R-universe search: topic:singlecell

bioc

SingleCellExperiment:S4 Classes for Single Cell Data

Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries.

Maintained by Davide Risso. Last updated 22 days ago.

immunooncology datarepresentation dataimport infrastructure singlecell

13.53 score 15k scripts 285 dependents

bioc

edgeR:Empirical Analysis of Digital Gene Expression Data in R

Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.

Maintained by Yunshun Chen. Last updated 19 days ago.

alternativesplicing batcheffect bayesian biomedicalinformatics cellbiology chipseq clustering coverage differentialexpression differentialmethylation differentialsplicing dnamethylation epigenetics functionalgenomics geneexpression genesetenrichment genetics immunooncology multiplecomparison normalization pathways proteomics qualitycontrol regression rnaseq sage sequencing singlecell systemsbiology timecourse transcription transcriptomics openblas

13.40 score 17k scripts 255 dependents

bioc

HDF5Array:HDF5 datasets as array-like objects in R

The HDF5Array package is an HDF5 backend for DelayedArray objects. It implements the HDF5Array, H5SparseMatrix, H5ADMatrix, and TENxMatrix classes, 4 convenient and memory-efficient array-like containers for representing and manipulating either: (1) a conventional (a.k.a. dense) HDF5 dataset, (2) an HDF5 sparse matrix (stored in CSR/CSC/Yale format), (3) the central matrix of an h5ad file (or any matrix in the /layers group), or (4) a 10x Genomics sparse matrix. All these containers are DelayedArray extensions and thus support all operations (delayed or block-processed) supported by DelayedArray objects.

Maintained by Hervé Pagès. Last updated 10 days ago.

infrastructure datarepresentation dataimport sequencing rnaseq coverage annotation genomeannotation singlecell immunooncology bioconductor-package core-package u24ca289073

12 stars 13.20 score 844 scripts 126 dependents

bioc

scran:Methods for Single-Cell RNA-Seq Data Analysis

Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows.

Maintained by Aaron Lun. Last updated 5 months ago.

immunooncology normalization sequencing rnaseq software geneexpression transcriptomics singlecell clustering bioconductor-package human-cell-atlas single-cell-rna-seq openblas cpp

41 stars 13.05 score 7.6k scripts 37 dependents

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 24 days ago.

cellbasedassays clustering dimensionreduction featureextraction geneexpression gui immunooncology shinyapps singlecell transcription transcriptomics visualization dimension-reduction feature-extraction gene-expression hacktoberfest human-cell-atlas shiny single-cell

225 stars 12.86 score 380 scripts 9 dependents

bioc

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 1 months ago.

software singlecell geneexpression transcriptomics classification clustering annotation bioconductor singler cpp

184 stars 12.83 score 2.1k scripts 2 dependents

bioc

SpatialExperiment:S4 Class for Spatially Resolved -omics Data

Defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordinates, images, and image metadata. A specialized constructor function is included for data from the 10x Genomics Visium platform.

Maintained by Dario Righelli. Last updated 5 months ago.

datarepresentation dataimport infrastructure immunooncology geneexpression transcriptomics singlecell spatial

59 stars 12.63 score 1.8k scripts 71 dependents

bioc

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 10 days ago.

preprocessing singlecell rnaseq atacseq doublets single-cell

184 stars 12.38 score 888 scripts 1 dependents

bioc

glmGamPoi:Fit a Gamma-Poisson Generalized Linear Model

Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments.

Maintained by Constantin Ahlmann-Eltze. Last updated 13 days ago.

regression rnaseq software singlecell gamma-poisson glm negative-binomial-regression on-disk openblas cpp

111 stars 12.16 score 1.0k scripts 4 dependents

bioc

slingshot:Tools for ordering single-cell sequencing

Provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction.

Maintained by Kelly Street. Last updated 5 months ago.

clustering differentialexpression geneexpression rnaseq sequencing software singlecell transcriptomics visualization

283 stars 12.01 score 1.0k scripts 4 dependents

bioc

MAST:Model-based Analysis of Single Cell Transcriptomics

Methods and models for handling zero-inflated single cell assay data.

Maintained by Andrew McDavid. Last updated 5 months ago.

geneexpression differentialexpression genesetenrichment rnaseq transcriptomics singlecell

232 stars 11.28 score 1.8k scripts 5 dependents

bioc

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 21 days ago.

singlecell dataimport datarepresentation bioconductor conversion scrna-seq

159 stars 11.25 score 660 scripts 4 dependents

bioc

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.

rnaseq atacseq geneexpression transcription singlecell principalcomponent cpp

348 stars 11.12 score 832 scripts 2 dependents

bioc

scater:Single-Cell Analysis Toolkit for Gene Expression Data in R

A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization.

Maintained by Alan OCallaghan. Last updated 23 days ago.

immunooncology singlecell rnaseq qualitycontrol preprocessing normalization visualization dimensionreduction transcriptomics geneexpression sequencing software dataimport datarepresentation infrastructure coverage

11.07 score 12k scripts 43 dependents

bioc

CATALYST:Cytometry dATa anALYSis Tools

CATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively.

Maintained by Helena L. Crowell. Last updated 4 months ago.

clustering dataimport differentialexpression experimentaldesign flowcytometry immunooncology massspectrometry normalization preprocessing singlecell software statisticalmethod visualization

67 stars 10.99 score 362 scripts 2 dependents

bioc

infercnv:Infer Copy Number Variation from Single-Cell RNA-Seq Data

Using single-cell RNA-Seq expression to visualize CNV in cells.

Maintained by Christophe Georgescu. Last updated 5 months ago.

software copynumbervariation variantdetection structuralvariation genomicvariation genetics transcriptomics statisticalmethod bayesian hiddenmarkovmodel singlecell jags cpp

601 stars 10.92 score 674 scripts

bioc

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.

immunooncology differentialexpression sequencing singlecell software statisticalmethod visualization

184 stars 10.74 score 686 scripts 1 dependents

bioc

tximeta:Transcript Quantification Import with Automatic Metadata

Transcript quantification import from Salmon and other quantifiers with automatic attachment of transcript ranges and release information, and other associated metadata. De novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for computational reproducibility.

Maintained by Michael Love. Last updated 2 months ago.

annotation genomeannotation dataimport preprocessing rnaseq singlecell transcriptomics transcription geneexpression functionalgenomics reproducibleresearch reportwriting immunooncology

67 stars 10.58 score 466 scripts 1 dependents

bioc

miloR:Differential neighbourhood abundance testing on a graph

Milo performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using either a negative bionomial generalized linear model or negative binomial generalized linear mixed model.

Maintained by Mike Morgan. Last updated 5 months ago.

singlecell multiplecomparison functionalgenomics software openblas cpp openmp

362 stars 10.49 score 340 scripts 1 dependents

bioc

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 1 months ago.

singlecell geneexpression clustering sequencing bayesian immunooncology dataimport cpp openmp

147 stars 10.47 score 256 scripts 2 dependents

bioc

UCell:Rank-based signature enrichment analysis for single-cell data

UCell is a package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with SingleCellExperiment and Seurat objects.

Maintained by Massimo Andreatta. Last updated 5 months ago.

singlecell genesetenrichment transcriptomics geneexpression cellbasedassays

143 stars 10.43 score 454 scripts 2 dependents

bioc

scRepertoire:A toolkit for single-cell immune receptor profiling

scRepertoire is a toolkit for processing and analyzing single-cell T-cell receptor (TCR) and immunoglobulin (Ig). The scRepertoire framework supports use of 10x, AIRR, BD, MiXCR, Omniscope, TRUST4, and WAT3R single-cell formats. The functionality includes basic clonal analyses, repertoire summaries, distance-based clustering and interaction with the popular Seurat and SingleCellExperiment/Bioconductor R workflows.

Maintained by Nick Borcherding. Last updated 10 days ago.

software immunooncology singlecell classification annotation sequencing cpp

327 stars 10.42 score 240 scripts

bioc

zinbwave:Zero-Inflated Negative Binomial Model for RNA-Seq Data

Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data.

Maintained by Davide Risso. Last updated 5 months ago.

immunooncology dimensionreduction geneexpression rnaseq software transcriptomics sequencing singlecell

43 stars 10.21 score 190 scripts 6 dependents

bioc

scuttle:Single-Cell RNA-Seq Analysis Utilities

Provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. Also provides some helper functions to assist development of other packages.

Maintained by Aaron Lun. Last updated 5 months ago.

immunooncology singlecell rnaseq qualitycontrol preprocessing normalization transcriptomics geneexpression sequencing software dataimport openblas cpp

10.21 score 1.7k scripts 80 dependents

bioc

singleCellTK:Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk.

Maintained by Joshua David Campbell. Last updated 1 months ago.

singlecell geneexpression differentialexpression alignment clustering immunooncology batcheffect normalization qualitycontrol dataimport gui

182 stars 10.17 score 252 scripts

bioc

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.

immunooncology normalization sequencing rnaseq software geneexpression transcriptomics singlecell differentialexpression bayesian cellbiology bioconductor-package gene-expression rcpp rcpparmadillo scrna-seq single-cell openblas cpp openmp

83 stars 10.14 score 368 scripts 1 dependents

bioc

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.

immunooncology singlecell software classification clustering dimensionreduction supportvectormachine rnaseq visualization transcriptomics datarepresentation gui differentialexpression transcription bioconductor-package human-cell-atlas single-cell-rna-seq openblas cpp

125 stars 10.10 score 374 scripts 1 dependents

bioc

tradeSeq:trajectory-based differential expression analysis for sequencing data

tradeSeq provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM.

Maintained by Hector Roux de Bezieux. Last updated 5 months ago.

clustering regression timecourse differentialexpression geneexpression rnaseq sequencing software singlecell transcriptomics multiplecomparison visualization

251 stars 10.06 score 440 scripts

bioc

DropletUtils:Utilities for Handling Single-Cell Droplet Data

Provides a number of utility functions for handling single-cell (RNA-seq) data from droplet technologies such as 10X Genomics. This includes data loading from count matrices or molecule information files, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix.

