Showing 138 of total 138 results (show query)
bioc
clustifyr:Classifier for Single-cell RNA-seq Using Cell Clusters
Package designed to aid in classifying cells from single-cell RNA sequencing data using external reference data (e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists). A variety of correlation based methods and gene list enrichment methods are provided to assist cell type assignment.
Maintained by Rui Fu. Last updated 5 months ago.
singlecellannotationsequencingmicroarraygeneexpressionassign-identitiesclustersmarker-genesrna-seqsingle-cell-rna-seq
51.6 match 119 stars 9.63 score 296 scriptsbioc
scPipe:Pipeline for single cell multi-omic data pre-processing
A preprocessing pipeline for single cell RNA-seq/ATAC-seq data that starts from the fastq files and produces a feature count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols.
Maintained by Shian Su. Last updated 3 months ago.
immunooncologysoftwaresequencingrnaseqgeneexpressionsinglecellvisualizationsequencematchingpreprocessingqualitycontrolgenomeannotationdataimportcurlbzip2xz-utilszlibcpp
50.5 match 68 stars 9.02 score 84 scripts
gfellerlab
SuperCell:Simplification of scRNA-seq data by merging together similar cells
Aggregates large single-cell data into metacell dataset by merging together gene expression of very similar cells.
Maintained by The package maintainer. Last updated 8 months ago.
softwarecoarse-grainingscrna-seq-analysisscrna-seq-data
41.5 match 72 stars 8.08 score 93 scriptsbioc
scBubbletree:Quantitative visual exploration of scRNA-seq data
scBubbletree is a quantitative method for the visual exploration of scRNA-seq data, preserving key biological properties such as local and global cell distances and cell density distributions across samples. It effectively resolves overplotting and enables the visualization of diverse cell attributes from multiomic single-cell experiments. Additionally, scBubbletree is user-friendly and integrates seamlessly with popular scRNA-seq analysis tools, facilitating comprehensive and intuitive data interpretation.
Maintained by Simo Kitanovski. Last updated 5 months ago.
visualizationclusteringsinglecelltranscriptomicsrnaseqbig-databigdatascrna-seqscrna-seq-analysisvisualvisual-exploration
51.3 match 6 stars 5.82 score 8 scriptsbioc
BgeeDB:Annotation and gene expression data retrieval from Bgee database. TopAnat, an anatomical entities Enrichment Analysis tool for UBERON ontology
A package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns.
Maintained by Julien Wollbrett. Last updated 5 months ago.
softwaredataimportsequencinggeneexpressionmicroarraygogenesetenrichmentbioinformaticsenrichment-analysisrna-seqscrna-seqsingle-cell
34.6 match 15 stars 8.46 score 19 scripts 1 dependentsbioc
zellkonverter:Conversion Between scRNA-seq Objects
Provides methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes functions to read and write H5AD files used for saving AnnData objects to disk.
Maintained by Luke Zappia. Last updated 6 days ago.
singlecelldataimportdatarepresentationbioconductorconversionscrna-seq
24.9 match 159 stars 11.25 score 660 scripts 4 dependentsbioc
BgeeCall:Automatic RNA-Seq present/absent gene expression calls generation
BgeeCall allows to generate present/absent gene expression calls without using an arbitrary cutoff like TPM<1. Calls are generated based on reference intergenic sequences. These sequences are generated based on expression of all RNA-Seq libraries of each species integrated in Bgee (https://bgee.org).
Maintained by Julien Wollbrett. Last updated 5 months ago.
softwaregeneexpressionrnaseqbiologygene-expressiongene-levelintergenic-regionspresent-absent-callsrna-seqrna-seq-librariesscrna-seq
37.5 match 3 stars 5.56 score 9 scripts
immunogenomics
harmony:Fast, Sensitive, and Accurate Integration of Single Cell Data
Implementation of the Harmony algorithm for single cell integration, described in Korsunsky et al <doi:10.1038/s41592-019-0619-0>. Package includes a standalone Harmony function and interfaces to external frameworks.
Maintained by Ilya Korsunsky. Last updated 4 months ago.
algorithmdata-integrationscrna-seqopenblascpp
15.0 match 554 stars 13.74 score 5.5k scripts 8 dependentsbioc
decoupleR:decoupleR: Ensemble of computational methods to infer biological activities from omics data
Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase.
Maintained by Pau Badia-i-Mompel. Last updated 5 months ago.
differentialexpressionfunctionalgenomicsgeneexpressiongeneregulationnetworksoftwarestatisticalmethodtranscription
17.6 match 230 stars 11.27 score 316 scripts 3 dependents
samuel-marsh
scCustomize:Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing
Collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using 'R'. 'scCustomize' aims to provide 1) Customized visualizations for aid in ease of use and to create more aesthetic and functional visuals. 2) Improve speed/reproducibility of common tasks/pieces of code in scRNA-seq analysis with a single or group of functions. For citation please use: Marsh SE (2021) "Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing" <doi:10.5281/zenodo.5706430> RRID:SCR_024675.
Maintained by Samuel Marsh. Last updated 3 months ago.
customizationggplot2scrna-seqseuratsingle-cellsingle-cell-genomicssingle-cell-rna-seqvisualization
22.3 match 242 stars 8.75 score 1.1k scripts
rezakj
iCellR:Analyzing High-Throughput Single Cell Sequencing Data
A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550> and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.
Maintained by Alireza Khodadadi-Jamayran. Last updated 8 months ago.
10xgenomics3dbatch-normalizationcell-type-classificationcite-seqclusteringclustering-algorithmdiffusion-mapsdropouticellrimputationintractive-graphnormalizationpseudotimescrna-seqscvdj-seqsingel-cell-sequencingumapcpp
33.2 match 121 stars 5.56 score 7 scripts 1 dependents
kharchenkolab
pagoda2:Single Cell Analysis and Differential Expression
Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.
Maintained by Evan Biederstedt. Last updated 1 years ago.
scrna-seqsingle-cellsingle-cell-rna-seqtranscriptomicsopenblascppopenmp
22.0 match 222 stars 8.00 score 282 scriptsbioc
scruff:Single Cell RNA-Seq UMI Filtering Facilitator (scruff)
A pipeline which processes single cell RNA-seq (scRNA-seq) reads from CEL-seq and CEL-seq2 protocols. Demultiplex scRNA-seq FASTQ files, align reads to reference genome using Rsubread, and generate UMI filtered count matrix. Also provide visualizations of read alignments and pre- and post-alignment QC metrics.
Maintained by Zhe Wang. Last updated 5 months ago.
softwaretechnologysequencingalignmentrnaseqsinglecellworkflowsteppreprocessingqualitycontrolvisualizationimmunooncologybioinformaticsscrna-seqsingle-cellumi
26.8 match 8 stars 6.20 score 22 scriptsbioc
BASiCS:Bayesian Analysis of Single-Cell Sequencing data
Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend.
Maintained by Catalina Vallejos. Last updated 5 months ago.
immunooncologynormalizationsequencingrnaseqsoftwaregeneexpressiontranscriptomicssinglecelldifferentialexpressionbayesiancellbiologybioconductor-packagegene-expressionrcpprcpparmadilloscrna-seqsingle-cellopenblascppopenmp
16.0 match 83 stars 10.26 score 368 scripts 1 dependentskharchenkolab
conos:Clustering on Network of Samples
Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/conos>. The size of the 'conosPanel' package is approximately 12 MB.
Maintained by Evan Biederstedt. Last updated 1 years ago.
batch-correctionscrna-seqsingle-cell-rna-seqopenblascppopenmp
21.8 match 204 stars 7.32 score 258 scriptsbioc
DifferentialRegulation:Differentially regulated genes from scRNA-seq data
DifferentialRegulation is a method for detecting differentially regulated genes between two groups of samples (e.g., healthy vs. disease, or treated vs. untreated samples), by targeting differences in the balance of spliced and unspliced mRNA abundances, obtained from single-cell RNA-sequencing (scRNA-seq) data. From a mathematical point of view, DifferentialRegulation accounts for the sample-to-sample variability, and embeds multiple samples in a Bayesian hierarchical model. Furthermore, our method also deals with two major sources of mapping uncertainty: i) 'ambiguous' reads, compatible with both spliced and unspliced versions of a gene, and ii) reads mapping to multiple genes. In particular, ambiguous reads are treated separately from spliced and unsplced reads, while reads that are compatible with multiple genes are allocated to the gene of origin. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques (Metropolis-within-Gibbs).
Maintained by Simone Tiberi. Last updated 5 months ago.
differentialsplicingbayesiangeneticsrnaseqsequencingdifferentialexpressiongeneexpressionmultiplecomparisonsoftwaretranscriptionstatisticalmethodvisualizationsinglecellgenetargetopenblascpp
29.2 match 10 stars 5.30 score 4 scriptsbioc
splatter:Simple Simulation of Single-cell RNA Sequencing Data
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. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets.
Maintained by Luke Zappia. Last updated 4 months ago.
singlecellrnaseqtranscriptomicsgeneexpressionsequencingsoftwareimmunooncologybioconductorbioinformaticsscrna-seqsimulation
15.0 match 224 stars 9.92 score 424 scripts 1 dependents
waldronlab
SingleCellMultiModal:Integrating Multi-modal Single Cell Experiment datasets
SingleCellMultiModal is an ExperimentHub package that serves multiple datasets obtained from GEO and other sources and represents them as MultiAssayExperiment objects. We provide several multi-modal datasets including scNMT, 10X Multiome, seqFISH, CITEseq, SCoPE2, and others. The scope of the package is is to provide data for benchmarking and analysis. To cite, use the 'citation' function and see <https://doi.org/10.1371/journal.pcbi.1011324>.
