https://notebooklm.google.com/notebook/ab5c5e5c-6907-4883-8918-f4868f15ff88

https://fbnrst.github.io/awesome-single-cell-stars/

alevin-fry

[Rust] - 🐟 Rapid, accurate and memory-frugal preprocessing of single-cell and single-nucleus RNA-seq data.

anchor

[Python] - ⚓ Find bimodal, unimodal, and multimodal features in your data

AnnSQL

[Python] - ⛃ The AnnSQL package enables SQL based queries on AnnData objects using the DuckDB in-process database engine.

ascend

[R] - ascend is an R package comprised of fast, streamlined analysis functions optimized to address the statistical challenges of single cell RNA-seq. The package incorporates novel and established methods to provide a flexible framework to perform filtering, quality control, normalization,…

BayesPrism

[R] - Bayesian cell Proportion Reconstruction Inferred using Statistical Marginalization (BayesPrism): A Fully Bayesian Inference of Tumor Microenvironment composition and gene expression.

bigSCale

[matlab] - An analytical framework for big-scale single cell data.

bixverse

[R, Rust] - Rust-accelerated R package with a streaming single cell engine. Counts stay on disk and are processed in chunks, so a million cell data set goes from raw counts to clusters in minutes on a laptop. Methods cover preprocessing (QC, doublet detection, ambient RNA removal), batch…

bonvoyage

[Python] - 📐 Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction.

bustools

[C++] - A suite of tools for manipulating BUS files for single cell RNA-Seq pre-processing. bustools can be used to error correct barcodes, collapse UMIs, produce gene count or transcript compatibility count matrices, and is useful for many other tasks.

ccRemover

[R] - Removes the Cell-Cycle Effect from Single-Cell RNA-Sequencing Data. Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data.

celda

[R] - A suite of Bayesian hierarchical models and supporting functions to perform clustering of cells and genes for count data generated by scRNA-seq. Celda: a Bayesian model to perform co-clustering of genes into modules and cells into subpopulations using single-cell RNA-seq data. The package…

Cell_BLAST

[Python] - A BLAST-like toolkit for scRNA-seq data querying and automated annotation.

CellCNN

[Python] - Representation Learning for detection of phenotype-associated cell subsets

CellRanger

[Linux Binary] - Cell Ranger is a set of analysis pipelines that process Chromium single-cell RNA-seq output to align reads, generate gene-cell matrices and perform clustering and gene expression analysis. Software requires registration with 10xgenomics.

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.

Clustergrammer

[Python, JavaScript] - Interative web-based heatmap for visualizing and analyzing high dimensional biological data, including single-cell RNA-seq. Clustergrammer can be used within a Jupyter notebook as an interative widget that can be shared using GitHub and NBviewer, see example notebook.

Clustergrammer2

[Python, JavaScript] - Interative WebGL web-based heatmap for visualizing and analyzing single-cell high-dimensional and location-based biological data. Clustergrammer can be used within a Jupyter notebook as an interative widget that can be shared using GitHub and NBviewer, see case studies.

CountClust

[R] - Functions for fitting Grade-of-Membership models, also known as "Topic models", to RNA-seq counts. These models generalize clustering methods to allow that each cell may belong to more than one cluster/topic.

countsimQC

[R] - Compare characteristics of one or more synthetic (e.g., RNA-seq) count matrices to a real count matrix, possibly the one based on which the synthetic data sets were generated.

cyclum

[python] - Cyclum is a novel AutoEncoder approach that characterizes circular trajectories in the high-dimensional gene expression space. Applying Cyclum to removing cell-cycle effects leads to substantially improved delineations of cell subpopulations, which is useful for establishing various…

CytoGuide

[C++,D3] - CyteGuide: Visual Guidance for Hierarchical Single-Cell Analysis

DESCEND

[R] - DESCEND deconvolves the true gene expression distribution across cells for UMI scRNA-seq counts. It provides estimates of several distribution based statistics (five distribution measurements and the coefficients of covariates (such as batches or cell size)).

DeLorean

[R] - Bayesian pseudotime estimation algorithm that uses Gaussian processes to model gene expression profiles and provides a full posterior for the pseudotimes.

dittoSeq

[R] - Bioconductor package offering user friendly visualization tools for single-cell and Bulk RNA Sequencing. Color blindness friendly by default; novice coder friendly; highly customizable and powerful enough to build publication-ready figures; universal in that it works directly with Seurat,…

dropkick

[Python] - Automated cell filtering for single-cell RNA sequencing data.

dynamo

[Python] - Inclusive model of expression dynamics with scSLAM-seq and multiomics, vector field reconstruction and potential landscape mapping.

embeddr

[R] - Embeddr creates a reduced dimensional representation of the gene space using a high-variance gene correlation graph and laplacian eigenmaps. It then fits a smooth pseudotime trajectory using principal curves.

Falco

[AWS cloud] - Falco: A quick and flexible single-cell RNA-seq processing framework on the cloud.

FastProject

[Python] - Signature analysis on low-dimensional projections of single-cell expression data.

flotilla

[Python] - Reproducible machine learning analysis of gene expression and alternative splicing data

GPfates

[Python] - Model transcriptional cell fates as mixtures of Gaussian Processes

GSEApy

[Python] - GSEApy: Gene Set Enrichment Analysis in Python. GSEApy is a Python/Rust implementation for GSEA and wrapper for Enrichr. GSEApy can be used for RNA-seq, ChIP-seq, Microarray data. It can be used for convenient GO enrichment and to produce publication quality figures in python.

HocusPocus

[R] - Basic PCA-based workflow for analysis and plotting of single cell RNA-seq data.

HTSeq

[Python] - A Python library to facilitate programmatic analysis of data from high-throughput sequencing (HTS) experiments. A popular component of HTSeq is htseq-count, a script to quantify gene expression in bulk and single-cell RNA-Seq and similar experiments.

IA-SVA

[R] - Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated with the biological variable of interest. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted…

ICGS

[Python] - Iterative Clustering and Guide-gene Selection (Olsson et al. Nature 2016). Identify discrete, transitional and mixed-lineage states from diverse single-cell transcriptomics platforms. Integrated FASTQ pseudoalignment /quantification (Kallisto), differential expression, cell-type…

InMoose

[Python] - InMoose is the Integrated Multi Omic Open Source Environment. It is a collection of tools for the analysis of omic data. Allows for batch effect correction, cohort QC, Differential Expression Analysis and Consensus Clustering.

ivis

[Python or R] - Structure-preserving dimensionality reduction in single-cell datasets.

kallisto

[C++] - kallisto is a program for quantifying abundances of transcripts or genes from bulk or single-cell RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on pseudoalignment for rapidly determining the compatibility of reads with targets,…

kb-python

[Python] - kb-python is a python package for processing single-cell RNA-sequencing. It wraps the kallisto | bustools single-cell RNA-seq command line tools in order to unify multiple processing workflows.

knn-smoothing

[python or R or matlab] - The algorithm is based on the observation that across protocols, the technical noise exhibited by UMI-filtered scRNA-Seq data closely follows Poisson statistics. Smoothing is performed by first identifying the nearest neighbors of each cell in a step-wise fashion, based…

mfa

[R] - Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers

M3Drop

[R] - Michaelis-Menten Modelling of Dropouts for scRNASeq.

memento-de

[Python] - Method-of-moments estimators for parameter estimation, with efficient resampling. Associated paper.

MetaCell

[R, C++] - Analysis of single cell RNA-seq data by computing partitions of a cell similarity graph into small homogeneous groups of cells called metacells.

MIMOSCA

[python] - A repository for the design and analysis of pooled single cell RNA-seq perturbation experiments (Perturb-seq).

Monocle

[R] - Differential expression and time-series analysis for single-cell RNA-Seq.

Muscat

[R] - muscat (Multi-sample multi-group scRNA-seq analysis tools ) provides various methods for Differential State (DS) analyses in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data.

netSmooth

[R] - netSmooth is a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell…

NetworkInference

[Julia] - Fast implementation of single-cell network inference algorithms: Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures

nimfa

[Python] - Nimfa is a Python scripting library which includes a number of published matrix factorization algorithms, initialization methods, quality and performance measures and facilitates the combination of these to produce new strategies. The library represents a unified and efficient interface…

novoSpaRc

[Python] - Predict locations of single cells in space by solely using single-cell RNA sequencing data. An existing reference database of marker genes is not required, but significantly enhances performance if available. bioRxiv.