Maintained by Jonathan Griffiths. Last updated 4 months ago.

immunooncology singlecell sequencing rnaseq geneexpression transcriptomics dataimport coverage zlib cpp

10.01 score 2.7k scripts 9 dependents

bioc

diffcyt:Differential discovery in high-dimensional cytometry via high-resolution clustering

Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.

Maintained by Lukas M. Weber. Last updated 2 months ago.

immunooncology flowcytometry proteomics singlecell cellbasedassays cellbiology clustering featureextraction software

20 stars 9.98 score 225 scripts 5 dependents

bioc

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.

singlecell rnaseq transcriptomics geneexpression sequencing software immunooncology bioconductor bioinformatics scrna-seq simulation

224 stars 9.92 score 424 scripts 1 dependents

bioc

OmnipathR:OmniPath web service client and more

A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github).

Maintained by Denes Turei. Last updated 1 months ago.

graphandnetwork network pathways software thirdpartyclient dataimport datarepresentation genesignaling generegulation systemsbiology transcriptomics singlecell annotation kegg complexes enzyme-ptm networks networks-biology omnipath proteins quarto

130 stars 9.90 score 226 scripts 2 dependents

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.

singlecell annotation sequencing microarray geneexpression assign-identities clusters marker-genes rna-seq single-cell-rna-seq

120 stars 9.63 score 296 scripts

bioc

clusterExperiment:Compare Clusterings for Single-Cell Sequencing

Provides functionality for running and comparing many different clusterings of single-cell sequencing data or other large mRNA Expression data sets.

Maintained by Elizabeth Purdom. Last updated 5 months ago.

clustering rnaseq sequencing software singlecell cpp

38 stars 9.62 score 192 scripts 1 dependents

bioc

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.

immunooncology software singlecell onechannel twochannel multiplecomparison normalization dataimport bioimaging imaging-mass-cytometry single-cell spatial-analysis

32 stars 9.61 score 354 scripts 5 dependents

bioc

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.

batcheffect geneexpression normalization rnaseq sequencing singlecell software transcriptomics bioinformatics single-cell

67 stars 9.52 score 137 scripts 1 dependents

bioc

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.

software geneexpression singlecell visualization dimensionreduction single-cell single-cell-analysis single-cell-multiomics single-cell-rna-seq

99 stars 9.52 score 494 scripts

bioc

bluster:Clustering Algorithms for Bioconductor

Wraps common clustering algorithms in an easily extended S4 framework. Backends are implemented for hierarchical, k-means and graph-based clustering. Several utilities are also provided to compare and evaluate clustering results.

Maintained by Aaron Lun. Last updated 5 months ago.

immunooncology software geneexpression transcriptomics singlecell clustering cpp

9.43 score 636 scripts 51 dependents

bioc

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.

geneexpression transcription differentialexpression genesetenrichment genetics microarray mrnamicroarray onechannel rnaseq biomedicalinformatics proteomics visualization functionalgenomics singlecell deconvolution single-cell single-cell-rna-seq transcriptomics

56 stars 9.29 score 99 scripts

bioc

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 9 days ago.

sequencing alignment sequencematching rnaseq chipseq singlecell geneexpression generegulation genetics immunooncology snp geneticvariability preprocessing qualitycontrol genomeannotation genefusiondetection indeldetection variantannotation variantdetection multiplesequencealignment zlib

9.24 score 892 scripts 10 dependents

bioc

batchelor:Single-Cell Batch Correction Methods

Implements a variety of methods for batch correction of single-cell (RNA sequencing) data. This includes methods based on detecting mutually nearest neighbors, as well as several efficient variants of linear regression of the log-expression values. Functions are also provided to perform global rescaling to remove differences in depth between batches, and to perform a principal components analysis that is robust to differences in the numbers of cells across batches.

Maintained by Aaron Lun. Last updated 16 days ago.

sequencing rnaseq software geneexpression transcriptomics singlecell batcheffect normalization cpp

9.10 score 1.2k scripts 10 dependents

bioc

Banksy:Spatial transcriptomic clustering

Banksy is an R package that incorporates spatial information to cluster cells in a feature space (e.g. gene expression). To incorporate spatial information, BANKSY computes the mean neighborhood expression and azimuthal Gabor filters that capture gene expression gradients. These features are combined with the cell's own expression to embed cells in a neighbor-augmented product space which can then be clustered, allowing for accurate and spatially-aware cell typing and tissue domain segmentation.

Maintained by Joseph Lee. Last updated 26 days ago.

clustering spatial singlecell geneexpression dimensionreduction clustering-algorithm single-cell-omics spatial-omics

90 stars 9.03 score 248 scripts

bioc

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.

immunooncology software sequencing rnaseq geneexpression singlecell visualization sequencematching preprocessing qualitycontrol genomeannotation dataimport curl bzip2 xz-utils zlib cpp

68 stars 9.02 score 84 scripts

bioc

scone:Single Cell Overview of Normalized Expression data

SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses.

Maintained by Davide Risso. Last updated 1 months ago.

immunooncology normalization preprocessing qualitycontrol geneexpression rnaseq software transcriptomics sequencing singlecell coverage

53 stars 9.00 score 104 scripts

bioc

schex:Hexbin plots for single cell omics data

Builds hexbin plots for variables and dimension reduction stored in single cell omics data such as SingleCellExperiment. The ideas used in this package are based on the excellent work of Dan Carr, Nicholas Lewin-Koh, Martin Maechler and Thomas Lumley.

Maintained by Saskia Freytag. Last updated 5 months ago.

software sequencing singlecell dimensionreduction visualization immunooncology dataimport

74 stars 8.96 score 102 scripts 2 dependents

bioc

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 30 days ago.

geneexpression proteomics singlecell massspectrometry preprocessing cellbasedassays bioconductor mass-spectrometry single-cell software

25 stars 8.94 score 115 scripts

bioc

assorthead:Assorted Header-Only C++ Libraries

Vendors an assortment of useful header-only C++ libraries. Bioconductor packages can use these libraries in their own C++ code by LinkingTo this package without introducing any additional dependencies. The use of a central repository avoids duplicate vendoring of libraries across multiple R packages, and enables better coordination of version updates across cohorts of interdependent C++ libraries.

Maintained by Aaron Lun. Last updated 26 days ago.

singlecell qualitycontrol normalization datarepresentation dataimport differentialexpression alignment

8.89 score 167 dependents

bioc

tidySingleCellExperiment:Brings SingleCellExperiment to the Tidyverse

'tidySingleCellExperiment' is an adapter that abstracts the 'SingleCellExperiment' container in the form of a 'tibble'. This allows *tidy* data manipulation, nesting, and plotting. For example, a 'tidySingleCellExperiment' is directly compatible with functions from 'tidyverse' packages `dplyr` and `tidyr`, as well as plotting with `ggplot2` and `plotly`. In addition, the package provides various utility functions specific to single-cell omics data analysis (e.g., aggregation of cell-level data to pseudobulks).

Maintained by Stefano Mangiola. Last updated 5 months ago.

assaydomain infrastructure rnaseq differentialexpression singlecell geneexpression normalization clustering qualitycontrol sequencing bioconductor dplyr ggplot2 plotly single-cell-rna-seq single-cell-sequencing singlecellexperiment tibble tidyr tidyverse

36 stars 8.86 score 125 scripts 2 dependents

bioc

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.

immunooncology singlecell software classification supportvectormachine rnaseq visualization transcriptomics datarepresentation transcription sequencing preprocessing geneexpression dataimport bioconductor-package human-cell-atlas projection-mapping single-cell-rna-seq openblas cpp

95 stars 8.82 score 172 scripts

bioc

CellBench:Construct Benchmarks for Single Cell Analysis Methods

This package contains infrastructure for benchmarking analysis methods and access to single cell mixture benchmarking data. It provides a framework for organising analysis methods and testing combinations of methods in a pipeline without explicitly laying out each combination. It also provides utilities for sampling and filtering SingleCellExperiment objects, constructing lists of functions with varying parameters, and multithreaded evaluation of analysis methods.

Maintained by Shian Su. Last updated 5 months ago.

software infrastructure singlecell benchmark bioinformatics

31 stars 8.73 score 98 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.

software singlecell visualization data-visualization ggplot2 publication-quality-plots seurat single-cell single-cell-genomics single-cell-rna-seq

178 stars 8.71 score 194 scripts

bioc

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.

singlecell genesetenrichment transcriptomics transcription geneexpression workflowstep normalization

8.59 score 860 scripts 4 dependents

bioc

SPIAT:Spatial Image Analysis of Tissues

SPIAT (**Sp**atial **I**mage **A**nalysis of **T**issues) is an R package with a suite of data processing, quality control, visualization and data analysis tools. SPIAT is compatible with data generated from single-cell spatial proteomics platforms (e.g. OPAL, CODEX, MIBI, cellprofiler). SPIAT reads spatial data in the form of X and Y coordinates of cells, marker intensities and cell phenotypes. SPIAT includes six analysis modules that allow visualization, calculation of cell colocalization, categorization of the immune microenvironment relative to tumor areas, analysis of cellular neighborhoods, and the quantification of spatial heterogeneity, providing a comprehensive toolkit for spatial data analysis.

Maintained by Yuzhou Feng. Last updated 14 days ago.

biomedicalinformatics cellbiology spatial clustering dataimport immunooncology qualitycontrol singlecell software visualization

22 stars 8.59 score 69 scripts

bioc

SIMLR:Single-cell Interpretation via Multi-kernel LeaRning (SIMLR)

Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization.

Maintained by Luca De Sano. Last updated 4 days ago.

immunooncology clustering geneexpression sequencing singlecell openblas cpp

110 stars 8.48 score 69 scripts

bioc

SCnorm:Normalization of single cell RNA-seq data

This package implements SCnorm — a method to normalize single-cell RNA-seq data.