Maintained by Marcel Ramos. Last updated 4 months ago.
experimentdatasinglecelldatareproducibleresearchexperimenthubgeobioconductor-packageu24ca289073
18.7 match 17 stars 7.29 score 60 scriptsbioc
TREG:Tools for finding Total RNA Expression Genes in single nucleus RNA-seq data
RNA abundance and cell size parameters could improve RNA-seq deconvolution algorithms to more accurately estimate cell type proportions given the different cell type transcription activity levels. A Total RNA Expression Gene (TREG) can facilitate estimating total RNA content using single molecule fluorescent in situ hybridization (smFISH). We developed a data-driven approach using a measure of expression invariance to find candidate TREGs in postmortem human brain single nucleus RNA-seq. This R package implements the method for identifying candidate TREGs from snRNA-seq data.
Maintained by Louise Huuki-Myers. Last updated 3 months ago.
softwaresinglecellrnaseqgeneexpressiontranscriptomicstranscriptionsequencingbioconductordeconvolutionrnascopescrna-seqsmfishsnrna-seqtreg
26.1 match 4 stars 5.20 score 5 scriptsbioc
SC3:Single-Cell Consensus Clustering
A tool for unsupervised clustering and analysis of single cell RNA-Seq data.
Maintained by Vladimir Kiselev. Last updated 5 months ago.
immunooncologysinglecellsoftwareclassificationclusteringdimensionreductionsupportvectormachinernaseqvisualizationtranscriptomicsdatarepresentationguidifferentialexpressiontranscriptionbioconductor-packagehuman-cell-atlassingle-cell-rna-seqopenblascpp
13.4 match 122 stars 10.09 score 374 scripts 1 dependents
feiyoung
ProFAST:Probabilistic Factor Analysis for Spatially-Aware Dimension Reduction
Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations. More details can be referred to Wei Liu, et al. (2023) <doi:10.1101/2023.07.11.548486>.
Maintained by Wei Liu. Last updated 1 months ago.
22.7 match 2 stars 5.86 score 12 scripts 1 dependentsbioc
muscat:Multi-sample multi-group scRNA-seq data analysis tools
`muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data.
Maintained by Helena L. Crowell. Last updated 5 months ago.
immunooncologydifferentialexpressionsequencingsinglecellsoftwarestatisticalmethodvisualization
12.8 match 181 stars 10.26 score 686 scripts
scmethods
scregclust:Reconstructing the Regulatory Programs of Target Genes in scRNA-Seq Data
Implementation of the scregclust algorithm described in Larsson, Held, et al. (2024) <doi:10.1038/s41467-024-53954-3> which reconstructs regulatory programs of target genes in scRNA-seq data. Target genes are clustered into modules and each module is associated with a linear model describing the regulatory program.
Maintained by Felix Held. Last updated 2 months ago.
clusteringregulatory-programsscrna-seq-analysiscppopenmp
20.0 match 9 stars 6.45 score 21 scriptsbioc
scQTLtools:An R package for single-cell eQTL analysis and visualization
This package specializes in analyzing and visualizing eQTL at the single-cell level. It can read gene expression matrices or Seurat data, or SingleCellExperiment object along with genotype data. It offers a function for cis-eQTL analysis to detect eQTL within a given range, and another function to fit models with three methods. Using this package, users can also generate single-cell level visualization result.
Maintained by Xiaofeng Wu. Last updated 2 months ago.
softwaregeneexpressiongeneticvariabilitysnpdifferentialexpressiongenomicvariationvariantdetectiongeneticsfunctionalgenomicssystemsbiologyregressionsinglecellnormalizationvisualizationrna-seqsc-eqtl
26.0 match 3 stars 4.95 scorebioc
scBFA:A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq
This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis.
Maintained by Ruoxin Li. Last updated 5 months ago.
singlecelltranscriptomicsdimensionreductiongeneexpressionatacseqbatcheffectkeggqualitycontrol
29.9 match 4.30 score 4 scriptsbioc
Linnorm:Linear model and normality based normalization and transformation method (Linnorm)
Linnorm is an algorithm for normalizing and transforming RNA-seq, single cell RNA-seq, ChIP-seq count data or any large scale count data. It has been independently reviewed by Tian et al. on Nature Methods (https://doi.org/10.1038/s41592-019-0425-8). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM.
Maintained by Shun Hang Yip. Last updated 5 months ago.
immunooncologysequencingchipseqrnaseqdifferentialexpressiongeneexpressiongeneticsnormalizationsoftwaretranscriptionbatcheffectpeakdetectionclusteringnetworksinglecellcpp
19.9 match 6.26 score 61 scripts 5 dependentsbioc
scmap:A tool for unsupervised projection of single cell RNA-seq data
Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types or individual cells identified in a different experiment.
Maintained by Vladimir Kiselev. Last updated 5 months ago.
immunooncologysinglecellsoftwareclassificationsupportvectormachinernaseqvisualizationtranscriptomicsdatarepresentationtranscriptionsequencingpreprocessinggeneexpressiondataimportbioconductor-packagehuman-cell-atlasprojection-mappingsingle-cell-rna-seqopenblascpp
14.1 match 95 stars 8.82 score 172 scriptsbioc
MuData:Serialization for MultiAssayExperiment Objects
Save MultiAssayExperiments to h5mu files supported by muon and mudata. Muon is a Python framework for multimodal omics data analysis. It uses an HDF5-based format for data storage.
Maintained by Ilia Kats. Last updated 19 days ago.
dataimportanndatabioconductormudatamulti-omicsmultimodal-omicsscrna-seq
21.0 match 5 stars 5.89 score 26 scriptsbioc
SingleR:Reference-Based Single-Cell RNA-Seq Annotation
Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.
Maintained by Aaron Lun. Last updated 27 days ago.
softwaresinglecellgeneexpressiontranscriptomicsclassificationclusteringannotationbioconductorsinglercpp
9.4 match 182 stars 12.60 score 2.1k scripts 1 dependentsbioc
cardelino:Clone Identification from Single Cell Data
Methods to infer clonal tree configuration for a population of cells using single-cell RNA-seq data (scRNA-seq), and possibly other data modalities. Methods are also provided to assign cells to inferred clones and explore differences in gene expression between clones. These methods can flexibly integrate information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. A flexible beta-binomial error model that accounts for stochastic dropout events as well as systematic allelic imbalance is used.
Maintained by Davis McCarthy. Last updated 5 months ago.
singlecellrnaseqvisualizationtranscriptomicsgeneexpressionsequencingsoftwareexomeseqclonal-clusteringgibbs-samplingscrna-seqsingle-cellsomatic-mutations
16.8 match 61 stars 7.05 score 62 scriptsbioc
NewWave:Negative binomial model for scRNA-seq
A model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise.
Maintained by Federico Agostinis. Last updated 5 months ago.
softwaregeneexpressiontranscriptomicssinglecellbatcheffectsequencingcoverageregressionbatch-effectsdimensionality-reductionnegative-binomialscrna-seq
22.0 match 4 stars 5.33 score 27 scripts
lazappi
clustree:Visualise Clusterings at Different Resolutions
Deciding what resolution to use can be a difficult question when approaching a clustering analysis. One way to approach this problem is to look at how samples move as the number of clusters increases. This package allows you to produce clustering trees, a visualisation for interrogating clusterings as resolution increases.
Maintained by Luke Zappia. Last updated 1 years ago.
clusteringclustering-treesvisualisationvisualization
9.9 match 219 stars 11.40 score 1.9k scripts 5 dependentsbioc
TAPseq:Targeted scRNA-seq primer design for TAP-seq
Design primers for targeted single-cell RNA-seq used by TAP-seq. Create sequence templates for target gene panels and design gene-specific primers using Primer3. Potential off-targets can be estimated with BLAST. Requires working installations of Primer3 and BLASTn.
Maintained by Andreas R. Gschwind. Last updated 5 months ago.
singlecellsequencingtechnologycrisprpooledscreens
19.9 match 4 stars 5.38 score 9 scriptsbioc
raer:RNA editing tools in R
Toolkit for identification and statistical testing of RNA editing signals from within R. Provides support for identifying sites from bulk-RNA and single cell RNA-seq datasets, and general methods for extraction of allelic read counts from alignment files. Facilitates annotation and exploratory analysis of editing signals using Bioconductor packages and resources.
Maintained by Kent Riemondy. Last updated 5 months ago.
multiplecomparisonrnaseqsinglecellsequencingcoverageepitranscriptomicsfeatureextractionannotationalignmentbioconductor-packagerna-seq-analysissingle-cell-analysissingle-cell-rna-seqcurlbzip2xz-utilszlib
16.9 match 8 stars 5.98 score 6 scriptskharchenkolab
numbat:Haplotype-Aware CNV Analysis from scRNA-Seq
A computational method that infers copy number variations (CNVs) in cancer scRNA-seq data and reconstructs the tumor phylogeny. 'numbat' integrates signals from gene expression, allelic ratio, and population haplotype structures to accurately infer allele-specific CNVs in single cells and reconstruct their lineage relationship. 'numbat' can be used to: 1. detect allele-specific copy number variations from single-cells; 2. differentiate tumor versus normal cells in the tumor microenvironment; 3. infer the clonal architecture and evolutionary history of profiled tumors. 'numbat' does not require tumor/normal-paired DNA or genotype data, but operates solely on the donor scRNA-data data (for example, 10x Cell Ranger output). Additional examples and documentations are available at <https://kharchenkolab.github.io/numbat/>. For details on the method please see Gao et al. Nature Biotechnology (2022) <doi:10.1038/s41587-022-01468-y>.
