R-universe search: singlecellexperiment

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 9 days ago.

immunooncology datarepresentation dataimport infrastructure singlecell

79.4 match 13.53 score 15k scripts 285 dependents

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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 23 days ago.

singlecell geneexpression differentialexpression alignment clustering immunooncology batcheffect normalization qualitycontrol dataimport gui

75.1 match 181 stars 10.16 score 252 scripts

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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

19.6 match 68 stars 9.02 score 84 scripts

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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 1 months ago.

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

30.0 match 5.38 score 2 scripts 2 dependents

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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

16.1 match 36 stars 8.86 score 125 scripts 2 dependents

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CuratedAtlasQueryR:Queries the Human Cell Atlas

Provides access to a copy of the Human Cell Atlas, but with harmonised metadata. This allows for uniform querying across numerous datasets within the Atlas using common fields such as cell type, tissue type, and patient ethnicity. Usage involves first querying the metadata table for cells of interest, and then downloading the corresponding cells into a SingleCellExperiment object.

Maintained by Stefano Mangiola. Last updated 5 months ago.

assaydomain infrastructure rnaseq differentialexpression geneexpression normalization clustering qualitycontrol sequencing transcription transcriptomics database duckdb hdf5 human-cell-atlas single-cell singlecellexperiment tidyverse

15.8 match 90 stars 7.04 score 41 scripts

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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 13 days ago.

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

18.1 match 5.08 score

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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 27 days ago.

singlecell geneexpression clustering sequencing bayesian immunooncology dataimport cpp openmp

8.5 match 147 stars 10.47 score 256 scripts 2 dependents

satijalab

Seurat:Tools for Single Cell Genomics

A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.

Maintained by Paul Hoffman. Last updated 1 years ago.

human-cell-atlas single-cell-genomics single-cell-rna-seq cpp

4.9 match 2.4k stars 16.86 score 50k scripts 73 dependents

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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 9 days ago.

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

7.3 match 11.07 score 12k scripts 43 dependents

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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

6.5 match 67 stars 11.06 score 362 scripts 2 dependents

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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

11.5 match 14 stars 5.83 score 16 scripts

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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

7.0 match 67 stars 9.52 score 137 scripts 1 dependents

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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

6.6 match 122 stars 10.09 score 374 scripts 1 dependents

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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

8.8 match 7.56 score 760 scripts 2 dependents

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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

6.7 match 32 stars 9.61 score 354 scripts 5 dependents

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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

4.8 match 230 stars 12.75 score 1.8k scripts 5 dependents

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alabaster.sce:Load and Save SingleCellExperiment from File

Save SingleCellExperiment into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

Maintained by Aaron Lun. Last updated 5 months ago.

dataimport datarepresentation

10.8 match 5.51 score 4 scripts 3 dependents

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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 30 days ago.

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

6.1 match 55 stars 9.28 score 99 scripts

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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

8.6 match 63 stars 6.30 score 16 scripts

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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

5.2 match 143 stars 10.43 score 454 scripts 2 dependents

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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

5.2 match 83 stars 10.26 score 368 scripts 1 dependents

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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 17 days ago.

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

10.3 match 5.08 score 2 scripts

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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 2 days ago.

sequencing rnaseq software geneexpression transcriptomics singlecell batcheffect normalization cpp

5.8 match 9.10 score 1.2k scripts 10 dependents

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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

7.5 match 27 stars 6.59 score 18 scripts

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ASURAT:Functional annotation-driven unsupervised clustering for single-cell data

ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).

Maintained by Keita Iida. Last updated 5 months ago.

geneexpression singlecell sequencing clustering genesignaling cpp

11.2 match 4.32 score 21 scripts

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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

6.4 match 44 stars 7.27 score 70 scripts

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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 1 months ago.

biologicalquestion statisticalmethod geneexpression singlecell transcriptomics spatial

7.2 match 3 stars 6.50 score

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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 7 days ago.

singlecell dataimport datarepresentation bioconductor conversion scrna-seq

3.8 match 159 stars 11.25 score 660 scripts 4 dependents

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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

7.4 match 6 stars 5.60 score 22 scripts

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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

4.0 match 224 stars 9.92 score 424 scripts 1 dependents

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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

4.5 match 30 stars 8.71 score 98 scripts

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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 1 months ago.

datarepresentation dataimport infrastructure transcriptomics singlecell spatial geneexpression

6.7 match 9 stars 5.81 score 16 scripts

rcannood

SCORPIUS:Inferring Developmental Chronologies from Single-Cell RNA Sequencing Data

An accurate and easy tool for performing linear trajectory inference on single cells using single-cell RNA sequencing data. In addition, 'SCORPIUS' provides functions for discovering the most important genes with respect to the reconstructed trajectory, as well as nice visualisation tools. Cannoodt et al. (2016) <doi:10.1101/079509>.

