awesome-single-cell

Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
https://github.com/seandavi/awesome-single-cell

Last synced: 4 days ago
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Image-based profiling
- Spatial transcriptomics
  - Pycytominer - [Python] Pycytominer is a suite of common functions used to process high dimensional readouts from high-throughput cell experiments. Manuscript: [Reproducible image-based profiling with Pycytominer](https://www.nature.com/articles/s41592-025-02611-8)
Journal articles of general interest
- Big data approach overview
  - Single-cell Transcriptome Study as Big Data
- Experimental design
  - Sensei - type abundance change estimated from scRNA-seq experiment](https://www.biorxiv.org/content/10.1101/2020.05.31.126565v1).
  - How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives
  - How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives
  - Design and computational analysis of single-cell RNA-sequencing experiments
- Methods comparisons
  - Bias, Robustness And Scalability In Differential Expression Analysis Of Single-Cell RNA-Seq Data - comparison of 36 statistical methods to detect differentially expressed genes between two annotated populations from the [conquer](http://imlspenticton.uzh.ch:3838/conquer/) database of consistently processed scRNA-seq datasets.
  - Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods - an assessment of main bulk and single-cell differential analysis methods used to analyze scRNA-seq data.
  - A comparison of single-cell trajectory inference methods - Unsure which of the more than 70 **trajectory inference** methods to use for your single-cell dataset? We evaluated 45 methods based on four criteria: the accuracy of the trajectory, how scalable the method is, how stable its outputs are, and the usability of the tool. These are summarised in a *"funky heatmap"* (Figures 2 & 3). Check out [dynverse.org](https://dynverse.org) for more information.
  - Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data - In this study, we benchmarked five methods (CIBERSORT, GSEA, GSVA, ORA and METANEIGHBOR) for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used five scRNA-seq datasets: human liver, 11 Tabula Muris mouse tissues, two human peripheral blood mononuclear cell datasets, and mouse retinal neurons, for which reference cell type signatures were available. Our results show that, in general, all five methods perform well in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.91, sd = 0.06), whereas precision-recall analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). GSVA was the overall top performer and was more robust in cell type signature subsampling simulations, although different methods performed well using different datasets. METANEIGHBOR and GSVA were the fastest methods.
  - Evaluation of single-cell classifiers for single-cell RNA sequencing data sets - In this article, nine tools have been systematically compared. The article provides a guideline for researchers to select and apply suitable single cell and cluster classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.
  - Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data - a comparison of gene regulatory network inference methods using simulated and real single-cell RNA-seq datasets
  - Comparison of computational methods for imputing single-cell RNA-sequencing data - We compared eight imputation methods, evaluated their power in recovering original real data, and performed broad analyses to explore their effects on clustering cell types, detecting differentially expressed genes, and reconstructing lineage trajectories in the context of both simulated and real data. Simulated datasets and case studies highlight that there are no one method performs the best in all the situations.
  - Comparison of methods to detect differentially expressed genes between single-cell populations - comparison of five statistical methods to detect differentially expressed genes between two distinct single-cell populations.
  - Evaluation of single-cell classifiers for single-cell RNA sequencing data sets - In this article, nine tools have been systematically compared. The article provides a guideline for researchers to select and apply suitable single cell and cluster classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.
- Paper collections
  - Mendeley Single Cell Sequencing Analysis
  - Single-Cell Genomics in the Journal Science - Special issue on Single-Cell Genomics
  - The emerging field of single-cell analysis - Special issue on single cell analysis
People
Similar lists and collections
- Methods comparisons
  - CrazyHotTommy's RNA-seq analysis list - Very broad list that includes some single cell RNA-seq packages and papers.
