Showing 51 of total 51 results (show query)
bioc
iSEE:Interactive SummarizedExperiment Explorer
Create an interactive Shiny-based graphical user interface for exploring data stored in SummarizedExperiment objects, including row- and column-level metadata. The interface supports transmission of selections between plots and tables, code tracking, interactive tours, interactive or programmatic initialization, preservation of app state, and extensibility to new panel types via S4 classes. Special attention is given to single-cell data in a SingleCellExperiment object with visualization of dimensionality reduction results.
Maintained by Kevin Rue-Albrecht. Last updated 23 days ago.
cellbasedassaysclusteringdimensionreductionfeatureextractiongeneexpressionguiimmunooncologyshinyappssinglecelltranscriptiontranscriptomicsvisualizationdimension-reductionfeature-extractiongene-expressionhacktoberfesthuman-cell-atlasshinysingle-cell
225 stars 12.86 score 380 scripts 9 dependentsbioc
scDblFinder:scDblFinder
The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly found in the scran package, the new fast and comprehensive scDblFinder method, and a reimplementation of the Amulet detection method for single-cell ATAC-seq.
Maintained by Pierre-Luc Germain. Last updated 9 days ago.
preprocessingsinglecellrnaseqatacseqdoubletssingle-cell
184 stars 12.38 score 888 scripts 1 dependents
stuart-lab
Signac:Analysis of Single-Cell Chromatin Data
A framework for the analysis and exploration of single-cell chromatin data. The 'Signac' package contains functions for quantifying single-cell chromatin data, computing per-cell quality control metrics, dimension reduction and normalization, visualization, and DNA sequence motif analysis. Reference: Stuart et al. (2021) <doi:10.1038/s41592-021-01282-5>.
Maintained by Tim Stuart. Last updated 7 months ago.
atacbioinformaticssingle-cellzlibcpp
355 stars 12.18 score 3.7k scripts 1 dependents
welch-lab
rliger:Linked Inference of Genomic Experimental Relationships
Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors. See Welch J, Kozareva V, et al (2019) <doi:10.1016/j.cell.2019.05.006>, and Liu J, Gao C, Sodicoff J, et al (2020) <doi:10.1038/s41596-020-0391-8> for more details.
Maintained by Yichen Wang. Last updated 2 months ago.
nonnegative-matrix-factorizationsingle-cellopenblascpp
408 stars 10.77 score 334 scripts 1 dependentsbioc
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
83 stars 10.14 score 368 scripts 1 dependentsbioc
cytomapper:Visualization of highly multiplexed imaging data in R
Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells.
Maintained by Lasse Meyer. Last updated 5 months ago.
immunooncologysoftwaresinglecellonechanneltwochannelmultiplecomparisonnormalizationdataimportbioimagingimaging-mass-cytometrysingle-cellspatial-analysis
32 stars 9.61 score 354 scripts 5 dependentsbioc
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
67 stars 9.52 score 137 scripts 1 dependentsbioc
Nebulosa:Single-Cell Data Visualisation Using Kernel Gene-Weighted Density Estimation
This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features (e.g. genes). Seurat and SingleCellExperiment objects can be used within Nebulosa.
Maintained by Jose Alquicira-Hernandez. Last updated 5 months ago.
softwaregeneexpressionsinglecellvisualizationdimensionreductionsingle-cellsingle-cell-analysissingle-cell-multiomicssingle-cell-rna-seq
99 stars 9.52 score 494 scripts
immunomind
immunarch:Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires
A comprehensive framework for bioinformatics exploratory analysis of bulk and single-cell T-cell receptor and antibody repertoires. It provides seamless data loading, analysis and visualisation for AIRR (Adaptive Immune Receptor Repertoire) data, both bulk immunosequencing (RepSeq) and single-cell sequencing (scRNAseq). Immunarch implements most of the widely used AIRR analysis methods, such as: clonality analysis, estimation of repertoire similarities in distribution of clonotypes and gene segments, repertoire diversity analysis, annotation of clonotypes using external immune receptor databases and clonotype tracking in vaccination and cancer studies. A successor to our previously published 'tcR' immunoinformatics package (Nazarov 2015) <doi:10.1186/s12859-015-0613-1>.