Maintained by Robrecht Cannoodt. Last updated 2 years ago.

4.5 match 59 stars 8.17 score 126 scripts

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ReactomeGSA:Client for the Reactome Analysis Service for comparative multi-omics gene set analysis

The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited.

Maintained by Johannes Griss. Last updated 4 months ago.

genesetenrichment proteomics transcriptomics systemsbiology geneexpression reactome

4.5 match 23 stars 8.05 score 67 scripts

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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

6.6 match 5.42 score 19 scripts

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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

5.2 match 6.57 score 176 scripts 1 dependents

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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

5.4 match 3 stars 6.05 score 84 scripts

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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

3.6 match 123 stars 8.89 score 278 scripts 1 dependents

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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

3.3 match 119 stars 9.63 score 296 scripts

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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

5.0 match 20 stars 6.23 score 42 scripts

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aggregateBioVar:Differential Gene Expression Analysis for Multi-subject scRNA-seq

For single cell RNA-seq data collected from more than one subject (e.g. biological sample or technical replicates), this package contains tools to summarize single cell gene expression profiles at the level of subject. A SingleCellExperiment object is taken as input and converted to a list of SummarizedExperiment objects, where each list element corresponds to an assigned cell type. The SummarizedExperiment objects contain aggregate gene-by-subject count matrices and inter-subject column metadata for individual subjects that can be processed using downstream bulk RNA-seq tools.

Maintained by Jason Ratcliff. Last updated 5 months ago.

software singlecell rnaseq transcriptomics transcription geneexpression differentialexpression

6.3 match 5 stars 4.95 score 18 scripts

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corral:Correspondence Analysis for Single Cell Data

Correspondence analysis (CA) is a matrix factorization method, and is similar to principal components analysis (PCA). Whereas PCA is designed for application to continuous, approximately normally distributed data, CA is appropriate for non-negative, count-based data that are in the same additive scale. The corral package implements CA for dimensionality reduction of a single matrix of single-cell data, as well as a multi-table adaptation of CA that leverages data-optimized scaling to align data generated from different sequencing platforms by projecting into a shared latent space. corral utilizes sparse matrices and a fast implementation of SVD, and can be called directly on Bioconductor objects (e.g., SingleCellExperiment) for easy pipeline integration. The package also includes additional options, including variations of CA to address overdispersion in count data (e.g., Freeman-Tukey chi-squared residual), as well as the option to apply CA-style processing to continuous data (e.g., proteomic TOF intensities) with the Hellinger distance adaptation of CA.

Maintained by Lauren Hsu. Last updated 5 months ago.

batcheffect dimensionreduction geneexpression preprocessing principalcomponent sequencing singlecell software visualization

6.7 match 4.64 score 22 scripts

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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 10 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

2.3 match 225 stars 12.86 score 380 scripts 9 dependents

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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

5.6 match 2 stars 5.26 score 3 scripts

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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 5 days ago.

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

3.5 match 31 stars 7.95 score 12 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

4.5 match 12 stars 6.09 score 34 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

3.7 match 19 stars 7.26 score 35 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 14 days ago.

software singlecell sequencing geneexpression spatial

3.5 match 89 stars 7.59 score 25 scripts

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

2.0 match 41 stars 13.14 score 7.6k scripts 36 dependents

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

4.0 match 24 stars 6.52 score 46 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

4.0 match 8 stars 6.20 score 22 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

4.3 match 6 stars 5.60 score 11 scripts

immunogenomics

harmony:Fast, Sensitive, and Accurate Integration of Single Cell Data

Implementation of the Harmony algorithm for single cell integration, described in Korsunsky et al <doi:10.1038/s41592-019-0619-0>. Package includes a standalone Harmony function and interfaces to external frameworks.

Maintained by Ilya Korsunsky. Last updated 4 months ago.

algorithm data-integration scrna-seq openblas cpp

1.8 match 554 stars 13.74 score 5.5k scripts 8 dependents

bioc

MultimodalExperiment:Integrative Bulk and Single-Cell Experiment Container

MultimodalExperiment is an S4 class that integrates bulk and single-cell experiment data; it is optimally storage-efficient, and its methods are exceptionally fast. It effortlessly represents multimodal data of any nature and features normalized experiment, subject, sample, and cell annotations, which are related to underlying biological experiments through maps. Its coordination methods are opt-in and employ database-like join operations internally to deliver fast and flexible management of multimodal data.

