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230 Free Single-cell RNA-seq (scRNA-seq) Tools - Software and Resources

230 Free Single-cell RNA-seq (scRNA-seq) Tools - Software and Resources


  1. ascend
    • Description : ascend (Analysis of Single Cell Expression, Normalisation, and Differential expression) is an R tool for analysis of single-cell RNA-seq data. The ascend tool kit contains functions for data quality control, normalization, reduction of dimensionality, sequence clustering, and differential expression analysis.
  2. BALDR
    • Description : BALDR is a pipeline tool to build immunoglobulin(Ig)/B cell receptor(BCR) sequences from Illumina single-cell RNA-Seq data. The BALDR offers the following reconstruction methods for human and rhesus macaque: 1. Transcript assembly using all reads with a selected Ig model, 2. Read assembly mapped to Ig loci, and alternatively including unmapped reads. For human sequences only: 1. Read assembly mapping to IMGT V(D)J and C sequences, 2. Reas assembly mapping to the Ig recombinome. For rhesus macaque, it can assemble reads filtered not to match Ig genes. The BALDR pipeline requires the following tools: Trimmomatic 0.32, Trinity v2.3.2, bowtie2 2.3.0, STAR v2.5.2b, samtools v1.3.1, IgBLAST v1.6.1, seqtk 1.2, and Perl 5.
  3. DendroSplit
    • Description : A tool for the analysis of single-cell RNA-seq data. The DendroSplit algorithm uses a feature selection approach to cluster and to reveal different population data hierarchies.
  4. RAFSIL
    • Description : A tool for the detection of similarities between single-cell scRNA-seq data sets. The RAFSIL algorithm uses the random forest method in which it first detects features and then learns similarities.
  5. BTR
    • Description : BTR is an R tool for analysis of differential gene expression in single-cell RNA-seq data. The BTR algorithm learns models and expression data using a new Boolean state space scoring function.
  6. COMET
    • Description : COMET is a tool to identify gene markers for cell populations in single-cell RNA-seq data. The COMET algorithm uses a standard non-parametric statistical structure and is suitable for use in high-throughput experiments.
  7. ClusterMine
    • Description : ClusterMine is an R tool for clustering single-cell RNA-seq data. The ClusterMine algorithm uses a gene set that contains functionally related genes for data partitioning into sub-categories. It analyses the sub-categories and combines these into a final clustering. ClusterMine requires a list of gene sets and a gene expression data matrix as inputs.
  8. SCANPY
    • Description : SCANPY is a Python library to analyze single-cell RNA-seq data. The SCANPY package has functions for preprocessing, clustering, differential expression testing, pseudo-time and trajectory inference, gene regulatory networks simulation, and visualization,
  9. BPSC
    • Description : BPSC (Beta-Poisson model for Single-Cell RNA-seq data analyses) is an R tool for the examination of single-cell RNA-seq data. The BPSC algorithm uses a beta-Poisson mixture model to estimate the bimodality of expression distribution.
  10. scPipe
    • Description : scPipe is a pipeline tool to preprocess single-cell RNA-seq data. The scPipe package has methods for barcode demultiplexing, read alignment, UMI-aware gene-level quantification, and quality control. It can use the following data from CEL-seq, MARS-seq, Drop-seq, Chromium 10x, and SMART-seq protocols.
  11. BASiCS
    • Description : BASiCS (Bayesian Analysis of Single-Cell Sequencing), is a tool for the analysis of single-cell RNA-seq data. The BASiCS algorithm uses a Bayesian hierarchical approach in supervised experiments. It estimates cell-specific normalization parameters from the model. It can quantify technical variability using spiking genes included in a cell's lysate, and it reports the total variability as experimental and biological components. Requires: SingleCellExperiment.
  12. SCnorm
    • Description : SCnorm is an R tool to normalize single-cell RNA-seq data. The SCnorm algorithm estimates the sequencing coverage for each gene by quantile regression and uses estimated scaling factors for group-wise calculations for expression levels.
  13. phenopath
    • Description : phenopath is an R tool to estimate pseud-time trajectories in single-cell RNA-seq data. The phenopath algorithm uses a new statistical approach, a mixture regression-latent variable model, which can detect known and unique covariate-pseudo-time interaction influences.
  14. ZINB-WaVE
    • Description : ZINB-WaVE is a tool to produce low-dimensional representations of single-cell RNA-seq data sets. The ZINB-WaVE algorithm uses a zero-inflated negative binomial model that estimates zero inflation (dropouts), over-dispersion, and the counts. It also considers varied library sizes and covariates, that eliminates the need to normalize data.
  15. BEARscc
    • Description : BEARscc (Bayesian ERCC Assessment of Robustness of Single Cell Clusters) is a tool to assess single-cell RNA-seq clusters. The BEARscc algorithm estimates experimental noise using ERCC spike-in controls.
  16. ccfindR
    • Description : ccfindR (Cancer Clone findeR) is a tool for the analysis of cancer cells from single-cell RNA-seq data. ccfindR contains functions for quality control, unsupervised clustering, and visualization. The ccfindR algorithm uses Bayesian non-negative matrix factorization for feature selection and clustering.
  17. ClusterMap
    • Description : ClusterMap is an R tool for the analysis of single-cell RNA-seq data. The ClusterMap algorithm uses hierarchical clustering and computes similarity scores, representing the match quality. The ClusterMap package also has visualization tools.
  18. mfa
    • Description : mfa is an R tool for modeling bifurcations in single-cell RNA-seq data. The mfa algorithm uses a Bayesian hierarchical mixture of factor analyzers and Markov-Chain Monte Carlo sampling.
  19. scDD
    • Description : A tool for analysis of single-cell RNA-seq data sets. The scDD algorithm uses Dirichlet Process mixture models to characterize and classify various expression patterns. This tool also has methods to simulate data.
  20. COAC
    • Description : COAC (Component Overlapping Attribute Clustering) is a tool to make a sub-population gene co-expression network analysis from single-cell RNA-seq data. The COAC algorithm uses a new Component Overlapping Attribute Clustering, inferring co-expression patterns from the elements of a matrix of scRNA-seq profiles.
  21. DEsingle
    • Description : DEsingle is an R tool for differential expression analysis in single-cell RNA-seq. The DEsingle algorithm differentiates and evaluates status, abundance, and general differential expressions. The algorithm uses a zero-inflated Negative Binomial method.
  22. SPAR
    • Description : A web portal for analysis, annotation, and visualization of RNA-seq data sets. The SPAR portal supports data originating from smRNA-seq, short total RNA sequencing, single-cell small RNA-seq, and microRNA-seq. Additionally, SPAR contains sncRNA data sets and data from 185 human tissues from ENCODE.
  23. Citrus
    • Description : Citrus is a tool for deducing of confounding effects in single-cell RNA-seq data. The Citrus algorithm uses a new statistical technique, single-cell partial least squares (scPLS).