Maintained by Teng Gao. Last updated 15 days ago.
cancer-genomicscnv-detectionlineage-tracingphylogenysingle-cellsingle-cell-analysissingle-cell-rna-seqspatial-transcriptomicscpp
13.4 match 179 stars 7.41 score 120 scripts
qile0317
APackOfTheClones:Visualization of Clonal Expansion for Single Cell Immune Profiles
Visualize clonal expansion via circle-packing. 'APackOfTheClones' extends 'scRepertoire' to produce a publication-ready visualization of clonal expansion at a single cell resolution, by representing expanded clones as differently sized circles. The method was originally implemented by Murray Christian and Ben Murrell in the following immunology study: Ma et al. (2021) <doi:10.1126/sciimmunol.abg6356>.
Maintained by Qile Yang. Last updated 4 months ago.
clonal-analysisimmune-repertoireimmune-systemscrna-seqscrnaseqseuratsingle-cellsingle-cell-genomicscpp
15.0 match 15 stars 6.45 score 15 scriptsbioc
scAnnotatR:Pretrained learning models for cell type prediction on single cell RNA-sequencing data
The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scAnnotatR also allows users to train their own models to predict new cell types based on specific research needs.
Maintained by Johannes Griss. Last updated 5 months ago.
singlecelltranscriptomicsgeneexpressionsupportvectormachineclassificationsoftware
13.4 match 15 stars 6.73 score 20 scriptsbioc
MEB:A normalization-invariant minimum enclosing ball method to detect differentially expressed genes for RNA-seq and scRNA-seq data
This package provides a method to identify differential expression genes in the same or different species. Given that non-DE genes have some similarities in features, a scaling-free minimum enclosing ball (SFMEB) model is built to cover those non-DE genes in feature space, then those DE genes, which are enormously different from non-DE genes, being regarded as outliers and rejected outside the ball. The method on this package is described in the article 'A minimum enclosing ball method to detect differential expression genes for RNA-seq data'. The SFMEB method is extended to the scMEB method that considering two or more potential types of cells or unknown labels scRNA-seq dataset DEGs identification.
Maintained by Jiadi Zhu. Last updated 5 months ago.
differentialexpressiongeneexpressionnormalizationclassificationsequencing
18.5 match 4.78 score 1 scriptsbioc
miQC:Flexible, probabilistic metrics for quality control of scRNA-seq data
Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA encoded genes (mtDNA) and (ii) if a small number of genes are detected. miQC is data-driven QC metric that jointly models both the proportion of reads mapping to mtDNA and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset.
Maintained by Ariel Hippen. Last updated 5 months ago.
singlecellqualitycontrolgeneexpressionpreprocessingsequencing
13.5 match 19 stars 6.39 score 65 scripts
igordot
clustermole:Unbiased Single-Cell Transcriptomic Data Cell Type Identification
Assignment of cell type labels to single-cell RNA sequencing (scRNA-seq) clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging when unexpected or poorly described populations are present. The clustermole R package provides methods to query thousands of human and mouse cell identity markers sourced from a variety of databases.
Maintained by Igor Dolgalev. Last updated 1 years ago.
cell-typecell-type-annotationcell-type-classificationcell-type-identificationcell-type-matchinggene-expression-signaturesscrna-seqsingle-cell
16.0 match 13 stars 5.37 score 36 scripts
papatheodorou-group
scGOclust:Measuring Cell Type Similarity with Gene Ontology in Single-Cell RNA-Seq
Traditional methods for analyzing single cell RNA-seq datasets focus solely on gene expression, but this package introduces a novel approach that goes beyond this limitation. Using Gene Ontology terms as features, the package allows for the functional profile of cell populations, and comparison within and between datasets from the same or different species. Our approach enables the discovery of previously unrecognized functional similarities and differences between cell types and has demonstrated success in identifying cell types' functional correspondence even between evolutionarily distant species.
Maintained by Yuyao Song. Last updated 1 years ago.
17.9 match 9 stars 4.80 score 14 scripts
jiang-junyao
CACIMAR:cross-species analysis of cell identities, markers and regulations
A toolkit to perform cross-species analysis based on scRNA-seq data. CACIMAR contains 5 main features. (1) identify Markers in each cluster. (2) Cell type annotaion (3) identify conserved markers. (4) identify conserved cell types. (5) identify conserved modules of regulatory networks.
Maintained by Junyao Jiang. Last updated 3 months ago.
cross-species-analysisscrna-seq
16.0 match 12 stars 5.26 score 6 scriptsbioc
peco:A Supervised Approach for **P**r**e**dicting **c**ell Cycle Pr**o**gression using scRNA-seq data
Our approach provides a way to assign continuous cell cycle phase using scRNA-seq data, and consequently, allows to identify cyclic trend of gene expression levels along the cell cycle. This package provides method and training data, which includes scRNA-seq data collected from 6 individual cell lines of induced pluripotent stem cells (iPSCs), and also continuous cell cycle phase derived from FUCCI fluorescence imaging data.
Maintained by Chiaowen Joyce Hsiao. Last updated 5 months ago.
sequencingrnaseqgeneexpressiontranscriptomicssinglecellsoftwarestatisticalmethodclassificationvisualizationcell-cyclesingle-cell-rna-seq
13.1 match 12 stars 6.09 score 34 scriptsbioc
SiPSiC:Calculate Pathway Scores for Each Cell in scRNA-Seq Data
Infer biological pathway activity of cells from single-cell RNA-sequencing data by calculating a pathway score for each cell (pathway genes are specified by the user). It is recommended to have the data in Transcripts-Per-Million (TPM) or Counts-Per-Million (CPM) units for best results. Scores may change when adding cells to or removing cells off the data. SiPSiC stands for Single Pathway analysis in Single Cells.
Maintained by Daniel Davis. Last updated 5 months ago.
softwaredifferentialexpressiongenesetenrichmentbiomedicalinformaticscellbiologytranscriptomicsrnaseqsinglecelltranscriptionsequencingimmunooncologydataimport
15.3 match 6 stars 5.18 score 3 scripts
liuy12
SCdeconR:Deconvolution of Bulk RNA-Seq Data using Single-Cell RNA-Seq Data as Reference
Streamlined workflow from deconvolution of bulk RNA-seq data to downstream differential expression and gene-set enrichment analysis. Provide various visualization functions.
Maintained by Yuanhang Liu. Last updated 10 months ago.
bulk-rna-seq-deconvolutiondeconvolutiondifferential-expressionffpegeneset-enrichment-analysisscdeconrsingle-cell
20.8 match 3 stars 3.78 score 4 scriptsbioc
scds:In-Silico Annotation of Doublets for Single Cell RNA Sequencing Data
In single cell RNA sequencing (scRNA-seq) data combinations of cells are sometimes considered a single cell (doublets). The scds package provides methods to annotate doublets in scRNA-seq data computationally.
Maintained by Dennis Kostka. Last updated 5 months ago.
singlecellrnaseqqualitycontrolpreprocessingtranscriptomicsgeneexpressionsequencingsoftwareclassification
11.9 match 6.57 score 176 scripts 1 dependents
blaserlab
blaseRdata:Supporting Data for the blaseRtools Package
What the package does (one paragraph).
Maintained by Brad Blaser. Last updated 12 months ago.
42.2 match 1.70 score 6 scriptsbioc
GloScope:Population-level Representation on scRNA-Seq data
This package aims at representing and summarizing the entire single-cell profile of a sample. It allows researchers to perform important bioinformatic analyses at the sample-level such as visualization and quality control. The main functions Estimate sample distribution and calculate statistical divergence among samples, and visualize the distance matrix through MDS plots.
Maintained by William Torous. Last updated 5 months ago.
datarepresentationqualitycontrolrnaseqsequencingsoftwaresinglecell
11.5 match 3 stars 6.05 score 84 scriptsbioc
adverSCarial:adverSCarial, generate and analyze the vulnerability of scRNA-seq classifier to adversarial attacks
adverSCarial is an R Package designed for generating and analyzing the vulnerability of scRNA-seq classifiers to adversarial attacks. The package is versatile and provides a format for integrating any type of classifier. It offers functions for studying and generating two types of attacks, single gene attack and max change attack. The single-gene attack involves making a small modification to the input to alter the classification. The max-change attack involves making a large modification to the input without changing its classification. The package provides a comprehensive solution for evaluating the robustness of scRNA-seq classifiers against adversarial attacks.
Maintained by Ghislain FIEVET. Last updated 5 months ago.
softwaresinglecelltranscriptomicsclassification
12.7 match 5.42 score 19 scripts
ocbe-uio
DIscBIO:A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics
An open, multi-algorithmic pipeline for easy, fast and efficient analysis of cellular sub-populations and the molecular signatures that characterize them. The pipeline consists of four successive steps: data pre-processing, cellular clustering with pseudo-temporal ordering, defining differential expressed genes and biomarker identification. More details on Ghannoum et. al. (2021) <doi:10.3390/ijms22031399>. This package implements extensions of the work published by Ghannoum et. al. (2019) <doi:10.1101/700989>.
Maintained by Waldir Leoncio. Last updated 1 years ago.
biomarker-discoveryjupyter-notebookscrna-seqsingle-cell-analysistranscriptomicsopenjdk
15.0 match 12 stars 4.38 score 5 scriptsbioc
CARDspa:Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics
CARD is a reference-based deconvolution method that estimates cell type composition in spatial transcriptomics based on cell type specific expression information obtained from a reference scRNA-seq data. A key feature of CARD is its ability to accommodate spatial correlation in the cell type composition across tissue locations, enabling accurate and spatially informed cell type deconvolution as well as refined spatial map construction. CARD relies on an efficient optimization algorithm for constrained maximum likelihood estimation and is scalable to spatial transcriptomics with tens of thousands of spatial locations and tens of thousands of genes.