OEFinder

[R] - Identify ordering effect genes in single cell RNA-seq data. OEFinder shiny impelemention depends on packages shiny, shinyFiles, gdata, and EBSeq.

OncoNEM

[R] - OncoNEM is a probabilistic method for inferring intra-tumor evolutionarylineage trees from somatic single nucleotide variants of single cells. OncoNEM identifies homogeneous cellularsubpopulations and infers their genotypes as well as a tree describing their evolutionary relationships.

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.

pcaReduce

[R] - hierarchical clustering of single cell transcriptional profiles.

PyGMNormalize

[Python] - Python implementation of edgeR normalization method for count matrices.

RAPIDS-singlecell

[Python] - A GPU-accelerated tool leveraging RAPIDS for scRNA analysis. Seamless scverse compatibility for efficient single-cell data processing and analysis. Replcates features from Scanpy, while also incorporating select functionalities from Squidpy and Decoupler.

RNAnorm

[Python] - Python implementation of common RNA-seq normalization methods (CPM, FPKM, TPM, UQ, CUF, TMM, CTF).

rMATS

[Python] - RNA-Seq Multavariate Analysis of Transcript Splicing.

robustSingleCell

[R] - robustSingleCell is a pipeline designed to identify robust cell subpopulations using scRNAseq data and compare population compositions across tissues and experimental models via similarity analysis as described in Magen et al. (2019) bioRxiv.

SAVER

[R] - SAVER (Single-cell Analysis Via Expression Recovery) implements a regularized regression prediction and empirical Bayes method to recover the true gene expression profile in noisy and sparse single-cell RNA-seq data.

SAKE

[R] - Single-cell RNA-Seq Analysis and Clustering Evaluation.

SCALE

[R] - SCALE is a statistical framework for Single Cell ALlelic Expression analysis. SCALE estimates kinetic parameters that characterize the transcriptional bursting process at the allelic level, while accounting for technical bias.

scAnalyzer

[Python] - An end-to-end, modular, and lightweight toolkit for scRNA-seq analysis. It provides comprehensive functions for preprocessing, quality control, doublet detection, batch correction, cell cycle scoring, trajectory inference, dimensionality reduction, and interactive visualization.

Scanpy

[Python] - Scanpy provides computationally efficient tools that scale up to very large data sets and enables simple integration of advanced machine learning algorithms.

scbean

[Python] - Scbean integrates a range of models for single-cell data analysis, including dimensionality reduction, removing batch effects, and transferring well-annotated cell type labels from scRNA-seq to scATAC-seq and spatial resolved transcriptomics, and joint-analysis of paired multimodal…

SCCAF

[Python] Single Cell Clustering Assessment Framework (SCCAF) is a method for automated identification of putative cell types from single cell data by iteratively applying clustering and a machine learning approach. Putative cell type discovery from single-cell gene expression data

scedar

[Python] - An easy-to-use package for normalization, clustering and visualization of large-scale scRNA-seq datasets. Scedar: A scalable Python package for single-cell RNA-seq exploratory data analysis.

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…

scGEAToolbox

[matlab] - a Matlab toolbox for single-cell RNA-seq data analyses.

schist

[Python] - schist is a scanpy-compatible python library which implements Nested Stochastic Block Models to identify cell groups in single cell experiments.

Scillus

[R] - Scillus is an R wrapper package for enhanced processing and visualization of Seurat-based scRNA-seq data.

SCINA

[R] - A semi-supervised category identification and assignment tool.

SCP

[R] - SCP(Single Cell Pipeline) is an R package that provides a comprehensive set of tools for single cell data processing and downstream analysis.

scVI

[python] - scVI is a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells (batch correction, visualization, clustering, and differential expression). Deep generative modeling for single-cell transcriptomics

scLM

[R] - Automatic detection of consensus gene clusters across multiple single-cell datasets

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…

scTDA

[Python] - scTDA is an object oriented python library for topological data analysis of high-throughput single-cell RNA-seq data. It includes tools for the preprocessing, analysis, and exploration of single-cell RNA-seq data based on topological representations.

SCODE

[R/Julia]- an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

SCORE

[R] - Enhancing single-cell cellular state inference by incorporating molecular network features.

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.

SCRL

[C++] - Network embedding-based representation learning for single cell RNA-seq data

scruff

[R] - An R package for preprocessing single cell RNA-seq (scRNA-seq) FASTQ reads generated by CEL-Seq and CEL-Seq2 protocols. It demultiplexes reads according to a predetermined list of cell barcodes, maps reads to reference genome using Rsubread, and reports filtered UMI (Unique Molecular…

scSemiProfiler

[Python] - Deep generative AI tool for cost-effective single-cell data generation. It has two main functions: 1. Single-cell-level bulk deconvolution – Generates single-cell gene expression profiles from bulk RNA-seq data using reference single-cell data from a similar tissue. 2. Semi-profiling –…

SCSPARKL

[Python/PySpark] - An Apache spark based approach for performing of preprocessing and downstream analysis of scRNA-seq data.

scSVA

[R] - An R package for interactive two- and three-dimensional visualization and exploration of massive single-cell omics data (2-10^9 cells). scSVA supports interactive analytics in a cloud with containerized tools. It contains optimized implementation of diffusion maps and multi-threaded 3D…

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…

SIMLR

[R, matlab] - SIMLR (Single-cell Interpretation via Multi-kernel LeaRning) learns an appropriate distance metric from the data for dimension reduction, clustering and visualization. SIMLR is capable of separating known subpopulations more accurately in single-cell data sets than do existing…

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…

sincera

[R] - R-based pipeline for single-cell analysis including clustering and visualization.

SingleSplice

[R, perl, C++] - A tool for detecting biological variation in alternative splicing within a population of single cells. See Welch et al. 2016.

singlet

[Python] - Single cell RNA-Seq analysis with phenotypes.

soupX

[R] - An R package for the estimation and removal of cell free mRNA contamination in droplet based single cell RNA-seq data. The problem this package attempts to solve is that all droplet based single cell RNA-seq experiments also capture ambient mRNAs present in the input solution along with cell…

SPRING

[matlab, javascript, python] - SPRING is a collection of pre-processing scripts and a web browser-based tool for visualizing and interacting with high dimensional data. SPRING was developed for single cell RNA-Seq data but can be applied more generally.

scTOP

[Python] - Single-cell type order parameters. Physics-inspired method of processing single-cell RNA-seq and identifying cell fate, motivated by the epigenetic landscape.

trendsceek

[R] - Identification of spatial expression trends in single-cell gene expression data

VISION

[] - A tool for annotating the sources of variation in single cell RNA-seq data in an automated, unbiased and scalable manner. It produces an interactive, low latency and feature rich web-based report that can be easily shared amongst researchers.

zUMIs

[R, perl, shell] - zUMIs: A fast and flexible pipeline to process RNA-seq data with UMIs.

STAR

[C/C++] - Splice-aware aligner for RNA-seq data, capable of mapping reads to a reference genome with high accuracy and speed.

Cellity

[R] - Classification of low quality cells in scRNA-seq data using R

gene_network_evaluation

[Python] - A flexible framework to evaluate the plausibility of gene programs inferred from single-cell genomic data. The assessment is broken down into themes such as goodness of fit (ability to explain the data), co-regulation, mechanistic interactions etc. Under each theme, multiple evaluation…

scDiagnostics

[R] - Package specifically designed to evaluate the fidelity of annotation transfer in scRNA-seq data. scDiagnostics provides a comprehensive set of diagnostic tools that assess the compatibility between query and reference datasets, helping to identify and mitigate risks of erroneous annotations.

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.

SinQC

[R] - A Method and Tool to Control Single-cell RNA-seq Data Quality.