  24. CIDR
    • Description : CIDR (Clustering through Imputation and Dimensionality Reduction) is an R tool to reduce the dimensionality and clustering of single-cell RNA-seq data. The CIDR algorithm uses an ‘implicit imputation’ method to deal with dropouts.
  25. scEpath
    • Description : A tool for analysis of single-cell RNA-seq data. The scEpath (single-cell Energy path) algorithm induces cellular trajectories based on Waddington's landscape metaphor of cell development. The algorithm also identifies significant marker genes and transcription factors. Note, requires the R package "princurve."
  26. CHETAH
    • Description : CHETAH (CHaracterization of cEll Types Aided by Hierarchical classification) is an R tool to classify cells in single-cell RNA-seq datasets. The CHETAH algorithm uses a reference dataset and hierarchical clustering of reference data to compute a tree for top-to-bottom classification.
  27. VDJPuzzle
    • Description : VDJPuzzle is a tool to reconstruct full-length B-cell receptors from single-cell RNA-seq data sets. The VDJPuzzle algorithm can reconstruct both heavy and light chains, and detect mutations in the V(D)J region.
  28. CellTrails
    • Description : A tool for expression analysis, de novo chronological ordering, and visualization of single-cell RNA-seq data. The CellTrails algorithm uses a geometric lower-dimensional manifold learning to estimate noise, drop-outs, experimental variance, and redundancy of predictive variables. CellTrails also has functions for branching reconstruction and graphical visualization of expression patterns and branches.
  29. celda
    • Description : celda (CEllular Latent Dirichlet Allocation) is an R tool for clustering cells and features in single-cell RNA-seq data sets. The celda algorithm uses a Bayesian hierarchical method.
  30. scater
    • Description : An R package for pre-processing, quality control, expression normalization, and visualization of single-cell RNA-seq data. The scater algorithm includes a suite of plotting functions.
  31. ASAP
    • Description : ASAP is a pipeline tool for the analysis of single-cell RNA-seq (scRNA) data. The ASAP tool includes functions for the identification of cell clusters, differentially expressed genes, enrichment analysis, and a variety of visualization tools.
  32. K-Branches
    • Description : K-Branches is a tool to detect branching points in single-cell RNA-seq (scRNBA-seq) data. The K-Branches algorithm uses K-branches clustering technique
  33. FateID
    • Description : FateID is a tool to quantify and visualize the bias of cell fate among multipotent progenitors in single-cell RNA-seq (scRNA-seq) data sets. The FateID uses the RaceID3 clustering algorithm for the identification of the cell types and an iterative supervised machine learning technique for quantification of biases in progenitors.
  34. TSCAN
    • Description : TSCAN (Tools for Single Cell Analysis) is a tool for differential analysis of single-cell RNA-seq data. The TSCAN algorithm uses a cluster-based minimum spanning tree (MST) method for the pseudo-temporal ordering of cells. TSCAN package has a graphical user interface implemented in Shiny.
  35. scran
    • Description : scran is an R tool for single-cell RNA-seq data experiments. The scran algorithm has functions for low-level analyses of single-cell RNA-seq data, including normalization, the designation of the cell-cycle phase, and the determination of notably variable and significantly correlated gene expressions. Requires: SingleCellExperiment tool.
  36. CaSTLe
    • Description : A tool for the classification of cells in single-cell RNA-seq data. The CaSTLe algorithm applies information transferred from past experiments with similar cell types and the XGBoost classification model to label cells.
  37. switchde
    • Description : switchde is an R tool to detect switch-like differential expression in single-cell RNA-seq data. The switchde algorithm uses a fast model fitting to produce interpretable parameter estimates for the speed of up- and downregulation and trajectories. It also models zero-inflation data.
  38. Oscope
    • Description : Oscope is a tool for the identification of oscillatory genes single-cell RNA-seq data. The package has functions for searching candidate oscillatory pairs, k-medoids clustering, and to recover the base cycle order for oscillatory groups.
  39. BSeq-sc
    • Description : BSeq-sc is a pipeline tool for the estimation of differential expression and cell types from single-cell RNA-seq data as bulk tissue samples.
  40. clusterExperiment
    • Description : clusterExperiment is a tool for clustering single-cell RNA-seq data. The clusterExperiment algorithm uses the Resampling-based Sequential Ensemble Clustering (RSEC) method for the creation of various user-defined clusters and computation of a consensus cluster. The package also contains visualization tools and functions for the detection of cluster signatures.
  41. ccRemover
    • Description : ccRemover is an R tool to remove the cell-cycle effects from single-cell RNA-seq data. The ccRemover algorithm uses a method that preserves significant features after removing the cell-cycle effect.
  42. alevinQC
    • Description : alevinQC is an R tool to generate quality control reports that summarize the "alevin" output.
  43. BAMMSC
    • Description : A tool for clustering single-cell RNA-seq data. The BAMMSC algorithm uses a Bayesian hierarchical Dirichlet multinomial mixture model and can characterize variabilities of genes, cell types, and individuals. It also accounts for batch effects, variable read lengths, and technical bias, within multiple individual samples. Besides, it combines DIMMSC in analyses of individuals.
  44. BASIC (uchicago)
    • Description : BASIC is a tool for assembling B-cell receptor (BCR) single-cell RNA-seq data. The BASIC algorithm first identifies known constant and variable regions to anchor sequences and then uses the recognized anchors to guide the assembly of BCR.
  45. BEER
    • Description : BEER (Batch EffEct Remover) is an R tool to detect and remove batch effects in single-cell RNA-seq data. The BEER algorithm converts data into one-dimensional values by stochastically embedding neighboring tSNEs and groups the cells and evaluates the expression profiles. It uses Kendall’s tau to determine cell-pair distances. It detects and removes batch effects using the fastMNN method, which identifies mutual nearest neighbor cells in separate batches. The BEER also has a workflow for users to learn the potential biological significance of removed PCs.
  46. bayNorm
    • Description : A tool for normalization, true count recovery, and imputation of single-cell RNA data. The bayNorm algorithm uses a new Bayesian approach for scaling and inference of counts. The likelihood approach uses a binomial model, and it estimates the priors using empirical Bayesian estimates of values across cells.
  47. DECENT
    • Description : DECENT is an R tool for differential expression analysis of single-cell RNA-seq data. The DECENT algorithm requires UMIs and it has a hierarchical approach in modeling the observed and unobserved counts to detect dropouts.
  48. BBKNN
    • Description : A tool for batch alignment of single-cell RNA-seq transcriptome data. The BBKNN algorithm uses graph-based data integration to increase alignment speed. It first identifies k nearest neighbors and transforms candidates to connectivities for subsequent analyses.
  49. BCseq (USC)
    • Description : BCseq (bias-corrected sequencing analysis) is a tool to quantify and correct bias in single-cell RNA-seq data sets. The BCseq algorithm detects dropouts by measurement of outputs from similar cells and assigns a quality score for each gene.