Maintained by Jing Fu. Last updated 14 days ago.
spatialsinglecelltranscriptomicsvisualizationopenblascppopenmp
14.2 match 4.54 score 3 scriptsbioc
demuxmix:Demultiplexing oligo-barcoded scRNA-seq data using regression mixture models
A package for demultiplexing single-cell sequencing experiments of pooled cells labeled with barcode oligonucleotides. The package implements methods to fit regression mixture models for a probabilistic classification of cells, including multiplet detection. Demultiplexing error rates can be estimated, and methods for quality control are provided.
Maintained by Hans-Ulrich Klein. Last updated 5 months ago.
singlecellsequencingpreprocessingclassificationregression
11.2 match 5 stars 5.76 score 19 scripts 1 dependentsbioc
DCATS:Differential Composition Analysis Transformed by a Similarity matrix
Methods to detect the differential composition abundances between conditions in singel-cell RNA-seq experiments, with or without replicates. It aims to correct bias introduced by missclaisification and enable controlling of confounding covariates. To avoid the influence of proportion change from big cell types, DCATS can use either total cell number or specific reference group as normalization term.
Maintained by Xinyi Lin. Last updated 5 months ago.
13.8 match 4.53 score 34 scripts
zhiyuan-hu-lab
CIDER:Meta-Clustering for scRNA-Seq Integration and Evaluation
A workflow of (a) meta-clustering based on inter-group similarity measures and (b) a ground-truth-free test metric to assess the biological correctness of integration in real datasets. See Hu Z, Ahmed A, Yau C (2021) <doi:10.1101/2021.03.29.437525> for more details.
Maintained by Zhiyuan Hu. Last updated 1 months ago.
11.7 match 5.30 scorebioc
aggregateBioVar:Differential Gene Expression Analysis for Multi-subject scRNA-seq
For single cell RNA-seq data collected from more than one subject (e.g. biological sample or technical replicates), this package contains tools to summarize single cell gene expression profiles at the level of subject. A SingleCellExperiment object is taken as input and converted to a list of SummarizedExperiment objects, where each list element corresponds to an assigned cell type. The SummarizedExperiment objects contain aggregate gene-by-subject count matrices and inter-subject column metadata for individual subjects that can be processed using downstream bulk RNA-seq tools.
Maintained by Jason Ratcliff. Last updated 5 months ago.
softwaresinglecellrnaseqtranscriptomicstranscriptiongeneexpressiondifferentialexpression
12.4 match 5 stars 4.95 score 18 scripts
vivianstats
scINSIGHT:Interpretation of Heterogeneous Single-Cell Gene Expression Data
We develop a novel matrix factorization tool named 'scINSIGHT' to jointly analyze multiple single-cell gene expression samples from biologically heterogeneous sources, such as different disease phases, treatment groups, or developmental stages. Given multiple gene expression samples from different biological conditions, 'scINSIGHT' simultaneously identifies common and condition-specific gene modules and quantify their expression levels in each sample in a lower-dimensional space. With the factorized results, the inferred expression levels and memberships of common gene modules can be used to cluster cells and detect cell identities, and the condition-specific gene modules can help compare functional differences in transcriptomes from distinct conditions. Please also see Qian K, Fu SW, Li HW, Li WV (2022) <doi:10.1186/s13059-022-02649-3>.
Maintained by Kun Qian. Last updated 3 years ago.
bioinformaticsgene-expressionintegrationscrna-seqopenblascpp
15.0 match 21 stars 4.02 score 10 scriptsbioc
DEsingle:DEsingle for detecting three types of differential expression in single-cell RNA-seq data
DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It defines and detects 3 types of differentially expressed genes between two groups of single cells, with regard to different expression status (DEs), differential expression abundance (DEa), and general differential expression (DEg). DEsingle employs Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect the 3 types of DE genes. Results showed that DEsingle outperforms existing methods for scRNA-seq DE analysis, and can reveal different types of DE genes that are enriched in different biological functions.
Maintained by Zhun Miao. Last updated 5 months ago.
differentialexpressiongeneexpressionsinglecellimmunooncologyrnaseqtranscriptomicssequencingpreprocessingsoftware
7.8 match 31 stars 7.42 score 28 scripts
xudonghan-bio
scapGNN:Graph Neural Network-Based Framework for Single Cell Active Pathways and Gene Modules Analysis
It is a single cell active pathway analysis tool based on the graph neural network (F. Scarselli (2009) <doi:10.1109/TNN.2008.2005605>; Thomas N. Kipf (2017) <arXiv:1609.02907v4>) to construct the gene-cell association network, infer pathway activity scores from different single cell modalities data, integrate multiple modality data on the same cells into one pathway activity score matrix, identify cell phenotype activated gene modules and parse association networks of gene modules under multiple cell phenotype. In addition, abundant visualization programs are provided to display the results.
Maintained by Xudong Han. Last updated 2 years ago.
28.8 match 2.00 score 7 scripts
rcannood
SCORPIUS:Inferring Developmental Chronologies from Single-Cell RNA Sequencing Data
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. Cannoodt et al. (2016) <doi:10.1101/079509>.
Maintained by Robrecht Cannoodt. Last updated 2 years ago.
7.0 match 59 stars 8.17 score 126 scripts
lhe17
nebula:Negative Binomial Mixed Models Using Large-Sample Approximation for Differential Expression Analysis of ScRNA-Seq Data
A fast negative binomial mixed model for conducting association analysis of multi-subject single-cell data. It can be used for identifying marker genes, differential expression and co-expression analyses. The model includes subject-level random effects to account for the hierarchical structure in multi-subject single-cell data. See He et al. (2021) <doi:10.1038/s42003-021-02146-6>.
Maintained by Liang He. Last updated 1 years ago.
8.6 match 35 stars 6.40 score 145 scriptsbioc
scMerge:scMerge: Merging multiple batches of scRNA-seq data
Like all gene expression data, single-cell data suffers from batch effects and other unwanted variations that makes accurate biological interpretations difficult. The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple single-cell data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of single-cell data.
Maintained by Yingxin Lin. Last updated 5 months ago.
batcheffectgeneexpressionnormalizationrnaseqsequencingsinglecellsoftwaretranscriptomicsbioinformaticssingle-cell
5.7 match 67 stars 9.52 score 137 scripts 1 dependentsbioc
mitch:Multi-Contrast Gene Set Enrichment Analysis
mitch is an R package for multi-contrast enrichment analysis. At it’s heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. The rank-MANOVA concept dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for pathway analysis of profiling studies with one, two or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. We have an established routine for pathway enrichment of Infinium Methylation Array data (see vignette). The main strengths of mitch are that it can import datasets easily from many upstream tools and has advanced plotting features to visualise these enrichments.
Maintained by Mark Ziemann. Last updated 4 months ago.
geneexpressiongenesetenrichmentsinglecelltranscriptomicsepigeneticsproteomicsdifferentialexpressionreactomednamethylationmethylationarraygene-regulationgene-seq-analysispathway-analysis
7.7 match 16 stars 7.06 score 15 scriptsbioc
POWSC:Simulation, power evaluation, and sample size recommendation for single cell RNA-seq
Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship.
Maintained by Kenong Su. Last updated 5 months ago.
differentialexpressionimmunooncologysinglecellsoftware
13.3 match 4.00 score 7 scriptsbioc
CHETAH:Fast and accurate scRNA-seq cell type identification
CHETAH (CHaracterization of cEll Types Aided by Hierarchical classification) is an accurate, selective and fast scRNA-seq classifier. Classification is guided by a reference dataset, preferentially also a scRNA-seq dataset. By hierarchical clustering of the reference data, CHETAH creates a classification tree that enables a step-wise, top-to-bottom classification. Using a novel stopping rule, CHETAH classifies the input cells to the cell types of the references and to "intermediate types": more general classifications that ended in an intermediate node of the tree.
Maintained by Jurrian de Kanter. Last updated 5 months ago.
classificationrnaseqsinglecellclusteringgeneexpressionimmunooncology
7.3 match 44 stars 7.27 score 70 scriptsbioc
tenXplore:ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics
Perform ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics.
Maintained by VJ Carey. Last updated 5 months ago.
immunooncologydimensionreductionprincipalcomponenttranscriptomicssinglecell
12.0 match 4.18 score 7 scriptsbioc
scRecover:scRecover for imputation of single-cell RNA-seq data
scRecover is an R package for imputation of single-cell RNA-seq (scRNA-seq) data. It will detect and impute dropout values in a scRNA-seq raw read counts matrix while keeping the real zeros unchanged, since there are both dropout zeros and real zeros in scRNA-seq data. By combination with scImpute, SAVER and MAGIC, scRecover not only detects dropout and real zeros at higher accuracy, but also improve the downstream clustering and visualization results.
Maintained by Zhun Miao. Last updated 5 months ago.
geneexpressionsinglecellrnaseqtranscriptomicssequencingpreprocessingsoftware
9.4 match 8 stars 5.20 score 9 scripts
bioc
BUSseq:Batch Effect Correction with Unknow Subtypes for scRNA-seq data
BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value can be used for downstream analysis as if all cells were sequenced in a single batch. BUSseq can integrate read count matrices obtained from different scRNA-seq platforms and allow cell types to be measured in some but not all of the batches as long as the experimental design fulfills the conditions listed in our manuscript.
Maintained by Fangda Song. Last updated 5 months ago.
experimentaldesigngeneexpressionstatisticalmethodbayesianclusteringfeatureextractionbatcheffectsinglecellsequencingcppopenmp
10.8 match 4.48 score 30 scripts
baderlab
FLASHMM:Fast and Scalable Single Cell Differential Expression Analysis using Mixed-Effects Models
A fast and scalable linear mixed-effects model (LMM) estimation algorithm for analysis of single-cell differential expression. The algorithm uses summary-level statistics and requires less computer memory to fit the LMM.