Maintained by Vadim I. Nazarov. Last updated 1 years ago.
airr-analysisb-cell-receptorbcrbcr-repertoirebioinformaticsigig-repertoireimmune-repertoireimmune-repertoire-analysisimmune-repertoire-dataimmunoglobulinimmunoinformaticsimmunologyrep-seqrepertoire-analysissingle-cellsingle-cell-analysist-cell-receptortcrtcr-repertoirecpp
316 stars 9.49 score 203 scripts
stemangiola
tidyseurat:Brings Seurat to the Tidyverse
It creates an invisible layer that allow to see the 'Seurat' object as tibble and interact seamlessly with the tidyverse.
Maintained by Stefano Mangiola. Last updated 8 months ago.
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicsdplyrggplot2pcapurrrsctseuratsingle-cellsingle-cell-rna-seqtibbletidyrtidyversetranscriptstsneumap
159 stars 9.48 score 398 scripts 1 dependentsbioc
EWCE:Expression Weighted Celltype Enrichment
Used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses.
Maintained by Alan Murphy. Last updated 1 months ago.
geneexpressiontranscriptiondifferentialexpressiongenesetenrichmentgeneticsmicroarraymrnamicroarrayonechannelrnaseqbiomedicalinformaticsproteomicsvisualizationfunctionalgenomicssinglecelldeconvolutionsingle-cellsingle-cell-rna-seqtranscriptomics
56 stars 9.29 score 99 scriptsbioc
scp:Mass Spectrometry-Based Single-Cell Proteomics Data Analysis
Utility functions for manipulating, processing, and analyzing mass spectrometry-based single-cell proteomics data. The package is an extension to the 'QFeatures' package and relies on 'SingleCellExpirement' to enable single-cell proteomics analyses. The package offers the user the functionality to process quantitative table (as generated by MaxQuant, Proteome Discoverer, and more) into data tables ready for downstream analysis and data visualization.
Maintained by Christophe Vanderaa. Last updated 29 days ago.
geneexpressionproteomicssinglecellmassspectrometrypreprocessingcellbasedassaysbioconductormass-spectrometrysingle-cellsoftware
25 stars 8.94 score 115 scripts
enblacar
SCpubr:Generate Publication Ready Visualizations of Single Cell Transcriptomics Data
A system that provides a streamlined way of generating publication ready plots for known Single-Cell transcriptomics data in a “publication ready” format. This is, the goal is to automatically generate plots with the highest quality possible, that can be used right away or with minimal modifications for a research article.
Maintained by Enrique Blanco-Carmona. Last updated 1 months ago.
softwaresinglecellvisualizationdata-visualizationggplot2publication-quality-plotsseuratsingle-cellsingle-cell-genomicssingle-cell-rna-seq
178 stars 8.71 score 194 scriptsbioc
M3Drop:Michaelis-Menten Modelling of Dropouts in single-cell RNASeq
This package fits a model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. Also includes an method for calculating exact Pearson residuals in UMI-tagged data using a library-size aware negative binomial model.
Maintained by Tallulah Andrews. Last updated 5 months ago.
rnaseqsequencingtranscriptomicsgeneexpressionsoftwaredifferentialexpressiondimensionreductionfeatureextractionhuman-cell-atlasrna-seqsingle-cellsingle-cell-rna-seq
29 stars 8.53 score 119 scripts 2 dependentsbioc
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
15 stars 8.46 score 19 scripts 1 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
246 stars 8.45 score 1.1k scriptsbioc
sccomp:Tests differences in cell-type proportion for single-cell data, robust to outliers
A robust and outlier-aware method for testing differences in cell-type proportion in single-cell data. This model can infer changes in tissue composition and heterogeneity, and can produce realistic data simulations based on any existing dataset. This model can also transfer knowledge from a large set of integrated datasets to increase accuracy further.