Maintained by Rhonda Bacher. Last updated 5 months ago.

normalization rnaseq singlecell immunooncology

47 stars 8.46 score 76 scripts

bioc

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 15 days ago.

bayesian regression differentialexpression singlecell metagenomics flowcytometry spatial batch-correction composition cytof differential-proportion microbiome multilevel proportions random-effects single-cell unwanted-variation

99 stars 8.43 score 69 scripts

bioc

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.

singlecell spatial statisticalmethod

172 stars 8.37 score 170 scripts

bioc

TRONCO:TRONCO, an R package for TRanslational ONCOlogy

The TRONCO (TRanslational ONCOlogy) R package collects algorithms to infer progression models via the approach of Suppes-Bayes Causal Network, both from an ensemble of tumors (cross-sectional samples) and within an individual patient (multi-region or single-cell samples). The package provides parallel implementation of algorithms that process binary matrices where each row represents a tumor sample and each column a single-nucleotide or a structural variant driving the progression; a 0/1 value models the absence/presence of that alteration in the sample. The tool can import data from plain, MAF or GISTIC format files, and can fetch it from the cBioPortal for cancer genomics. Functions for data manipulation and visualization are provided, as well as functions to import/export such data to other bioinformatics tools for, e.g, clustering or detection of mutually exclusive alterations. Inferred models can be visualized and tested for their confidence via bootstrap and cross-validation. TRONCO is used for the implementation of the Pipeline for Cancer Inference (PICNIC).

Maintained by Luca De Sano. Last updated 3 days ago.

biomedicalinformatics bayesian graphandnetwork somaticmutation networkinference network clustering dataimport singlecell immunooncology algorithms cancer-inference tumors

30 stars 8.35 score 38 scripts

bioc

dreamlet:Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs

Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample.

Maintained by Gabriel Hoffman. Last updated 4 days ago.

rnaseq geneexpression differentialexpression batcheffect qualitycontrol regression genesetenrichment generegulation epigenetics functionalgenomics transcriptomics normalization singlecell preprocessing sequencing immunooncology software cpp

12 stars 8.14 score 128 scripts

bioc

velociraptor:Toolkit for Single-Cell Velocity

This package provides Bioconductor-friendly wrappers for RNA velocity calculations in single-cell RNA-seq data. We use the basilisk package to manage Conda environments, and the zellkonverter package to convert data structures between SingleCellExperiment (R) and AnnData (Python). The information produced by the velocity methods is stored in the various components of the SingleCellExperiment class.

Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.

singlecell geneexpression sequencing coverage rna-velocity

55 stars 8.06 score 52 scripts

bioc

spicyR:Spatial analysis of in situ cytometry data

The spicyR package provides a framework for performing inference on changes in spatial relationships between pairs of cell types for cell-resolution spatial omics technologies. spicyR consists of three primary steps: (i) summarizing the degree of spatial localization between pairs of cell types for each image; (ii) modelling the variability in localization summary statistics as a function of cell counts and (iii) testing for changes in spatial localizations associated with a response variable.

Maintained by Ellis Patrick. Last updated 25 days ago.

singlecell cellbasedassays spatial

9 stars 8.02 score 57 scripts 1 dependents

bioc

FLAMES:FLAMES: Full Length Analysis of Mutations and Splicing in long read RNA-seq data

Semi-supervised isoform detection and annotation from both bulk and single-cell long read RNA-seq data. Flames provides automated pipelines for analysing isoforms, as well as intermediate functions for manual execution.

Maintained by Changqing Wang. Last updated 19 days ago.

rnaseq singlecell transcriptomics dataimport differentialsplicing alternativesplicing geneexpression longread zlib curl bzip2 xz-utils cpp

31 stars 7.95 score 12 scripts

bioc

scDD:Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions

This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions.

Maintained by Keegan Korthauer. Last updated 5 months ago.

immunooncology bayesian clustering rnaseq singlecell multiplecomparison visualization differentialexpression

33 stars 7.92 score 50 scripts

bioc

AneuFinder:Analysis of Copy Number Variation in Single-Cell-Sequencing Data

AneuFinder implements functions for copy-number detection, breakpoint detection, and karyotype and heterogeneity analysis in single-cell whole genome sequencing and strand-seq data.

Maintained by Aaron Taudt. Last updated 3 days ago.

immunooncology software sequencing singlecell copynumbervariation genomicvariation hiddenmarkovmodel wholegenome cpp

18 stars 7.90 score 37 scripts

bioc

BayesSpace:Clustering and Resolution Enhancement of Spatial Transcriptomes

Tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into "sub-spots", for which features such as gene expression or cell type composition can be imputed.

Maintained by Matt Stone. Last updated 5 months ago.

software clustering transcriptomics geneexpression singlecell immunooncology dataimport openblas cpp openmp

126 stars 7.90 score 278 scripts 1 dependents

bioc

mistyR:Multiview Intercellular SpaTial modeling framework

mistyR is an implementation of the Multiview Intercellular SpaTialmodeling framework (MISTy). MISTy is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views. Each of these views can describe a different spatial context, i.e., define a relationship among the observed expressions of the markers, such as intracellular regulation or paracrine regulation, but also, the views can also capture cell-type specific relationships, capture relations between functional footprints or focus on relations between different anatomical regions. Each MISTy view is considered as a potential source of variability in the measured marker expressions. Each MISTy view is then analyzed for its contribution to the total expression of each marker and is explained in terms of the interactions with other measurements that led to the observed contribution.

Maintained by Jovan Tanevski. Last updated 5 months ago.

software biomedicalinformatics cellbiology systemsbiology regression decisiontree singlecell spatial bioconductor biology intercellular machine-learning modular molecular-biology multiview spatial-transcriptomics

51 stars 7.87 score 160 scripts

bioc

COTAN:COexpression Tables ANalysis

Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate p-value for the associated independence test. COTAN can also evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Moreover, this approach provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions and becoming a new tool to identify cell-identity marker genes.

Maintained by Galfrè Silvia Giulia. Last updated 1 months ago.

systemsbiology transcriptomics geneexpression singlecell

16 stars 7.85 score 96 scripts

bioc

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.

sequencing rnaseq geneexpression transcription normalization regression multiplecomparison batcheffect visualization differentialexpression differentialsplicing alternativesplicing singlecell bioconductor gene-expression genomics salmon scrnaseq statistics transcriptomics

28 stars 7.83 score 150 scripts

bioc

scDiagnostics:Cell type annotation diagnostics

The scDiagnostics package provides diagnostic plots to assess the quality of cell type assignments from single cell gene expression profiles. The implemented functionality allows to assess the reliability of cell type annotations, investigate gene expression patterns, and explore relationships between different cell types in query and reference datasets allowing users to detect potential misalignments between reference and query datasets. The package also provides visualization capabilities for diagnostics purposes.

Maintained by Anthony Christidis. Last updated 5 months ago.

annotation classification clustering geneexpression rnaseq singlecell software transcriptomics

8 stars 7.77 score 46 scripts

bioc

LACE:Longitudinal Analysis of Cancer Evolution (LACE)

LACE is an algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a weighed likelihood function computed on multiple time points.

Maintained by Davide Maspero. Last updated 15 days ago.

biomedicalinformatics singlecell somaticmutation

15 stars 7.75 score 3 scripts

bioc

lemur:Latent Embedding Multivariate Regression

Fit a latent embedding multivariate regression (LEMUR) model to multi-condition single-cell data. The model provides a parametric description of single-cell data measured with treatment vs. control or more complex experimental designs. The parametric model is used to (1) align conditions, (2) predict log fold changes between conditions for all cells, and (3) identify cell neighborhoods with consistent log fold changes. For those neighborhoods, a pseudobulked differential expression test is conducted to assess which genes are significantly changed.

Maintained by Constantin Ahlmann-Eltze. Last updated 5 months ago.

transcriptomics differentialexpression singlecell dimensionreduction regression openblas cpp

87 stars 7.69 score 81 scripts

bioc

countsimQC:Compare Characteristic Features of Count Data Sets

countsimQC provides functionality to create a comprehensive report comparing a broad range of characteristics across a collection of count matrices. One important use case is the comparison of one or more synthetic count matrices to a real count matrix, possibly the one underlying the simulations. However, any collection of count matrices can be compared.

Maintained by Charlotte Soneson. Last updated 3 months ago.

microbiome rnaseq singlecell experimentaldesign qualitycontrol reportwriting visualization immunooncology

27 stars 7.69 score 24 scripts

bioc

scDesign3:A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics

We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools.

Maintained by Dongyuan Song. Last updated 28 days ago.

software singlecell sequencing geneexpression spatial

89 stars 7.59 score 25 scripts

bioc

ggsc:Visualizing Single Cell and Spatial Transcriptomics

Useful functions to visualize single cell and spatial data. It supports visualizing 'Seurat', 'SingleCellExperiment' and 'SpatialExperiment' objects through grammar of graphics syntax implemented in 'ggplot2'.

Maintained by Guangchuang Yu. Last updated 5 months ago.

dimensionreduction geneexpression singlecell software spatial transcriptomics visualization openblas cpp openmp

47 stars 7.59 score 18 scripts

bioc

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.

immunooncology singlecell spatial dataimport clustering imc single-cell

24 stars 7.58 score 126 scripts

bioc

nnSVG:Scalable identification of spatially variable genes in spatially-resolved transcriptomics data

Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more spatial locations.

Maintained by Lukas M. Weber. Last updated 1 months ago.

spatial singlecell transcriptomics geneexpression preprocessing

17 stars 7.57 score 183 scripts 1 dependents

bioc

dittoSeq:User Friendly Single-Cell and Bulk RNA Sequencing Visualization

A universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors().

Maintained by Daniel Bunis. Last updated 5 months ago.

software visualization rnaseq singlecell geneexpression transcriptomics dataimport

7.56 score 760 scripts 2 dependents

bioc

SimBu:Simulate Bulk RNA-seq Datasets from Single-Cell Datasets

SimBu can be used to simulate bulk RNA-seq datasets with known cell type fractions. You can either use your own single-cell study for the simulation or the sfaira database. Different pre-defined simulation scenarios exist, as are options to run custom simulations. Additionally, expression values can be adapted by adding an mRNA bias, which produces more biologically relevant simulations.

Maintained by Alexander Dietrich. Last updated 2 days ago.

software rnaseq singlecell

15 stars 7.50 score 29 scripts 1 dependents

bioc

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.

differentialexpression geneexpression singlecell immunooncology rnaseq transcriptomics sequencing preprocessing software

31 stars 7.42 score 28 scripts

bioc

mbkmeans:Mini-batch K-means Clustering for Single-Cell RNA-seq

Implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation.