  50. BEELINE
    • Description : BEELINE (Benchmarking gEnE reguLatory network Inference from siNgle-cEll) is a tool for benchmarking algorithms inferring gene regulatory networks (GRNs). The BEELINE algorithm simulates single-cell expression data and uses artificial networks having predictable cell trajectories and Boolean models to evaluate the accuracy of inferences.
  51. SingleCellExperiment
    • Description : SingleCellExperiment is an R tool for defining an S4 class for data storage in single-cell RNA-seq. The SingleCellExperiment algorithm stores and retrieves spike-in information, dimensionality reduction coordinates, metadata, and size factors for each cell sample.
  52. correct_bacode_py
    • Description : correct_bacode.py is a tool to correct sequencing errors in cell barcoding that contain RT primers for Quartz-Seq2 (a high-throughput single-cell RNA-sequencing method, see links).
  53. TraCeR
    • Description : An R tool to reconstruct T-cell receptor genes from single-cell RNA-seq data. The TraCeR algorithm can also identify cells containing identical T-cell receptors to determine clonally propagated cells.
  54. BUSseq
    • Description : BUSseq (Batch Effects Correction with Unknown Subtypes for scRNA seq) is an R tool to correct batch effects in single-cell RNA-seq data. The BUSseq algorithm can also cluster cell types, correct for overdispersion, cell-specific sequencing depth, and dropout events.
  55. pySCENIC
    • Description : A Python pipeline for inference of gene regulatory networks and identification of cell states in RNA-seq data sets. The pySCENIC workflow is a Python implementation of the original SCENIC R package and much faster (see links).
  56. cb_sniffer
    • Description : cb_sniffer is a tool to identify for mutant and reference barcodes in single-cell RNA-seq data.
  57. LungGENS
    • Description : LungGENS (Lung Gene Expression in Single-cell) is a tool to map the gene expression of developing lung tissue in single-cell RNA-seq (scRNA-seq) data. The LungGENS algorithm delivers associated gene signatures, surface markers, and transcription factors as an interactive heatmap including tables.
  58. netSmooth
    • Description : netSmooth is an R tool for smoothing noisy single-cell scRNA-seq data. The netSmooth algorithm uses a network-diffusion based approach in which it uses priors to analyse the covariance of expression profiles.
  59. clonealign
    • Description : clonealign is a tool for the assignment of expression profiles to cancer clones in single-cell RNA-seq and DNA sequencing data. The clonealign algorithm uses a probabilistic, reparametrization gradient variational inference, approach to map expression profiles to copy number profiles.
  60. MAGIC (mskcc.org)
    • Description : MAGIC (Markov Affinity-based Graph Imputation of Cells) is a tool for denoising high-dimensional single-cell RNA-seq data. The MAGIC algorithm works by information sharing over alike cells to denoise cell counts and to detect dropouts.
  61. cellassign
    • Description : cellassign is an R tool for the assignment of known and de novo cell types of scRNA-seq based on marker genes.
  62. cellity
    • Description : cellity is a tool to detect and filter cells in single-cell RNA-seq data. The cellity algorithm works based on over 20 curated sets of biological and experimental characteristics. A Python version is also available, see links.
  63. BRAPeS
    • Description : BRAPeS (BCR Reconstruction Algorithm for Paired-End Single-cell) is a tool to reconstruct B cell receptors (BCR) paired-end single-cell RNA-seq data. The BRAPeS algorithm can use short sequences, down to 25bp of length.
  64. bigSCale
    • Description : A framework tool to analyze and visualize single-cell RNA-seq data. The bigSCale functions support clustering, differential expression analysis, phenotype, pseudo-time analysis, inference of gene regulatory networks (GRNs), dataset minimizing. The algorithm estimates models using large sample sizes using a directed convolution strategy to estimate models and cope with noise. Alternative name: bigSCale2
  65. CellBench
    • Description : CellBench is an R tool to benchmark single-cell analysis approaches. CellBench has the means to organize and test mixtures of analysis methods. The CellBench also implements methods to build lists of functions with many parameters, sample, and filter single-cell test targets.
  66. SCRL
    • Description : SCRL (Single Cell Representation Learning) is a tool for collecting representations of single-cell RNA-seq information, such as pathways, from multiple sources.
  67. Bisque (ucla)
    • Description : Bisque is an R tool to estimate cell composition from single-cell RNA-seq data. The Bisque algorithm uses a regression approach to compute reference expression profiles and, consequently, to learn gene-specific bulk expression transformations for decomposition.
  68. CellRanger
    • Description : CellRanger is a tool for sequence read alignment, clustering, gene expression analysis, and generation of gene-cell matrices from Chromium single cell 3’ RNA-seq data.
  69. ZIFA
    • Description : ZIFA (Zero Inflated Factor Analysis) is a tool to reduce dimensionality in single-cell RNA-seq data. The ZIFA algorithm also has models for characteristics of dropouts.
  70. CALISTA
    • Description : CALISTA (Clustering and Lineage Inference in Single-Cell Transcriptional Analysis) is a tool for analysis of single-cell RNA-seq data. The CALISTA algorithm includes functions for analysis of differential expression, single-cell clustering, cell-lineage reconstruction, and pseudo-time ordering.
  71. scTDA
    • Description : scTDA (single-cell topological data analysis) is a Python library for the analysis of single-cell RNA-seq data. The scTDA algorithm uses an unsupervised statistical structure to characterize cellular states. It is non-linear and model-independent.
  72. CellView
    • Description : CellView is a tool for the analysis of differential and co-expression patterns, and for viewing 3D representations of single-cell RNA-seq data. The CellView has functions for knowledge-based and hypothesis-driven investigations of data.
  73. SigEMD
    • Description : SigEMD is an R tool to identify differential expression in single-cell RNA-seq data. The SigEMD algorithm uses imputation, logistic regression, gene interaction network information, and a nonparametric method based on the Earth Mover's Distance for the identification.
  74. Conos
    • Description : Conos is a tool to analyze heterogeneous single-cell RNA-seq datasets. The Conos algorithm estimates multiple inter-sample mappings to compute a graph over cells that identify cell clusters and information proliferation.
  75. scVEGs
    • Description : scVEGs is a tool for analysis of differentially expressed genes in single-cell RNA-seq data. The scVEGs algorithm uses a gene expression variation model (GEVM) and makes use of the relation between the coefficient of variation (CV) and mean expression level to adjust for over-dispersion and compute statistical significance.
  76. CellBender
    • Description : CellBender is a tool to eliminate experimental artifacts in droplet-based single-cell RNA-seq (scRNA-seq) assays.
  77. wot
    • Description : wot (Waddington-OT) is a tool to analyze Optimal-Transport of single-cell expression in RNA-seq data sets. The wot algorithm applies time-course data to compute the probability distributions of cells.