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,…

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

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

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

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…

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

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

SINCERITIES

[R/Matlab] - Inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles

Spectra

[python] - Spectra is factor analysis method that infers cell type specific and global gene programs in scRNA-seq data using gene sets for cellular processes from our scRNA-seq knowledge base Cytopus and cell type annotations. Spectra factors generalize across tens of studies, hundreds of patients…

APackOfTheClones

[R] - APackOfTheClones: Visualization of clonal expansion with circle packing

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.

Ibex

[R] - Ibex: Variational autoencoder for single-cell BCR sequencing

Scirpy

[Python] - A Scanpy extension for analyzing single-cell T-cell receptor (TCR) sequencing data.

scRepertoire

[R] - scRepertoire 2: Enhanced and efficient toolkit for single-cell immune profiling

TraCeR

[python] - Reconstruction of T-Cell receptor sequences from single-cell RNA-seq data.

TRAPeS

[python, C++] - TRAPeS (TCR Reconstruction Algorithm for Paired-End Single-cell), a software for reconstruction of T cell receptors (TCR) using short, paired-end single-cell RNA-sequencing.

TRUST4

[bash] - TRUST4: immune repertoire reconstruction from bulk and single-cell RNA-seq data

GPseudoClust

[Python] - Software that clusters genes for pseudotemporally ordered data and quantifies the uncertainty in cluster allocations arising from the uncertainty in the pseudotime ordering.

GiniClust

[Python/R] - GiniClust is a clustering method implemented in Python and R for detecting rare cell-types from large-scale single-cell gene expression data. GiniClust can be applied to datasets originating from different platforms, such as multiplex qPCR data, traditional single-cell RNAseq or newly…

DECENT

[R] - The unique features of scRNA-seq data have led to the development of novel methods for differential expression (DE) analysis. However, few of the existing DE methods for scRNA-seq data estimate the number of molecules pre-dropout and therefore do not explicitly distinguish technical and…

MetaMarkers

[R] - MetaMarkers proposes a simple methodology to pool marker information across dataset while keeping dataset independents to identify robust marker signatures from single-cell data. How many markers are needed to robustly determine a cell's type?

pbcheck

[Python] - Audits a single-cell differential-expression design for pseudoreplication: checks donor nesting within condition, then runs a naive per-cell test and a donor-pseudobulk test against a donor-permutation null and reports each arm's permutation floor.

Phenotype Cover

[Python] - Provides two algorithms for marker selection (G-PC, CEM-PC) introduced in Multiset multicover methods for discriminative marker selection. Most marker selection methods focus on differential expression (DE) analysis. Although such methods work well for data with a few non-overlapping…

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.

SCMarker

[R] - SCMarker is a method performing ab initial marker gene set selection from scRNA-seq data to achieve improved clustering/cell-typing results. SCMarker: ab initio marker selection for single cell transcriptome profiling.

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.

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.

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..

torchdr

[python] - Dimensionality reduction toolbox using PyTorch, featuring various algorithms such as TSNE, UMAP, and more. Supports GPU acceleration to maximize computational efficiency.

destiny

[R] - Diffusion maps are spectral method for non-linear dimension reduction introduced by Coifman et al.(2005). Diffusion maps are based on a distance metric (diffusion distance) which is conceptually relevant to how differentiating cells follow noisy diffusion-like dynamics, moving from a…

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…

scvis

[python] - Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

SWNE

[R] - Visualizing single-cell RNA-seq datasets with Similarity Weighted Nonnegative Embedding (SWNE)

ZIFA

[Python] - Zero-inflated dimensionality reduction algorithm for single-cell data.

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

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

p-SNE

[Python] - Poisson Stochastic Neighbor Embedding, a nonlinear dimensionality reduction method for sparse count data using Poisson KL divergence and Hellinger distance. Paper.

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.

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.

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…

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.

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. Manuscript open access: Normalizing and…

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…

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.

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…

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.…

BatchEffectRemoval

[Python] - Removal of Batch Effects using Distribution-Matching Residual Networks

ResPAN

[Python] - ResPAN is a light structured Residual autoencoder and mutual nearest neighbor Paring guided Adversarial Network 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…

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…

UNCURL

[Python] - Unsupervised and semi-supervised sampling effect removal for single-cell RNA-seq data.

scmap

[R] - scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types identified in a different experiment.

Monet

[python] - A package for analyzing and integrating scRNA-Seq data using PCA-based latent spaces.

dropsim

[R] - Simulating droplet based scRNA-seq data.

powsimR

[R] - Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it…

splatter

[R] - Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented.

symsim

[R] - SymSim (Synthetic model of multiple variability factors for Simulation) is an R package for simulation of single cell RNA-Seq data.

CALISTA

[R] - CALISTA provides a user-friendly toolbox for the analysis of single cell expression data. CALISTA accomplishes three major tasks: 1) Identification of cell clusters in a cell population based on single-cell gene expression data, 2) Reconstruction of lineage progression and produce transition…

CellRank

[python] - CellRank 2 uses multiple modalities—such as gene expression similarity, pseudotime, developmental potential, RNA velocity, experimental time points, and metabolic labeling—to define cell–cell transitions.

CoSpar

[python] - CoSpar is a toolkit for dynamic inference by integrating state and lineage information. It gains superior robustness and accuracy by exploiting both the local coherence and sparsity of differentiation transitions, i.e., neighboring initial states share similar yet sparse fate outcomes.…

DensityPath

[.] - DensityPath: a level-set algorithm to visualize and reconstruct cell developmental trajectories for large-scale single-cell RNAseq data

dynverse

[R] - A comparison of single-cell trajectory inference methods: towards more accurate and robust tools

ECLAIR

[python] - ECLAIR stands for Ensemble Clustering for Lineage Analysis, Inference and Robustness. Robust and scalable inference of cell lineages from gene expression data.

K-Branches

[R] - The main idea behind the K-Branches method is to identify regions of interest (branching regions and tips) in differentiation trajectories of single cells. So far, K-Branches is intended to be used on the diffusion map representation of the data, so the user should either provide the data in…

MERLoT

[R/python] - Reconstructing complex lineage trees from scRNA-seq data using MERLoT.

ouija

[R] - A descriptive marker gene approach to single-cell pseudotime inference

ouijaflow

[python] - A descriptive marker gene approach to single-cell pseudotime inference

Palantir

[Python] - Characterization of cell fate probabilities in single-cell data with Palantir

PhenoPath

[R] - Single-cell pseudotime with heterogeneous genetic and environmental backgrounds, including Bayesian significance testing of iteractions.

pseudodynamics

[MATLAB] - Inferring population dynamics from single-cell RNA-sequencing time series data

psupertime

[R] - psupertime is an R package which uses single cell RNAseq data, where the cells have labels following a known sequence (e.g. a time series), to identify a small number of genes which place cells in that known order. It can be used for discovery of relevant genes, for exploration of unlabelled…

SCDIFF

[Python, JavaScript] - SCDIFF is a single-cell trajectory inference method with interactive visualizations powered by D3.js. SCDIFF utilized the TF regulatory information to mitigate the impact of enormous single-cell RNA-seq noise (such as drop-out). With the TF regulatory information, SCDIFF is…

SCIMITAR

[Python] - Single Cell Inference of Morphing Trajectories and their Associated Regulation module (SCIMITAR) is a method for inferring biological properties from a pseudotemporal ordering. It can also be used to obtain progression-associated genes that vary along the trajectory, and genes that…

SCORPIUS

[R] - An accurate and easy tool for performing linear trajectory inference on single cells using single-cell RNA sequencing data. In addition, SCORPIUS provides functions for discovering the most important genes with respect to the reconstructed trajectory, as well as nice visualisation tools.…

SCUBA

[matlab/R] - SCUBA stands for "Single-cell Clustering Using Bifurcation Analysis." SCUBA is a novel computational method for extracting lineage relationships from single-cell gene expression data, and modeling the dynamic changes associated with cell differentiation.

scVelo

[Python] - scVelo is a scalable toolkit for RNA velocity analysis in single cells. It generalizes the concept of RNA velocity by relaxing previously made assumptions with a dynamical model. It allows to identify putative driver genes, infer a latent time, estimate reaction rates of transcription,…

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.