Maintained by Davide Risso. Last updated 5 months ago.

clustering geneexpression rnaseq software transcriptomics sequencing singlecell human-cell-atlas cpp

10 stars 7.41 score 54 scripts 2 dependents

bioc

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.

network graphandnetwork singlecell rnaseq geneexpression sequencing transcriptomics normalization preprocessing clustering dimensionreduction bioinformatics genomics single-cell

27 stars 7.41 score 4 scripts

bioc

scry:Small-Count Analysis Methods for High-Dimensional Data

Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq.

Maintained by Kelly Street. Last updated 5 months ago.

dimensionreduction geneexpression normalization principalcomponent rnaseq software sequencing singlecell transcriptomics

19 stars 7.34 score 116 scripts

bioc

chromVAR:Chromatin Variation Across Regions

Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments.

Maintained by Alicia Schep. Last updated 5 months ago.

singlecell sequencing generegulation immunooncology cpp

7.31 score 772 scripts

bioc

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.

classification rnaseq singlecell clustering geneexpression immunooncology

44 stars 7.27 score 70 scripts

bioc

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.

singlecell flowcytometry bioinformatics cytometry data-science single-cell tidyverse cpp

18 stars 7.24 score 35 scripts

bioc

iSEEu:iSEE Universe

iSEEu (the iSEE universe) contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels, or modes allowing easy configuration of iSEE applications.

Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.

immunooncology visualization gui dimensionreduction featureextraction clustering transcription geneexpression transcriptomics singlecell cellbasedassays hacktoberfest

9 stars 7.15 score 35 scripts 1 dependents

bioc

SPsimSeq:Semi-parametric simulation tool for bulk and single-cell RNA sequencing data

SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size.

Maintained by Joris Meys. Last updated 5 months ago.

geneexpression rnaseq singlecell sequencing dnaseq

10 stars 7.14 score 29 scripts 1 dependents

bioc

scMultiSim:Simulation of Multi-Modality Single Cell Data Guided By Gene Regulatory Networks and Cell-Cell Interactions

scMultiSim simulates paired single cell RNA-seq, single cell ATAC-seq and RNA velocity data, while incorporating mechanisms of gene regulatory networks, chromatin accessibility and cell-cell interactions. It allows users to tune various parameters controlling the amount of each biological factor, variation of gene-expression levels, the influence of chromatin accessibility on RNA sequence data, and so on. It can be used to benchmark various computational methods for single cell multi-omics data, and to assist in experimental design of wet-lab experiments.

Maintained by Hechen Li. Last updated 5 months ago.

singlecell transcriptomics geneexpression sequencing experimentaldesign

23 stars 7.08 score 11 scripts

bioc

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.

geneexpression genesetenrichment singlecell transcriptomics epigenetics proteomics differentialexpression reactome dnamethylation methylationarray gene-regulation gene-seq-analysis pathway-analysis

16 stars 7.06 score 15 scripts

bioc

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.

singlecell rnaseq visualization transcriptomics geneexpression sequencing software exomeseq clonal-clustering gibbs-sampling scrna-seq single-cell somatic-mutations

61 stars 7.05 score 62 scripts

bioc

satuRn:Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell RNA-sequencing Applications

satuRn provides a higly performant and scalable framework for performing differential transcript usage analyses. The package consists of three main functions. The first function, fitDTU, fits quasi-binomial generalized linear models that model transcript usage in different groups of interest. The second function, testDTU, tests for differential usage of transcripts between groups of interest. Finally, plotDTU visualizes the usage profiles of transcripts in groups of interest.

Maintained by Jeroen Gilis. Last updated 5 months ago.

regression experimentaldesign differentialexpression geneexpression rnaseq sequencing software singlecell transcriptomics multiplecomparison visualization

21 stars 6.97 score 74 scripts 1 dependents

bioc

scClassify:scClassify: single-cell Hierarchical Classification

scClassify is a multiscale classification framework for single-cell RNA-seq data based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references.

Maintained by Yingxin Lin. Last updated 5 months ago.

singlecell geneexpression classification

23 stars 6.92 score 30 scripts

bioc

alevinQC:Generate QC Reports For Alevin Output

Generate QC reports summarizing the output from an alevin, alevin-fry, or simpleaf run. Reports can be generated as html or pdf files, or as shiny applications.

Maintained by Charlotte Soneson. Last updated 3 months ago.

qualitycontrol singlecell cpp

32 stars 6.91 score 21 scripts

bioc

mnem:Mixture Nested Effects Models

Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, ... and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm.

Maintained by Martin Pirkl. Last updated 4 days ago.

pathways systemsbiology networkinference network rnaseq pooledscreens singlecell crispr atacseq dnaseq geneexpression cpp

4 stars 6.81 score 15 scripts 4 dependents

bioc

ggspavis:Visualization functions for spatial transcriptomics data

Visualization functions for spatial transcriptomics data. Includes functions to generate several types of plots, including spot plots, feature (molecule) plots, reduced dimension plots, spot-level quality control (QC) plots, and feature-level QC plots, for datasets from the 10x Genomics Visium and other technological platforms. Datasets are assumed to be in either SpatialExperiment or SingleCellExperiment format.

Maintained by Lukas M. Weber. Last updated 5 months ago.

spatial singlecell transcriptomics geneexpression qualitycontrol dimensionreduction

3 stars 6.80 score 264 scripts

bioc

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.

spatial singlecell transcriptomics visualization software multidimensional single-cell spatial-omics

6 stars 6.74 score 153 scripts 1 dependents

bioc

waddR:Statistical tests for detecting differential distributions based on the 2-Wasserstein distance

The package offers statistical tests based on the 2-Wasserstein distance for detecting and characterizing differences between two distributions given in the form of samples. Functions for calculating the 2-Wasserstein distance and testing for differential distributions are provided, as well as a specifically tailored test for differential expression in single-cell RNA sequencing data.

Maintained by Julian Flesch. Last updated 5 months ago.

software statisticalmethod singlecell differentialexpression cpp

25 stars 6.70 score 6 scripts

bioc

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 20 days ago.

singlecell generegulation networkinference network geneexpression transcription genetarget cpp

14 stars 6.67 score 17 scripts

bioc

cellxgenedp:Discover and Access Single Cell Data Sets in the CELLxGENE Data Portal

The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summaries. The cellxgenedp package provides an alternative, R-based inteface, allowind data discovery, viewing, and downloading.

Maintained by Martin Morgan. Last updated 5 months ago.

singlecell dataimport thirdpartyclient

8 stars 6.64 score 27 scripts

bioc

lisaClust:lisaClust: Clustering of Local Indicators of Spatial Association

lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution.

Maintained by Ellis Patrick. Last updated 4 months ago.

singlecell cellbasedassays spatial

3 stars 6.64 score 48 scripts

bioc

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.

singlecell transcriptomics geneexpression supportvectormachine classification software

15 stars 6.61 score 20 scripts

bioc

bayNorm:Single-cell RNA sequencing data normalization

bayNorm is used for normalizing single-cell RNA-seq data.

Maintained by Wenhao Tang. Last updated 5 months ago.

immunooncology normalization rnaseq singlecell sequencing scrnaseq cpp openmp

9 stars 6.59 score 36 scripts

bioc

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.

singlecell geneexpression bioinformatics single-cell cpp

27 stars 6.59 score 18 scripts

bioc

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.

singlecell rnaseq qualitycontrol preprocessing transcriptomics geneexpression sequencing software classification

6.57 score 176 scripts 1 dependents

bioc

SpaceMarkers:Spatial Interaction Markers

Spatial transcriptomic technologies have helped to resolve the connection between gene expression and the 2D orientation of tissues relative to each other. However, the limited single-cell resolution makes it difficult to highlight the most important molecular interactions in these tissues. SpaceMarkers, R/Bioconductor software, can help to find molecular interactions, by identifying genes associated with latent space interactions in spatial transcriptomics.

Maintained by Atul Deshpande. Last updated 12 days ago.

singlecell geneexpression software spatial transcriptomics

5 stars 6.55 score 21 scripts

bioc

tricycle:tricycle: Transferable Representation and Inference of cell cycle

The package contains functions to infer and visualize cell cycle process using Single Cell RNASeq data. It exploits the idea of transfer learning, projecting new data to the previous learned biologically interpretable space. We provide a pre-learned cell cycle space, which could be used to infer cell cycle time of human and mouse single cell samples. In addition, we also offer functions to visualize cell cycle time on different embeddings and functions to build new reference.

Maintained by Shijie Zheng. Last updated 5 months ago.

singlecell software transcriptomics rnaseq transcription biologicalquestion dimensionreduction immunooncology

25 stars 6.54 score 46 scripts

bioc

SpotClean:SpotClean adjusts for spot swapping in spatial transcriptomics data

SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontaminate the spot swapping effect, thus increase the sensitivity and precision of downstream analyses.

Maintained by Zijian Ni. Last updated 5 months ago.

dataimport rnaseq sequencing geneexpression spatial singlecell transcriptomics preprocessing rna-seq spatial-transcriptomics

31 stars 6.52 score 36 scripts

bioc

condiments:Differential Topology, Progression and Differentiation

This package encapsulate many functions to conduct a differential topology analysis. It focuses on analyzing an 'omic dataset with multiple conditions. While the package is mostly geared toward scRNASeq, it does not place any restriction on the actual input format.

Maintained by Hector Roux de Bezieux. Last updated 4 months ago.

rnaseq sequencing software singlecell transcriptomics multiplecomparison visualization

26 stars 6.52 score 17 scripts

bioc

CatsCradle:This package provides methods for analysing spatial transcriptomics data and for discovering gene clusters

This package addresses two broad areas. It allows for in-depth analysis of spatial transcriptomic data by identifying tissue neighbourhoods. These are contiguous regions of tissue surrounding individual cells. 'CatsCradle' allows for the categorisation of neighbourhoods by the cell types contained in them and the genes expressed in them. In particular, it produces Seurat objects whose individual elements are neighbourhoods rather than cells. In addition, it enables the categorisation and annotation of genes by producing Seurat objects whose elements are genes.