Maintained by Lucas Schiffer. Last updated 5 months ago.

datarepresentation infrastructure singlecell

6.0 match 4.00 score 3 scripts

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

2.3 match 10.21 score 1.7k scripts 80 dependents

bioc

partCNV:Infer locally aneuploid cells using single cell RNA-seq data

This package uses a statistical framework for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. Our method uses an EM algorithm with mixtures of Poisson distributions while incorporating cytogenetics information (e.g., regional deletion or amplification) to guide the classification (partCNV). When applicable, we further improve the accuracy by integrating a Hidden Markov Model for feature selection (partCNVH).

Maintained by Ziyi Li. Last updated 5 months ago.

software copynumbervariation hiddenmarkovmodel singlecell classification

5.2 match 4.18 score 4 scripts

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 1 months ago.

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

1.8 match 110 stars 12.11 score 1.0k scripts 4 dependents

waldronlab

SingleCellMultiModal:Integrating Multi-modal Single Cell Experiment datasets

SingleCellMultiModal is an ExperimentHub package that serves multiple datasets obtained from GEO and other sources and represents them as MultiAssayExperiment objects. We provide several multi-modal datasets including scNMT, 10X Multiome, seqFISH, CITEseq, SCoPE2, and others. The scope of the package is is to provide data for benchmarking and analysis. To cite, use the 'citation' function and see <https://doi.org/10.1371/journal.pcbi.1011324>.

Maintained by Marcel Ramos. Last updated 4 months ago.

experimentdata singlecelldata reproducibleresearch experimenthub geo bioconductor-package u24ca289073

2.7 match 17 stars 7.29 score 60 scripts

lhe17

nebula:Negative Binomial Mixed Models Using Large-Sample Approximation for Differential Expression Analysis of ScRNA-Seq Data

A fast negative binomial mixed model for conducting association analysis of multi-subject single-cell data. It can be used for identifying marker genes, differential expression and co-expression analyses. The model includes subject-level random effects to account for the hierarchical structure in multi-subject single-cell data. See He et al. (2021) <doi:10.1038/s42003-021-02146-6>.

Maintained by Liang He. Last updated 1 years ago.

cpp

2.9 match 35 stars 6.40 score 145 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

3.0 match 9 stars 6.02 score 13 scripts

bioc

MOFA2:Multi-Omics Factor Analysis v2

The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, vizualisation, imputation etc are available.

Maintained by Ricard Argelaguet. Last updated 5 months ago.

dimensionreduction bayesian visualization factor-analysis mofa multi-omics

1.8 match 319 stars 10.02 score 502 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

3.2 match 5.41 score 258 scripts

bioc

simPIC:simPIC: flexible simulation of paired-insertion counts for single-cell ATAC-sequencing data

simPIC is a package for simulating single-cell ATAC-seq count data. It provides a user-friendly, well documented interface for data simulation. Functions are provided for parameter estimation, realistic scATAC-seq data simulation, and comparing real and simulated datasets.

Maintained by Sagrika Chugh. Last updated 5 months ago.

singlecell atacseq software sequencing immunooncology dataimport

3.8 match 4.54 score 3 scripts

lazappi

clustree:Visualise Clusterings at Different Resolutions

Deciding what resolution to use can be a difficult question when approaching a clustering analysis. One way to approach this problem is to look at how samples move as the number of clusters increases. This package allows you to produce clustering trees, a visualisation for interrogating clusterings as resolution increases.

Maintained by Luke Zappia. Last updated 1 years ago.

clustering clustering-trees visualisation visualization

1.5 match 219 stars 11.40 score 1.9k scripts 5 dependents

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

2.5 match 6 stars 6.74 score 153 scripts 1 dependents

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

1.9 match 74 stars 8.96 score 102 scripts 2 dependents

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 5 months ago.

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

2.0 match 12 stars 8.09 score 128 scripts

bioc

jazzPanda:Finding spatially relevant marker genes in image based spatial transcriptomics data

This package contains the function to find marker genes for image-based spatial transcriptomics data. There are functions to create spatial vectors from the cell and transcript coordiantes, which are passed as inputs to find marker genes. Marker genes are detected for every cluster by two approaches. The first approach is by permtuation testing, which is implmented in parallel for finding marker genes for one sample study. The other approach is to build a linear model for every gene. This approach can account for multiple samples and backgound noise.

Maintained by Melody Jin. Last updated 14 days ago.

spatial geneexpression differentialexpression statisticalmethod transcriptomics correlation linear-models marker-genes spatial-transcriptomics

3.2 match 2 stars 5.00 score

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

1.5 match 357 stars 10.49 score 340 scripts 1 dependents

gfellerlab

SuperCell:Simplification of scRNA-seq data by merging together similar cells

Aggregates large single-cell data into metacell dataset by merging together gene expression of very similar cells.