Maintained by Changjiang Xu. Last updated 3 days ago.
10.3 match 1 stars 4.54 score 3 scriptsbioc
AUCell:AUCell: Analysis of 'gene set' activity in single-cell RNA-seq data (e.g. identify cells with specific gene signatures)
AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed.
Maintained by Gert Hulselmans. Last updated 5 months ago.
singlecellgenesetenrichmenttranscriptomicstranscriptiongeneexpressionworkflowstepnormalization
5.4 match 8.59 score 860 scripts 4 dependentsbioc
Rsubread:Mapping, quantification and variant analysis of sequencing data
Alignment, quantification and analysis of RNA sequencing data (including both bulk RNA-seq and scRNA-seq) and DNA sequenicng data (including ATAC-seq, ChIP-seq, WGS, WES etc). Includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. Can be applied to all major sequencing techologies and to both short and long sequence reads.
Maintained by Wei Shi. Last updated 14 hours ago.
sequencingalignmentsequencematchingrnaseqchipseqsinglecellgeneexpressiongeneregulationgeneticsimmunooncologysnpgeneticvariabilitypreprocessingqualitycontrolgenomeannotationgenefusiondetectionindeldetectionvariantannotationvariantdetectionmultiplesequencealignmentzlib
4.9 match 9.24 score 892 scripts 10 dependentsbioc
scFeatures:scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction
scFeatures constructs multi-view representations of single-cell and spatial data. scFeatures is a tool that generates multi-view representations of single-cell and spatial data through the construction of a total of 17 feature types. These features can then be used for a variety of analyses using other software in Biocondutor.
Maintained by Yue Cao. Last updated 5 months ago.
cellbasedassayssinglecellspatialsoftwaretranscriptomics
7.5 match 10 stars 5.95 score 15 scripts
pfh
langevitour:Langevin Tour
An HTML widget that randomly tours 2D projections of numerical data. A random walk through projections of the data is shown. The user can manipulate the plot to use specified axes, or turn on Guided Tour mode to find an informative projection of the data. Groups within the data can be hidden or shown, as can particular axes. Points can be brushed, and the selection can be linked to other widgets using crosstalk. The underlying method to produce the random walk and projection pursuit uses Langevin dynamics. The widget can be used from within R, or included in a self-contained R Markdown or Quarto document or presentation, or used in a Shiny app.
Maintained by Paul Harrison. Last updated 1 months ago.
javascript-applicationslangevin-dynamicstourvisualization
6.9 match 26 stars 6.41 score 22 scripts 1 dependentsbioc
epiregulon:Gene regulatory network inference from single cell epigenomic data
Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.
Maintained by Xiaosai Yao. Last updated 5 days ago.
singlecellgeneregulationnetworkinferencenetworkgeneexpressiontranscriptiongenetargetcpp
6.4 match 14 stars 6.67 score 17 scriptsbioc
cellity:Quality Control for Single-Cell RNA-seq Data
A support vector machine approach to identifying and filtering low quality cells from single-cell RNA-seq datasets.
Maintained by Tomislav Ilicic. Last updated 5 months ago.
immunooncologyrnaseqqualitycontrolpreprocessingnormalizationvisualizationdimensionreductiontranscriptomicsgeneexpressionsequencingsoftwaresupportvectormachine
10.6 match 4.00 score 9 scriptsbioc
NeuCA:NEUral network-based single-Cell Annotation tool
NeuCA is is a neural-network based method for scRNA-seq data annotation. It can automatically adjust its classification strategy depending on cell type correlations, to accurately annotate cell. NeuCA can automatically utilize the structure information of the cell types through a hierarchical tree to improve the annotation accuracy. It is especially helpful when the data contain closely correlated cell types.
Maintained by Hao Feng. Last updated 5 months ago.
singlecellsoftwareclassificationneuralnetworkrnaseqtranscriptomicsdatarepresentationtranscriptionsequencingpreprocessinggeneexpressiondataimport
12.7 match 3.30 score 3 scriptsbioc
EasyCellType:Annotate cell types for scRNA-seq data
We developed EasyCellType which can automatically examine the input marker lists obtained from existing software such as Seurat over the cell markerdatabases. Two quantification approaches to annotate cell types are provided: Gene set enrichment analysis (GSEA) and a modified versio of Fisher's exact test. The function presents annotation recommendations in graphical outcomes: bar plots for each cluster showing candidate cell types, as well as a dot plot summarizing the top 5 significant annotations for each cluster.
Maintained by Ruoxing Li. Last updated 5 months ago.
singlecellsoftwaregeneexpressiongenesetenrichment
9.3 match 4.30 score 6 scriptsbioc
ROSeq:Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data
ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. ROSeq takes filtered and normalized read matrix and cell-annotation/condition as input and determines the differentially expressed genes between the contrasting groups of single cells. One of the input parameters is the number of cores to be used.
Maintained by Krishan Gupta. Last updated 5 months ago.
geneexpressiondifferentialexpressionsinglecellcount-datagene-expressiongene-expression-profilesnormalizationpopulationsranktmmtungtung-datasettutorialvignette
8.7 match 2 stars 4.34 score 11 scriptsbioc
concordexR:Identify Spatial Homogeneous Regions with concordex
Spatial homogeneous regions (SHRs) in tissues are domains that are homogenous with respect to cell type composition. We present a method for identifying SHRs using spatial transcriptomics data, and demonstrate that it is efficient and effective at finding SHRs for a wide variety of tissue types. concordex relies on analysis of k-nearest-neighbor (kNN) graphs. The tool is also useful for analysis of non-spatial transcriptomics data, and can elucidate the extent of concordance between partitions of cells derived from clustering algorithms, and transcriptomic similarity as represented in kNN graphs.
Maintained by Kayla Jackson. Last updated 2 months ago.
singlecellclusteringspatialtranscriptomics
5.7 match 13 stars 6.23 score 13 scripts
jli-stat
ccRemover:Removes the Cell-Cycle Effect from Single-Cell RNA-Sequencing Data
Implements a method for identifying and removing the cell-cycle effect from scRNA-Seq data. The description of the method is in Barron M. and Li J. (2016) <doi:10.1038/srep33892>. Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. Different from previous methods, ccRemover implements a mechanism that formally tests whether a component is cell-cycle related or not, and thus while it often thoroughly removes the cell-cycle effect, it preserves other features/signals of interest in the data.
Maintained by Jun Li. Last updated 8 years ago.
11.9 match 2 stars 3.00 scorebioc
RCSL:Rank Constrained Similarity Learning for single cell RNA sequencing data
A novel clustering algorithm and toolkit RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both lo-cal similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearman’s rank correlations of a cell’s expression vector with those of other cells to measure its global similar-ity, and adaptively learns neighbour representation of a cell as its local similarity. The overall similar-ity of a cell to other cells is a linear combination of its global similarity and local similarity.
Maintained by Qinglin Mei. Last updated 5 months ago.
singlecellsoftwareclusteringdimensionreductionrnaseqvisualizationsequencing
7.9 match 2 stars 4.48 score 10 scriptsbioc
partCNV:Infer locally aneuploid cells using single cell RNA-seq data
This package uses a statistical framework for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. Our method uses an EM algorithm with mixtures of Poisson distributions while incorporating cytogenetics information (e.g., regional deletion or amplification) to guide the classification (partCNV). When applicable, we further improve the accuracy by integrating a Hidden Markov Model for feature selection (partCNVH).
Maintained by Ziyi Li. Last updated 5 months ago.
softwarecopynumbervariationhiddenmarkovmodelsinglecellclassification
8.4 match 4.18 score 4 scriptsbioc
ILoReg:ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data
ILoReg is a tool for identification of cell populations from scRNA-seq data. In particular, ILoReg is useful for finding cell populations with subtle transcriptomic differences. The method utilizes a self-supervised learning method, called Iteratitive Clustering Projection (ICP), to find cluster probabilities, which are used in noise reduction prior to PCA and the subsequent hierarchical clustering and t-SNE steps. Additionally, functions for differential expression analysis to find gene markers for the populations and gene expression visualization are provided.
Maintained by Johannes Smolander. Last updated 5 months ago.
singlecellsoftwareclusteringdimensionreductionrnaseqvisualizationtranscriptomicsdatarepresentationdifferentialexpressiontranscriptiongeneexpression
6.5 match 5 stars 4.88 score 2 scripts
yuelyu21
SCIntRuler:Guiding the Integration of Multiple Single-Cell RNA-Seq Datasets
The accumulation of single-cell RNA-seq (scRNA-seq) studies highlights the potential benefits of integrating multiple datasets. By augmenting sample sizes and enhancing analytical robustness, integration can lead to more insightful biological conclusions. However, challenges arise due to the inherent diversity and batch discrepancies within and across studies. SCIntRuler, a novel R package, addresses these challenges by guiding the integration of multiple scRNA-seq datasets.
Maintained by Yue Lyu. Last updated 5 months ago.
sequencinggeneticvariabilitysinglecellcpp
6.2 match 2 stars 4.85 score 3 scriptsbioc
veloviz:VeloViz: RNA-velocity informed 2D embeddings for visualizing cell state trajectories
VeloViz uses each cell’s current observed and predicted future transcriptional states inferred from RNA velocity analysis to build a nearest neighbor graph between cells in the population. Edges are then pruned based on a cosine correlation threshold and/or a distance threshold and the resulting graph is visualized using a force-directed graph layout algorithm. VeloViz can help ensure that relationships between cell states are reflected in the 2D embedding, allowing for more reliable representation of underlying cellular trajectories.