TASIC

[matlab] - TASIC is a new method for determining temporal trajectories, branching and cell assignments in single cell time series experiments. Unlike prior approaches TASIC uses on a probabilistic graphical model to integrate expression and time information making it more robust to noise and…

TopSLAM

[python] - Extracting and using probabilistic Waddington's landscape recreation from single cell gene expression measurements

Truffle

[Python] - Truffle utilizes multicommodity flow algorithms for trajectory inference in time series clinical transcriptomics data. Integrating patients in time series clinical transcriptomics data

TSCAN

[R] - Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.

VELOCYTO

[Python, R] - Estimating RNA velocity in single cell RNA sequencing datasets.

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).

cellassign

[R] - Automated, probabilistic assignment of scRNA-seq to known types. cellassign automatically assigns single-cell RNA-seq data to known cell types across thousands of cells accounting for patient and batch specific effects. Information about a priori known markers for cell types is provided as…

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…

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…

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…

easybio

[R] - easybio is an R pacakge for cell type annotation using the CellMarker2.0 database. bioRxiv

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…

scVI

[python] - scVI is a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells (batch correction, visualization, clustering, and differential expression). Deep generative modeling for single-cell transcriptomics

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…

singleCellNet

[R] - A near-universal step in the analysis of single cell RNA-Seq data is to hypothesize the identity of each cell. Often, this is achieved by finding cells that express combinations of marker genes that had previously been implicated as being cell-type specific, an approach that is not…

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)

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…

DeepSort

[python] - A reference-free cell-type annotation tool for single-cell RNA-seq data using deep learning with a weighted graph neural network, which is learned based on the most comprehensive single-cell transcriptomics atlases involving 764,741 cells across 88 tissues of human and mouse. bioRxiv

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…

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…

clustifyr

[R] - Classifies cells and clusters in single-cell RNA-seq experiments using external reference data, gene signatures, or marker gene lists.

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

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…

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.

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…

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

scMalignantFinder

[Python] - scMalignantFinder is a Python package designed for analyzing cancer single-cell RNA-seq and spatial transcriptomics datasets to distinguish malignant cells from their normal counterparts. Trained on over 400,000 high-quality single-cell transcriptomes, scMalignantFinder uses curated…

AMULET

[shell, Python, R] - A count based method for detecting multiplets from single nucleus ATAC-seq (snATAC-seq) data. Genome Biology

demuxlet

[shell] - Multiplexed droplet single-cell RNA-sequencing using natural genetic variation

DoubletFinder

[R] - Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. BioRxiv

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 is in development.

Scrublet

[Python] - Computational identification of cell doublets in single-cell transcriptomic data. BioRxiv

solo

[Python] - Doublet detection via semi-supervised deep learning.

geosketch

[Python] - Method to subsample massive scRNA-seq datasets while preserving rare cell states. Resulting “sketch” accelerates clustering, visualization, and integration analyses. Paper

G2S3

[R] - G2S3: a Sparse and Smooth Signal of Gene Graph-based imputation method for scRNA-seq data

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 and published in Cell.

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…

scImpute

[R] - scImpute: Accurate And Robust Imputation For Single Cell RNA-Seq Data

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…

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

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.

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

Ginkgo

[R, C] - Ginkgo is a web application for single-cell copy-number variation analysis.

HoneyBADGER

[R] - HoneyBADGER identifies and infers the presence of CNV and LOH events in single cells and reconstructs subclonal architecture using allele and expression information from single-cell RNA-sequencing data.

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…

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…

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…

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.

cb_sniffer

[python] - Mutation barcode caller, calls mutant and ref barcodes from 10x single cell data.

cerebra

[python] - Cerebra is a tool for high-throughput summarizing of vcf entries following traditional variant calling for a sequencing experiment. Helps to extract relevant mutation information from among tens of thousands of vcf lines.

monovar

[python] - Monovar is a single nucleotide variant (SNV) detection and genotyping algorithm for single-cell DNA sequencing data. It takes a list of bam files as input and outputs a vcf file containing the detected SNVs.

octopus

[C++] - Bayesian haplotype-based mutation calling with single-cell calling model. Identifies clonal and subclonal mutations using phylogeny inference and accounts for allelic dropout.

SCIPhi

[python] - Single-cell mutation identification via phylogenetic inference (SCIPhI) is a new approach to mutation detection in individual tumor cells by leveraging the evolutionary relationship among cells.

SComatic

SComatic is a tool that provides functionalities to detect somatic single-nucleotide mutations in high-throughput single-cell genomics and transcriptomics data sets, such as single-cell RNA-seq and single-cell ATAC-seq. Paper

SSrGE

[python] - SSrGE is an approach to identify SNVs correlated with Gene Expression using multiple regularized linear regressions. It contains its own pipeline to infer SNVs from scRNA-seq reads and is able to identify and sort genes and SNVs for a given cell subgroup. Nature Communication (2017)…

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.

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…

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

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

ChromA

[C++/Fortran] - Chromatin Accessibility Annotation Tool.

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

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

cicero

[R] - Predicts enhancer-gene pairs by co-accessibility. Also adapts monocle 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.

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

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…

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,…

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

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

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…

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…

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

Signac

[R] - Signac is an extension of Seurat for the analysis, interpretation, and exploration of single-cell chromatin datasets.

bindSC

[R] - bindSC (Bi-dimensional INtegration of multi-omics Data from Single Cell sequencing technologies) is an R package for single cell multi-omic integration analysis, developed and maintained by Ken chen's lab in MDACC. bindSC is developed to address the challenge of single-cell multi-omic data…

CellWalkR

[R] - An R Package for integrating single-cell and bulk data to resolve regulatory elements.

CITE-seq-Count

[python] Cite-seq-Count is a python package that deals with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) and cell hashing data. CITE-seq is a multimodal single cell phenotyping method that allows for immunophenotyping of cells with a potentially limitless number of…

clonealign

[R] - Integrate single-cell RNA and single-cell DNA-seq measured in separate cells from the same tumour to infer cancer-clone-specific gene expression profiles clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers, Genome Biology 2019

Cobolt

[python] - Cobolt is a novel method that not only allows for analyzing the data from joint-modality platforms, but provides a coherent framework for the integration of multiple datasets measured on different modalities. Cobolt: integrative analysis of multimodal single-cell sequencing data

FigR

[R] - FigR (Functional inference of gene Regulation) is a computational framework for supporting the integration of single cell chromatin accessibility and gene expression data to infer transcriptional regulators of target genes. Functional inference of gene regulation using single-cell multi-omics

scVI

[python] - scVI is a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells (batch correction, visualization, clustering, and differential expression). Deep generative modeling for single-cell transcriptomics

GLUE

[python] - GLUE (Graph-Linked Unified Embedding) is a deep learning method for unpaired single-cell multi-omics data integration and regulatory inference (Paper).

LIGER

[R] - LIGER (Linked Inference of Genomic Experimental Relationships) is a package for integrating and analyzing multiple single-cell datasets. Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity

MATCHER

[python] - MATCHER: An algorithm for integrating single cell transcriptomic and epigenomic data using manifold alignment. MATCHER takes multiple types of single cell measurements performed on distinct single cells and infers single cell multi-omic profiles.