Maintained by Stefano Mangiola. Last updated 14 days ago.
bayesianregressiondifferentialexpressionsinglecellmetagenomicsflowcytometryspatialbatch-correctioncompositioncytofdifferential-proportionmicrobiomemultilevelproportionsrandom-effectssingle-cellunwanted-variation
99 stars 8.43 score 69 scripts
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 2 months ago.
filteringmarker-genesscgatesignaturessingle-cell
106 stars 8.38 score 163 scripts
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
223 stars 8.00 score 282 scriptsbioc
imcRtools:Methods for imaging mass cytometry data analysis
This R package supports the handling and analysis of imaging mass cytometry and other highly multiplexed imaging data. The main functionality includes reading in single-cell data after image segmentation and measurement, data formatting to perform channel spillover correction and a number of spatial analysis approaches. First, cell-cell interactions are detected via spatial graph construction; these graphs can be visualized with cells representing nodes and interactions representing edges. Furthermore, per cell, its direct neighbours are summarized to allow spatial clustering. Per image/grouping level, interactions between types of cells are counted, averaged and compared against random permutations. In that way, types of cells that interact more (attraction) or less (avoidance) frequently than expected by chance are detected.
Maintained by Daniel Schulz. Last updated 5 months ago.
immunooncologysinglecellspatialdataimportclusteringimcsingle-cell
24 stars 7.58 score 126 scriptsbioc
scde:Single Cell Differential Expression
The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734).
Maintained by Evan Biederstedt. Last updated 5 months ago.
immunooncologyrnaseqstatisticalmethoddifferentialexpressionbayesiantranscriptionsoftwareanalysisbioinformaticsheterogenityngssingle-celltranscriptomicsopenblascppopenmp
173 stars 7.53 score 141 scripts
dynverse
dyngen:A Multi-Modal Simulator for Spearheading Single-Cell Omics Analyses
A novel, multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution. 'dyngen' is more flexible than current single-cell simulation engines. It allows better method development and benchmarking, thereby stimulating development and testing of novel computational methods. Cannoodt et al. (2021) <doi:10.1038/s41467-021-24152-2>.
Maintained by Robrecht Cannoodt. Last updated 2 years ago.
benchmarkingsingle-cellsingle-cell-analysissingle-cell-omics
74 stars 7.53 score 57 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 10 days ago.
cancer-genomicscnv-detectionlineage-tracingphylogenysingle-cellsingle-cell-analysissingle-cell-rna-seqspatial-transcriptomicscpp
180 stars 7.48 score 120 scriptsbioc
netSmooth:Network smoothing for scRNAseq
netSmooth is an R package for network smoothing of single cell RNA sequencing data. Using bio networks such as protein-protein interactions as priors for gene co-expression, netsmooth improves cell type identification from noisy, sparse scRNAseq data.
Maintained by Jonathan Ronen. Last updated 5 months ago.
networkgraphandnetworksinglecellrnaseqgeneexpressionsequencingtranscriptomicsnormalizationpreprocessingclusteringdimensionreductionbioinformaticsgenomicssingle-cell
27 stars 7.41 score 4 scriptsbioc
tidytof:Analyze High-dimensional Cytometry Data Using Tidy Data Principles
This package implements an interactive, scientific analysis pipeline for high-dimensional cytometry data built using tidy data principles. It is specifically designed to play well with both the tidyverse and Bioconductor software ecosystems, with functionality for reading/writing data files, data cleaning, preprocessing, clustering, visualization, modeling, and other quality-of-life functions. tidytof implements a "grammar" of high-dimensional cytometry data analysis.
Maintained by Timothy Keyes. Last updated 5 months ago.
singlecellflowcytometrybioinformaticscytometrydata-sciencesingle-celltidyversecpp
18 stars 7.24 score 35 scripts
zjufanlab
scCATCH:Single Cell Cluster-Based Annotation Toolkit for Cellular Heterogeneity
An automatic cluster-based annotation pipeline based on evidence-based score by matching the marker genes with known cell markers in tissue-specific cell taxonomy reference database for single-cell RNA-seq data. See Shao X, et al (2020) <doi:10.1016/j.isci.2020.100882> for more details.