  - Museum of Spatial Transcriptomics - A comprehensive catalog of spatial transcriptomics data sets and methods.
  - agitter's Pseudotime estimation list - An overview of algorithms for estimating pseudotime in single-cell RNA-seq data.
  - scRNA-tools.org - Database of scRNA-seq analysis tools and their functions. Managed through this [Github repository](https://github.com/Oshlack/scRNA-tools).
Software packages
- Archetypal analysis
  - scAAnet - [Python] - scAAnet performs non-linear archetypal analysis through autoencoder networks to identify shared gene expression programs (GEPs) among heterogenous cell populations and infer relative activity of each GEP across cells.
  - ACTIONet - [R/Python] - A multiresolution framework that combines archetypal analysis with manifold learning to characterize continuous single-cell state landscapes. [A multiresolution framework to characterize single-cell state landscapes](https://doi.org/10.1038/s41467-020-18416-6).
- Batch-effect removal
  - BatchEffectRemoval - [Python] - [Removal of Batch Effects using Distribution-Matching Residual Networks](https://doi.org/10.1093/bioinformatics/btx196)
  - ResPAN - [Python] - ResPAN is a light structured **Res**idual autoencoder and mutual nearest neighbor **P**aring guided **A**dversarial **N**etwork for scRNA-seq batch correction.
  - scPLS - [C++, R] - A normalization method to remove unwanted variation using both control and target genes. It takes advantage of the fact that genes in a scRNAseq study often can be naturally classified into two sets: a control set of genes that are free of effects of the predictor variables and a target set of genes that are of primary interest. By modeling the two sets of genes jointly using the partial least squares regression, scPLS is capable of making full use of the data to improve the inference of confounding effects. https://www.nature.com/articles/s41598-017-13665-w
  - TASC - [C++, python] - To account for cell-to-cell technical differences, we propose a statistical framework, TASC (Toolkit for Analysis of Single Cell RNA-seq), an empirical Bayes approach to reliably model the cell-specific dropout rates and amplification bias by use of external RNA spike-ins. TASC incorporates the technical parameters, which reflect cell-to-cell batch effects, into a hierarchical mixture model to estimate the biological variance of a gene and detect differentially expressed genes. More importantly, TASC is able to adjust for covariates to further eliminate confounding that may originate from cell size and cell cycle differences.
  - UNCURL - [Python] - Unsupervised and semi-supervised sampling effect removal for single-cell RNA-seq data.
- Cell clustering
  - BackSPIN - [Python] - Biclustering algorithm developed taking into account intrinsic features of single-cell RNA-seq experiments.
  - dropClust - [R/Python] - Efficient clustering of ultra-large scRNA-seq data.
  - SC3 - [R] - SC3 is a tool for the unsupervised clustering of cells from single cell RNA-Seq experiments.
  - TooManyCells - [Haskell, CLI program] - [Suite of graph-based tools for efficient, global, and unbiased identification and visualization of cell clades.](https://www.biorxiv.org/content/10.1101/519660v1).
- Cell projection and unimodal integration
  - Monet - [python] - A package for analyzing and integrating scRNA-Seq data using PCA-based latent spaces.
- Cell subsampling
  - geosketch - [Python] - Method to subsample massive scRNA-seq datasets while preserving rare cell states. Resulting “sketch” accelerates clustering, visualization, and integration analyses. [Paper](https://doi.org/10.1016/j.cels.2019.05.003)
- Cell type identification and classification
  - CIPR - [R] - (Cluster Identity PRedictor-pronounced cy-per). A Shiny web applet (and R-package) that helps annotating the cluster identities in single-cell RNA-sequencing (scRNA-seq) experiments. The algorithm compares gene expression signature of experimental clusters with known reference datasets. In addition to 7 reference datasets implemented in CIPR (2 from mouse and 5 from human), users can upload custom high-throughput reference data for specialized studies. The CIPR pipeline can be further tailored to different analytical contexts by excluding irrelevant reference subsets and low-variance reference genes from the analysis. The manuscript describing CIPR and comparing its performance against other similar software was published in [BMC Bioinformatics](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3538-2). CIPR's fast and computationally efficient calculations and graphical outputs will facilitate scRNA-seq analysis where the user wants to try different clustering parameters iteratively and examine the cluster identities. Source code for the [Shiny](https://github.com/atakanekiz/CIPR-Shiny) and [R-package](https://github.com/atakanekiz/CIPR-Package) implementations are available on GitHub.