MultiVI

[python] - MultiVI is a probabilistic framework that leverages deep neural networks to jointly analyze scRNA, scATAC and multiomic (scRNA + scATAC) data. MultiVI: deep generative model for the integration of multi-modal data

MOFA

[python, R] - Multi‐Omics Factor Analysis, a framework for unsupervised integration of multi‐omics data sets. MOFA is a method for disentangling the different sources of heterogeneity in bulk and single-cell multi-omics data sets. It identifies the latent factors that drive unique and shared…

SCALEX

[python] - SCALEX provides a VAE framework for integration of heterogeneous single-cell data by disentangling batch-invariant components from batch-related variations and projecting the batch-invariant components into a generalized, low-dimensional cell-embedding space. Construction of…

scarf

[python] - 🧣 Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop. Preprint

scDART

[python] - scDART is a deep learning framework that integrates scRNA-seq and scATAC-seq data and learns cross-modalities relationships simultaneously. scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously

SISUA

[python] - In this study, we propose models based on the Bayesian generative approach, where protein quantification available as CITE-seq counts from the same cells are used to constrain the learning process, thus forming a semi-supervised model. The generative model is based on the deep…

TorchGW

[Python] - Fast Sampled Gromov-Wasserstein optimal transport solver in pure PyTorch with Triton GPU kernels. Aligns datasets across modalities by matching internal distance structures, even in different dimensions. 3-175x faster than POT. Docs

FiRE

[python, R, C++] - Finder of rare entities (FiRE) helps identify rare cell types in voluminous single-cell datasets. Design of FiRE is inspired by the observation that rareness estimation of a particular data point is the flip side of measuring the density around it. In principle, FiRE uses the…

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

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

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…

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…

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

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…

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…

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…

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.…

LIANA

[R, python] - LIANA enables the use of any combination of ligand-receptor methods and resources, and their consensus. Paper

geneformer

[Python] a single-cell large model training on 30 million human single-cell transcriptomics, supporting batch integration, gene dosage sensitivity predictions, chromatin dynamics prediction, network dynamics prediction, etc. manuscript open access: Transfer learning enables predictions in network…

scGPT

[Python] a single-cell large model training on 33 million human single-cell transcriptomics, supporting single-cell annotation, batch integration, perturbation prediction manuscript open access: scGPT: toward building a foundation model for single-cell multi-omics using generative AI

scFoundation

[Python] a single-cell large model training on 50 million human single-cell transcriptomics with 100 million parameters, supporting single-cell clustering, drug response prediction, perturbation prediction, single-cell annotation, gene module inference, etc. manuscript open access: Large-scale…

CellPLM

[Python] the first single-Cell Pre-trained Language Model that encodes cell-cell relations and it consistently outperforms existing pre-trained and non-pre-trained models in diverse downstream tasks, with 100x higher inference speed compared to existing pre-trained models, training on 9 million…

autozyme

[python, R, C++] - Autonomous multi-agent framework that speeds up single-cell software (e.g. Scanpy, Seurat) and other scientific code on CPUs while preserving the original results.

BASIC

[python] - BASIC is a semi-de novo assembly method to determine the full-length sequence of the BCR in single B cells from scRNA-seq data.

BPCells

[R] - A package for high performance single cell analysis of RNA-seq and ATAC-seq datasets. BPCells provides disk-backed streaming calculations that reduce memory requirements for normalization and PCA of large scRNA-seq datasets by nearly 100-fold compared to in-memory workflows such as Scanpy.

CytoSpill

[R] - The goal of CytoSpill is to estimate and compensate spillover noises in CyTOF data, without requiring any training data.

dropEst

[C++, R] - High-performance pipeline for initial analysis of droplet-based single-cell RNA-seq data (Drop-seq, inDrop, 10x and some others). Allows to estimate gene count matrix as well as diagnostic stats from fastq files with raw reads. Implements corrections for different noise sources.

dropSeqPipe

[python, R, snakemake] - An automatic data handling pipeline for drop-seq/scrb-seq data. It runs from raw fastq.gz data until the final count matrix with QC plots along the way.

ffq

[python] - Fetch run and metadata information for single-cell genomics datasets.

gget

[Python] - gget is a free, open-source command-line tool and Python package that enables efficient querying of genomic databases. gget consists of a collection of separate but interoperable modules, each designed to facilitate one type of database querying in a single line of code.

immunarch

[R] - R Package for Fast and Painless Exploration of Single-cell and Bulk T-cell/Antibody Immune Repertoires

sasc

[C] - sasc stands for Simulated Annealing Single-Cell, an algorithm for performing phylogenetic analysis of single-cell cancer samples. Manuscript here.

scDataviz

[R] - scDataviz: single cell dataviz and downstream analyses, with a primary focus on flow and mass cytometry.

SCIFIL

[Matlab] - SCIFIL: Single Cell Inference of FItness Landscape is a computational method for in vivo inference of clonal selection and estimate of fitness landscapes of heterogeneous cancer cell populations from single cell sequencing data.

SCope

[python] - SCope is a fast visualization tool for large-scale and high dimensional scRNA-seq datasets. Publication here.

scTE

[python] - Quantifying transposable element expression from single-cell sequencing data.

SCIFER

[shell, python] - Approach for analysis of LINE-1 mRNA expression in single cells at a single locus resolution.

SiFit

[Java] - SiFit: A Method for Inferring Tumor Trees from Single-Cell Sequencing Data under Finite-site Models

Sinto

[python] - A toolkit for working with aligned single-cell reads. Includes functions to split BAM files by cell barcode, add cell barcodes as read tags, move cell barcode information to read groups, and create a scATAC-seq fragment file from a BAM file.

sircel

[python] - sircel (pronounced "circle") separates reads in a fastq file based on barcode sequences that occur at known positions of reads. This is an essential first step in analyzing single-cell genomics data from experiments such as Drop-Seq. Barcode sequences often contain deletion and/or…

Snakemake single-cell-rna-seq workflow

[python, R, snakemake] - An automated pipeline for single cell RNA-seq analysis.

VisCello

[R] - VisCello for C.elegans embryogenesis.

Wishbone

[python] - Wishbone is an algorithm to identify bifurcating developmental trajectories from single cell data. Wishbone can applied to both single cell RNA-seq and mass cytometry datasets.

AESTETIK

[Python] Representation learning for multi-modal spatially resolved transcriptomics data. manuscript: Representation learning for multi-modal spatially resolved transcriptomics data.

BayesSpace

[R] A Bayesian statistical model for clustering and resolution enhancement of spatial gene expression experiments. manuscript open access: Spatial transcriptomics at subspot resolution with BayesSpace

CellTrek

[R] CellTrek is a computational method to achieve single-cell spatial mapping through coembedding, random forest and metric learning approaches. manuscript open access: Spatial charting of single-cell transcriptomes in tissues

cell2location

[Python] A Bayesian model that perform spatial deconvolution in SRT data and create cellular maps of diverse tissues based on negative binomial distribution. manuscript open access: Cell2location maps fine-grained cell types in spatial transcriptomics

ChatSpatial

[Python] An MCP server enabling spatial transcriptomics analysis via natural language. Integrates 60+ methods including SpaGCN, Cell2location, RCTD, LIANA+, CellRank for spatial domains, deconvolution, cell communication, and trajectory analysis. Supports Visium, Xenium, MERFISH, Slide-seq.…

DeepSpot

[Python] Predicts spatial transcriptomics from H&E histology images using spatial context. manuscript: DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E Images.

DeepSpot-M

[Python] Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology. manuscript: DeepSpot-M: a multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology.

DeepSpot2Cell

[Python] Predicts virtual single-cell spatial transcriptomics from H&E images using spot-level supervision. manuscript: DeepSpot2Cell: Predicting Virtual Single-Cell Spatial Transcriptomics from H&E images using Spot-Level Supervision.

MultiVI

[python] - MultiVI is a probabilistic framework that leverages deep neural networks to jointly analyze scRNA, scATAC and multiomic (scRNA + scATAC) data. MultiVI: deep generative model for the integration of multi-modal data

DSTG

[Python] A spatial deconvolution method designed with graph-based convolutional networks. manuscript open access: DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence

FlashDeconv

[Python] A high-performance spatial transcriptomics deconvolution method using structure-preserving randomized sketching for atlas-scale and subcellular-resolution platforms. Processes 1 million spots in ~3 minutes with linear O(N) scaling. manuscript: FlashDeconv enables atlas-scale,…

Giotto Suite

[R] Flexible, composable, and technology-agnostic data framework in R, which allows the representation and integration of virtually any type of spatial omics data at any spatial resolution. manuscript open access:Giotto Suite: a multi-scale and technology-agnostic spatial multi-omics analysis…

Merfishtools

[Python] - MERFISHtools implement a Bayesian framework for accurately predicting gene or transcript expression from MERFISH data.

MINGL

[Python] - MINGL (Mixture-based Identification of Neighborhood Gradients with Likelihood estimates), a probabilistic framework that converts existing neighborhood annotations into continuous measures of tissue architecture.

NMFreg

[Python] - The method is proposed in Slide-seq paper and reconstructs expression of each Slide-seq bead as a weighted combination of metagene factors, each corresponding to the expression signature of an individual cell type, defined from scRNA-seq.