Maintained by Xin Shao. Last updated 2 years ago.
cell-markerscluster-annotationmarker-genesrna-seqsequencingseuratsingle-celltranscriptometranscriptomics
226 stars 7.13 score 75 scriptsbioc
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
61 stars 7.05 score 62 scriptsbioc
CuratedAtlasQueryR:Queries the Human Cell Atlas
Provides access to a copy of the Human Cell Atlas, but with harmonised metadata. This allows for uniform querying across numerous datasets within the Atlas using common fields such as cell type, tissue type, and patient ethnicity. Usage involves first querying the metadata table for cells of interest, and then downloading the corresponding cells into a SingleCellExperiment object.
Maintained by Stefano Mangiola. Last updated 5 months ago.
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicsdatabaseduckdbhdf5human-cell-atlassingle-cellsinglecellexperimenttidyverse
90 stars 7.04 score 41 scriptsstemangiola
tidygate:Interactively Gate Points
Interactively gate points on a scatter plot. Interactively drawn gates are recorded and can be applied programmatically to reproduce results exactly. Programmatic gating is based on the package gatepoints by Wajid Jawaid (who is also an author of this package).
Maintained by Stefano Mangiola. Last updated 6 months ago.
assaydomaininfrastructureclusteringdatavisdatavizdplyrdrawingfacsgateggplot2interactivepipeprogrammaticseuratsingle-cellsingle-cell-rna-seqtibbletidy-datatidyverse
23 stars 6.89 score 14 scripts 1 dependentsbioc
escheR:Unified multi-dimensional visualizations with Gestalt principles
The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide this open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows.
Maintained by Boyi Guo. Last updated 5 months ago.
spatialsinglecelltranscriptomicsvisualizationsoftwaremultidimensionalsingle-cellspatial-omics
6 stars 6.74 score 153 scripts 1 dependents
alexisvdb
singleCellHaystack:A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data
One key exploratory analysis step in single-cell genomics data analysis is the prediction of features with different activity levels. For example, we want to predict differentially expressed genes (DEGs) in single-cell RNA-seq data, spatial DEGs in spatial transcriptomics data, or differentially accessible regions (DARs) in single-cell ATAC-seq data. 'singleCellHaystack' predicts differentially active features in single cell omics datasets without relying on the clustering of cells into arbitrary clusters. 'singleCellHaystack' uses Kullback-Leibler divergence to find features (e.g., genes, genomic regions, etc) that are active in subsets of cells that are non-randomly positioned inside an input space (such as 1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For the theoretical background of 'singleCellHaystack' we refer to our original paper Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3> and our update Vandenbon and Diez (Scientific Reports, 2023) <doi:10.1038/s41598-023-38965-2>.
Maintained by Alexis Vandenbon. Last updated 1 years ago.
bioinformaticscite-seqpseudotimescatac-seqsingle-cellspatial-proteomicsspatial-transcriptomicstranscriptomics
81 stars 6.71 score 64 scripts
person-c
easybio:Comprehensive Single-Cell Annotation and Transcriptomic Analysis Toolkit
Provides a comprehensive toolkit for single-cell annotation with the 'CellMarker2.0' database (see Xia Li, Peng Wang, Yunpeng Zhang (2023) <doi: 10.1093/nar/gkac947>). Streamlines biological label assignment in single-cell RNA-seq data and facilitates transcriptomic analysis, including preparation of TCGA<https://portal.gdc.cancer.gov/> and GEO<https://www.ncbi.nlm.nih.gov/geo/> datasets, differential expression analysis and visualization of enrichment analysis results. Additional utility functions support various bioinformatics workflows. See Wei Cui (2024) <doi: 10.1101/2024.09.14.609619> for more details.
Maintained by Wei Cui. Last updated 26 days ago.
limmageoqueryedgerfgseabioinformaticscellmarker2gsearna-seqsingle-cell
10 stars 6.62 score 35 scriptsbioc
CiteFuse:CiteFuse: multi-modal analysis of CITE-seq data
CiteFuse pacakage implements a suite of methods and tools for CITE-seq data from pre-processing to integrative analytics, including doublet detection, network-based modality integration, cell type clustering, differential RNA and protein expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualisation of the analyses.