  - SingleR - [R] - SingleR leverages reference transcriptomic datasets of pure cell types to infer the cell of origin of each of the single cells independently. [Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nature Immunology (2019)](https://www.nature.com/articles/s41590-018-0276-y)
  - Celltypist - [Python] - Celltypist is an automated cell type annotation tool for scRNA-seq datasets on the basis of logistic regression classifiers optimized by the stochastic gradient descent algorithm. Celltypist provides several different models for predictions, with a current focus on immune sub-populations, in order to assist in the accurate classification of different cell types and subtypes.
  - scExtract - [Python] - scExtract is a tool for automating annotation and integration of published single-cell RNA-seq datas. This tool uses LLMs agents to extract relevant information from scientific articles, process the data, and use annotations to guide multi-datasets integration.
  - CyteType - [Python] - CyteType is a Python package for deep chracterization of cell clusters from single-cell RNA-seq data. This package interfaces with Anndata objects to call CyteType API.
  - ceLLama - [R/Python] - ceLLama is a streamlined automation pipeline for cell type annotations using local large-language models (LLMs).
  - CHETAH - [R] - CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing. CHETAH (CHaracterization of cEll Types Aided by Hierarchical clustering) is an accurate cell type identification algorithm that is rapid and selective, including the possibility of intermediate or unassigned categories. Evidence for assignment is based on a classification tree of previously available scRNA-seq reference data and includes a confidence score based on the variance in gene expression per cell type. For cell types represented in the reference data, CHETAH's accuracy is as good as existing methods. Its specificity is superior when cells of an unknown type are encountered, such as malignant cells in tumor samples which it pinpoints as intermediate or unassigned. [bioRxiv](https://doi.org/10.1101/558908)
  - easybio - [R] - easybio is an R pacakge for cell type annotation using the CellMarker2.0 database. [bioRxiv](https://doi.org/10.1101/2024.09.14.609619)
  - Garnett - [R] - Garnett is a software package that facilitates automated cell type classification from single-cell expression data. Garnett works by taking single-cell data, along with a cell type definition (marker) file, and training a regression-based classifier. Once a classifier is trained for a tissue/sample type, it can be applied to classify future datasets from similar tissues. In addition to describing training and classifying functions, this website aims to be a repository of previously trained classifiers. [Supervised Classification Enables Rapid Annotation of Cell Atlases](https://www.nature.com/articles/s41592-019-0535-3)
  - SignacX - [R] - Signac classifies the cellular phenotype for each individual cell in scRNA-seq data using neural networks trained with sorted bulk gene expression data from the Human Primary Cell Atlas. Signac can: map cells from one data set to another, classify non-human single cell data, identify novel cell types, and classify single cell data across many tissues, diseases and technologies. [Cell type classification and discovery across diseases, technologies and tissues reveals conserved gene signatures and enables standardized single-cell readouts](https://www.biorxiv.org/content/10.1101/2021.02.01.429207v3.full)
  - scCATCH - [R] - A single cell cluster-based annotation package from cluster marker genes identification to cluster annotation based on evidence-based score by matching the identified potential marker genes with known cell markers in tissue-specific cell taxonomy reference database (CellMatch) [Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data. iScience (2020)](https://www.sciencedirect.com/science/article/pii/S2589004220300663)
  - ImmClassifier - [R,python,Docker] - A cell type annotation algorithm that employs a knowledge-based approach to annotating cells based on their underlying ontology and multitudes of previously-published data. By encoding immune cell hierarchy in a neural network, ImmClassifier is able to identify fine-grained cell types with high accuracy. By running in Docker the tool is platform-agnostic. [bioRxiv](https://www.biorxiv.org/content/10.1101/2020.03.23.002758v1)