  78. CellBIC
    • Description : CellBIC is a tool for clustering single-cell RNA-seq data. The CellBIC algorithm does top-down hierarchical clustering.
  79. Wishbone
    • Description : Wishbone is a tool to position cells with accompanying bifurcating developmental trajectories in single-cell RNA-seq or cytometry data.
  80. trendsceek
    • Description : trendsceek is an R tool for the identification of statistically significant spatial expression trends in single-cell RNA-seq data. The trendsceek algorithm uses modified marked point processes and also discovers genes in sequential fluorescence in situ hybridization data. It can unveil significant gene expression gradients and hot spots.
  81. cellAlign
    • Description : cellAlign is an R tool to compare and quantify cell expression and trajectories among and between cells in single-cell RNA-seq data sets.
  82. txburst
    • Description : txburst is a tool to infer transcriptional burst kinetics and analysis in single-cell RNA-seq data.
  83. RCA (GIS)
    • Description : RCA (clustering single-cell RNA-seq data using reference component analysis) is an R tool for clustering single-cell RNA-seq data.
  84. BranchedGP
    • Description : BranchedGP is a tool to detect gene-specific branching dynamics and estimate branching times. The algorithm can also predict likely branching regions. Other names: BGP (Branching Gaussian process).
  85. p-Creode
    • Description : p-Creode is a tool for mapping multi-branched cellular differentiation pathways using single-cell RNA-seq data. The p-Creode algorithm uses an unsupervised method to create, compare multi-branching graphs, and infer developmental trajectories. Alternative name: Creode.
  86. ouijaflow
    • Description : A tool for estimating pseudo-times in RNA-seq data. The Ouija algorithm uses an orthogonal Bayesian method to learn pseudo-times using marker genes.
  87. CellO
    • Description : CellO (Cell Ontology-based classification) is a tool to classify cell types in single-cell RNA-seq data. The Authors trained the CellO algorithm using curated datasets from Sequence Read Archive at NCBI.
  88. MUSIC (Tongji University)
    • Description : MUSIC is an R tool to analyze single-cell data from a combination of Perturb-Seq, CRISP-seq, CROP-seq, and single-cell RNA-seq. The MUSIC algorithm analysis step consists of preprocessing, model construction, and perturbation impact prioritizing.
  89. Cell BLAST
    • Description : Cell BLAST is a tool for searching single-cell RNA-seq databases and annotation of RNA-seq data. Apart from the standalone version, there is also a web server http://cblast.gao-lab.org (see links).
  90. Expedition
    • Description : Expedition is a suite of tools for visualization and analysis of single-cell alternative splicing RNA-seq data, consisting of anchor, bonvoyage, and outrigger packages.
  91. singlecell_pnm
    • Description : singlecell_pnm is the Jupyter notebook code for single-cell alternative splicing. This is the code for the paper by Song and Botvinnik, et al., Molecular Cell (2017), investigating alternative splicing in single cells in neurodegenerative disease.
  92. CellRouter
    • Description : CellRouter is a tool for the identification of cell-state transition trajectories in single-cell RNA-seq data. The CellRouter algorithm uses flow networks to identify subpopulations.
  93. Palantir
    • Description : A tool for computation of cellular differentiation trajectories in single-cell RNA-seq and Mass cytometry data. The Palantir algorithm models differentiation as a stochastic process to estimate cell fates.
  94. BraCeR
    • Description : An R tool to reconstruct full-length B-cell-receptor (BCR) sequences from paired-end or single-end single-cell RNA-seq data. BraCeR is an extension of the TraCeR pipeline and can also determine clonality, estimate somatic hypermutations (SHMs), isotype switching, and create resource files for analyses of additional species. The default version is human and mice.
  95. Alevin
    • Description : Alevin is a tool for estimating gene abundances in droplet-based single-cell dscRNA-seq data. The Alevin tool is included in the Salmon package (see links).
  96. Cardelino
    • Description : Cardelino is an R tool to identify clones in single-cell RNA-seq data. The Cardelino algorithm has methods for inferring clonal structures in a cell population and analyses of differential gene expression.
  97. batchelor
    • Description : batchelor is an R tool for batch correction in single-cell RNA-seq data. The batchelor algorithm uses mutual nearest neighbors (MNNs) method in the high-dimensional expression space for the detection of batch effects. The algorithm does not depend on predefined population compositions. Requires: SingleCellExperiment.
  98. CNNC
    • Description : CNNC is a tool for co-expression analysis of single-cell RNA-seq data. The CNNC algorithm implements a Convolutional neural network for the purpose.
  99. scvis
    • Description : scvis is a tool to reduce dimensionality in single-cell RNA-seq data. The scvis algorithm learns a probabilistic parametric mapping function for adding new data.
  100. BISCUIT
    • Description : BISCUIT is an R tool to normalize and cluster single-cell RNA-seq data. The BISCUIT algorithm uses a hierarchical Bayesian mixture model that scales related to each cell, facilitating iterative normalization and clustering of cells, and reduces experimental variation. The algorithm also applies a scalable Gibbs inference algorithm to enhance the deduction of cluster building.
  101. FeatureCounts
    • Description : featureCounts is a tool to quantify RNA-seq and gDNA-seq data as counts. It is also suitable for single-cell RNA-seq (scRNA-seq) data. It supports multi-threading. The featureCounts is part of the Subread package (see links). An R version is also available as Rsubread .
  102. HTSeq
    • Description : HTSeq is a tool for the analysis of high-throughput sequencing data. It processes reads aligned with HISTAT or STAR and assign expression value counts. The HTSeq is also suitable for the quantification of single-cell RNA-seq data (scRNA-seq). The package also includes a htseq-count tool for pre-processing RNA-seq reads before differential expression analysis and a htseq-qa tool that assesses the read quality.
  103. edgeR
    • Description : edgeR is a tool for differential expression (DE) analysis of RNA-seq, ChIP-seq, CAGE, and SAGE data with biological replicates. The edgeR algorithm uses information from all the genes, computes the dispersion using a weighted likelihood and F-test techniques. For the normalization, it can use the trimmed mean of M-values, upper-quartile (UQ) procedure, Relative Log Expression (RLE), and DESeq. It can compare two groups, paired and unpaired, or use a Generalized Linear Model (GLM). The upper-quartile (UQ) procedure is also applicable to single-cell RNA-seq (scRNA-seq).
  104. ImpulseDE
    • Description : ImpulseDE is an R tool for differentially expressed genes (DEGs) in RNA-seq and scRNA-seq time-course data. The ImpulseDE can report DEGs across time points over time in datasets with single or multiple conditions. It includes quality values for DEGs, impulse model parameters, fitted values for genes, and can use multi-threading.
  105. LAmbDA
    • Description : LAmbDA is a tool for single-cell RNA-seq (scRNA-seq) data. The LAmbDA algorithm uses a transfer technique model to train models to decrease batch effects.