Maintained by The package maintainer. Last updated 8 months ago.

software coarse-graining scrna-seq-analysis scrna-seq-data

1.9 match 72 stars 8.08 score 93 scripts

bioc

ADImpute:Adaptive Dropout Imputer (ADImpute)

Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Here we propose two novel methods: a gene regulatory network-based approach using gene-gene relationships learnt from external data and a baseline approach corresponding to a sample-wide average. ADImpute can implement these novel methods and also combine them with existing imputation methods (currently supported: DrImpute, SAVER). ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble.

Maintained by Ana Carolina Leote. Last updated 5 months ago.

geneexpression network preprocessing sequencing singlecell transcriptomics

3.5 match 4.30 score 7 scripts

welch-lab

CytoSimplex:Simplex Visualization of Cell Fate Similarity in Single-Cell Data

Create simplex plots to visualize the similarity between single-cells and selected clusters in a 1-/2-/3-simplex space. Velocity information can be added as an additional layer. See Liu J, Wang Y et al (2023) <doi:10.1101/2023.12.07.570655> for more details.

Maintained by Yichen Wang. Last updated 6 months ago.

openblas cpp

3.5 match 1 stars 4.18 score 3 scripts

bioc

scTreeViz:R/Bioconductor package to interactively explore and visualize single cell RNA-seq datasets with hierarhical annotations

scTreeViz provides classes to support interactive data aggregation and visualization of single cell RNA-seq datasets with hierarchies for e.g. cell clusters at different resolutions. The `TreeIndex` class provides methods to manage hierarchy and split the tree at a given resolution or across resolutions. The `TreeViz` class extends `SummarizedExperiment` and can performs quick aggregations on the count matrix defined by clusters.

Maintained by Jayaram Kancherla. Last updated 5 months ago.

visualization infrastructure gui singlecell

3.6 match 4.00 score 3 scripts

bioc

ILoReg:ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data

ILoReg is a tool for identification of cell populations from scRNA-seq data. In particular, ILoReg is useful for finding cell populations with subtle transcriptomic differences. The method utilizes a self-supervised learning method, called Iteratitive Clustering Projection (ICP), to find cluster probabilities, which are used in noise reduction prior to PCA and the subsequent hierarchical clustering and t-SNE steps. Additionally, functions for differential expression analysis to find gene markers for the populations and gene expression visualization are provided.

Maintained by Johannes Smolander. Last updated 5 months ago.

singlecell software clustering dimensionreduction rnaseq visualization transcriptomics datarepresentation differentialexpression transcription geneexpression

3.0 match 5 stars 4.88 score 2 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

1.9 match 24 stars 7.58 score 126 scripts

shufeyangyi2015310117

SC.MEB:Spatial Clustering with Hidden Markov Random Field using Empirical Bayes

Spatial clustering with hidden markov random field fitted via EM algorithm, details of which can be found in Yi Yang (2021) <doi:10.1101/2021.06.05.447181>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.

Maintained by Yi Yang. Last updated 3 years ago.

openblas cpp

4.5 match 3.04 score 11 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 6 days ago.

singlecell generegulation networkinference network geneexpression transcription genetarget cpp

2.0 match 14 stars 6.67 score 17 scripts

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

1.5 match 95 stars 8.82 score 172 scripts

bioc

POWSC:Simulation, power evaluation, and sample size recommendation for single cell RNA-seq

Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship.

Maintained by Kenong Su. Last updated 5 months ago.

differentialexpression immunooncology singlecell software

3.3 match 4.00 score 7 scripts

bioc

slalom:Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data

slalom is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity, thereby allowing to identify biological drivers of cell-to-cell variability and model confounding factors. The method uses Bayesian factor analysis with a latent variable model to identify active pathways (selected by the user, e.g. KEGG pathways) that explain variation in a single-cell RNA-seq dataset. This an R/C++ implementation of the f-scLVM Python package. See the publication describing the method at https://doi.org/10.1186/s13059-017-1334-8.

Maintained by Davis McCarthy. Last updated 5 months ago.

immunooncology singlecell rnaseq normalization visualization dimensionreduction transcriptomics geneexpression sequencing software reactome kegg openblas cpp

3.1 match 4.08 score 12 scripts

chanzuckerberg

cellxgene.census:CZ CELLxGENE Discover Cell Census

API to facilitate the use of the CZ CELLxGENE Discover Census. For more information about the API and the project visit https://github.com/chanzuckerberg/cellxgene-census/

Maintained by Chan Zuckerberg Initiative Foundation. Last updated 5 months ago.