Maintained by Michael Shapiro. Last updated 13 days ago.

biologicalquestion statisticalmethod geneexpression singlecell transcriptomics spatial

3 stars 6.52 score

bioc

nipalsMCIA:Multiple Co-Inertia Analysis via the NIPALS Method

Computes Multiple Co-Inertia Analysis (MCIA), a dimensionality reduction (jDR) algorithm, for a multi-block dataset using a modification to the Nonlinear Iterative Partial Least Squares method (NIPALS) proposed in (Hanafi et. al, 2010). Allows multiple options for row- and table-level preprocessing, and speeds up computation of variance explained. Vignettes detail application to bulk- and single cell- multi-omics studies.

Maintained by Maximilian Mattessich. Last updated 1 months ago.

software clustering classification multiplecomparison normalization preprocessing singlecell

6 stars 6.51 score 10 scripts

bioc

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.

singlecell spatial classification single-cell

5 stars 6.49 score 23 scripts

bioc

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.

singlecell qualitycontrol geneexpression preprocessing sequencing

19 stars 6.39 score 65 scripts

bioc

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.

genetics rnaseq sequencing differentialexpression geneexpression multiplecomparison software transcription statisticalmethod visualization singlecell flowcytometry genetarget openblas cpp

11 stars 6.35 score 34 scripts 1 dependents

bioc

CellMixS:Evaluate Cellspecific Mixing

CellMixS provides metrics and functions to evaluate batch effects, data integration and batch effect correction in single cell trancriptome data with single cell resolution. Results can be visualized and summarised on different levels, e.g. on cell, celltype or dataset level.

Maintained by Almut Lütge. Last updated 5 months ago.

singlecell transcriptomics geneexpression batcheffect

7 stars 6.35 score 64 scripts

bioc

APL:Association Plots

APL is a package developed for computation of Association Plots (AP), a method for visualization and analysis of single cell transcriptomics data. The main focus of APL is the identification of genes characteristic for individual clusters of cells from input data. The package performs correspondence analysis (CA) and allows to identify cluster-specific genes using Association Plots. Additionally, APL computes the cluster-specificity scores for all genes which allows to rank the genes by their specificity for a selected cell cluster of interest.

Maintained by Clemens Kohl. Last updated 5 months ago.

statisticalmethod dimensionreduction singlecell sequencing rnaseq geneexpression

15 stars 6.31 score 15 scripts

bioc

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.

singlecell immunooncology rnaseq geneexpression transcription flowcytometry massspectrometry dataimport

63 stars 6.30 score 16 scripts

bioc

signifinder:Collection and implementation of public transcriptional cancer signatures

signifinder is an R package for computing and exploring a compendium of tumor signatures. It allows to compute a variety of signatures, based on gene expression values, and return single-sample scores. Currently, signifinder contains more than 60 distinct signatures collected from the literature, relating to multiple tumors and multiple cancer processes.

Maintained by Stefania Pirrotta. Last updated 3 months ago.

geneexpression genetarget immunooncology biomedicalinformatics rnaseq microarray reportwriting visualization singlecell spatial genesignaling

7 stars 6.28 score 15 scripts

bioc

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.

immunooncology sequencing chipseq rnaseq differentialexpression geneexpression genetics normalization software transcription batcheffect peakdetection clustering network singlecell cpp

6.26 score 61 scripts 5 dependents

bioc

spatialHeatmap:spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Large-Scale Data Extensions

The spatialHeatmap package offers the primary functionality for visualizing cell-, tissue- and organ-specific assay data in spatial anatomical images. Additionally, it provides extended functionalities for large-scale data mining routines and co-visualizing bulk and single-cell data. A description of the project is available here: https://spatialheatmap.org.

Maintained by Jianhai Zhang. Last updated 4 months ago.

spatial visualization microarray sequencing geneexpression datarepresentation network clustering graphandnetwork cellbasedassays atacseq dnaseq tissuemicroarray singlecell cellbiology genetarget

5 stars 6.26 score 12 scripts

bioc

xCell2:A Tool for Generic Cell Type Enrichment Analysis

xCell2 provides methods for cell type enrichment analysis using cell type signatures. It includes three main functions - 1. xCell2Train for training custom references objects from bulk or single-cell RNA-seq datasets. 2. xCell2Analysis for conducting the cell type enrichment analysis using the custom reference. 3. xCell2GetLineage for identifying dependencies between different cell types using ontology.

Maintained by Almog Angel. Last updated 10 days ago.

geneexpression transcriptomics microarray rnaseq singlecell differentialexpression immunooncology genesetenrichment

7 stars 6.24 score 15 scripts

bioc

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.

singlecell clustering spatial transcriptomics

13 stars 6.23 score 13 scripts

bioc

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.

immunooncology dnamethylation differentialmethylation differentialexpression geneexpression generegulation epigenetics genetics clustering featureextraction regression bayesian sequencing coverage singlecell bayesian-inference generalised-linear-models heterogeneity hierarchical-models methylation-analysis single-cell cpp

20 stars 6.23 score 42 scripts

bioc

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.

software technology sequencing alignment rnaseq singlecell workflowstep preprocessing qualitycontrol visualization immunooncology bioinformatics scrna-seq single-cell umi

8 stars 6.20 score 22 scripts

bioc

dominoSignal:Cell Communication Analysis for Single Cell RNA Sequencing

dominoSignal is a package developed to analyze cell signaling through ligand - receptor - transcription factor networks in scRNAseq data. It takes as input information transcriptomic data, requiring counts, z-scored counts, and cluster labels, as well as information on transcription factor activation (such as from SCENIC) and a database of ligand and receptor pairings (such as from CellPhoneDB). This package creates an object storing ligand - receptor - transcription factor linkages by cluster and provides several methods for exploring, summarizing, and visualizing the analysis.

Maintained by Jacob T Mitchell. Last updated 12 days ago.

systemsbiology singlecell transcriptomics network

5 stars 6.20 score 5 scripts

bioc

SingleCellAlleleExperiment:S4 Class for Single Cell Data with Allele and Functional Levels for Immune Genes

Defines a S4 class that is based on SingleCellExperiment. In addition to the usual gene layer the object can also store data for immune genes such as HLAs, Igs and KIRs at allele and functional level. The package is part of a workflow named single-cell ImmunoGenomic Diversity (scIGD), that firstly incorporates allele-aware quantification data for immune genes. This new data can then be used with the here implemented data structure and functionalities for further data handling and data analysis.

Maintained by Jonas Schuck. Last updated 2 months ago.

datarepresentation infrastructure singlecell transcriptomics geneexpression genetics immunooncology dataimport

7 stars 6.18 score 12 scripts

bioc

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.

sequencing singlecell geneexpression differentialexpression clustering

18 stars 6.16 score 4 scripts

bioc

knowYourCG:Functional analysis of DNA methylome datasets

KnowYourCG (KYCG) is a supervised learning framework designed for the functional analysis of DNA methylation data. Unlike existing tools that focus on genes or genomic intervals, KnowYourCG directly targets CpG dinucleotides, featuring automated supervised screenings of diverse biological and technical influences, including sequence motifs, transcription factor binding, histone modifications, replication timing, cell-type-specific methylation, and trait-epigenome associations. KnowYourCG addresses the challenges of data sparsity in various methylation datasets, including low-pass Nanopore sequencing, single-cell DNA methylomes, 5-hydroxymethylation profiles, spatial DNA methylation maps, and array-based datasets for epigenome-wide association studies and epigenetic clocks.

Maintained by Goldberg David. Last updated 3 months ago.

epigenetics dnamethylation sequencing singlecell spatial methylationarray zlib

2 stars 6.10 score 4 scripts

bioc

peco:A Supervised Approach for Predicting cell Cycle Progression 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.

sequencing rnaseq geneexpression transcriptomics singlecell software statisticalmethod classification visualization cell-cycle single-cell-rna-seq

12 stars 6.09 score 34 scripts

bioc

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.

datarepresentation qualitycontrol rnaseq sequencing software singlecell

3 stars 6.05 score 84 scripts

bioc

Dino:Normalization of Single-Cell mRNA Sequencing Data

Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such as those produced by 10X genomics and other uninque molecular identifier (UMI) based microfluidics protocols for which the depth-dependent proportion of zeros in the raw expression data can otherwise present a challenge.

Maintained by Jared Brown. Last updated 5 months ago.

software normalization rnaseq singlecell sequencing geneexpression transcriptomics regression cellbasedassays

9 stars 6.02 score 13 scripts

bioc

gemma.R:A wrapper for Gemma's Restful API to access curated gene expression data and differential expression analyses

Low- and high-level wrappers for Gemma's RESTful API. They enable access to curated expression and differential expression data from over 10,000 published studies. Gemma is a web site, database and a set of tools for the meta-analysis, re-use and sharing of genomics data, currently primarily targeted at the analysis of gene expression profiles.

Maintained by Ogan Mancarci. Last updated 4 months ago.

software dataimport microarray singlecell thirdpartyclient differentialexpression geneexpression bayesian annotation experimentaldesign normalization batcheffect preprocessing bioinformatics gemma genomics transcriptomics

10 stars 5.99 score 26 scripts

bioc

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.

multiplecomparison rnaseq singlecell sequencing coverage epitranscriptomics featureextraction annotation alignment bioconductor-package rna-seq-analysis single-cell-analysis single-cell-rna-seq curl bzip2 xz-utils zlib

8 stars 5.98 score 6 scripts

bioc

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.

immunooncology software transcriptomics geneexpression rnaseq regression differentialexpression singlecell gene-expression genomics single-cell

19 stars 5.98 score 7 scripts

bioc

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.

sequencing singlecell clustering featureextraction

10 stars 5.97 score 47 scripts

bioc

simpleSeg:A package to perform simple cell segmentation

Image segmentation is the process of identifying the borders of individual objects (in this case cells) within an image. This allows for the features of cells such as marker expression and morphology to be extracted, stored and analysed. simpleSeg provides functionality for user friendly, watershed based segmentation on multiplexed cellular images in R based on the intensity of user specified protein marker channels. simpleSeg can also be used for the normalization of single cell data obtained from multiple images.