  - mLLMCelltype - [R/Python] - A multi-model framework for single-cell RNA-seq cell type annotation using large language models (LLMs). It implements an interactive consensus mechanism where multiple LLMs collaborate to reach agreement on cell type annotations, with uncertainty quantification through consensus proportion and entropy metrics. Supports OpenAI, Anthropic, Google, and Alibaba models.
  - CASSIA - [R/Python/Web] - CASSIA is a multi-agent large language model (LLM) framework for automated, reference-free, and interpretable cell type annotation of single-cell RNA-seq data. It includes dedicated agents for annotation, validation, formatting, quality scoring, and reporting, along with optional modules for subclustering, uncertainty quantification, retrieval-augmented generation (RAG), and annotation refinement via the Annotation Boost agent. CASSIA has been applied to correct errors in gold-standard annotations, detect mixed cell types, and accurately annotate rare cell populations across diverse species. [CASSIA: a multi-agent large language model for reference free, interpretable, and automated cell annotation of single-cell RNA-sequencing data](https://www.biorxiv.org/content/10.1101/2024.12.04.626476v2)
  - ScType - [Web/R/Python] - ScType is an automated ultra-fast, marker-based cell type annotation tool for single-cell and spatial transcriptomics data. [Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data](https://www.nature.com/articles/s41467-022-28803-w)
  - Compocyte - [python] - Compocyte is a composite classifier for modular hierarchical cell type annotation of single cell data. Using Compocyte you can build different hierarchical classifier architectures (local classifier per parent node, local classifer per node and local classifier per level) using all relevant models from pytorch, TensorFlow and keras. Local classifiers can be individually modified to account for alterations in classification taxonomies or selectively improve specific annotations in human-in-the-loop approaches.
  - mtSC - [Python] - A multitask deep metric learning framework that integrates multiple references for single-cell assignment, including cross-species settings. [Integrating multiple references for single-cell assignment](https://doi.org/10.1093/nar/gkab380).
  - clustifyr - [R] - Classifies cells and clusters in single-cell RNA-seq experiments using external reference data, gene signatures, or marker gene lists.
- Cellular interactions/communication
  - CellPhoneDB - [python] - Publicly available repository of curated receptors, ligands and their interactions in humam (subunit architecture is included for both ligands and receptors, representing heteromeric complexes accurately). [Paper](https://www.nature.com/articles/s41596-020-0292-x)
  - Celcomen - [python] - Causal generative model that disentangles intra- and inter-cellular gene regulation programs in spatial transcriptomics and single-cell data through a generative graph neural network. Can generate post-perturbation counterfactual spatial transcriptomics. [Paper](https://arxiv.org/abs/2409.05804)
  - NicheNet - [R] - To study intercellular communication from a computational perspective. It uses human or mouse gene expression data of interacting cells as input and combines this with a prior model that integrates existing knowledge on ligand-to-target signaling paths. This allows to predict ligand-receptor interactions that might drive gene expression changes in cells of interest. [Paper](https://www.nature.com/articles/s41592-019-0667-5)
  - NICHES - [R] - Computational toolset that analyzes cell-cell signaling by creating unique one-to-one cell pairs rather than using prior knowledge networks. Unlike NicheNet, NICHES enables low-dimensional embedding of cellular interactions in signal-space and specifically supports spatial datasets through nearest-neighbor constraints. Integrates with standard single-cell analysis packages for visualization and trajectory analysis. [Paper](https://doi.org/10.1093/bioinformatics/btac775)
  - COMUNET - [python] - It streamlines the interpretation of the results from cell-cell communication analyses by using multiplex networks to represent and cluster all potential communication pathways between cell types. [Paper](https://academic.oup.com/bioinformatics/article/36/15/4296/5836497)
  - CellChat - [R] - It predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. [Paper](https://www.nature.com/articles/s41467-021-21246-9)
  - CellNEST - [Python] - Cell Neural Networks on Spatial Transcriptomics (CellNEST) deciphers patterns of cell-cell communication by introducing relay-network detection that identifies ligand-receptor-ligand-receptor communication chains. Uses attention mechanisms to analyze spatial transcriptomics data, detect T cell homing signals, and predict new communication patterns in various cancer types. [Paper](https://www.nature.com/articles/s41592-025-02721-3)