1.9 match 96 stars 6.60 score 15 scripts

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 5 months ago.

software visualization differentialexpression geneexpression transcription rnaseq singlecell sequencing clustering

2.5 match 2 stars 4.85 score 2 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

1.3 match 39 stars 9.63 score 192 scripts 1 dependents

bioc

traviz:Trajectory functions for visualization and interpretation.

traviz provides a suite of functions to plot trajectory related objects from Bioconductor packages. It allows plotting trajectories in reduced dimension, as well as averge gene expression smoothers as a function of pseudotime. Asides from general utility functions, traviz also allows plotting trajectories estimated by Slingshot, as well as smoothers estimated by tradeSeq. Furthermore, it allows for visualization of Slingshot trajectories using ggplot2.

Maintained by Koen Van den Berge. Last updated 5 months ago.

geneexpression rnaseq sequencing software singlecell transcriptomics visualization

3.8 match 3.00 score 2 scripts

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

1.5 match 31 stars 7.42 score 28 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

1.8 match 18 stars 6.16 score 4 scripts

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

1.9 match 5.89 score 78 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 4 months ago.

statisticalmethod dimensionreduction singlecell sequencing rnaseq geneexpression

1.8 match 15 stars 6.31 score 15 scripts

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

1.8 match 5 stars 6.26 score 12 scripts

bioc

airpart:Differential cell-type-specific allelic imbalance

Airpart identifies sets of genes displaying differential cell-type-specific allelic imbalance across cell types or states, utilizing single-cell allelic counts. It makes use of a generalized fused lasso with binomial observations of allelic counts to partition cell types by their allelic imbalance. Alternatively, a nonparametric method for partitioning cell types is offered. The package includes a number of visualizations and quality control functions for examining single cell allelic imbalance datasets.

Maintained by Wancen Mu. Last updated 5 months ago.

singlecell rnaseq atacseq chipseq sequencing generegulation geneexpression transcription transcriptomevariant cellbiology functionalgenomics differentialexpression graphandnetwork regression clustering qualitycontrol

2.3 match 2 stars 4.78 score 2 scripts

bioc

MuData:Serialization for MultiAssayExperiment Objects

Save MultiAssayExperiments to h5mu files supported by muon and mudata. Muon is a Python framework for multimodal omics data analysis. It uses an HDF5-based format for data storage.

Maintained by Ilia Kats. Last updated 20 days ago.

dataimport anndata bioconductor mudata multi-omics multimodal-omics scrna-seq

1.8 match 5 stars 5.89 score 26 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 16 days ago.

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

1.3 match 99 stars 8.41 score 69 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 4 days ago.

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

1.9 match 5.55 score 32 scripts

bioc

ccImpute:ccImpute: an accurate and scalable consensus clustering based approach to impute dropout events in the single-cell RNA-seq data (https://doi.org/10.1186/s12859-022-04814-8)

Dropout events make the lowly expressed genes indistinguishable from true zero expression and different than the low expression present in cells of the same type. This issue makes any subsequent downstream analysis difficult. ccImpute is an imputation algorithm that uses cell similarity established by consensus clustering to impute the most probable dropout events in the scRNA-seq datasets. ccImpute demonstrated performance which exceeds the performance of existing imputation approaches while introducing the least amount of new noise as measured by clustering performance characteristics on datasets with known cell identities.

Maintained by Marcin Malec. Last updated 5 months ago.

singlecell sequencing principalcomponent dimensionreduction clustering rnaseq transcriptomics openblas cpp openmp

2.3 match 2 stars 4.48 score 2 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

1.7 match 5.96 score 19 scripts 2 dependents

bioc

Spaniel:Spatial Transcriptomics Analysis

Spaniel includes a series of tools to aid the quality control and analysis of Spatial Transcriptomics data. Spaniel can import data from either the original Spatial Transcriptomics system or 10X Visium technology. The package contains functions to create a SingleCellExperiment Seurat object and provides a method of loading a histologial image into R. The spanielPlot function allows visualisation of metrics contained within the S4 object overlaid onto the image of the tissue.

Maintained by Rachel Queen. Last updated 5 months ago.

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

2.3 match 4.34 score 22 scripts

yuelyu21

SCIntRuler:Guiding the Integration of Multiple Single-Cell RNA-Seq Datasets

The accumulation of single-cell RNA-seq (scRNA-seq) studies highlights the potential benefits of integrating multiple datasets. By augmenting sample sizes and enhancing analytical robustness, integration can lead to more insightful biological conclusions. However, challenges arise due to the inherent diversity and batch discrepancies within and across studies. SCIntRuler, a novel R package, addresses these challenges by guiding the integration of multiple scRNA-seq datasets.

Maintained by Yue Lyu. Last updated 5 months ago.

sequencing geneticvariability singlecell cpp

1.9 match 2 stars 4.85 score 3 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

1.7 match 7 stars 5.32 score 1 scripts

bioc

ExperimentSubset:Manages subsets of data with Bioconductor Experiment objects

Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for one or more matrix-like assays along with the associated row and column data. Often only a subset of the original data is needed for down-stream analysis. For example, filtering out poor quality samples will require excluding some columns before analysis. The ExperimentSubset object is a container to efficiently manage different subsets of the same data without having to make separate objects for each new subset.