Maintained by Ellis Patrick. Last updated 5 months ago.

classification survival singlecell normalization spatial spatial-statistics

5.96 score 19 scripts 2 dependents

bioc

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.

cellbasedassays singlecell spatial software transcriptomics

10 stars 5.95 score 15 scripts

bioc

ClustIRR:Clustering of immune receptor repertoires

ClustIRR analyzes repertoires of B- and T-cell receptors. It starts by identifying communities of immune receptors with similar specificities, based on the sequences of their complementarity-determining regions (CDRs). Next, it employs a Bayesian probabilistic models to quantify differential community occupancy (DCO) between repertoires, allowing the identification of expanding or contracting communities in response to e.g. infection or cancer treatment.

Maintained by Simo Kitanovski. Last updated 30 days ago.

clustering immunooncology singlecell software classification b-cell-receptor bioinformatics immunoinformatics immunology quantitative-methods rep-seq repertoire-analysis t-cell-receptor cpp

2 stars 5.95 score 2 scripts

bioc

StabMap:Stabilised mosaic single cell data integration using unshared features

StabMap performs single cell mosaic data integration by first building a mosaic data topology, and for each reference dataset, traverses the topology to project and predict data onto a common embedding. Mosaic data should be provided in a list format, with all relevant features included in the data matrices within each list object. The output of stabMap is a joint low-dimensional embedding taking into account all available relevant features. Expression imputation can also be performed using the StabMap embedding and any of the original data matrices for given reference and query cell lists.

Maintained by Shila Ghazanfar. Last updated 5 months ago.

singlecell dimensionreduction software

5.95 score 60 scripts

bioc

transformGamPoi:Variance Stabilizing Transformation for Gamma-Poisson Models

Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., 'acosh', 'log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals).

Maintained by Constantin Ahlmann-Eltze. Last updated 5 months ago.

singlecell normalization preprocessing regression cpp

21 stars 5.95 score 21 scripts

bioc

immApex:Tools for Adaptive Immune Receptor Sequence-Based Machine and Deep Learning

A set of tools to build tensorflow/keras3-based models in R from amino acid and nucleotide sequences focusing on adaptive immune receptors. The package includes pre-processing of sequences, unifying gene nomenclature usage, encoding sequences, and combining models. This package will serve as the basis of future immune receptor sequence functions/packages/models compatible with the scRepertoire ecosystem.

Maintained by Nick Borcherding. Last updated 5 days ago.

software immunooncology singlecell classification annotation sequencing motifannotation cpp

8 stars 5.94 score 3 scripts

bioc

SCOPE:A normalization and copy number estimation method for single-cell DNA sequencing

Whole genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we propose SCOPE, a normalization and copy number estimation method for scDNA-seq data. The distinguishing features of SCOPE include: (i) utilization of cell-specific Gini coefficients for quality controls and for identification of normal/diploid cells, which are further used as negative control samples in a Poisson latent factor model for normalization; (ii) modeling of GC content bias using an expectation-maximization algorithm embedded in the Poisson generalized linear models, which accounts for the different copy number states along the genome; (iii) a cross-sample iterative segmentation procedure to identify breakpoints that are shared across cells from the same genetic background.

Maintained by Rujin Wang. Last updated 5 months ago.

singlecell normalization copynumbervariation sequencing wholegenome coverage alignment qualitycontrol dataimport dnaseq

5.92 score 84 scripts

bioc

TrajectoryUtils:Single-Cell Trajectory Analysis Utilities

Implements low-level utilities for single-cell trajectory analysis, primarily intended for re-use inside higher-level packages. Include a function to create a cluster-level minimum spanning tree and data structures to hold pseudotime inference results.

Maintained by Aaron Lun. Last updated 5 months ago.

geneexpression singlecell

5.91 score 16 scripts 9 dependents

bioc

MetaNeighbor:Single cell replicability analysis

MetaNeighbor allows users to quantify cell type replicability across datasets using neighbor voting.

Maintained by Stephan Fischer. Last updated 5 months ago.

immunooncology geneexpression go multiplecomparison singlecell transcriptomics

5.89 score 78 scripts

bioc

tidySpatialExperiment:SpatialExperiment with tidy principles

tidySpatialExperiment provides a bridge between the SpatialExperiment package and the tidyverse ecosystem. It creates an invisible layer that allows you to interact with a SpatialExperiment object as if it were a tibble; enabling the use of functions from dplyr, tidyr, ggplot2 and plotly. But, underneath, your data remains a SpatialExperiment object.

Maintained by William Hutchison. Last updated 5 months ago.

infrastructure rnaseq geneexpression sequencing spatial transcriptomics singlecell

6 stars 5.88 score 12 scripts

bioc

miRspongeR:Identification and analysis of miRNA sponge regulation

This package provides several functions to explore miRNA sponge (also called ceRNA or miRNA decoy) regulation from putative miRNA-target interactions or/and transcriptomics data (including bulk, single-cell and spatial gene expression data). It provides eight popular methods for identifying miRNA sponge interactions, and an integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of miRNA sponge modules, and conduct survival analysis of miRNA sponge modules. By using a sample control variable strategy, it provides a function to infer sample-specific miRNA sponge interactions. In terms of sample-specific miRNA sponge interactions, it implements three similarity methods to construct sample-sample correlation network.

Maintained by Junpeng Zhang. Last updated 5 months ago.

geneexpression biomedicalinformatics networkenrichment survival microarray software singlecell spatial rnaseq cerna mirna sponge

5 stars 5.88 score 8 scripts

bioc

BUSpaRse:kallisto | bustools R utilities

The kallisto | bustools pipeline is a fast and modular set of tools to convert single cell RNA-seq reads in fastq files into gene count or transcript compatibility counts (TCC) matrices for downstream analysis. Central to this pipeline is the barcode, UMI, and set (BUS) file format. This package serves the following purposes: First, this package allows users to manipulate BUS format files as data frames in R and then convert them into gene count or TCC matrices. Furthermore, since R and Rcpp code is easier to handle than pure C++ code, users are encouraged to tweak the source code of this package to experiment with new uses of BUS format and different ways to convert the BUS file into gene count matrix. Second, this package can conveniently generate files required to generate gene count matrices for spliced and unspliced transcripts for RNA velocity. Here biotypes can be filtered and scaffolds and haplotypes can be removed, and the filtered transcriptome can be extracted and written to disk. Third, this package implements utility functions to get transcripts and associated genes required to convert BUS files to gene count matrices, to write the transcript to gene information in the format required by bustools, and to read output of bustools into R as sparses matrices.

Maintained by Lambda Moses. Last updated 5 months ago.

singlecell rnaseq workflowstep cpp

9 stars 5.87 score 165 scripts

bioc

SingleCellSignalR:Cell Signalling Using Single Cell RNAseq Data Analysis

Allows single cell RNA seq data analysis, clustering, creates internal network and infers cell-cell interactions.

Maintained by Jacques Colinge Developer. Last updated 5 months ago.

singlecell network clustering rnaseq classification

5.87 score 35 scripts 1 dependents

bioc

escape:Easy single cell analysis platform for enrichment

A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize ssGSEA, GSVA, AUCell, and UCell-based enrichment calculations across individual cells.

Maintained by Nick Borcherding. Last updated 8 days ago.

software singlecell classification annotation genesetenrichment sequencing genesignaling pathways

5.84 score 138 scripts

bioc

ISAnalytics:Analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies

In gene therapy, stem cells are modified using viral vectors to deliver the therapeutic transgene and replace functional properties since the genetic modification is stable and inherited in all cell progeny. The retrieval and mapping of the sequences flanking the virus-host DNA junctions allows the identification of insertion sites (IS), essential for monitoring the evolution of genetically modified cells in vivo. A comprehensive toolkit for the analysis of IS is required to foster clonal trackign studies and supporting the assessment of safety and long term efficacy in vivo. This package is aimed at (1) supporting automation of IS workflow, (2) performing base and advance analysis for IS tracking (clonal abundance, clonal expansions and statistics for insertional mutagenesis, etc.), (3) providing basic biology insights of transduced stem cells in vivo.

Maintained by Francesco Gazzo. Last updated 4 months ago.

biomedicalinformatics sequencing singlecell

3 stars 5.83 score 15 scripts

bioc

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.

shinyapps software singlecell chipseq atacseq methylseq classification clustering epigenetics principalcomponent annotation batcheffect multiplecomparison normalization pathways preprocessing qualitycontrol reportwriting visualization genesetenrichment differentialpeakcalling epigenomics shiny single-cell cpp

14 stars 5.83 score 16 scripts

bioc

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.

visualization clustering singlecell transcriptomics rnaseq big-data bigdata scrna-seq scrna-seq-analysis visual visual-exploration

6 stars 5.82 score 8 scripts

bioc

SpatialExperimentIO:Read in Xenium, CosMx, MERSCOPE or STARmapPLUS data as SpatialExperiment object

Read in imaging-based spatial transcriptomics technology data. Current available modules are for Xenium by 10X Genomics, CosMx by Nanostring, MERSCOPE by Vizgen, or STARmapPLUS from Broad Institute. You can choose to read the data in as a SpatialExperiment or a SingleCellExperiment object.

Maintained by Yixing E. Dong. Last updated 2 months ago.

datarepresentation dataimport infrastructure transcriptomics singlecell spatial geneexpression

9 stars 5.81 score 16 scripts

bioc

CCPlotR:Plots For Visualising Cell-Cell Interactions

CCPlotR is an R package for visualising results from tools that predict cell-cell interactions from single-cell RNA-seq data. These plots are generic and can be used to visualise results from multiple tools such as Liana, CellPhoneDB, NATMI etc.

Maintained by Sarah Ennis. Last updated 5 months ago.

singlecell network visualization cellbiology systemsbiology

43 stars 5.81 score 7 scripts

bioc

dandelionR:Single-cell Immune Repertoire Trajectory Analysis in R

dandelionR is an R package for performing single-cell immune repertoire trajectory analysis, based on the original python implementation. It provides the necessary functions to interface with scRepertoire and a custom implementation of an absorbing Markov chain for pseudotime inference, inspired by the Palantir Python package.