  - Connectome - [R] - Software package that facilitates calculation and visualization of cell-cell signaling network topologies in single-cell RNA-seq data. Supports analysis of ligand-receptor interactions, differential connectomics between tissue systems, and interactive exploration of cellular communication patterns. [Paper](https://www.nature.com/articles/s41598-022-07959-x)
  - GEARS - [Python] - Graph-enhanced gene activation and repression simulator that predicts transcriptional responses to both single and multigene perturbations. Integrates deep learning with knowledge graphs of gene-gene relationships to predict outcomes of novel gene perturbations not seen experimentally. Shows high precision in predicting genetic interaction subtypes. [Paper](https://www.nature.com/articles/s41587-023-01905-6)
  - LIANA - [R, python] - LIANA enables the use of any combination of ligand-receptor methods and resources, and their consensus. [Paper](https://www.nature.com/articles/s41467-022-30755-0)
- Copy number analysis
  - aneufinder - [R] - Bioconductor module for copy-number detection in single-cell whole genome sequencing (scWGS) and strand-seq data using a Hidden Markov Model or binary bisection method.
  - CopyKAT - [R] - Inference of genomic copy number and subclonal structure from scRNA-seq data. Outperforms *inferCNV*. [Paper](https://doi.org/10.1038/s41587-020-00795-2)
  - Ginkgo - [R, C] - Ginkgo is a web application for single-cell copy-number variation analysis.
  - inferCNV - [R] - Part of the TrinityCTAT (Trinity Cancer Transcriptome Analysis Toolkit). Provides tools for copy-number inference from single-cell RNA-seq data.
  - inferCNVpy - [Python] - A Python/Scanpy re-implementation of `inferCNV`. Significantly faster than the R version.
  - MEDALT - [R, Python] - This package performs lineage tracing using copy number profile from single cell sequencing technology. It will infer: 1. An rooted directed minimal spanning tree (RDMST) to represent aneuploidy evolution of tumor cells. 2. The focal and broad copy number alterations associated with lineage expansion.
  - Numbat - [R] - Numbat is a haplotype-aware CNV caller from single-cell and spatial transcriptomics data. It integrates signals from gene expression, allelic ratio, and population-derived haplotype information to accurately infer allele-specific CNVs in single cells and reconstruct their lineage relationship. [Paper](https://www.nature.com/articles/s41587-022-01468-y)
  - SCEVAN - [R] - Easy-to-use package that starting from the raw count matrix of scRNA data automatically classifies the cells present in the biopsy by segregating non-malignant cells of tumor microenviroment from the malignant cells, outperforms *copyKAT*. It also infers the copy number profile of malignant cells, identifies subclonal structures and automatically analyses the specific and shared alterations of each subpopulation. [Preprint](https://www.biorxiv.org/content/10.1101/2021.11.20.469390v1)
  - SCICoNE - [C++, Python] - Single-cell copy number calling and event history reconstruction. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells.
- Count modelling and normalization
  - BEARscc - [R] - BEARscc makes use of ERCC spike-in measurements to model technical variance as a function of gene expression and technical dropout effects on lowly expressed genes.
  - 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. BASiCS can also identify genes with differential expression/over-dispersion between two or more groups of cells.
  - BPSC - [R] - Beta-Poisson model for single-cell RNA-seq data analyses
  - dsb - [R or Python] - a method for normalizing and denoising protein data from antibody derived tags (ADT). Compatible with CITE-seq, ASAP-seq, TEA-seq, ICICLE-seq, MissionBio etc. Removes ambient and cell to cell technical noise from ADTs see vignettes on [CRAN](https://CRAN.R-project.org/package=dsb). Manuscript open access: [Normalizing and denoising protein expression data from droplet-sed single cell profiling. *Nature Communications* (2022)](https://www.nature.com/articles/s41467-022-29356-8)
  - 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
  - SCnorm - [R] - A quantile regression based approach for robust normalization of single cell RNA-seq data.
  - zinbwaveZinger - [R] - We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq. https://doi.org/10.1186/s13059-018-1406-4
  - Dino - [R] - normalizes single-cell RNA-seq data by constructing a flexible negative-binomial mixture model of gene expression and sampling from the posterior distribution of expected expression conditional on observed sequencing depth. [Normalization by distributional resampling of high throughput single-cell RNA-sequencing data. *Bioinformatics* (2021)](https://doi.org/10.1093/bioinformatics/btab450)