Novae

[Python] - Graph-based foundation model for spatial transcriptomics data. Zero-shot spatial domain inference, batch-effect correction, and many other features.

PASTE

[Python] A spatial alignment tool for aligning homogeneous spatial transcriptomic slices based on optimal transport and euclidean distance. manuscript open access: Alignment and integration of spatial transcriptomics data

PASTE2

[Python] A spatial alignment tool for aligning homogeneous spatial transcriptomic slices based on the partial extension of the Fused Gromov-Wasserstein optimal transport. manuscript open access: PASTE2: Partial Alignment of Multi-slice Spatially Resolved Transcriptomics Data

RCTD

[R] A statistical model to deconvolute cell types of spatial spots based on scRNA-seq reference by poisson distribution and maximum likelihood estimation. manuscript open access: Robust decomposition of cell type mixtures in spatial transcriptomics

SLAT

[Python] SLAT is to align both homogeneous and heterogeneous (the first work) single cell spatial omics data by employing a graph alignment framework consists of LGCN and adversarial discriminator. manuscript open access: Spatial-linked alignment tool (SLAT) for aligning heterogenous slices

Sopa

[Python] Technology-invariant pipeline for spatial omics analysis that scales to millions of cells (Xenium / Visium HD / MERSCOPE / CosMx / PhenoCycler / MACSima / etc). Useful for cell segmentation, using StarDist and Proseg for segmentation. Works in pair with the package Novae for batch…

SpaGCN

[Python] SpaGCN is a graph convolutional network to integrate gene expression and histology to identify spatial domains and spatially variable genes. manuscript open access: SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes…

SpaTalk

[R] - SpaTalk is a cell-cell communication inference method for either single-cell or spot-based spatially resolved transcriptomic data, e.g., STARmap, MERFISH, seqFISH, Slide-seq, 10X Visium.

SpatialDe

[Python] - SpatialDE is a statistical test to identify genes with spatial patterns of expression variation from multiplexed imaging or spatial RNA-sequencing data.

SpatialDWLS

[R] A method to identify the cell types at each location with Giotto and determine the cell type composition using dampened weighted least squares. manuscript open access: SpatialDWLS: accurate deconvolution of spatial transcriptomic data

SpatialPrompt

[Python] SpatialPrompt is a spot deconvolution and domain identification method for spatially resolved transcriptomics datasets. Main advantage of this tool is, it is highly scalable for large datasets.

spatialGE

[R] An analysis suite allowing users to study spatial transcriptomics data from multiple platforms (e.g., Visium, CosMx). The package includes methods for pre-processing, clustering/domain detection, spatially variable genes, and functional analysis via the detection of gene expression gradients…

Splotch

[Python] Splotch is a hierarchical generative probabilistic model for analyzing Spatial Transcriptomics data.

SPOTlight

[R] SPOTlight enables the deconvolution of SRT data from a single-cell reference by a non-negative matrix factorization regression(NMFreg) model. manuscript open access: SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes

squidpy

[Python] - Squidpy is a Python package for the analysis and visualization of spatial molecular data. It provides tools to process, analyze, and visualize spatial transcriptomics data, including spatially resolved transcriptomics and spatial proteomics. Squidpy: a scalable framework for spatial…

Starspace

[Python] - Defines a schema for gene or protein expression data containing spatially localized information. Converts data from a variety of assay types, including Spatial Transcriptomics, CODEX, In-situ Sequencing, MERFISH, osmFISH, and starMAP. Demonstrates how to visualize and interact with…

STAGATE

[Python] STAGATE is designed for spatial clustering and denoising expressions of spatial resolved transcriptomics (ST) data by learning low-dimensional latent embeddings with both spatial information and gene expressions via a graph attention auto-encoder(GATE). manuscript open access: Deciphering…

STAligner

[Python] STAligner is designed for alignment and integration of spatially resolved transcriptomics data, it employs a graph attention auto-encoder neural network(GATE) to extract spatially aware embedding and introduces the triplet loss to update the spot embedding to reduce the distance from the…

Tangram

[Python] Tangram is used to map single-cell (or single-nucleus) gene expression data onto spatial gene expression data designed with optimizing a specially designed mapping objective loss. manuscript open access: Deep learning and alignment of spatially resolved single-cell transcriptomes with…

TorchGW

[Python] - Fast Sampled Gromov-Wasserstein optimal transport solver in pure PyTorch with Triton GPU kernels. Aligns datasets across modalities by matching internal distance structures, even in different dimensions. 3-175x faster than POT. Docs

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

BioDockify Learn - Computational Biomedicine: Command Line to Single-Cell

[Video course] - Free AI-narrated video course covering the full scRNA-seq path: command line, R, genomics foundations, then Seurat workflows (object creation, QC/filtering, normalization, PCA/UMAP, clustering, cell-type annotation, doublet detection, integration). Includes Bioinformatics &…

Analysis of single cell RNA-seq data

[R and Python] - The course is taught through the University of Cambridge Bioinformatics training unit, but the material found on these pages is meant to be used for anyone interested in learning about computational analysis of scRNA-seq data.

Aaron Lun's Single Cell workflow on Bioconductor

[R] - This article describes a computational workflow for basic analysis of scRNA-seq data using software packages from the open-source Bioconductor project.

ATAC-Seq Pipeline

[Shell and R] - Chromatin accessibility landscape of pediatric T-lymphoblastic leukemia and human T-cell precursors.

Bambu-Pipe

Bambu-Pipe is a Nextflow pipeline that performs context-aware transcript discovery and quantification from long-read single-cell and spatial transcriptomics data. Manuscript: Isoform-level discovery, quantification and fusion analysis from single-cell and spatial long-read RNA-seq data with…

Bioconductor2016 Single-cell-RNA-sequencing workshop by Sandrine Dudoit lab

[R] - SCONE, clusterExperiment, and slingshot tutorial.

BiomedCentral Single Cell Omics collectin

collection of papers describing techniques for single-cell analysis and protocols.

Clustering 3K PBMCs with Scanpy in Galaxy

Galaxy Training Material.

CRUK CI Introduction to single-cell RNA-seq data analysis

| website

CSHL Single Cell Analysis - Bioinformatics

course materials - Uses Shalek 2013 and Macaulay 2016 datasets to teach machine learning to biologists

CyTOF Workflow

[R] - An R-based pipeline for differential analyses of high dimensional mass cytometry data, primarily based on Bioconductor packages. Link to the paper describing a high-level introduction to the core concepts and code.

Dana-Farber Cancer Institute Trajectory inference across conditions: differential expression and differential…

| website

Dan Beiting DIY Transcriptomics

| website - A hybrid course covering best practices for bulk and single cell RNA-seq data analysis, with a primary focus on empowering students to be independent in the use of lightweight and open-source software and the R/bioconductor environment.

EBI Single cell RNA-seq tutorial

| website

ELIXIR EXCELERATE Single RNA-seq data analysis with R

| website

Festival of Genomics California Single Cell Workshop

[R] - Explores basic workflow from exploratory data analysis to normalization and downstream analyses using a dataset of 1679 cells from the Allen Brain Atlas.

Gilad Lab Single Cell Data Exploration

R-based exploration of single cell sequence data. Lots of experimentation.

GPU accelerated single-cell analysis using RAPIDS

NVIDIA tutorials on using RAPIDS (https://www.rapids.ai/) to accelerate single-cell analysis on GPUs.

Harvard Chan Bioinformatics Core Single-cell RNA-seq data analysis workshop

| website

Harvard STEM Cell Institute Single Cell Workshop 2015

workshop on common computational analysis techniques for scRNA-seq data from differential expression to subpopulation identification and network analysis. See course description for more information

Hemberg Lab scRNA-seq course materials

kallistobustools

kallisto | bustools workflow for pre-processing single-cell RNA-seq data.