Maintained by Irzam Sarfraz. Last updated 5 months ago.

infrastructure software dataimport datarepresentation

2.2 match 4.00 score 8 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

1.6 match 10 stars 5.30 score 4 scripts

bioc

scDDboost:A compositional model to assess expression changes from single-cell rna-seq data

scDDboost is an R package to analyze changes in the distribution of single-cell expression data between two experimental conditions. Compared to other methods that assess differential expression, scDDboost benefits uniquely from information conveyed by the clustering of cells into cellular subtypes. Through a novel empirical Bayesian formulation it calculates gene-specific posterior probabilities that the marginal expression distribution is the same (or different) between the two conditions. The implementation in scDDboost treats gene-level expression data within each condition as a mixture of negative binomial distributions.

Maintained by Xiuyu Ma. Last updated 2 months ago.

singlecell software clustering sequencing geneexpression differentialexpression bayesian cpp

1.8 match 4.58 score 19 scripts

ocbe-uio

DIscBIO:A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

An open, multi-algorithmic pipeline for easy, fast and efficient analysis of cellular sub-populations and the molecular signatures that characterize them. The pipeline consists of four successive steps: data pre-processing, cellular clustering with pseudo-temporal ordering, defining differential expressed genes and biomarker identification. More details on Ghannoum et. al. (2021) <doi:10.3390/ijms22031399>. This package implements extensions of the work published by Ghannoum et. al. (2019) <doi:10.1101/700989>.

Maintained by Waldir Leoncio. Last updated 1 years ago.

biomarker-discovery jupyter-notebook scrna-seq single-cell-analysis transcriptomics openjdk

1.9 match 12 stars 4.38 score 5 scripts

bioc

epiregulon.extra:Companion package to epiregulon with additional plotting, differential and graph functions

Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.

Maintained by Xiaosai Yao. Last updated 6 days ago.

generegulation network geneexpression transcription chiponchip differentialexpression genetarget normalization graphandnetwork

1.6 match 4.90 score 10 scripts

bioc

cytofQC:Labels normalized cells for CyTOF data and assigns probabilities for each label

cytofQC is a package for initial cleaning of CyTOF data. It uses a semi-supervised approach for labeling cells with their most likely data type (bead, doublet, debris, dead) and the probability that they belong to each label type. This package does not remove data from the dataset, but provides labels and information to aid the data user in cleaning their data. Our algorithm is able to distinguish between doublets and large cells.

Maintained by Jill Lundell. Last updated 5 months ago.

software singlecell annotation

1.8 match 2 stars 4.30 score 3 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 2 months ago.

geneexpression transcriptomics microarray rnaseq singlecell differentialexpression immunooncology genesetenrichment

1.3 match 6 stars 6.17 score 15 scripts

bioc

methylscaper:Visualization of Methylation Data

methylscaper is an R package for processing and visualizing data jointly profiling methylation and chromatin accessibility (MAPit, NOMe-seq, scNMT-seq, nanoNOMe, etc.). The package supports both single-cell and single-molecule data, and a common interface for jointly visualizing both data types through the generation of ordered representational methylation-state matrices. The Shiny app allows for an interactive seriation process of refinement and re-weighting that optimally orders the cells or DNA molecules to discover methylation patterns and nucleosome positioning.

Maintained by Bacher Rhonda. Last updated 5 months ago.

dnamethylation epigenetics sequencing visualization singlecell nucleosomepositioning

1.6 match 1 stars 4.90 score 3 scripts

bioc

scPCA:Sparse Contrastive Principal Component Analysis

A toolbox for sparse contrastive principal component analysis (scPCA) of high-dimensional biological data. scPCA combines the stability and interpretability of sparse PCA with contrastive PCA's ability to disentangle biological signal from unwanted variation through the use of control data. Also implements and extends cPCA.

Maintained by Philippe Boileau. Last updated 1 months ago.

principalcomponent geneexpression differentialexpression sequencing microarray rnaseq bioconductor contrastive-learning dimensionality-reduction

1.3 match 12 stars 5.94 score 29 scripts

bioc

CellTrails:Reconstruction, visualization and analysis of branching trajectories

CellTrails is an unsupervised algorithm for the de novo chronological ordering, visualization and analysis of single-cell expression data. CellTrails makes use of a geometrically motivated concept of lower-dimensional manifold learning, which exhibits a multitude of virtues that counteract intrinsic noise of single cell data caused by drop-outs, technical variance, and redundancy of predictive variables. CellTrails enables the reconstruction of branching trajectories and provides an intuitive graphical representation of expression patterns along all branches simultaneously. It allows the user to define and infer the expression dynamics of individual and multiple pathways towards distinct phenotypes.