Maintained by Kelvin Tuong. Last updated 27 days ago.

software immunooncology singlecell

8 stars 5.81 score 7 scripts

bioc

HuBMAPR:Interface to 'HuBMAP'

'HuBMAP' provides an open, global bio-molecular atlas of the human body at the cellular level. The `datasets()`, `samples()`, `donors()`, `publications()`, and `collections()` functions retrieves the information for each of these entity types. `*_details()` are available for individual entries of each entity type. `*_derived()` are available for retrieving derived datasets or samples for individual entries of each entity type. Data files can be accessed using `bulk_data_transfer()`.

Maintained by Christine Hou. Last updated 2 months ago.

software singlecell dataimport thirdpartyclient spatial infrastructure bioconductor-package client hubmap rstudio

3 stars 5.80 score 1 scripts

bioc

cicero:Predict cis-co-accessibility from single-cell chromatin accessibility data

Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends monocle 2 for use in chromatin accessibility data.

Maintained by Hannah Pliner. Last updated 5 months ago.

sequencing clustering cellbasedassays immunooncology generegulation genetarget epigenetics atacseq singlecell

5.80 score 312 scripts

bioc

deconvR:Simulation and Deconvolution of Omic Profiles

This package provides a collection of functions designed for analyzing deconvolution of the bulk sample(s) using an atlas of reference omic signature profiles and a user-selected model. Users are given the option to create or extend a reference atlas and,also simulate the desired size of the bulk signature profile of the reference cell types.The package includes the cell-type-specific methylation atlas and, Illumina Epic B5 probe ids that can be used in deconvolution. Additionally,we included BSmeth2Probe, to make mapping WGBS data to their probe IDs easier.

Maintained by Irem B. Gündüz. Last updated 5 months ago.

dnamethylation regression geneexpression rnaseq singlecell statisticalmethod transcriptomics bioconductor-package deconvolution dna-methylation omics

10 stars 5.78 score 15 scripts

bioc

ompBAM:C++ Library for OpenMP-based multi-threaded sequential profiling of Binary Alignment Map (BAM) files

This packages provides C++ header files for developers wishing to create R packages that processes BAM files. ompBAM automates file access, memory management, and handling of multiple threads 'behind the scenes', so developers can focus on creating domain-specific functionality. The included vignette contains detailed documentation of this API, including quick-start instructions to create a new ompBAM-based package, and step-by-step explanation of the functionality behind the example packaged included within ompBAM.

Maintained by Alex Chit Hei Wong. Last updated 5 months ago.

alignment dataimport rnaseq software sequencing transcriptomics singlecell

4 stars 5.78 score 3 scripts 1 dependents

bioc

TENxIO:Import methods for 10X Genomics files

Provides a structured S4 approach to importing data files from the 10X pipelines. It mainly supports Single Cell Multiome ATAC + Gene Expression data among other data types. The main Bioconductor data representations used are SingleCellExperiment and RaggedExperiment.

Maintained by Marcel Ramos. Last updated 4 months ago.

software infrastructure dataimport singlecell bioconductor-package u24ca289073

5.77 score 7 scripts 3 dependents

bioc

scRNAseqApp:A single-cell RNAseq Shiny app-package

The scRNAseqApp is a Shiny app package designed for interactive visualization of single-cell data. It is an enhanced version derived from the ShinyCell, repackaged to accommodate multiple datasets. The app enables users to visualize data containing various types of information simultaneously, facilitating comprehensive analysis. Additionally, it includes a user management system to regulate database accessibility for different users.

Maintained by Jianhong Ou. Last updated 16 days ago.

visualization singlecell rnaseq interactive-visualizations multiple-users shiny-apps single-cell-rna-seq

4 stars 5.76 score 3 scripts

bioc

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.

singlecell sequencing preprocessing classification regression

5 stars 5.76 score 19 scripts 1 dependents

bioc

BPRMeth:Model higher-order methylation profiles

The BPRMeth package is a probabilistic method to quantify explicit features of methylation profiles, in a way that would make it easier to formally use such profiles in downstream modelling efforts, such as predicting gene expression levels or clustering genomic regions or cells according to their methylation profiles.

Maintained by Chantriolnt-Andreas Kapourani. Last updated 5 months ago.

immunooncology dnamethylation geneexpression generegulation epigenetics genetics clustering featureextraction regression rnaseq bayesian kegg sequencing coverage singlecell openblas cpp

5.75 score 94 scripts 1 dependents

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 2 months ago.

software singlecell rnaseq atacseq normalization preprocessing dimensionreduction visualization qualitycontrol clustering classification annotation geneexpression differentialexpression bioinformatics genomics machine-learning parameter-optimization robustness single-cell unsupervised-learning cpp

23 stars 5.70 score 18 scripts

bioc

CytoGLMM:Conditional Differential Analysis for Flow and Mass Cytometry Experiments

The CytoGLMM R package implements two multiple regression strategies: A bootstrapped generalized linear model (GLM) and a generalized linear mixed model (GLMM). Most current data analysis tools compare expressions across many computationally discovered cell types. CytoGLMM focuses on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. As a result, CytoGLMM finds differential proteins in flow and mass cytometry data while reducing biases arising from marker correlations and safeguarding against false discoveries induced by patient heterogeneity.

Maintained by Christof Seiler. Last updated 5 months ago.

flowcytometry proteomics singlecell cellbasedassays cellbiology immunooncology regression statisticalmethod software

2 stars 5.68 score 1 scripts 1 dependents

bioc

lute:Framework for cell size scale factor normalized bulk transcriptomics deconvolution experiments

Provides a framework for adjustment on cell type size when performing bulk transcripomics deconvolution. The main framework function provides a means of reference normalization using cell size scale factors. It allows for marker selection and deconvolution using non-negative least squares (NNLS) by default. The framework is extensible for other marker selection and deconvolution algorithms, and users may reuse the generics, methods, and classes for these when developing new algorithms.

Maintained by Sean K Maden. Last updated 5 months ago.

rnaseq sequencing singlecell coverage transcriptomics normalization

3 stars 5.65 score 3 scripts

bioc

breakpointR:Find breakpoints in Strand-seq data

This package implements functions for finding breakpoints, plotting and export of Strand-seq data.

Maintained by David Porubsky. Last updated 5 months ago.

software sequencing dnaseq singlecell coverage

8 stars 5.64 score 11 scripts

bioc

cydar:Using Mass Cytometry for Differential Abundance Analyses

Identifies differentially abundant populations between samples and groups in mass cytometry data. Provides methods for counting cells into hyperspheres, controlling the spatial false discovery rate, and visualizing changes in abundance in the high-dimensional marker space.

Maintained by Aaron Lun. Last updated 5 months ago.

immunooncology flowcytometry multiplecomparison proteomics singlecell cpp

5.64 score 48 scripts

bioc

scviR:experimental inferface from R to scvi-tools

This package defines interfaces from R to scvi-tools. A vignette works through the totalVI tutorial for analyzing CITE-seq data. Another vignette compares outputs of Chapter 12 of the OSCA book with analogous outputs based on totalVI quantifications. Future work will address other components of scvi-tools, with a focus on building understanding of probabilistic methods based on variational autoencoders.

Maintained by Vincent Carey. Last updated 5 months ago.

infrastructure singlecell dataimport bioconductor cite-seq scverse

6 stars 5.60 score 11 scripts

bioc

scrapper:Bindings to C++ Libraries for Single-Cell Analysis

Implements R bindings to C++ code for analyzing single-cell (expression) data, mostly from various libscran libraries. Each function performs an individual step in the single-cell analysis workflow, ranging from quality control to clustering and marker detection. It is mostly intended for other Bioconductor package developers to build more user-friendly end-to-end workflows.

Maintained by Aaron Lun. Last updated 12 days ago.

normalization rnaseq software geneexpression transcriptomics singlecell batcheffect qualitycontrol differentialexpression featureextraction principalcomponent clustering openblas cpp

5.57 score 32 scripts

bioc

iSEEhub:iSEE for the Bioconductor ExperimentHub

This package defines a custom landing page for an iSEE app interfacing with the Bioconductor ExperimentHub. The landing page allows users to browse the ExperimentHub, select a data set, download and cache it, and import it directly into a Bioconductor iSEE app.

Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.

dataimport immunooncology infrastructure shinyapps singlecell software bioconductor bioconductor-package hacktoberfest isee

3 stars 5.56 score 4 scripts

bioc

scifer:Scifer: Single-Cell Immunoglobulin Filtering of Sanger Sequences

Have you ever index sorted cells in a 96 or 384-well plate and then sequenced using Sanger sequencing? If so, you probably had some struggles to either check the electropherogram of each cell sequenced manually, or when you tried to identify which cell was sorted where after sequencing the plate. Scifer was developed to solve this issue by performing basic quality control of Sanger sequences and merging flow cytometry data from probed single-cell sorted B cells with sequencing data. scifer can export summary tables, 'fasta' files, electropherograms for visual inspection, and generate reports.

Maintained by Rodrigo Arcoverde Cerveira. Last updated 4 months ago.

preprocessing qualitycontrol sangerseq sequencing software flowcytometry singlecell

5 stars 5.54 score 9 scripts

bioc

demuxSNP:scRNAseq demultiplexing using cell hashing and SNPs

This package assists in demultiplexing scRNAseq data using both cell hashing and SNPs data. The SNP profile of each group os learned using high confidence assignments from the cell hashing data. Cells which cannot be assigned with high confidence from the cell hashing data are assigned to their most similar group based on their SNPs. We also provide some helper function to optimise SNP selection, create training data and merge SNP data into the SingleCellExperiment framework.

Maintained by Michael Lynch. Last updated 5 months ago.

classification singlecell

6 stars 5.52 score 22 scripts

bioc

cytoviewer:An interactive multi-channel image viewer for R

This R package supports interactive visualization of multi-channel images and segmentation masks generated by imaging mass cytometry and other highly multiplexed imaging techniques using shiny. The cytoviewer interface is divided into image-level (Composite and Channels) and cell-level visualization (Masks). It allows users to overlay individual images with segmentation masks, integrates well with SingleCellExperiment and SpatialExperiment objects for metadata visualization and supports image downloads.