  106. scVCMD
    • Description : scVCMD is a method to cluster cells in single-cell RNA-seq (scRNA-seq) data. The scVCMD algorithm can cluster cells originating from multiple experiments and can distinguish sub-populations with shared markers.
  107. scmap
    • Description : scmap is an R tool to compare cells from separate expression single-cell RNA-seq (scRNA-seq) experiments.
  108. scfind
    • Description : scfind is a tool to index single-cell RNA-seq (scRNA-seq) data sets to allow efficient searching. See also scmap.
  109. singleCellTK
    • Description : singleCellTK (Single Cell Toolkit) is an R tool for analysis of single-cell RNA-seq (scRNA-seq) data. The singleCellTK algorithm has functions for analysis of differential expression, downsampling, and clustering.
  110. f-scLVM
    • Description : f-scLVM (factorial single-cell latent variable model) implemented in the slalom package to estimate factors, refine gene set annotations to assess and model confounding factors contributing to cell-cell variability.
  111. slalom
    • Description : slalom is a tool to model confounding factors for cell-cell variability in single-cell RNA-seq (scRNA-seq) data sets.
  112. scMatch
    • Description : scMatch is a tool for annotation of single-cell RNA-seq (scRNA-seq) data.
  113. SCOPIT
    • Description : SCOPIT (Single-Cell One-sided Probability Interactive Tool) is a tool to calculate sample sizes for single-cell RNA-seq (scRNA-seq) data experiments. The SCOPIT algorithm uses a multinomial distribution, and a separate R package, pmultinom, contains the scripts for the computations.
  114. scone (Bioconductor)
    • Description : scone is a tool to compare and rank the performance of single-cell RNA-seq (scRNA-seq) techniques.
  115. splatter
    • Description : splatter is a tool for simulation of single-cell RNA-seq (scRNA-seq) data. The algorithm can estimate the parameters from real data sets. The splatter tool also combines an interface for several simulations methods, such as Splat which is based on a gamma-Poisson distribution.
  116. GRM
    • Description : GRM is a tool for technical noise removal in single-cell RNA-seq (scRNA-seq).
  117. SC3
    • Description : SC3 is a tool to cluster consensus sequences of single-cell RNA-seq (scRNA-sq) data. The SC3 algorithm uses unsupervised learning method.
  118. scde
    • Description : scde is a tool to analyze differential gene expression in single-cell RNA-seq (scRNA-seq) data. The scde algorithm uses individual error models and detects differential expression and identifies subpopulations.
  119. IRIS-EDA
    • Description : IRIS-EDA is a comprehensive tool for differential gene expression (DGE) analysis for RNA-seq and single-cell RNA-seq (scRNA-seq) data. The IRIS-EDA algorithm implements edgeR, DESeq2, and limma tools. IRIS-EDA package also includes correlation analysis, heatmap, clustering, biclustering, Principal Component Analysis (PCA), Multidimensional Scaling (MDS), t-distributed Stochastic Neighbor Embedding (t-SNE), and several visualization tools.
  120. M3Drop
    • Description : M3Drop is a tool to meld dropout patterns in single-cell RNA-seq (scRNA-seq) data.
  121. OEFinder
    • Description : OEFinder is a visualization tool for ordering effects in single-cell RNA-seq (scRNA-seq) data.
  122. scLVM
    • Description : scLVM is a tool to model heterogeneity of cells in single-cell RNA-seq (scRNA-seq) datasets to pinpoint confounding sources.
  123. sincell
    • Description : sincell is an R tool for the assessment of cell-state hierarchies in single-cell RNA-seq (scRNA-seq) datasets. The package includes functions to compute cell-state hierarchies, statistics, accounts for random variations in the data, functional association tests, and graphics.
  124. monocle
    • Description : monocle is a tool for quantification of expressed mRNA in single-cell RNA-seq (scRNA-sec) data. The monocle algorithm has functions for differential expression and time-series analysis and uses the Census algorithm to convert relative expression levels into relative transcript counts without experimental spike-in controls.
  125. RaceID
    • Description : RaceID is a tool
  126. StemID
    • Description : StemID is a tool for the identification of stem cells in single-cell RNA-seq (scRNA-seq) data based on RaceID results. See also StemID2 and RaceID3 (links).
  127. RaceID2_StemID2_package
    • Description : RaceID2_StemID2_package. RaceID is a tool for the identification of abundant cell types in single-cell RNA-seq (scRNA-seq) data. The RaceID algorithm uses unique molecular identifiers to compute the counts. See also a more recent version (links). StemID is a tool for the identification of stem cells in single-cell RNA-seq (scRNA-seq) data based on RaceID2 results. See also StemID2 and RaceID3 (links).
  128. RaceID3_StemID2_package
    • Description : RaceID3_StemID2_package is a tool for the identification and clustering fo cell types in single-cell RNA-seq (scRNA-seq) data. The RaceID3 algorithm requires a gene-by-cell expression matrix as input and computes partitions of cell types. The StemID2 tool assembles cell types into a lineage tree.
  129. VarID analysis
    • Description : VarID analysis is a tool is part of the RaceID package starting with RaceID v0.1.4 (see links). The VarID algorithm has functions for clustering using pruned k-nearest neighbor networks and a background gene expression model.
  130. SinQC
    • Description : SinQC (Single-cell RNA-seq Quality Control) is a tool for the detection of technical artifacts in single-cell RNA-seq (scRNA-seq) data. The SinQC algorithm uses both gene expression patterns and data quality information for the detection.
  131. SLICE
    • Description : SLICE is a tool to estimate cellular differentiation states in single-cell RNA-seq (scRNA-seq) data. The SLICE algorithm also predicts differentiation lineages by computing cell trajectory entropies.
  132. Slingshot
    • Description : Slingshot is a tool for the identification of developmental trajectories in single-cell RNA-seq (scRNA-seq) data. The Slingshot algorithm can use prior knowledge via supervised graph construction.
  133. SPARSim
    • Description : SPARSim is a tool to simulate single-cell RNa-seq (scRNA-seq) count data. The SPARSim algorithm uses Gamma-Multivariate Hypergeometric model.
  134. SSrGE
    • Description : SSrGE is a tool for the identification of subpopulations in single-cell RNA-seq (sc-RNA-seq) data. The SSrGE algorithm uses nucleotide variations for the identification.
  135. STEMNET
    • Description : STEMNET is a tool to cluster and classify cell populations in single-cell RNA-seq (scRNA-seq) data.
  136. SymSim
    • Description : SymSim (Synthetic model of multiple variability factors for Simulation) is a tool to simulate single-cell RNA-seq (scRNA-seq) data.
  137. TASIC
    • Description : TASIC is a tool to estimate temporal cell trajectories, branching, and cell assignments in single-cell RNA-seq (scRNA-seq) time series data.
  138. TSEE
    • Description : TSEE is a tool for visualization of gene expression patterns in single-cell RNA-seq (scRNA-seq) time-series data.