  - Sanity - [C] - (SAmpling-Noise-corrected Inference of Transcription ActivitY) is a Bayesian procedure that infers the log expression levels (log transcription quotients) of genes by filtering out Poisson noise from UMI count matrices. It estimates expression values and error bars directly without tunable parameters. [Bayesian inference of gene expression states from single-cell RNA-seq data. *Nature Biotechnology* (2021](https://doi.org/10.1038/s41587-021-00875-x)
- Dimension reduction
  - scvis - [python] - [Interpretable dimensionality reduction of single cell transcriptome data with deep generative models](https://doi.org/10.1101/178624)
  - torchdr - [python] - Dimensionality reduction toolbox using PyTorch, featuring various algorithms such as TSNE, UMAP, and more. Supports GPU acceleration to maximize computational efficiency.
  - PHATE - Potential of Heat-diffusion for Affinity-based Transition Embedding - [Python, R, matlab] - PHATE is a tool for visualizing high dimensional single-cell data with natural progressions or trajectories. PHATE uses a novel conceptual framework for learning and visualizing the manifold inherent to biological systems in which smooth transitions mark the progressions of cells from one state to another.
  - SWNE - [R] - [Visualizing single-cell RNA-seq datasets with Similarity Weighted Nonnegative Embedding (SWNE)](https://www.biorxiv.org/content/early/2018/03/05/276261)
  - ZIFA - [Python] - Zero-inflated dimensionality reduction algorithm for single-cell data.
  - scDEED - [R] optimizing hyperparameters of UMAP/t-SNE, assigning each embedding a “reliability score” by permutation , manuscript open access: [Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters](https://www.nature.com/articles/s41467-024-45891-y)
  - p-SNE - [Python] - Poisson Stochastic Neighbor Embedding, a nonlinear dimensionality reduction method for sparse count data using Poisson KL divergence and Hellinger distance. [Paper](https://arxiv.org/abs/2604.16932).
- Doublet Identification
  - AMULET - [shell, Python, R] - A count based method for detecting multiplets from single nucleus ATAC-seq (snATAC-seq) data. [Genome Biology](https://doi.org/10.1186/s13059-021-02469-x)
  - demuxlet - [shell] - [Multiplexed droplet single-cell RNA-sequencing using natural genetic variation](https://www.nature.com/articles/nbt.4042)
  - DoubletFinder - [R] - Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. [BioRxiv](https://www.biorxiv.org/content/early/2018/06/20/352484)
  - DoubletDecon - [R] - Cell-State Aware Removal of Single-Cell RNA-Seq Doublets. [BioRxiv](DoubletDecon: Cell-State Aware Removal of Single-Cell RNA-Seq Doublets)
  - DoubletDetection - [R, Python] - A Python3 package to detect doublets (technical errors) in single-cell RNA-seq count matrices. An [R implementation](https://github.com/TomKellyGenetics/DoubletDetection) is in development.
  - Scrublet - [Python] - Computational identification of cell doublets in single-cell transcriptomic data. [BioRxiv](https://www.biorxiv.org/content/early/2018/07/09/357368)
  - solo - [Python] - Doublet detection via semi-supervised deep learning.
- Epigenomics
  - ArchR - [R] - ArchR is a full-featured R package for processing and analyzing single-cell ATAC-seq data. ArchR provides the most extensive suite of scATAC-seq analysis tools of any software available. [ArchR: An integrative and scalable software package for single-cell chromatin accessibility analysis](https://www.biorxiv.org/content/10.1101/2020.04.28.066498v1).
  - ChromVAR - [R] - Determine variations in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments. [BioRxiv](https://www.biorxiv.org/content/early/2017/02/21/110346)
  - EpiScanpy - [python] - EpiScanpy is the epigenomic extension of scRNA-seq analysis tool Scanpy. It analyses single-cell open chromatin (scATAC-seq) and single-cell DNA methylation (for example scBS-seq) data. [EpiScanpy: integrated single-cell epigenomic analysis](https://www.nature.com/articles/s41467-021-25131-3)
  - Signac - [R] - Signac is an extension of Seurat for the analysis, interpretation, and exploration of single-cell chromatin datasets.
  - ATACdemultiplex - [Go] - Suites of low-level multi-threaded utilities to efficiently manipulate large single-cell ATAC-Seq data (BAM, BED/fragments, Fastq files). Very efficient for creating sparse matrices, subset fragment/BED files, annotate peaks, estimate FDR corrected fisher features, create bigwig files and compute TSS enrichments (global and at the single-cell level).
  - AtacWorks - [python] - AtacWorks is a deep learning tool to denoise and identify accessible chromatin regions from low-coverage, low cell count, or low-quality ATAC-seq data. AtacWorks can denoise signal and identify peaks from rare cellular subtypes in a mixed population. [Biorxiv](https://www.biorxiv.org/content/10.1101/829481v2)