Maintained by Daniel Ellwanger. Last updated 5 months ago.

immunooncology clustering datarepresentation differentialexpression dimensionreduction geneexpression sequencing singlecell software timecourse

1.7 match 4.00 score 7 scripts

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

0.5 match 59 stars 12.63 score 1.8k scripts 71 dependents

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

0.8 match 54 stars 8.06 score 52 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

1.2 match 8 stars 5.20 score 9 scripts

bioc

mia:Microbiome analysis

mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization.

Maintained by Tuomas Borman. Last updated 2 days ago.

microbiome software dataimport analysis bioconductor

0.5 match 52 stars 11.50 score 316 scripts 5 dependents

bioc

scBFA:A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq

This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis.

Maintained by Ruoxin Li. Last updated 5 months ago.

singlecell transcriptomics dimensionreduction geneexpression atacseq batcheffect kegg qualitycontrol

1.3 match 4.30 score 4 scripts

bioc

scFeatureFilter:A correlation-based method for quality filtering of single-cell RNAseq data

An R implementation of the correlation-based method developed in the Joshi laboratory to analyse and filter processed single-cell RNAseq data. It returns a filtered version of the data containing only genes expression values unaffected by systematic noise.

Maintained by Guillaume Devailly. Last updated 5 months ago.

immunooncology singlecell rnaseq preprocessing geneexpression

1.3 match 4.30 score 20 scripts

bioc

scTensor:Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition

The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor.

Maintained by Koki Tsuyuzaki. Last updated 5 months ago.

dimensionreduction singlecell software geneexpression

1.3 match 4.18 score 2 scripts

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 2 months ago.

software immunooncology singlecell classification annotation sequencing cpp

0.5 match 326 stars 10.49 score 240 scripts

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

0.5 match 99 stars 9.66 score 494 scripts

bioc

SpatialFeatureExperiment:Integrating SpatialExperiment with Simple Features in sf

A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used.

Maintained by Lambda Moses. Last updated 1 months ago.

datarepresentation transcriptomics spatial

0.5 match 49 stars 9.40 score 322 scripts 1 dependents

mojaveazure

pbmc3k.sce:PBMC 3k Dataset as a SingleCellExperiment

The PBMC 3k dataset provided as a SingleCellExperiment object. Also includes a processed version, pbmc3k.sce.final, using Bioc-equivalents of the Seurat standard workflow

Maintained by Paul Hoffman. Last updated 10 months ago.

3.7 match 1.30 score

bioc

TreeSummarizedExperiment:TreeSummarizedExperiment: a S4 Class for Data with Tree Structures

TreeSummarizedExperiment has extended SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data.

Maintained by Ruizhu Huang. Last updated 5 months ago.

datarepresentation infrastructure

0.6 match 7.86 score 251 scripts 15 dependents

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

0.5 match 47 stars 7.59 score 18 scripts

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

0.5 match 3 stars 6.90 score 264 scripts

mojaveazure

pbmc3k:Raw and Processed Matrices of the PBMC 3k Dataset

What the package does (one paragraph).

Maintained by Paul Hoffman. Last updated 9 months ago.

3.5 match 1.00 score

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

0.5 match 7 stars 6.30 score 12 scripts

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 2 months ago.

software singlecell classification annotation genesetenrichment sequencing genesignaling pathways

0.5 match 5.92 score 138 scripts

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

0.5 match 5.59 score 7 scripts 2 dependents

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

0.5 match 7 stars 5.50 score 15 scripts

bioc

spaSim:Spatial point data simulator for tissue images

A suite of functions for simulating spatial patterns of cells in tissue images. Output images are multitype point data in SingleCellExperiment format. Each point represents a cell, with its 2D locations and cell type. Potential cell patterns include background cells, tumour/immune cell clusters, immune rings, and blood/lymphatic vessels.

Maintained by Yuzhou Feng. Last updated 5 months ago.

statisticalmethod spatial biomedicalinformatics

0.5 match 2 stars 5.18 score 25 scripts

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 2 months ago.