  139. UNCURL
    • Description : UNCURL is a tool to preprocess non-negative matrix factorization for single-cell RNA-seq (scRNA-seq) data. The UNCURL algorithm is able to include prior knowledge.
  140. URD
    • Description : URD is a tool to reconstruct transcriptional trajectories in single-cell RNA-seq (scRNA-seq) data. The URD algorithm computes a branching tree.
  141. VASC
    • Description : VASC (deep variational autoencoder) is a tool for dimension reduction in single-cell RNA-seq (scRNA-seq) data. The VASC algorithm uses Deep Variational Autoencoder.
  142. velocyto
    • Description : velocyto is a tool to estimate gene-specific transcriptional derivatives and visualization of single-cell RNA-seq (scRNA-seq) data. The velocyto algorithm uses velocity patterns.
  143. Vireo
    • Description : Vireo (Variational Inference for Reconstructing Ensemble Origin) is a tool to demultiplex pooled single-cell RNA-seq (scRNA-seq) data. The Vireo algorithm uses a Bayesian approach and works with data where only partial or no genotype information exists.
  144. VISION
    • Description : VISION is a tool that describes coordinated variation among cells in single-cell RNA-seq (scRNA-seq) data. The VISION tool computes an interactive, low latency and feature-rich HTML report.
  145. Cerebro
    • Description : Cerebro (cell report browser) is a tool to interactively visualize single-cell RNA-seq (scRNA-seq) data. The Cerebro is based on Shiny and Electron.
  146. visnormsc
    • Description : visnormsc is a graphical user interface (GUI) to normalize single-cell RNA-seq (scRNA-seq) data. visnormsc works with SCnorm normalization tool (see links).
  147. SNN-Cliq
    • Description : SNN-Cliq is a tool to cluster cells types based on the expression in single-cell RNA-seq (scRNA-seq) data, The SNN-Cliq algorithm uses the nearest neighbor method to detect similarities between data points.
  148. Wave-Crest
    • Description : Wave-Crest is a tool for the reconstruction of cell trajectories in single-cell RNA-seq (scRNA-seq) data.
  149. CONCLUS
    • Description : CONCLUS (CONsensus CLUSters to a meaningful CONCLUSion) is a tool to cluster and to select feature markers in single-cell RNA-seq (scRNA-seq) data.
  150. SINCERA
    • Description : SINCERA is a pipeline tool for profiling, analysis, and visualization of single-cell RNA-seq (scRNA-seq) data. The SINCERA algorithm has functions for the identification of cell type specific gene signatures, and the computation of driving forces of cell types.
  151. scRNABatchQC
    • Description : scRNABatchQC is a tool for QC and comparison of datasets, and generation reports in single-cell RNA-seq (scRNA-seq) datasets.
  152. SCQC
    • Description : SCQC is a tool for quality control of single-cell RNA-seq (scRNA-seq) datasets. The SCQC algorithm has functions for gene-wise screening and library-wise screening.
  153. WebGestalt
    • Description : WebGestalt is a web-based tool for the analysis of gene sets in various contects in single-cell RNA-seq (scRNA-seq) data.
  154. scphaser
    • Description : scphaser is a tool to phase haplotypes in single-cell RNA-seq (scRNA-seq) data. scphaser can phase de novo and rare variants.
  155. SCOUT
    • Description : SCOUT is a tool to infer pseudo-time ordering and bifurcation trajectories in single-cell RNA-seq (scRNA-seq) data. The SCOUT algorithm uses the fixed-radius near neighbors algorithms for cell densities to identify cell states, and use the minimum spanning tree to compute branching. SCOUT determines a weighted distance to using a projection of Apollonian circle to compute the pseudo-time trajectories cells.
  156. SCNS
    • Description : SCNS (Single Cell Network Synthesis) is a tool to compute and analyze regulatory networks in single-cell RNA-seq (scRNA-seq) data.
  157. scNBMF
    • Description : scNBMF (single-cell negative binomial matrix factorization) is a tool factorize matrices to detect cell types in single-cell RNA-seq (scRNA-seq) data. The scNBMF algorithm uses TensorFlow framework.
  158. scGESTALT
    • Description : scGESTALT is a docker image that processes GESTALT barcode sequencing data. scGESTALT can recostruct cell lineages using single-cell RNA-seq (scRNA-seq) and CRISPR-Cas9 barcodes.
  159. scDAPA
    • Description : scDAPA is a tool to detect and visualize alternative polyadenylations in single-cell RNA-seq (scRNA-seq) data. The scDAPA algorithm uses a histogram-based method and the Wilcoxon rank-sum test. It also visulaizes the candidtae genes.
  160. scAPA
    • Description : scAPA is a tool for the analysis of alternative polyadenylation in single-cell RNA-seq (scRNA-seq) data.
  161. SC2P
    • Description : SC2P is a tool for the differential analysis of single-cell RNA-seq (scRNA-seq) data. The SC2P algorithm can compute the probability of gene being expressed and the difference in expression levels.
  162. dropEst
    • Description : dropEst is a pipeline tool to estimate molecular counts in droplet-based single-cell RNA-seq (scRNA-seq) data. The dropEst package correct barcodes, classifies cell quality, computes diagnostic information about the libraries, corrects sequencing errors, and composition bias.
  163. DPre
    • Description : DPre (driver-predictor) is a tool to identify bias in differentiation and genes affecting cell type conversions in Single-cell RNA-seq (scRNA-seq) data. The DPre algorithm scores transcriptional similarity of each gene between data sets to estimate differentiation biases.
  164. densityCut
    • Description : densityCut is a tool to cluster single-cell RNA-seq (scRNA-seq) data. The densityCut algorithm uses estimates of the k-nearest neighbor graph and finetunes using a random walk strategy. The clustering unveils cells of the same functional states and types and can reveal clonal architectures of tumors.
  165. DENDRO
    • Description : DENDRO (Dna based EvolutioNary tree preDiction by scRna-seq technOlogy) is a tool to profile genetic heterogeneity and to detect subclones in single-cell RNA-seq (scRNA-seq) data. The DENDRO algorithm clusters subclones and computes a related phylogenetic tree.
  166. ddSeeker
    • Description : ddSeeker is a tool to process Bio-Rad ddSEQ single-cell RNA-seq (scRNA-seq) data. It does initial processing and computes quality metrics.
  167. D3E
    • Description : D3E is a tool for the identification of differentially expressed genes in single-cell RNA-seq (scRNA-seq) data. The D3E algorithm has a function to fit the parameters of a Poisson-Beta distribution. A web-based version is available for the analysis of small datasets (see links).
  168. scDC
    • Description : scDC (single cell differential composition) is a tool package containing a collection of functions for the analysis of single-cell RNA-seq (scRNA-seq) data. It analyses differential cell-type composition using bootstrap resampling to estimate the uncertainty in cell-type proportions and compute confidence intervals.