Maintained by Lyla Atta. Last updated 5 months ago.
transcriptomicsvisualizationgeneexpressionsequencingrnaseqdimensionreductioncpp
7.5 match 4.00 score 6 scriptsbioc
smartid:Scoring and Marker Selection Method Based on Modified TF-IDF
This package enables automated selection of group specific signature, especially for rare population. The package is developed for generating specifc lists of signature genes based on Term Frequency-Inverse Document Frequency (TF-IDF) modified methods. It can also be used as a new gene-set scoring method or data transformation method. Multiple visualization functions are implemented in this package.
Maintained by Jinjin Chen. Last updated 4 months ago.
softwaregeneexpressiontranscriptomics
6.8 match 1 stars 4.30 score 2 scriptsbioc
SplineDV:Differential Variability (DV) analysis for single-cell RNA sequencing data. (e.g. Identify Differentially Variable Genes across two experimental conditions)
A spline based scRNA-seq method for identifying differentially variable (DV) genes across two experimental conditions. Spline-DV constructs a 3D spline from 3 key gene statistics: mean expression, coefficient of variance, and dropout rate. This is done for both conditions. The 3D spline provides the “expected” behavior of genes in each condition. The distance of the observed mean, CV and dropout rate of each gene from the expected 3D spline is used to measure variability. As the final step, the spline-DV method compares the variabilities of each condition to identify differentially variable (DV) genes.
Maintained by Shreyan Gupta. Last updated 1 months ago.
softwaresinglecellsequencingdifferentialexpressionrnaseqgeneexpressiontranscriptomicsfeatureextraction
5.7 match 2 stars 5.08 score 3 scripts
cyrillagger
scDiffCom:Differential Analysis of Intercellular Communication from scRNA-Seq Data
Analysis tools to investigate changes in intercellular communication from scRNA-seq data. Using a Seurat object as input, the package infers which cell-cell interactions are present in the dataset and how these interactions change between two conditions of interest (e.g. young vs old). It relies on an internal database of ligand-receptor interactions (available for human, mouse and rat) that have been gathered from several published studies. Detection and differential analyses rely on permutation tests. The package also contains several tools to perform over-representation analysis and visualize the results. See Lagger, C. et al. (2023) <doi:10.1038/s43587-023-00514-x> for a full description of the methodology.
Maintained by Cyril Lagger. Last updated 1 years ago.
6.7 match 21 stars 4.25 score 17 scriptsbioc
genomicInstability:Genomic Instability estimation for scRNA-Seq
This package contain functions to run genomic instability analysis (GIA) from scRNA-Seq data. GIA estimates the association between gene expression and genomic location of the coding genes. It uses the aREA algorithm to quantify the enrichment of sets of contiguous genes (loci-blocks) on the gene expression profiles and estimates the Genomic Instability Score (GIS) for each analyzed cell.
Maintained by Mariano Alvarez. Last updated 5 months ago.
systemsbiologygeneexpressionsinglecell
7.0 match 5 stars 4.00 score 3 scriptsbioc
distinct:distinct: a method for differential analyses via hierarchical permutation tests
distinct is a statistical method to perform differential testing between two or more groups of distributions; differential testing is performed via hierarchical non-parametric permutation tests on the cumulative distribution functions (cdfs) of each sample. While most methods for differential expression target differences in the mean abundance between conditions, distinct, by comparing full cdfs, identifies, both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean (e.g., unimodal vs. bi-modal distributions with the same mean). distinct is a general and flexible tool: due to its fully non-parametric nature, which makes no assumptions on how the data was generated, it can be applied to a variety of datasets. It is particularly suitable to perform differential state analyses on single cell data (i.e., differential analyses within sub-populations of cells), such as single cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry (HDCyto) data. To use distinct one needs data from two or more groups of samples (i.e., experimental conditions), with at least 2 samples (i.e., biological replicates) per group.
Maintained by Simone Tiberi. Last updated 5 months ago.
geneticsrnaseqsequencingdifferentialexpressiongeneexpressionmultiplecomparisonsoftwaretranscriptionstatisticalmethodvisualizationsinglecellflowcytometrygenetargetopenblascpp
4.3 match 11 stars 6.35 score 34 scripts 1 dependentsbioc
Cepo:Cepo for the identification of differentially stable genes
Defining the identity of a cell is fundamental to understand the heterogeneity of cells to various environmental signals and perturbations. We present Cepo, a new method to explore cell identities from single-cell RNA-sequencing data using differential stability as a new metric to define cell identity genes. Cepo computes cell-type specific gene statistics pertaining to differential stable gene expression.
Maintained by Hani Jieun Kim. Last updated 5 months ago.
classificationgeneexpressionsinglecellsoftwaresequencingdifferentialexpression
5.7 match 4.62 score 14 scripts 1 dependentsbioc
fishpond:Fishpond: downstream methods and tools for expression data
Fishpond contains methods for differential transcript and gene expression analysis of RNA-seq data using inferential replicates for uncertainty of abundance quantification, as generated by Gibbs sampling or bootstrap sampling. Also the package contains a number of utilities for working with Salmon and Alevin quantification files.
Maintained by Michael Love. Last updated 5 months ago.
sequencingrnaseqgeneexpressiontranscriptionnormalizationregressionmultiplecomparisonbatcheffectvisualizationdifferentialexpressiondifferentialsplicingalternativesplicingsinglecellbioconductorgene-expressiongenomicssalmonscrnaseqstatisticstranscriptomics
3.2 match 28 stars 7.83 score 150 scriptsbioc
BioTIP:BioTIP: An R package for characterization of Biological Tipping-Point
Adopting tipping-point theory to transcriptome profiles to unravel disease regulatory trajectory.
Maintained by Yuxi (Jennifer) Sun. Last updated 5 months ago.
sequencingrnaseqgeneexpressiontranscriptionsoftware
3.6 match 18 stars 6.84 score 37 scripts
fentouxungui
SeuratExplorer:An 'Shiny' App for Exploring scRNA-seq Data Processed in 'Seurat'
A simple, one-command package which runs an interactive dashboard capable of common visualizations for single cell RNA-seq. 'SeuratExplorer' requires a processed 'Seurat' object, which is saved as 'rds' or 'qs2' file.
Maintained by Yongchao Zhang. Last updated 1 days ago.
6.1 match 3.90 scorebioc
epiregulon.extra:Companion package to epiregulon with additional plotting, differential and graph functions
Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.
Maintained by Xiaosai Yao. Last updated 5 days ago.
generegulationnetworkgeneexpressiontranscriptionchiponchipdifferentialexpressiongenetargetnormalizationgraphandnetwork
4.8 match 4.90 score 10 scriptsbioc
FEAST:FEAture SelcTion (FEAST) for Single-cell clustering
Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as “features”), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have significant impact on the clustering accuracy. FEAST is an R library for selecting most representative features before performing the core of scRNA-seq clustering. It can be used as a plug-in for the etablished clustering algorithms such as SC3, TSCAN, SHARP, SIMLR, and Seurat. The core of FEAST algorithm includes three steps: 1. consensus clustering; 2. gene-level significance inference; 3. validation of an optimized feature set.
Maintained by Kenong Su. Last updated 5 months ago.
sequencingsinglecellclusteringfeatureextraction
3.9 match 10 stars 5.97 score 47 scriptsbioc
CTdata:Data companion to CTexploreR
Data from publicly available databases (GTEx, CCLE, TCGA and ENCODE) that go with CTexploreR in order to re-define a comprehensive and thoroughly curated list of CT genes and their main characteristics.
Maintained by Laurent Gatto. Last updated 5 months ago.
transcriptomicsepigeneticsgeneexpressiondataimportexperimenthubsoftware
3.6 match 1 stars 5.69 score 1 scripts 1 dependentsbioc
scDotPlot:Cluster a Single-cell RNA-seq Dot Plot
Dot plots of single-cell RNA-seq data allow for an examination of the relationships between cell groupings (e.g. clusters) and marker gene expression. The scDotPlot package offers a unified approach to perform a hierarchical clustering analysis and add annotations to the columns and/or rows of a scRNA-seq dot plot. It works with SingleCellExperiment and Seurat objects as well as data frames.
Maintained by Benjamin I Laufer. Last updated 5 months ago.
softwarevisualizationdifferentialexpressiongeneexpressiontranscriptionrnaseqsinglecellsequencingclustering
4.2 match 2 stars 4.85 score 2 scriptsigordot
scooter:Streamlined scRNA-Seq Analysis Pipeline
Streamlined scRNA-Seq analysis pipeline.
Maintained by Igor Dolgalev. Last updated 1 years ago.
7.2 match 4 stars 2.51 score 16 scripts
princethewinner
FiRE:Finder of Rare Entities (FiRE)
The algorithm assigns rareness/ outlierness score to every sample in voluminous datasets. The algorithm makes multiple estimations of the proximity between a pair of samples, in low-dimensional spaces. To compute proximity, FiRE uses Sketching, a variant of locality sensitive hashing. For more details: Jindal, A., Gupta, P., Jayadeva and Sengupta, D., 2018. Discovery of rare cells from voluminous single cell expression data. Nature Communications, 9(1), p.4719. <doi:10.1038/s41467-018-07234-6>.
Maintained by Prashant Gupta. Last updated 4 years ago.
13.9 match 1.30 score 7 scriptsjli-stat
ClussCluster:Simultaneous Detection of Clusters and Cluster-Specific Genes in High-Throughput Transcriptome Data
Implements a new method 'ClussCluster' descried in Ge Jiang and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished). Simultaneously perform clustering analysis and signature gene selection on high-dimensional transcriptome data sets. To do so, 'ClussCluster' incorporates a Lasso-type regularization penalty term to the objective function of K- means so that cell-type-specific signature genes can be identified while clustering the cells.
Maintained by Li Jun. Last updated 6 years ago.