  - BIRD - [C++/R] - BIRD is a tool for predicting chromatin accessibility and inferring regulatory element activities in single cells using scRNA-seq. [Global prediction of chromatin accessibility using small-cell-number and single-cell RNA-seq](https://academic.oup.com/nar/article/47/19/e121/5552071)
  - ChromA - [C++/Fortran] - Chromatin Accessibility Annotation Tool.
  - cisTopic - [R/python] - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data. [cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data](https://www.nature.com/articles/s41592-019-0367-1)
  - cicero - [R] - Predicts enhancer-gene pairs by co-accessibility. Also adapts [monocle](http://cole-trapnell-lab.github.io/monocle-release/) for single-cell ATAC-seq (clustering, trajectories, differential accessibility).
  - DeepCpg - [python] - DeepCpG is a deep neural network for predicting the methylation state of CpG dinucleotides in multiple cells. It allows to accurately impute incomplete DNA methylation profiles, to discover predictive sequence motifs, and to quantify the effect of sequence mutations.
  - Enhlink - [Go/Binary] - Enhlink is a fast, easy to install, scalable, and robust computational approach that can infer linkages from high-dimensional, sparse, mono- or multi-omic single-cell datasets. Enhlink can be extended to infer distal, covariates, and clusters linkages. Compared to alternative methods such as Cicero, Archr, or Signac, Enhlink is more flexible, accurate and robust, and performs much faster. Enhlink can easily process data generated by the Cell Ranger pipelines, or any sparse matrices saved in the appropriate format. [Enhlink infers distal and context-specific enhancer–promoter linkages](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-024-03374-9)
  - Melissa - [R] - Melissa (MEthyLation Inference for Single cell Analysis), a Bayesian hierarchical method to quantify spatially-varying methylation profiles across genomic regions from single-cell bisulfite sequencing data (scBS-seq). Melissa clusters individual cells based on local methylation patterns, enabling the discovery of epigenetic differences and similarities among individual cells. The clustering also acts as an effective regularisation method for imputation of methylation on unassayed CpG sites, enabling transfer of information between individual cells. [BioRxiv](https://doi.org/10.1101/312025)
  - SCALE - [python] - SCALE is a deeplearning tool combining GMM with VAE for single-cell ATAC-seq analysis (visualization, clustering, imputation, batch effect removal, downstream analysis for celltype-specific TFs). [SCALE method for single-cell ATAC-seq analysis via latent feature extraction](https://www.nature.com/articles/s41467-019-12630-7)
  - SCATE - [R] - SCATE reconstructs activities of individual cis-regulatory elements (CREs) from single-cell ATAC-seq data by adaptively integrating information from co-activated CREs, similar cells, and publicly available regulome data. [Single-cell ATAC-seq signal extraction and enhancement with SCATE](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02075-3)
  - scbs - [python] - A command line tool for the analysis of Single-Cell Bisulfite-Sequencing data. scbs makes it easy to obtain a cell×region methylation matrix (≈count matrix) from raw single-cell methylation files and enables efficient storage, quality control and visualization. Furthermore, scbs allows you to scan the entire genome for variably or differentially methylated regions (VMRs or DMRs), and implements a novel approach for more accurate quantification of methylation in genomic intervals. [bioRχiv](https://doi.org/10.1101/2022.06.15.496318)
  - scE2G - [python] - Family of models to predict enhancer-gene regulation based on single-cell data. These models use features derived from either single-cell ATAC-seq or multiomicRNA and ATAC-seq data in supervised classifiers trained on a new harmonized CRISPR perturbation dataset including over 13,000 examples.
  - SCRAT - [R] - SCRAT provides essential tools for users to read in single-cell regulome 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. [Single-cell regulome data analysis by SCRAT](https://academic.oup.com/bioinformatics/article/33/18/2930/3823309)
- Feature (Gene) imputation
  - G2S3 - [R] - [G2S3: a Sparse and Smooth Signal of Gene Graph-based imputation method for scRNA-seq data](https://www.biorxiv.org/content/10.1101/2020.04.01.020586v1.full.pdf)
  - MAGIC - [R, Python, MATLAB] - Markov Affinity-based Graph Imputation of Cells (MAGIC). A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data. On [BioRviv](http://www.biorxiv.org/content/early/2017/02/25/111591) and [published in Cell](https://doi.org/10.1016/j.cell.2018.05.061).