Maintained by Yingxin Lin. Last updated 5 months ago.
singlecellgeneexpressionbioinformaticssingle-cellcpp
27 stars 6.59 score 18 scriptsbioc
Statial:A package to identify changes in cell state relative to spatial associations
Statial is a suite of functions for identifying changes in cell state. The functionality provided by Statial provides robust quantification of cell type localisation which are invariant to changes in tissue structure. In addition to this Statial uncovers changes in marker expression associated with varying levels of localisation. These features can be used to explore how the structure and function of different cell types may be altered by the agents they are surrounded with.
Maintained by Farhan Ameen. Last updated 5 months ago.
singlecellspatialclassificationsingle-cell
5 stars 6.49 score 23 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 stars 6.45 score 15 scriptsbioc
scMET:Bayesian modelling of cell-to-cell DNA methylation heterogeneity
High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression.
Maintained by Andreas C. Kapourani. Last updated 5 months ago.
immunooncologydnamethylationdifferentialmethylationdifferentialexpressiongeneexpressiongeneregulationepigeneticsgeneticsclusteringfeatureextractionregressionbayesiansequencingcoveragesinglecellbayesian-inferencegeneralised-linear-modelsheterogeneityhierarchical-modelsmethylation-analysissingle-cellcpp
20 stars 6.23 score 42 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
8 stars 6.20 score 22 scriptsbioc
switchde:Switch-like differential expression across single-cell trajectories
Inference and detection of switch-like differential expression across single-cell RNA-seq trajectories.
Maintained by Kieran Campbell. Last updated 5 months ago.
immunooncologysoftwaretranscriptomicsgeneexpressionrnaseqregressiondifferentialexpressionsinglecellgene-expressiongenomicssingle-cell
19 stars 5.98 score 7 scriptsbioc
ChromSCape:Analysis of single-cell epigenomics datasets with a Shiny App
ChromSCape - Chromatin landscape profiling for Single Cells - is a ready-to-launch user-friendly Shiny Application for the analysis of single-cell epigenomics datasets (scChIP-seq, scATAC-seq, scCUT&Tag, ...) from aligned data to differential analysis & gene set enrichment analysis. It is highly interactive, enables users to save their analysis and covers a wide range of analytical steps: QC, preprocessing, filtering, batch correction, dimensionality reduction, vizualisation, clustering, differential analysis and gene set analysis.
Maintained by Pacome Prompsy. Last updated 5 months ago.
shinyappssoftwaresinglecellchipseqatacseqmethylseqclassificationclusteringepigeneticsprincipalcomponentannotationbatcheffectmultiplecomparisonnormalizationpathwayspreprocessingqualitycontrolreportwritingvisualizationgenesetenrichmentdifferentialpeakcallingepigenomicsshinysingle-cellcpp
14 stars 5.83 score 16 scripts
core-bioinformatics
ClustAssess:Tools for Assessing Clustering
A set of tools for evaluating clustering robustness using proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) <doi:10.1038/srep06207>), as well as similarity across methods and method stability using element-centric clustering comparison (Gates et al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package enables stability-based parameter assessment for graph-based clustering pipelines typical in single-cell data analysis.
Maintained by Andi Munteanu. Last updated 1 months ago.
softwaresinglecellrnaseqatacseqnormalizationpreprocessingdimensionreductionvisualizationqualitycontrolclusteringclassificationannotationgeneexpressiondifferentialexpressionbioinformaticsgenomicsmachine-learningparameter-optimizationrobustnesssingle-cellunsupervised-learningcpp
23 stars 5.70 score 18 scripts
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 3 months ago.
differential-regulation-analysisgene-regulatory-networksmanifold-alignmentsingle-celltensor-decomposition
22 stars 5.63 score 65 scripts 1 dependents
pwwang
plotthis:High-Level Plotting Built Upon 'ggplot2' and Other Plotting Packages
Provides high-level API and a wide range of options to create stunning, publication-quality plots effortlessly. It is built upon 'ggplot2' and other plotting packages, and is designed to be easy to use and to work seamlessly with 'ggplot2' objects. It is particularly useful for creating complex plots with multiple layers, facets, and annotations. It also provides a set of functions to create plots for specific types of data, such as Venn diagrams, alluvial diagrams, and phylogenetic trees. The package is designed to be flexible and customizable, and to work well with the 'ggplot2' ecosystem. The API can be found at <https://pwwang.github.io/plotthis/reference/index.html>.