NBIS Single cell RNA-seq analysis workshop

| website

MGC/BioSB Course - Single Cell Analysis

nf-core/scrnaseq

nf-core/scrnaseq is a bioinformatics best-practice analysis pipeline for processing 10x Genomics single-cell RNA-seq data. The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers…

nf-core/scflow

nf-core/scflow is a bioinformatics pipeline for scalable, reproducible, best-practice analyses of single-cell/nuclei RNA-sequencing data. The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner.

nf-core/scnanoseq

nf-core/scnanoseq is a Nextflow analysis pipeline for processing 10X Genomics single-cell/nuclei RNA-seq data derived from the Oxford Nanopore long-read sequencer. The pipeline has been designed to be scalable to large datasets (including PromethION data), portable and reproducible.

Orchestrating Single-Cell Analysis with Bioconductor

[R] - This blogdown book describes a comprehensive and reproducible workflow for the analysis of single-cell RNA-sequencing data.

Pre-processing of 10X Single-Cell RNA Datasets in Galaxy

Galaxy Training Material.

Theis Lab Single Cell Tutorial

The main part of this repository is a case study where the best-practices established in the manuscript are applied to a mouse intestinal epithelium regions dataset from Haber et al., Nature 551 (2018) available from the GEO under GSE92332.

Using Seurat (v1.2) for unsupervised clustering and biomarker discovery

301 single cells across diverse tissues from (Pollen et al., Nature Biotechnology, 2014). Original tutorial using Seurat 1.2

Seurat (v3.0) - Guided Clustering Tutorial

new tutorial using Seurat 3.0

SIB Single-cell Transcriptomics

| website

SIB NBIS/SciLifeLab Advanced topics in Single Cell Omics

| website

SIB Advanced topics in single-cell transcriptomics

scRNA-Seq Cardiac Development

End-to-end scRNA-seq pipeline mapping 10 cardiac cell types using Scanpy, Nextflow and AWS Batch.

WEHI Single cell RNA-seq analysis workshop

| website

Wellcome Sanger Institute Analysis of single cell RNA-seq data

| website

10X Genomics datasets

10x genomics public datasets, including 1.3M cell mouse brain dataset.

ASAP

Automated Single-cell Analysis Pipeline (deposited in BioRXiv on December 22, 2016).

cellBrowser

[Python, Javascript] Python pipeline and Javascript scatter plot library for single-cell datasets. Demo

CellView

CellView is an R Shiny web application that allows knowledge-based and hypothesis-driven exploration of processed single cell transcriptomic data. ref.

Cell_BLAST

A Web portal powered by Cell_BLAST (scRNA-seq querying tool) and ACA (scRNA-seq database).

CELLxGENE

CELLxGENE is a suite of tools that help scientists to find, download, explore, analyze, annotate, and publish single cell datasets. It includes several powerful tools with various features to help you to engage with single cell data.

conquer

A repository of consistently processed, analysis-ready single-cell RNA-seq data sets.

Curated Database of single-cell studies

Available as a tsv download. Over 500 single cell transcriptomics studies have been published to date. Many of these have data available, but the links between data, study, and systems studied can be hard to identify through literature search. This manuscript describes a nearly exhaustive and…

Cytopus

[python] - Cytopus is a dedicated scRNA-seq knowledge base containing cell type markers and gene sets representing cellular processes. The packages includes methods to organize and query hierarchically organized single cell taxonomies and interfaces with Spectra) for gene program inference and…

D3E

Discrete Distributional Differential Expression (D3E) is a tool for identifying differentially-expressed genes, based on single-cell RNA-seq data.

dseqr

Dseqr runs end-to-end multi-sample single-cell and bulk RNA-seq analyses using a user friendly web app built around best practices from the OSCA handbook. Features include pseudobulk differential expression analysis, automated cluster annotation, reference mapping with Azimuth, Gene Ontology…

EBI Single Cell Expression Atlas

The Single Cell Expression Atlas contains uniformly re-analysed single cell expression data across different species and provides interactive visualizations to explore that data.

PanglaoDB

A database for exploring mouse and human single-cell RNA-seq experiments and curated cell-type marker genes.

Galaxy Single Cell Omics Workbench

dedicated Galaxy server for analyzing single cell data.

HEST Xenium virtual spatial transcriptomics

[Python] DeepSpot-M predicted single-cell transcriptome-wide profiles for all 59 HEST-1k 10x Xenium samples (~13.3M cells; gated). manuscript: DeepSpot-M: a multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology.

IRIS3

IRIS3 (integrated cell-type-specific regulon inference server from single-cell RNA-Seq) is an easy-to-use server empowered by over 20 functionalities to support comprehensive interpretations and graphical visualizations of identified cell-type-specific regulons.

JingleBells

A repository of standardized single cell RNA-Seq datasets for analysis and visualization in IGV at the single cell level. Currently focused on immune cells (http://www.jimmunol.org/content/198/9/3375.long).

SCPortalen

SCPortalen: human and mouse single-cell centric database. ref

scRNA.seq.datasets

Collection of public scRNA-Seq datasets used by Hemberg Lab

scRNASeqDB

A database aggregating human single-cell RNA-seq datasets. ref

Single Cell Portal

The Single-Cell Portal was developed to facilitate open data and open science in Single-cell Genomics. The portal currently focuses on sharing scientific results interactively, and sharing associated datasets.

TCGA virtual spatial transcriptomics atlas

[Python] DeepSpot-M predicted transcriptome-wide ST for 28,664 TCGA H&E slides across 32 cancers (295.3M spots, 10,865 patients; gated). manuscript: DeepSpot-M: a multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology.

Single-Cell Tumor Immune Atlas project

[R] - We generated a single-cell tumor immune atlas, jointly analyzing >500,000 cells from 217 patients and 13 cancer types, providing the basis for a patient stratification based on immune cell compositions.

V-SVA

An R Shiny application for detecting and annotating hidden sources of variation in single cell RNA-seq data.

WOT

Waddington Optimal Transport (wot) uses time-course data to infer how the probability distribution of cells in gene-expression space evolves over time, by using the mathematical approach of optimal transport.

Asc-Seurat

[R, Docker] - Asc-Seurat is a web application based on Shiny. Pronounced as “ask Seurat”, it provides an easy-to-install and easy-to-use interface that allows the execution of all steps necessary for scRNA-seq analysis. It integrates many of the capabilities of the Seurat and Dynverse and also…

Cellar

[Python] - Cellar is an easy to use, interactive, and comprehensive software tool for the assignment of cell types in single-cell studies. It supports preprocessing, dimensionality reduction, clustering, differential expression & enrichment analysis, spatial transcriptomics, label transfer,…

cellBrowser

[Python, Javascript] Python pipeline and Javascript scatter plot library for single-cell datasets. Demo

CellView

CellView is an R Shiny web application that allows knowledge-based and hypothesis-driven exploration of processed single cell transcriptomic data. ref.

cellxgene VIP

cellxgene VIP is a web-based interactive tool built upon cellxgene but greatly extended its plotting and analytical capabilities by integrating state-of-the-art tools in this field. It allows users with no programming experience to rapidly explore scRNA-seq data and create high-resolution figures…

Cerebro

[R] - Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows, which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user. Through an interactive and…

ChromSCape

[R] - Interactive & complete analysis of single-cell epigenomic landscapes with Shiny. Includes counting, QC, filtering, dimensionality reduction, clustering, visualisation, coverage, peak calling, differential & gene set analysis) - (scChIP-seq, scCUT&TAG, scATAC-seq, scChIC-seq...). (Prompsy et…

Cirrocumulus

Cirrocumulus is an interactive visualization tool for large-scale single-cell genomics (e.g. sc/snRNA-seq, spatial) data.

CReSCENT

[R, Javascript, Python] - CReSCENT: CanceR Single Cell ExpressioN Toolkit (Mohanraj et al. 2020), is an intuitive and scalable web portal incorporating a containerized pipeline execution engine for standardized analysis of cancer scRNA-seq data and associated metadata. CReSCENT uses public data…

FASTGenomics

[Python, R] - FASTGenomics is an online platform to share single-cell RNA sequencing data and analyses using reproducible workflows. Gene expression data can be shared meeting European data protection standards (GDPR). FASTGenomics enables the user to upload their own data and generate customized…

Ginkgo

[R, C] - Ginkgo is a web application for single-cell copy-number variation analysis and visualization.