  - NetDECODE - [R] - We develop an algorithm, called DECODE, to assess the extent of joint presence/absence of genes across different cells. We show that this network captures biologically-meaningful pathways, cell-type specific modules, and connectivity patterns characteristic of complex networks. We develop a model that uses this network to discriminate biological vs. technical zeros, by exploiting each gene's local neighborhood. For non-biological zeros, we build a predictive model to impute the missing value using their most informative neighbors.
  - scImpute - [R] - [scImpute: Accurate And Robust Imputation For Single Cell RNA-Seq Data](doi:10.1038/s41467-018-03405-7)
  - VIPER - [C++, R] - A fast and accurate tool to impute zero values in single-cell RNA sequencing studies to facilitate accurate transcriptome quantification at the single-cell level. VIPER is based on nonnegative sparse regression models and is capable of progressively inferring a sparse set of local neighborhood cells that are most predictive of the expression levels of the cell of interest for imputation. A key feature of VIPER is its ability to preserve gene expression variability across cells after imputation. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-018-1575-1
  - SoftImpute - [R] - Matrix completion via iterative soft-thresholded SVD. Originally developed for general low-rank matrix completion, it has been applied to scRNA-seq imputation by treating the gene expression matrix as a low-rank matrix with missing (dropout) entries.
- Gene regulatory network identification
  - scPRINT - [python] - scPRINT is pretrained on 50M cells to predict robust gene networks from single cell RNAseq data. [scPRINT: pre-training on 50 million cells allows robust gene network predictions](https://www.biorxiv.org/content/10.1101/2024.07.29.605556v1)
  - Dictys - [Python] - Dictys reconstructs and analyzes context specific and dynamic Gene Regulatory Networks from scRNA-seq and scATAC-seq datasets. [Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics](https://www.nature.com/articles/s41592-023-01971-3)
  - Marlene - [Python] - Marlene infers dynamic gene regulatory networks from scRNA-seq data using an evolving self-attention mechanism and meta-learning for few-shot adaptation to rare cell types. [Recovering time-varying networks from single-cell data](https://academic.oup.com/bioinformatics/article/41/Supplement_1/i628/8199402)
  - Normalisr - [Python, Shell] - Normalisr infers Gene Regulatory Networks from Perturb-seq and other single-cell CRISPR screens. Its normalization and statistical association testing framework also unifies single-cell differential expression and co-expression. [Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr](https://doi.org/10.1038/s41467-021-26682-1).
  - SCENIC - [R] - SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data. [SCENIC: single-cell regulatory network inference and clustering](https://www.nature.com/articles/nmeth.4463)
  - SCENIC+ - [python] - SCENIC+ is a python package to build gene regulatory networks using combined or separate scRNA-seq and scATAC-seq data. [SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks](https://www.biorxiv.org/content/10.1101/2022.08.19.504505v1)
  - SINCERITIES - [R/Matlab] - [Inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles](https://academic.oup.com/bioinformatics/article/34/2/258/4158033)
- Immune receptor profiling
  - APackOfTheClones - [R] - [APackOfTheClones: Visualization of clonal expansion with circle packing](https://joss.theoj.org/papers/eb9d2b2fc3ffb1d440bd7120b66f1f71)
  - DALI - [R] - Diversity Analysis Interface (DALI) is a tool that enables TCR and BCR analysis in the Seurat ecosystem. The functionality of the tool is also exposed via an interactive Shiny application.

Programming Languages

R 70 Python 62 Jupyter Notebook 24 HTML 14 C++ 12 C 4 MATLAB 2 JavaScript 2 TeX 2 OpenEdge ABL 1

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Awesome

awesome-single-cell

Citation

Image-based profiling

Spatial transcriptomics

Journal articles of general interest

Big data approach overview

Experimental design

Methods comparisons

Paper collections

People

Female

Male

Methods comparisons

Similar lists and collections

Methods comparisons

Software packages

Archetypal analysis

Batch-effect removal

Cell clustering

Cell projection and unimodal integration

Cell subsampling

Cell type identification and classification

Cellular interactions/communication

Copy number analysis

Count modelling and normalization

Dimension reduction

Doublet Identification

Epigenomics

Feature (Gene) imputation

Gene regulatory network identification

Immune receptor profiling