  169. GWASpi
    • Description : GWASpi (genome-wide association studies pipeline) is a tool to manage and analyze single-nucleotide polymorphism (SNPs) in genome-wide association studies (GWAS).
  170. DrImpute
    • Description : DrImpute is a tool for the imputation of dropout events in single-cell RNA-seq (sc-RNA-seq) data.
  171. dropbead
    • Description : dropbead is a tool is for the analysis of single-cell RNA-seq (scRNA-seq) data from droplet sequencing.
  172. DropletUtils
    • Description : DropletUtils is a utility tool for the analysis of single-cell RNA-seq (scRNA-seq) data from droplet technologies. DropletUtils includes functions for loading, identification of empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix.
  173. DTWscore
    • Description : DTWscore is a tool for the analysis of transcriptional heterogeneity in single-cell RNA-seq (scRNA-seq) time-series data.
  174. DC3
    • Description : DC3 (De-Convolution and Coupled-Clustering) is a tool for the joint analysis of bulk and single-cell RNA-seq (scRNA-seq) originating from the same heterogeneous cell population. The DC3 algorithm can identify subpopulations, cluster cells, and de-convolve the bulk data.
  175. DeepImpute
    • Description : DeepImpute is a tool to impute single-cell RNA-seq (scRNA-seq) data. The DeepImpute algorithm uses a deep neural network learning method.
  176. demuxlet
    • Description : demuxlet is a tool to multiplex barcoded droplet single-cell RNA-seq (scRNA-seq) data. The demuxlet algorithm uses a natural genetic variation to assign the identity of each droplet with a single cell and droplets containing two cells.
  177. DensityPath
    • Description : DensityPath is a tool to reconstruct and visualize developmental cell trajectories in single-cell RNA-seq (scRNA-seq) datasets.
  178. desc
    • Description : desc (deep embedding algorithm for single-cell clustering) is a tool to cluster single-cell RNA_seq (scRNA-seq) data. The desc algorithm uses deep neural network learning.
  179. Dhaka
    • Description : Dhaka is a tool to cluster and analyze single-cell RNA-seq (scRNA-seq) data. The Dhaka algorithm uses a variational autoencoder approach to reduce dimensionality. The method can also be applied to copy number variation (CNV).
  180. GiniClust
    • Description : GiniClust is a tool to detect rare cell types in single-cell RNA-seq (scRNA-seq) data.
  181. GiniClust2
    • Description : GiniClust2 is a tool to detect cell types in single-cell RNA-seq (scRNA-seq) data. The GiniClust2 uses a weighted ensemble clustering method.
  182. flowMap
    • Description : flowMap is a tool to quantify cell populations in flow cytometry data. The flowMap algorithm uses the Friedman-Rafsky statistical test.
  183. DigitalCellSorter
    • Description : DigitalCellSorter, Polled Digital Cell Sorter (p-DCS) is a tool for the identification of hematological cell types in single-cell RNA-seq (scRNA-seq) data. The DigitalCellSorter algorithm uses a voting approach based on molecular signatures to determine cell types and computes an approval score.
  184. DoubletFinder
    • Description : DoubletFinder is a tool to remove doublet artifacts in single-cell RNA-seq (scRNA-seq) data. The DoubletFinder algorithm creates artificial doublets and uses the proximity of real expression of a cell to predict doublets.
  185. DPT
    • Description : DPT (Diffusion pseudotime) is a tool to estimate the temporal order of differentiating cell in single-cell RNA-seq (scRNA-seq) data. The DPT algorithm using diffusion-like random walks to estimate transitions between cells.
  186. DrivAER
    • Description : DrivAER is a tool to identify and score pathways and transcriptions factors of user-specified outcomes in single-cell RNA-seq (scRNA-seq) data. The DrivAER algorithm uses a machine learning approach.
  187. Granatum
    • Description : Granatum is a pipeline tool for single-cell RNA-seq (scRNA-seq) data analysis. The Granatum package contains modules for quality control, normalization, clustering, differential gene expression, and enrichment analysis, protein network interaction visualization, and cell pseudo-time pathway construction. The graphical user interface guides through all the analysis steps.
  188. DoubletDecon
    • Description : DoubletDecon is a tool to remove doublet artifacts from single-cell RNA-seq (scRNA-seq) data. The DoubletDecon algorithm uses a combination of deconvolution analyses and the identification of unique cell-state gene expression to identify doublets.
  189. dropClust
    • Description : dropClust is a tool to cluster single-cell RNA-seq (scRNA-seq) data. The Locality Sensitive Hashing, an approximate nearest neighbor search technique algorithm uses Locality Sensitive Hashing, which is an approximate nearest neighbor search method.
  190. DUSC
    • Description : DUSC (Deep Unsupervised Single-cell Clustering) is a tool cluster cell types in single-cell RNA-seq (scRNA-seq) data. The DUSC algorithm uses a combination of deep learning and a mode-based clustering method.
  191. ECLAIR
    • Description : ECLAIR (Ensemble Cell Lineage Analysis with Improved Robustness) is a tool to cluster cell lineages in single-cell RNA-seq (scRNA-seq) data. The ECLAIR algorithm analyses in three steps: 1. ensemble generation, 2. consensus clustering, and 3. tree combination.
  192. EMEP
    • Description : EMEP (evolutionary multiobjective ensemble pruning) is a tool to cluster single-cell RNA-seq (scRNA-seq) data. The EMEP algorithm reduces the dimensionality and clusters the resulting subspaces into ensembles.
  193. EnImpute
    • Description : EnImpute is a tool to impute dropouts in single-cell RNA-seq (scRNA-seq) data. The EnImpute algorithm uses an ensemble learning method by combining results from multiple different learning methods.
  194. enhance
    • Description : enhance is a tool to remove noise single-cell RNA-seq (scRNA-seq) data. The enhance algorithm uses the principal component analysis (PCA).
  195. GiniClust3
    • Description : GiniClust3 is a tool to identify and cluster rare cell types in single-cell RNA-seq (scRNA-seq) data. The GiniClust3 is an improved version of GiniClust2.
  196. GPseudoClust
    • Description : GPseudoClust is a tool for temporal ordering of the cell in single-cell RNA-seq (scRNA-seq) data. The GPseudoClust algorithm uses a joint inference of pseudo-temporal ordering and gene clusters to quantify the uncertainty.
  197. EnsembleKQC
    • Description : EnsembleKQC is a tool for quality control of single-cell RNA-seq (scRNA-seq) data. The EnsembleKQC algorithm combines weak classifiers base on five selected features from housekeeping genes, sequencing reads mapping rate and detected genes.
  198. GMM-demux
    • Description : GMM-demux is a tool to remove Multi-Sample-Multiplets in a cell hashing dataset and to estimate the fraction of Same-Sample-Multiplets and singlets in barcoded single-cell RNA-seq (scRNA-seq) data.