Granatum

Granatum 🍇 is a graphical single-cell RNA-seq (scRNA-seq) analysis pipeline for genomics scientists. Published in December 2017.

histoCAT

[MATLAB]- Histology Topography Cytometry Analysis Toolbox (histoCAT) is a package to visualize and analyse highly multiplexed image cytometry data. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data

InterCellar

[R] - an R/Shiny app for interactive analysis and exploration of cell-cell communication based on single-cell transcriptomics data. Starting from pre-computed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore cell clusters,…

iS-CellR

iS-CellR (Interactive platform for Single-cell RNAseq) is a web-based Shiny app that integrates the Seurat package with Shiny's reactive programming framework to provide comprhensive analysis and interactive visualization of single-cell RNAseq data. Paper

iSEE

[R] - iSEE, interactive SummarizedExperiment Explorer. The iSEE package aims to provide an interactive user interface for exploring data in objects derived from the SummarizedExperiment class. Particular focus will be given to single-cell data in the SingleCellExperiment derived class. The…

Millefy

[R] - An R package and a Docker image with JupyterLab for visualizing read coverage of scRNA-seq datasets in genomic contexts. By dynamically and automatically reorder single cells based on 'locus-specific' pseudotime, Millefy highlights cell-to-cell heterogeneity in read coverage of scRNA-seq…

NASQAR

Nucleic Acid SeQuence Analysis Resource, a web-based platform that provides an intuitive interface for popular tools (like DESeq2, Seurat, and others) to perform standard downstream analysis workflows for RNAseq data. The portal hosts a number of R Shiny apps.

PIVOT

Platform for Interactive analysis and Visualization Of Transcriptomics data. ref

rakaia

[Python, Javascript] - Scalable in-browser visualization and analysis of single-cell spatial biology. website.

scClustViz

An interactive R Shiny tool for visualizing single-cell RNAseq clustering results from common analysis pipelines (SingleCellExperiment or Seurat, currently). Its main goal is two-fold: A: to help select a biologically appropriate resolution or K from clustering results by assessing differential…

scRNAseqApp

The scRNAseqApp is a Shiny app package designed for interactive visualization of single-cell data. It is an enhanced version derived from the ShinyCell, repackaged to accommodate multiple datasets. The app enables users to visualize data containing various types of information simultaneously,…

SeuratWizard

a web-based (wizard style) interactive R Shiny application to perform guided single-cell RNA-seq data analysis and clustering. demo

SeuratV3Wizard

a web-based (wizard style) interactive R Shiny application to perform guided single-cell RNA-seq data analysis and clustering based on Seurat v3. demo

ShinyArchRUiO

[R] - Shiny based web app for visualization of single-cell ATAC-seq data using ArchR. ShinyArchR.UiO: user-friendly, integrative and open-source tool for visualization of single-cell ATAC-seq data using ArchR

ShinyCortex

a resource that brings together data from recent scRNA-seq studies of the developing cortex for further analysis. ShinyCortex is based in R and displays recently published scRNA-seq data from the human and mouse cortex in a comprehensible, dynamic and accessible way, suitable for data exploration…

singleCellTK

The singleCellTK is an R/Shiny package and GUI for analyzing and visualizing scRNA-Seq through a web interface. Analysis modules include data summary and filtering, dimensionality reduction and clustering, batch correction, differential expression analysis, pathway activity analysis, and power…

spatialGE-web

A web application providing point-and-click access to the methods in the spatialGE R package and other tools (including STdeconvolve, InSituType, SpaGCN). The web application requires no coding experience. User accounts can be created to safely keep samples and results organized within projects.

STREAM

STREAM is an interactive computational pipeline for reconstructing complex celluar developmental trajectories from sc-qPCR, scRNA-seq or scATAC-seq data. preprint.

Vitessce

[JavaScript, Python, R] - A framework for integrative visualization of multi-modal single-cell data, supporting microscopy images, cell segmentations, dimensionality reduction scatterplots, gene expression heatmaps, and genome browser tracks. Vitessce is packaged as a React component, Jupyter…

V-SVA

An R Shiny application for detecting and annotating hidden sources of variation in single cell RNA-seq data.

Mendeley Single Cell Sequencing Analysis

BiomedCentral Single Cell Omics collectin

collection of papers describing techniques for single-cell analysis and protocols.

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

Single-cell Transcriptome Study as Big Data

Design and computational analysis of single-cell RNA-sequencing experiments

How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives

Sensei

. How to determine the number of patients required to ascertain a cell-type abundance change estimated from scRNA-seq experiment.

Comparative analysis of single-cell RNA sequencing methods

a comparison of wet lab protocols for scRNA sequencing.

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…

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.

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 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…

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…

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…

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

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.

scRNA-tools.org

Database of scRNA-seq analysis tools and their functions. Managed through this Github repository.

agitter's Pseudotime estimation list

An overview of algorithms for estimating pseudotime in single-cell RNA-seq data.

Rhonda Bacher (University of Wisconsin-Madison, USA)

Barbara Di Camillo (Information Engineering Department, University of Padova, Italy

Jinmiao Chen (Singapore Immunology Network, A*STAR, Singapore)

Jean Fan (Johns Hopkins University, USA)

Brooke Fridley (Children's Mercy Hospital, USA)

Sandrine Dudoit (UC Berkeley, USA)

Lana X. Garmire, (University of Hawaii Cancer Center, USA)

Laleh Haghverdi (EMBL, Germany)

Stephanie Hicks (Johns Hopkins Bloomberg School of Public Health, USA)

Christina Kendziorski (University of Wisconsin–Madison, USA)

Keegan Korthauer (Dana Farber Cancer Institute, USA)

Smita Krishnaswamy (Yale University)

Ning Leng (Morgridge Institute for Research, USA)

Elisabetta Mereu (Centre for Genomic Regulation, Barcelona)

Samantha Morris (Depts of Dev. Bio. and Genetics, Washington University, St. Louis)

Alicia Oshlack (Murdoch Children's Research Institute, Australia)

Dana Pe'er (Memorial Sloan Kettering Cancer Center, USA)

Emma Pierson (Stanford University, USA)

Aviv Regev (Broad Institute, USA)

Sarah Snelling (University of Oxford, UK)

Charlotte Soneson (Institute of Molecular Life Sciences, University of Zurich)

Sarah Teichmann (Wellcome Trust Sanger Institute, UK)

Barbara Treutlein (ETH Zurich, CH)

Catalina Vallejos (The Alan Turing Institute & UCL, UK)

Sanja Vickovic (New York Genome Center & Columbia University, USA)

Stein Aerts (KU Leuven Center for Human Genetics, Belgium)

Ahmet Coskun (Georgia Tech, USA)

Bart DePlancke (EPFL, School of Life sciences, Institute of Bioengineering, Switzerland)

Raphael Gottardo (Fred Hutchinson Cancer Research Center, USA)

Chung Chau Hon (RIKEN Centre for Integrative Medical Sciences, Yokohama)

Yanxiang Deng (University of Pennsylvania, USA)

Martin Hemberg (Sanger Institute, UK)

John Hickey (Duke University, USA)

Holger Heyn (Centre for Genomic Regulation, Barcelona)

Peter Kharchenko (Department of Biomedical Informatics, Harvard Medical School, USA)

Sten Linnarson (Karolinska Institutet, Sweden)

Aaron Lun (Cancer Research UK, UK)

John Marioni (EBI, UK)

Davis McCarthy (EBI, UK)

John Reid (MRC Biostatistics Unit, Cambridge University, UK)

Mark Robinson (Institute of Molecular Life Sciences, University of Zurich)

Yvan Saeys (Vlaams Instituut voor Biotechnologie, Ghent, Belgium)

Rickard Sandberg (Karolinska Institutet, SE)

Neville Sanjana (New York Genome Center & NYU)

Rahul Satija (New York Genome Center)

Peter Sims (Columbia University, Department of Systems Biology)

Oliver Stegle (EBI, UK)

Fabian Theis (Institute of Computational Biology, Helmholtz Zentrum München)

Cole Trapnell (University of Washington, Department of Genome Sciences)

Itai Yanai (New York University, School of Medicine, Institute for Computational Medicine, USA)