-linear dimension reduction
MOFAR/PythonFactor analysis model that provides a general framework for the integration of multi-omic data sets in a completely unsupervised fashion
SpatialDEMethod to identify genes which significantly depend on spatial coordinates in non-linear and non-parametric ways (spatially resolved RNA-sequencing from e.g. spatial transcriptomics, in situ gene expression measurements from e.g. SeqFISH or MERFISH)
tTt and qTfyEnabling single- cell tracking and quantification of cellular and molecular properties in time-lapse imaging data

External lists of sc-tools

Awesome single cellList of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
omicXPlatform that provides real-time access to the pathways used by life science practitioners in their published works, includes
scRNA toolsTable of tools for the analysis of single-cell RNA-seq data
scRNA-seq notesList of scRNA-seq analysis tools