Maintained by Lasse Meyer. Last updated 5 months ago.

immunooncology software singlecell onechannel twochannel multichannel spatial dataimport bioconductor imaging shiny visualization

7 stars 5.50 score 15 scripts

bioc

VisiumIO:Import Visium data from the 10X Space Ranger pipeline

The package allows users to readily import spatial data obtained from either the 10X website or from the Space Ranger pipeline. Supported formats include tar.gz, h5, and mtx files. Multiple files can be imported at once with *List type of functions. The package represents data mainly as SpatialExperiment objects.

Maintained by Marcel Ramos. Last updated 2 months ago.

software infrastructure dataimport singlecell spatial bioconductor-package genomics u24ca289073

5.50 score 14 scripts 1 dependents

bioc

TDbasedUFE:Tensor Decomposition Based Unsupervised Feature Extraction

This is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. It can perform unsupervised feature extraction. It uses tensor decomposition. It is applicable to gene expression, DNA methylation, and histone modification etc. It can perform multiomics analysis. It is also potentially applicable to single cell omics data sets.

Maintained by Y-h. Taguchi. Last updated 5 months ago.

geneexpression featureextraction methylationarray singlecell bioinformatics dna-methylation gene-expression-profiles histone-modifications multiomics tensor-decomposition

5 stars 5.48 score 9 scripts 1 dependents

bioc

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 9 days ago.

software visualization differentialexpression geneexpression transcription rnaseq singlecell sequencing clustering

7 stars 5.45 score 2 scripts

bioc

sketchR:An R interface for python subsampling/sketching algorithms

Provides an R interface for various subsampling algorithms implemented in python packages. Currently, interfaces to the geosketch and scSampler python packages are implemented. In addition it also provides diagnostic plots to evaluate the subsampling.

Maintained by Charlotte Soneson. Last updated 3 months ago.

singlecell

3 stars 5.43 score 3 scripts

bioc

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.

software singlecell transcriptomics classification

5.42 score 19 scripts

bioc

speckle:Statistical methods for analysing single cell RNA-seq data

The speckle package contains functions for the analysis of single cell RNA-seq data. The speckle package currently contains functions to analyse differences in cell type proportions. There are also functions to estimate the parameters of the Beta distribution based on a given counts matrix, and a function to normalise a counts matrix to the median library size. There are plotting functions to visualise cell type proportions and the mean-variance relationship in cell type proportions and counts. As our research into specialised analyses of single cell data continues we anticipate that the package will be updated with new functions.

Maintained by Belinda Phipson. Last updated 5 months ago.

singlecell rnaseq regression geneexpression

5.41 score 258 scripts

bioc

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.

singlecell sequencing technology crispr pooledscreens

4 stars 5.38 score 9 scripts

bioc

chevreulProcess:Tools for managing SingleCellExperiment objects as projects

Tools analyzing SingleCellExperiment objects as projects. for input into the Chevreul app downstream. Includes functions for analysis of single cell RNA sequencing data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik.

Maintained by Kevin Stachelek. Last updated 2 months ago.

coverage rnaseq sequencing visualization geneexpression transcription singlecell transcriptomics normalization preprocessing qualitycontrol dimensionreduction dataimport

5.38 score 2 scripts 2 dependents

bioc

scReClassify:scReClassify: post hoc cell type classification of single-cell RNA-seq data

A post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure with semi-supervised learning algorithm AdaSampling technique. The current version of scReClassify supports Support Vector Machine and Random Forest as a base classifier.

Maintained by Taiyun Kim. Last updated 5 months ago.

software transcriptomics singlecell classification supportvectormachine

10 stars 5.38 score 12 scripts

bioc

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.

software geneexpression transcriptomics singlecell batcheffect sequencing coverage regression batch-effects dimensionality-reduction negative-binomial scrna-seq

4 stars 5.33 score 27 scripts

bioc

SCArray:Large-scale single-cell omics data manipulation with GDS files

Provides large-scale single-cell omics data manipulation using Genomic Data Structure (GDS) files. It combines dense and sparse matrices stored in GDS files and the Bioconductor infrastructure framework (SingleCellExperiment and DelayedArray) to provide out-of-memory data storage and large-scale manipulation using the R programming language.

Maintained by Xiuwen Zheng. Last updated 5 days ago.

infrastructure datarepresentation dataimport singlecell rnaseq cpp

1 stars 5.32 score 9 scripts 1 dependents

bioc

hoodscanR:Spatial cellular neighbourhood scanning in R

hoodscanR is an user-friendly R package providing functions to assist cellular neighborhood analysis of any spatial transcriptomics data with single-cell resolution. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. The package can result in cell-level neighborhood annotation output, along with funtions to perform neighborhood colocalization analysis and neighborhood-based cell clustering.

Maintained by Ning Liu. Last updated 2 months ago.

spatial transcriptomics singlecell clustering cpp

4 stars 5.32 score 15 scripts

bioc

VDJdive:Analysis Tools for 10X V(D)J Data

This package provides functions for handling and analyzing immune receptor repertoire data, such as produced by the CellRanger V(D)J pipeline. This includes reading the data into R, merging it with paired single-cell data, quantifying clonotype abundances, calculating diversity metrics, and producing common plots. It implements the E-M Algorithm for clonotype assignment, along with other methods, which makes use of ambiguous cells for improved quantification.

Maintained by Kelly Street. Last updated 5 months ago.

software immunooncology singlecell annotation rnaseq targetedresequencing cpp

7 stars 5.32 score 1 scripts

bioc

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.

differentialsplicing bayesian genetics rnaseq sequencing differentialexpression geneexpression multiplecomparison software transcription statisticalmethod visualization singlecell genetarget openblas cpp

10 stars 5.30 score 4 scripts

bioc

scCB2:CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data

scCB2 is an R package implementing CB2 for distinguishing real cells from empty droplets in droplet-based single cell RNA-seq experiments (especially for 10x Chromium). It is based on clustering similar barcodes and calculating Monte-Carlo p-value for each cluster to test against background distribution. This cluster-level test outperforms single-barcode-level tests in dealing with low count barcodes and homogeneous sequencing library, while keeping FDR well controlled.

Maintained by Zijian Ni. Last updated 5 months ago.

dataimport rnaseq singlecell sequencing geneexpression transcriptomics preprocessing clustering

10 stars 5.30 score 5 scripts

bioc

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.

software differentialexpression genesetenrichment biomedicalinformatics cellbiology transcriptomics rnaseq singlecell transcription sequencing immunooncology dataimport

7 stars 5.24 score 3 scripts

bioc

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.

geneexpression singlecell rnaseq transcriptomics sequencing preprocessing software

8 stars 5.20 score 9 scripts

bioc

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 4 months ago.

software singlecell rnaseq geneexpression transcriptomics transcription sequencing bioconductor deconvolution rnascope scrna-seq smfish snrna-seq treg

4 stars 5.20 score 5 scripts

bioc

scGPS:A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation)

The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations.

Maintained by Quan Nguyen. Last updated 5 months ago.

singlecell clustering dataimport sequencing coverage openblas cpp

4 stars 5.20 score 7 scripts

timothy-barry

ondisc:Algorithms and data structures for large single-cell expression matrices

Single-cell datasets are growing in size, posing challenges as well as opportunities for genomics researchers. `ondisc` is an R package that facilitates analysis of large-scale single-cell data out-of-core on a laptop or distributed across tens to hundreds processors on a cluster or cloud. In both of these settings, `ondisc` requires only a few gigabytes of memory, even if the input data are tens of gigabytes in size. `ondisc` mainly is oriented toward single-cell CRISPR screen analysis, but ondisc also can be used for single-cell differential expression and single-cell co-expression analyses. ondisc is powered by several new, efficient algorithms for manipulating and querying large, sparse expression matrices.

Maintained by Timothy Barry. Last updated 12 months ago.

dataimport singlecell differentialexpression crispr zlib cpp

11 stars 5.13 score 62 scripts

bioc

smoppix:Analyze Single Molecule Spatial Omics Data Using the Probabilistic Index

Test for univariate and bivariate spatial patterns in spatial omics data with single-molecule resolution. The tests implemented allow for analysis of nested designs and are automatically calibrated to different biological specimens. Tests for aggregation, colocalization, gradients and vicinity to cell edge or centroid are provided.

Maintained by Stijn Hawinkel. Last updated 1 months ago.

transcriptomics spatial singlecell cpp

1 stars 5.10 score 4 scripts

bioc

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.

transcriptomics visualization rnaseq bayesian spatial software geneexpression dimensionreduction featureextraction singlecell cpp

3 stars 5.08 score 9 scripts

bioc

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 2 months ago.

software singlecell sequencing differentialexpression rnaseq geneexpression transcriptomics featureextraction

2 stars 5.08 score 3 scripts

bioc

chevreulPlot:Plots used in the chevreulPlot package

Tools for plotting SingleCellExperiment objects in the chevreulPlot package. Includes functions for analysis and visualization of single-cell data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik.

Maintained by Kevin Stachelek. Last updated 30 days ago.

coverage rnaseq sequencing visualization geneexpression transcription singlecell transcriptomics normalization preprocessing qualitycontrol dimensionreduction dataimport

5.08 score 2 scripts

bioc

DepecheR:Determination of essential phenotypic elements of clusters in high-dimensional entities

The purpose of this package is to identify traits in a dataset that can separate groups. This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. As certain clusters with separating information will be identified, and these clusters are defined by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters.

Maintained by Jakob Theorell. Last updated 5 months ago.

software cellbasedassays transcription differentialexpression datarepresentation immunooncology transcriptomics classification clustering dimensionreduction featureextraction flowcytometry rnaseq singlecell visualization cpp

5.08 score 15 scripts

bioc

chevreulShiny:Tools for managing SingleCellExperiment objects as projects

Tools for managing SingleCellExperiment objects as projects. Includes functions for analysis and visualization of single-cell data. Also included is a shiny app for visualization of pre-processed scRNA data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik.

Maintained by Kevin Stachelek. Last updated 26 days ago.

coverage rnaseq sequencing visualization geneexpression transcription singlecell transcriptomics normalization preprocessing qualitycontrol dimensionreduction dataimport

5.08 score

bioc

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 3 days ago.

software geneexpression geneticvariability snp differentialexpression genomicvariation variantdetection genetics functionalgenomics systemsbiology regression singlecell normalization visualization rna-seq sc-eqtl

3 stars 5.02 score