6.5 match 2.70 score
yuepan027
scpoisson:Single Cell Poisson Probability Paradigm
Useful to visualize the Poissoneity (an independent Poisson statistical framework, where each RNA measurement for each cell comes from its own independent Poisson distribution) of Unique Molecular Identifier (UMI) based single cell RNA sequencing (scRNA-seq) data, and explore cell clustering based on model departure as a novel data representation.
Maintained by Yue Pan. Last updated 3 years ago.
6.3 match 2.70 score 4 scripts
mohmedsoudy
ScRNAIMM:Performing Single-Cell RNA-Seq Imputation by Using Mean/Median Imputation
Performing single-cell imputation in a way that preserves the biological variations in the data. The package clusters the input data to do imputation for each cluster, and do a distribution check using the Anderson-Darling normality test to impute dropouts using mean or median (Yazici, B., & Yolacan, S. (2007) <DOI:10.1080/10629360600678310>).
Maintained by Mohamed Soudy. Last updated 1 years ago.
6.2 match 2.70 scorebioc
HGC:A fast hierarchical graph-based clustering method
HGC (short for Hierarchical Graph-based Clustering) is an R package for conducting hierarchical clustering on large-scale single-cell RNA-seq (scRNA-seq) data. The key idea is to construct a dendrogram of cells on their shared nearest neighbor (SNN) graph. HGC provides functions for building graphs and for conducting hierarchical clustering on the graph. The users with old R version could visit https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get HGC package built for R 3.6.
Maintained by XGlab. Last updated 5 months ago.
singlecellsoftwareclusteringrnaseqgraphandnetworkdnaseqcpp
3.5 match 4.70 score 25 scriptsbioc
celda:CEllular Latent Dirichlet Allocation
Celda is a suite of Bayesian hierarchical models for clustering single-cell RNA-sequencing (scRNA-seq) data. It is able to perform "bi-clustering" and simultaneously cluster genes into gene modules and cells into cell subpopulations. It also contains DecontX, a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. A variety of scRNA-seq data visualization functions is also included.
Maintained by Joshua Campbell. Last updated 26 days ago.
singlecellgeneexpressionclusteringsequencingbayesianimmunooncologydataimportcppopenmp
1.6 match 147 stars 10.47 score 256 scripts 2 dependentsbioc
ccImpute:ccImpute: an accurate and scalable consensus clustering based approach to impute dropout events in the single-cell RNA-seq data (https://doi.org/10.1186/s12859-022-04814-8)
Dropout events make the lowly expressed genes indistinguishable from true zero expression and different than the low expression present in cells of the same type. This issue makes any subsequent downstream analysis difficult. ccImpute is an imputation algorithm that uses cell similarity established by consensus clustering to impute the most probable dropout events in the scRNA-seq datasets. ccImpute demonstrated performance which exceeds the performance of existing imputation approaches while introducing the least amount of new noise as measured by clustering performance characteristics on datasets with known cell identities.
Maintained by Marcin Malec. Last updated 5 months ago.
singlecellsequencingprincipalcomponentdimensionreductionclusteringrnaseqtranscriptomicsopenblascppopenmp
3.6 match 2 stars 4.48 score 2 scripts
cran
VAM:Variance-Adjusted Mahalanobis
Contains logic for cell-specific gene set scoring of single cell RNA sequencing data.
Maintained by H. Robert Frost. Last updated 1 years ago.
3.3 match 4.78 score 4 dependentsbioc
PCAtools:PCAtools: Everything Principal Components Analysis
Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data.
Maintained by Kevin Blighe. Last updated 5 months ago.
rnaseqatacseqgeneexpressiontranscriptionsinglecellprincipalcomponentcpp
1.3 match 343 stars 11.12 score 832 scripts 2 dependentsbioc
scFeatureFilter:A correlation-based method for quality filtering of single-cell RNAseq data
An R implementation of the correlation-based method developed in the Joshi laboratory to analyse and filter processed single-cell RNAseq data. It returns a filtered version of the data containing only genes expression values unaffected by systematic noise.
Maintained by Guillaume Devailly. Last updated 5 months ago.
immunooncologysinglecellrnaseqpreprocessinggeneexpression
3.3 match 4.30 score 20 scriptsbioc
retrofit:RETROFIT: Reference-free deconvolution of cell mixtures in spatial transcriptomics
RETROFIT is a Bayesian non-negative matrix factorization framework to decompose cell type mixtures in ST data without using external single-cell expression references. RETROFIT outperforms existing reference-based methods in estimating cell type proportions and reconstructing gene expressions in simulations with varying spot size and sample heterogeneity, irrespective of the quality or availability of the single-cell reference. RETROFIT recapitulates known cell-type localization patterns in a Slide-seq dataset of mouse cerebellum without using any single-cell data.
Maintained by Adam Park. Last updated 5 months ago.
transcriptomicsvisualizationrnaseqbayesianspatialsoftwaregeneexpressiondimensionreductionfeatureextractionsinglecellcpp
2.7 match 3 stars 5.08 score 9 scriptsbioc
SPOTlight:`SPOTlight`: Spatial Transcriptomics Deconvolution
`SPOTlight`provides a method to deconvolute spatial transcriptomics spots using a seeded NMF approach along with visualization tools to assess the results. Spatially resolved gene expression profiles are key to understand tissue organization and function. However, novel spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots).
Maintained by Marc Elosua-Bayes. Last updated 5 months ago.
singlecellspatialstatisticalmethod
1.5 match 172 stars 8.37 score 170 scriptsbioc
InterCellar:InterCellar: an R-Shiny app for interactive analysis and exploration of cell-cell communication in single-cell transcriptomics
InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell communication in one or multiple conditions.
Maintained by Marta Interlandi. Last updated 5 months ago.
softwaresinglecellvisualizationgotranscriptomics
2.5 match 9 stars 4.95 score 7 scripts
chencxxy28
InteRD:The Integrated and Robust Deconvolution
We developed the Integrated and Robust Deconvolution algorithm to infer cell-type proportions from target bulk RNA-seq data. This package is able to effectively integrate deconvolution results from multiple scRNA-seq datasets and calibrates estimates from reference-based deconvolution by taking into account extra biological information as priors. Moreover, the proposed algorithm is robust to inaccurate external information imposed in the deconvolution system.
Maintained by Chixiang Chen. Last updated 3 years ago.
3.2 match 3.85 score 14 scripts
gangcai
pGRN:Single-Cell RNA Sequencing Pseudo-Time Based Gene Regulatory Network Inference
Inference and visualize gene regulatory network based on single-cell RNA sequencing pseudo-time information.
Maintained by Gangcai Xie. Last updated 2 years ago.
5.6 match 2.00 score 3 scriptsbioc
CDI:Clustering Deviation Index (CDI)
Single-cell RNA-sequencing (scRNA-seq) is widely used to explore cellular variation. The analysis of scRNA-seq data often starts from clustering cells into subpopulations. This initial step has a high impact on downstream analyses, and hence it is important to be accurate. However, there have not been unsupervised metric designed for scRNA-seq to evaluate clustering performance. Hence, we propose clustering deviation index (CDI), an unsupervised metric based on the modeling of scRNA-seq UMI counts to evaluate clustering of cells.
Maintained by Jiyuan Fang. Last updated 5 months ago.
singlecellsoftwareclusteringvisualizationsequencingrnaseqcellbasedassays
2.0 match 5 stars 5.00 score 4 scriptsjli-stat
scSorter:Implementation of 'scSorter' Algorithm
Implements the algorithm described in Guo, H., and Li, J., "scSorter: assigning cells to known cell types according to known marker genes". Cluster cells to known cell types based on marker genes specified for each cell type.
Maintained by Jun Li. Last updated 4 years ago.
3.8 match 2.30 score
carmonalab
scGate:Marker-Based Cell Type Purification for Single-Cell Sequencing Data
A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. 'scGate' automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. Briefly, 'scGate' takes as input: i) a gene expression matrix stored in a 'Seurat' object and ii) a “gating model” (GM), consisting of a set of marker genes that define the cell population of interest. The GM can be as simple as a single marker gene, or a combination of positive and negative markers. More complex GMs can be constructed in a hierarchical fashion, akin to gating strategies employed in flow cytometry. 'scGate' evaluates the strength of signature marker expression in each cell using the rank-based method 'UCell', and then performs k-nearest neighbor (kNN) smoothing by calculating the mean 'UCell' score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNA-seq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest. See the related publication Andreatta et al. (2022) <doi:10.1093/bioinformatics/btac141>.
Maintained by Massimo Andreatta. Last updated 1 months ago.
filteringmarker-genesscgatesignaturessingle-cell
1.0 match 106 stars 8.38 score 163 scriptsbioc
scDataviz:scDataviz: single cell dataviz and downstream analyses
In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment, it has a 'plug and play' feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz. Finally, the graphics in scDataviz are generated via the ggplot engine, which means that users can 'add on' features to these with ease.
Maintained by Kevin Blighe. Last updated 5 months ago.
singlecellimmunooncologyrnaseqgeneexpressiontranscriptionflowcytometrymassspectrometrydataimport
1.3 match 63 stars 6.30 score 16 scriptscran
RESET:Reconstruction Set Test
Contains logic for sample-level variable set scoring using randomized reduced rank reconstruction error. Frost, H. Robert (2023) "Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error" <doi:10.1101/2023.04.03.535366>.
Maintained by H. Robert Frost. Last updated 1 years ago.
3.3 match 2.30 score
cailab-tamu
scTenifoldNet:Construct and Compare scGRN from Single-Cell Transcriptomic Data
A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See <doi:10.1016/j.patter.2020.100139> for more details.