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

0.5 match 3 stars 4.95 score

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 4 months ago.

infrastructure datarepresentation dataimport singlecell rnaseq cpp

0.5 match 1 stars 4.02 score 9 scripts 1 dependents

SingleCellExperiment:S4 Classes for Single Cell Data

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

scPipe:Pipeline for single cell multi-omic data pre-processing

chevreulProcess:Tools for managing SingleCellExperiment objects as projects

tidySingleCellExperiment:Brings SingleCellExperiment to the Tidyverse

CuratedAtlasQueryR:Queries the Human Cell Atlas

chevreulShiny:Tools for managing SingleCellExperiment objects as projects

celda:CEllular Latent Dirichlet Allocation

Seurat:Tools for Single Cell Genomics

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

CATALYST:Cytometry dATa anALYSis Tools

ChromSCape:Analysis of single-cell epigenomics datasets with a Shiny App

scMerge:scMerge: Merging multiple batches of scRNA-seq data

SC3:Single-Cell Consensus Clustering

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

cytomapper:Visualization of highly multiplexed imaging data in R

MAST:Model-based Analysis of Single Cell Transcriptomics

alabaster.sce:Load and Save SingleCellExperiment from File

EWCE:Expression Weighted Celltype Enrichment

scDataviz:scDataviz: single cell dataviz and downstream analyses

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

BASiCS:Bayesian Analysis of Single-Cell Sequencing data

chevreulPlot:Plots used in the chevreulPlot package

batchelor:Single-Cell Batch Correction Methods

CiteFuse:CiteFuse: multi-modal analysis of CITE-seq data

ASURAT:Functional annotation-driven unsupervised clustering for single-cell data

CHETAH:Fast and accurate scRNA-seq cell type identification

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

zellkonverter:Conversion Between scRNA-seq Objects

demuxSNP:scRNAseq demultiplexing using cell hashing and SNPs

splatter:Simple Simulation of Single-cell RNA Sequencing Data

CellBench:Construct Benchmarks for Single Cell Analysis Methods

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

SCORPIUS:Inferring Developmental Chronologies from Single-Cell RNA Sequencing Data

ReactomeGSA:Client for the Reactome Analysis Service for comparative multi-omics gene set analysis

adverSCarial:adverSCarial, generate and analyze the vulnerability of scRNA-seq classifier to adversarial attacks

scds:In-Silico Annotation of Doublets for Single Cell RNA Sequencing Data

GloScope:Population-level Representation on scRNA-Seq data

BayesSpace:Clustering and Resolution Enhancement of Spatial Transcriptomes

clustifyr:Classifier for Single-cell RNA-seq Using Cell Clusters

scMET:Bayesian modelling of cell-to-cell DNA methylation heterogeneity

aggregateBioVar:Differential Gene Expression Analysis for Multi-subject scRNA-seq

corral:Correspondence Analysis for Single Cell Data

iSEE:Interactive SummarizedExperiment Explorer

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

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

peco:A Supervised Approach for **P**r**e**dicting **c**ell Cycle Pr**o**gression using scRNA-seq data

tidytof:Analyze High-dimensional Cytometry Data Using Tidy Data Principles

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

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

tricycle:tricycle: Transferable Representation and Inference of cell cycle

scruff:Single Cell RNA-Seq UMI Filtering Facilitator (scruff)

scviR:experimental inferface from R to scvi-tools

harmony:Fast, Sensitive, and Accurate Integration of Single Cell Data

MultimodalExperiment:Integrative Bulk and Single-Cell Experiment Container

scuttle:Single-Cell RNA-Seq Analysis Utilities

partCNV:Infer locally aneuploid cells using single cell RNA-seq data

glmGamPoi:Fit a Gamma-Poisson Generalized Linear Model

SingleCellMultiModal:Integrating Multi-modal Single Cell Experiment datasets

nebula:Negative Binomial Mixed Models Using Large-Sample Approximation for Differential Expression Analysis of ScRNA-Seq Data

Dino:Normalization of Single-Cell mRNA Sequencing Data

MOFA2:Multi-Omics Factor Analysis v2

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

simPIC:simPIC: flexible simulation of paired-insertion counts for single-cell ATAC-sequencing data

clustree:Visualise Clusterings at Different Resolutions

escheR:Unified multi-dimensional visualizations with Gestalt principles

schex:Hexbin plots for single cell omics data

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

jazzPanda:Finding spatially relevant marker genes in image based spatial transcriptomics data

miloR:Differential neighbourhood abundance testing on a graph

SuperCell:Simplification of scRNA-seq data by merging together similar cells

ADImpute:Adaptive Dropout Imputer (ADImpute)

CytoSimplex:Simplex Visualization of Cell Fate Similarity in Single-Cell Data

scTreeViz:R/Bioconductor package to interactively explore and visualize single cell RNA-seq datasets with hierarhical annotations

ILoReg:ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data

imcRtools:Methods for imaging mass cytometry data analysis

SC.MEB:Spatial Clustering with Hidden Markov Random Field using Empirical Bayes

epiregulon:Gene regulatory network inference from single cell epigenomic data

scmap:A tool for unsupervised projection of single cell RNA-seq data

POWSC:Simulation, power evaluation, and sample size recommendation for single cell RNA-seq

peco:A Supervised Approach for Predicting cell Cycle Progression using scRNA-seq data