Maintained by Panwen Wang. Last updated 13 days ago.
36 stars 5.51 score 2 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
13 stars 5.37 score 36 scripts
jgasmits
AnanseSeurat:Construct ANANSE GRN-Analysis Seurat
Enables gene regulatory network (GRN) analysis on single cell clusters, using the GRN analysis software 'ANANSE', Xu et al.(2021) <doi:10.1093/nar/gkab598>. Export data from 'Seurat' objects, for GRN analysis by 'ANANSE' implemented in 'snakemake'. Finally, incorporate results for visualization and interpretation.
Maintained by Jos Smits. Last updated 1 years ago.
grn-analysisseurat-objectssingle-cellsingle-cell-atac-seqsingle-cell-rna-seq
8 stars 4.90 score 4 scripts
dosorio
rPanglaoDB:Download and Merge Single-Cell RNA-Seq Data from the PanglaoDB Database
Download and merge labeled single-cell RNA-seq data from the PanglaoDB <https://panglaodb.se/> into a Seurat object.
Maintained by Daniel Osorio. Last updated 2 years ago.
data-integrationdata-miningrna-seqsingle-cellsingle-cell-rna-seq
26 stars 4.41 score 20 scripts
yunuuuu
BPCellsArray:Using BPCells as a DelayedArray Backend
Implements a DelayedArray backend for reading and writing arrays in the BPCells storage layout. The resulting BPCells*Arrays are compatible with all Bioconductor pipelines that can accept DelayedArray instances.
Maintained by Yun Peng. Last updated 8 months ago.
softwaredataimportdatarepresentationinfrastructuresingle-cell
7 stars 4.32 score
ruzhangzhao
mixhvg:Mixture of Multiple Highly Variable Feature Selection Methods
Highly variable gene selection methods, including popular public available methods, and also the mixture of multiple highly variable gene selection methods, <https://github.com/RuzhangZhao/mixhvg>. Reference: <doi:10.1101/2024.08.25.608519>.
Maintained by Ruzhang Zhao. Last updated 27 days ago.
rna-seq-analysisrna-seq-pipelinesingle-cellsingle-cell-rna-seqvariable-selection
5 stars 4.18 score 6 scriptskharchenkolab
scistreer:Maximum-Likelihood Perfect Phylogeny Inference at Scale
Fast maximum-likelihood phylogeny inference from noisy single-cell data using the 'ScisTree' algorithm by Yufeng Wu (2019) <doi:10.1093/bioinformatics/btz676>. 'scistreer' provides an 'R' interface and improves speed via 'Rcpp' and 'RcppParallel', making the method applicable to massive single-cell datasets (>10,000 cells).
Maintained by Teng Gao. Last updated 2 years ago.
evolutionphylogeneticssingle-cellcpp
7 stars 4.02 score 2 scripts 1 dependentsyunuuuu
rsahmi:Single-Cell Analysis of Host-Microbiome Interactions
A computational resource designed to accurately detect microbial nucleic acids while filtering out contaminants and false-positive taxonomic assignments from standard transcriptomic sequencing of mammalian tissues. For more details, see Ghaddar (2023) <doi:10.1038/s43588-023-00507-1>. This implementation leverages the 'polars' package for fast and systematic microbial signal recovery and denoising from host tissue genomic sequencing.
Maintained by Yun Peng. Last updated 2 days ago.
3.90 score
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
4 stars 3.60 score 4 scripts
obenno
scSpotlight:A Single Cell Analysis Shiny App
A single cell analysis (viewer) app based on Seurat.
Maintained by Zhixia Xiao. Last updated 7 months ago.
2 stars 2.78 score