  199. geosketch
    • Description : geosketch is a tool to summarize transcriptomic heterogeneity in single-cell RNA-seq (scRNA-seq) data.
  200. genesorteR
    • Description : genesorteR is an R tool to rank genes in cell clusters in single-cell RNA-seq (scRNA-seq) data.
  201. garnett
    • Description : garnett is a tool for annotation of cell type in single-cell RNA-seq (scRNA-seq) data. The garnett algorithm uses an interpretable, hierarchical markup language of cell type-specific genes.
  202. FORKS
    • Description : FORKS (Finding Orderings Robustly using K-means and Steiner trees) is a tool to order cells pseudo-temporally and to infer bifurcating state trajectories.
  203. FiRE - Finder of Rare Entities
    • Description : FiRE - Finder of Rare Entities is a tool to compute a rareness score to each individual expression profile in single-cell RNA-seq (scRNA-seq) data sets.
  204. Falco
    • Description : Falco is a cloud-based tool to parallelize RNA-seq processing pipelines using Apache Hadoop and Apache Spark.
  205. ESAT
    • Description : ESAT (End Sequence Analysis Toolkit) is a tool to compute read counts directly fromSAM/BAM and BED files for Digital expression (DGE) libraries in single-cell RNA-seq (scRNA-seq) data.
  206. DrSeq2
    • Description : DrSeq2 is a tool for quality control and analysis of single-cell RNA-seq (scRNA-seq), scATAC-seq, Drop-ChIP, and epigenome data. The DrSeq2 package includes four types of QC analyses and several other analyses.
  207. ACTIONet
    • Description : ACTIONet is a tool for the analysis of cell state landscape in single-cell RNA-seq (scRNA-seq) data. The ACTIONet algorithm uses multilevel matrix decomposition and network reconstruction to detect patterns.
  208. Augur
    • Description : Augur is a tool to prioritize cell types in RNA-seq (scRNA-seq) data. The Augur algorithm uses a machine-learning method.
  209. Cyclum
    • Description : Cyclum is a tool for the recovery and removal of cell cycle effects in RNA-seq (scRNA-seq) data.
  210. LATE
    • Description : LATE (Learning with AuToEncoder) is a tool to impute expression in single-cell RNA-seq (scRNA-seq) data. The LATE uses an autoencoder neural network.
  211. net-SNE
    • Description : net-SNE is a tool to visualize single-cell RNA-seq (scRNA-seq) data. The net-SNE algorithm uses neural networks to learn a mapping function from high-dimensional data.
  212. scClassify
    • Description : scClassify is a tool to identify cell types single-cell RNA-sequencing (scRNA-seq) data. The scClassify algorithm uses an ensemble learning approach.
  213. Scedar
    • Description : Scedar (Single-cell exploratory data analysis for RNA-Seq) is a tool for imputation and visualization of gene dropouts, transcriptomic profiling, and clustering of single-cell RNA-seq (scRNA-seq) datasets.
  214. SCENT
    • Description : SCENT is a tool for the analysis of single-cell TNA-seq (scRNA-seq) data. The SCENT algorithm uses a signaling-based entropy to estimate the potency. The SCENT package also includes a lineage-inference algorithm.
  215. scDGN
    • Description : scDGN is a tool that uses a domain adversarial neural network model to learn a reduced dimension representation of single-cell RNA-seq (scRNA-seq data).
  216. DepecheR
    • Description : DepecheR is a tool to cluster cytometry and single-cell RNA-seq (scRNA-seq) data. The DepecheR tool uses a parameter free and sparse k-means-based algorithm.
  217. DIEM
    • Description : DIEM (Debris Identification using Expectation Maximization) is a tool to remove debris-contaminated droplets in single nucleus snRNA-seq data. The DIEM algorithm uses the expression profile of background RNA droplets and expectation maximization (EM) to assign candidate debris.
  218. GRISLI
    • Description : GRISLI (Gene Regulation Inference for Single-cell with LInear) is a tool to infer gene regulation single-cell RNA-seq (scRNA-seq ) data. The GRISLI algorithm uses linear differential equations and velocity inference to reconstruct the underlying gene regulation networks (GRNs).
  219. HoneyBADGER
    • Description : HoneyBADGER (hidden Markov model integrated Bayesian approach for detecting CNV and LOH events from single-cell RNA-seq data) is a tool to identify copy number variation (CNV) and loss of heterozygosity (LOH) in single-cell RNA-seq (scRNA-seq) data. The HoneyBADGER algorithm can also reconstruct subclonal architecture.
  220. GRNVBEM
    • Description : GRNVBEM is a tool to infer gene regulatory network (GRN) from time-series or pseudo-time series in single-cell RNA-seq (scRNA-seq) data. The GRNVBEM algorithm uses a first-order autoregressive moving average model and a variational Bayesian Expectation-Maximization.
  221. DESCEND
    • Description : DESCEND is a tool to deconvolve gene expression in single-cell RNA-seq (scRNA-seq) counts. The DESCEND algorithm uses technical noise model and unique molecular identifiers (UMI).
  222. LIGER
    • Description : LIGER (Linked Inference of Genomic Experimental Relationships) is a tool for the analysis of multiple single-cell RNA-seq (scRNA-seq) data sets. The LIGER algorithm uses integrative non-negative matrix factorization to identify factors shared among data sets.
  223. ISOP
    • Description : ISOP (ISOform-level expression Patterns) is a tool to characterize isoform expressions patterns in single-cell RNA-seq (scRNA-seq) data. The ISOP uses mixture modeling for the characterization.
  224. KPNN
    • Description : KPNN (Knowledge-primed neural networks) is a tool to interpret single-cell RNA-seq (sc-RNA-seq) data using deep learning algorithms.
  225. MetaCell
    • Description : MetaCell is a tool to analyze single-cell RNA-seq (scRNA-seq) UMI matrices. The MetaCell metacell algorithm uses K-nn graph partitions.
  226. rCASC
    • Description : rCASC is a tool to classify single-cell RNA-seq (scRNA-seq) data. The rCASC had algorithms from count generation to cell sub-population identification.
  227. rpca
    • Description : rpca (RobustPCA and Truncated RobustPCA) is a tool to decompose Matrices into Low-Rank and Sparse Components, and with Truncated RobustPCA using L2 noise.
  228. ROGUE
    • Description : ROGUE is a tool for the quantification of the purity of identified cell clusters in single-cell RNA-seq (scRNA-seq) data.
  229. SCALE
    • Description : SCALE is a tool to model allele-specific gene expression in single-cell RNA-seq (scRNA-seq) data. SCALE identifies genes displaying allelic differences in bursting parameters and genes whose alleles not burst independently.
  230. CONICS
    • Description : CONICS (COpy-Number analysis In single-Cell RNA-Sequencing) is a tool to analyze copy-number variation (CNV) in single-cell RNA-seq data. CONICS quantifies CNV, maps gene expressions to tumor clones, and phylogenies.



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