Maintained by Daniel Osorio. Last updated 2 months ago.
differential-regulation-analysisgene-regulatory-networksmanifold-alignmentsingle-celltensor-decomposition
1.3 match 22 stars 5.63 score 65 scripts 1 dependents
ncchung
jackstraw:Statistical Inference for Unsupervised Learning
Test for association between the observed data and their estimated latent variables. The jackstraw package provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their latent variables. Depending on the data type and the analysis aim, the latent variables may be estimated by principal component analysis (PCA), factor analysis (FA), K-means clustering, and related unsupervised learning algorithms. The jackstraw methods learn over-fitting characteristics inherent in this circular analysis, where the observed data are used to estimate the latent variables and used again to test against that estimated latent variables. When latent variables are estimated by PCA, the jackstraw enables statistical testing for association between observed variables and latent variables, as estimated by low-dimensional principal components (PCs). This essentially leads to identifying variables that are significantly associated with PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds coherent groups in high-dimensional data. The jackstraw estimates statistical significance of cluster membership, by testing association between data and cluster centers. Clustering membership can be improved by using the resulting jackstraw p-values and posterior inclusion probabilities (PIPs), with an application to unsupervised evaluation of cell identities in single cell RNA-seq (scRNA-seq).
Maintained by Neo Christopher Chung. Last updated 3 months ago.
clusteringk-meansmachine-learningpcastatisticsunsupervised
1.3 match 16 stars 5.29 score 35 scriptsbioc
ADImpute:Adaptive Dropout Imputer (ADImpute)
Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Here we propose two novel methods: a gene regulatory network-based approach using gene-gene relationships learnt from external data and a baseline approach corresponding to a sample-wide average. ADImpute can implement these novel methods and also combine them with existing imputation methods (currently supported: DrImpute, SAVER). ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble.
Maintained by Ana Carolina Leote. Last updated 5 months ago.
geneexpressionnetworkpreprocessingsequencingsinglecelltranscriptomics
1.5 match 4.30 score 7 scriptsbioc
HIPPO:Heterogeneity-Induced Pre-Processing tOol
For scRNA-seq data, it selects features and clusters the cells simultaneously for single-cell UMI data. It has a novel feature selection method using the zero inflation instead of gene variance, and computationally faster than other existing methods since it only relies on PCA+Kmeans rather than graph-clustering or consensus clustering.
Maintained by Tae Kim. Last updated 5 months ago.
sequencingsinglecellgeneexpressiondifferentialexpressionclustering
1.0 match 18 stars 6.16 score 4 scriptsbioc
LRcell:Differential cell type change analysis using Logistic/linear Regression
The goal of LRcell is to identify specific sub-cell types that drives the changes observed in a bulk RNA-seq differential gene expression experiment. To achieve this, LRcell utilizes sets of cell marker genes acquired from single-cell RNA-sequencing (scRNA-seq) as indicators for various cell types in the tissue of interest. Next, for each cell type, using its marker genes as indicators, we apply Logistic Regression on the complete set of genes with differential expression p-values to calculate a cell-type significance p-value. Finally, these p-values are compared to predict which one(s) are likely to be responsible for the differential gene expression pattern observed in the bulk RNA-seq experiments. LRcell is inspired by the LRpath[@sartor2009lrpath] algorithm developed by Sartor et al., originally designed for pathway/gene set enrichment analysis. LRcell contains three major components: LRcell analysis, plot generation and marker gene selection. All modules in this package are written in R. This package also provides marker genes in the Prefrontal Cortex (pFC) human brain region, human PBMC and nine mouse brain regions (Frontal Cortex, Cerebellum, Globus Pallidus, Hippocampus, Entopeduncular, Posterior Cortex, Striatum, Substantia Nigra and Thalamus).
Maintained by Wenjing Ma. Last updated 5 months ago.
singlecellgenesetenrichmentsequencingregressiongeneexpressiondifferentialexpressionenrichmentmarker-genes
1.4 match 3 stars 4.48 score 5 scriptsbioc
OSTA.data:OSTA book data
'OSTA.data' is a companion package for the "Orchestrating Spatial Transcriptomics Analysis" (OSTA) with Bioconductor online book. Throughout OSTA, we rely on a set of publicly available datasets that cover different sequencing- and imaging-based platforms, such as Visium, Visium HD, Xenium (10x Genomics) and CosMx (NanoString). In addition, we rely on scRNA-seq (Chromium) data for tasks, e.g., spot deconvolution and label transfer (i.e., supervised clustering). These data been deposited in an Open Storage Framework (OSF) repository, and can be queried and downloaded using functions from the 'osfr' package. For convenience, we have implemented 'OSTA.data' to query and retrieve data from our OSF node, and cache retrieved Zip archives using 'BiocFileCache'.
Maintained by Yixing E. Dong. Last updated 18 days ago.
dataimportdatarepresentationexperimenthubsoftwareinfrastructureimmunooncologygeneexpressiontranscriptomicssinglecellspatial
1.0 match 2 stars 5.00 scorebioc
scShapes:A Statistical Framework for Modeling and Identifying Differential Distributions in Single-cell RNA-sequencing Data
We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs). We model each gene independently under each treatment condition using error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth. Since all four distributions considered in our framework belong to the same family of distributions, we first perform a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions. Genes passing the KS test will be then modeled using GLMs. Model selection is done by calculating the Bayesian Information Criterion (BIC) and likelihood ratio test (LRT) statistic.
Maintained by Malindrie Dharmaratne. Last updated 5 months ago.
rnaseqsinglecellmultiplecomparisongeneexpression
1.0 match 8 stars 4.90 score 6 scriptscailab-tamu
scTenifoldKnk:In-Silico Knockout Experiments from Single-Cell Gene Regulatory Networks
A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene’s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene’s function in the analyzed cells.
Maintained by Daniel Osorio. Last updated 2 months ago.
functional-genomicsgene-functiongene-knockoutgene-regulatory-networkvirtual-knockout-experiments
1.0 match 43 stars 4.85 score 11 scriptspapatheodorou-group
scOntoMatch:Aligning Ontology Annotation Across Single Cell Datasets with 'scOntoMatch'
Unequal granularity of cell type annotation makes it difficult to compare scRNA-seq datasets at scale. Leveraging the ontology system for defining cell type hierarchy, 'scOntoMatch' aims to align cell type annotations to make them comparable across studies. The alignment involves two core steps: first is to trim the cell type tree within each dataset so each cell type does not have descendants, and then map cell type labels cross-studies by direct matching and mapping descendants to ancestors. Various functions for plotting cell type trees and manipulating ontology terms are also provided. In the Single Cell Expression Atlas hosted at EBI, a compendium of datasets with curated ontology labels are great inputs to this package.
Maintained by Yuyao Song. Last updated 1 years ago.
1.0 match 7 stars 4.54 score 6 scriptsbioc
MICSQTL:MICSQTL (Multi-omic deconvolution, Integration and Cell-type-specific Quantitative Trait Loci)
Our pipeline, MICSQTL, utilizes scRNA-seq reference and bulk transcriptomes to estimate cellular composition in the matched bulk proteomes. The expression of genes and proteins at either bulk level or cell type level can be integrated by Angle-based Joint and Individual Variation Explained (AJIVE) framework. Meanwhile, MICSQTL can perform cell-type-specic quantitative trait loci (QTL) mapping to proteins or transcripts based on the input of bulk expression data and the estimated cellular composition per molecule type, without the need for single cell sequencing. We use matched transcriptome-proteome from human brain frontal cortex tissue samples to demonstrate the input and output of our tool.
Maintained by Qian Li. Last updated 5 months ago.
geneexpressiongeneticsproteomicsrnaseqsequencingsinglecellsoftwarevisualizationcellbasedassayscoverage
1.0 match 4.30 score 3 scripts
ellisdoro
jrSiCKLSNMF:Clustering Single-Cell Multimodal Omics Data with Joint Graph Regularized Single-Cell Kullback-Leibler Sparse Non-Negative Matrix Factorization
Methods to perform Joint graph Regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced "junior sickles NMF") on quality controlled multi-assay single-cell omics count data, specifically dual-assay scRNA-seq and scATAC-seq data. 'jrSiCKLSNMF' extracts meaningful latent factors that are shared across omics views. These factors enable accurate cell-type clustering, and facilitate visualizations. Also includes methods for mini- batch updates and other adaptations for larger datasets.
Maintained by Dorothy Ellis. Last updated 9 months ago.
1.3 match 3.00 score 6 scriptscran
scaper:Single Cell Transcriptomics-Level Cytokine Activity Prediction and Estimation
Generates cell-level cytokine activity estimates using relevant information from gene sets constructed with the 'CytoSig' and the 'Reactome' databases and scored using the modified 'Variance-adjusted Mahalanobis (VAM)' framework for single-cell RNA-sequencing (scRNA-seq) data. 'CytoSig' database is described in: Jiang at al., (2021) <doi:10.1038/s41592-021-01274-5>. 'Reactome' database is described in: Gillespie et al., (2021) <doi:10.1093/nar/gkab1028>. The 'VAM' method is outlined in: Frost (2020) <doi:10.1093/nar/gkaa582>.
Maintained by Azka Javaid. Last updated 1 years ago.
1.0 match 2.30 score
welch-lab
SiNMFiD:Supervised iNMF informed Deconvolution
A package for completing cell type deconvolution on bulk spatial transcriptomic data utilizing multiple reference scRNA-seq datasets.
Maintained by Joshua Sodicoff. Last updated 1 years ago.
1.1 match 2.00 score 1 scriptsjli-stat
SparseMDC:Implementation of SparseMDC Algorithm
Implements the algorithm described in Barron, M., and Li, J. (Not yet published). This algorithm clusters samples from multiple ordered populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseMDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers.
Maintained by Jun Li. Last updated 7 years ago.
1.0 match 2.00 score