R-universe search: topic:scrna-seq

Showing 21 of total 21 results (show query)

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 5 months ago.

algorithm data-integration scrna-seq openblas cpp

554 stars 13.74 score 5.5k scripts 8 dependents

bioc

zellkonverter:Conversion Between scRNA-seq Objects

Provides methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes functions to read and write H5AD files used for saving AnnData objects to disk.

Maintained by Luke Zappia. Last updated 20 days ago.

singlecell dataimport datarepresentation bioconductor conversion scrna-seq

159 stars 11.25 score 660 scripts 4 dependents

bioc

BASiCS:Bayesian Analysis of Single-Cell Sequencing data

Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend.

Maintained by Catalina Vallejos. Last updated 5 months ago.

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

83 stars 10.14 score 368 scripts 1 dependents

bioc

splatter:Simple Simulation of Single-cell RNA Sequencing Data

Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets.

Maintained by Luke Zappia. Last updated 4 months ago.

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

224 stars 9.92 score 424 scripts 1 dependents

bioc

BgeeDB:Annotation and gene expression data retrieval from Bgee database. TopAnat, an anatomical entities Enrichment Analysis tool for UBERON ontology

A package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns.

Maintained by Julien Wollbrett. Last updated 5 months ago.

software dataimport sequencing geneexpression microarray go genesetenrichment bioinformatics enrichment-analysis rna-seq scrna-seq single-cell

15 stars 8.46 score 19 scripts 1 dependents

samuel-marsh

scCustomize:Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing

Collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using 'R'. 'scCustomize' aims to provide 1) Customized visualizations for aid in ease of use and to create more aesthetic and functional visuals. 2) Improve speed/reproducibility of common tasks/pieces of code in scRNA-seq analysis with a single or group of functions. For citation please use: Marsh SE (2021) "Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing" <doi:10.5281/zenodo.5706430> RRID:SCR_024675.

Maintained by Samuel Marsh. Last updated 3 months ago.

customization ggplot2 scrna-seq seurat single-cell single-cell-genomics single-cell-rna-seq visualization

246 stars 8.45 score 1.1k scripts

kharchenkolab

pagoda2:Single Cell Analysis and Differential Expression

Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.

Maintained by Evan Biederstedt. Last updated 1 years ago.

scrna-seq single-cell single-cell-rna-seq transcriptomics openblas cpp openmp

223 stars 8.00 score 282 scripts

kharchenkolab

conos:Clustering on Network of Samples

Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/conos>. The size of the 'conosPanel' package is approximately 12 MB.

Maintained by Evan Biederstedt. Last updated 1 years ago.

batch-correction scrna-seq single-cell-rna-seq openblas cpp openmp

205 stars 7.33 score 258 scripts

bioc

cardelino:Clone Identification from Single Cell Data

Methods to infer clonal tree configuration for a population of cells using single-cell RNA-seq data (scRNA-seq), and possibly other data modalities. Methods are also provided to assign cells to inferred clones and explore differences in gene expression between clones. These methods can flexibly integrate information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. A flexible beta-binomial error model that accounts for stochastic dropout events as well as systematic allelic imbalance is used.

Maintained by Davis McCarthy. Last updated 5 months ago.

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

61 stars 7.05 score 62 scripts

qile0317

APackOfTheClones:Visualization of Clonal Expansion for Single Cell Immune Profiles

Visualize clonal expansion via circle-packing. 'APackOfTheClones' extends 'scRepertoire' to produce a publication-ready visualization of clonal expansion at a single cell resolution, by representing expanded clones as differently sized circles. The method was originally implemented by Murray Christian and Ben Murrell in the following immunology study: Ma et al. (2021) <doi:10.1126/sciimmunol.abg6356>.

Maintained by Qile Yang. Last updated 4 months ago.

clonal-analysis immune-repertoire immune-system scrna-seq scrnaseq seurat single-cell single-cell-genomics cpp

15 stars 6.45 score 15 scripts

bioc

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

A pipeline which processes single cell RNA-seq (scRNA-seq) reads from CEL-seq and CEL-seq2 protocols. Demultiplex scRNA-seq FASTQ files, align reads to reference genome using Rsubread, and generate UMI filtered count matrix. Also provide visualizations of read alignments and pre- and post-alignment QC metrics.

Maintained by Zhe Wang. Last updated 5 months ago.

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

8 stars 6.20 score 22 scripts

bioc

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 1 months ago.

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

5 stars 5.89 score 26 scripts

bioc

scBubbletree:Quantitative visual exploration of scRNA-seq data

scBubbletree is a quantitative method for the visual exploration of scRNA-seq data, preserving key biological properties such as local and global cell distances and cell density distributions across samples. It effectively resolves overplotting and enables the visualization of diverse cell attributes from multiomic single-cell experiments. Additionally, scBubbletree is user-friendly and integrates seamlessly with popular scRNA-seq analysis tools, facilitating comprehensive and intuitive data interpretation.

Maintained by Simo Kitanovski. Last updated 5 months ago.

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

6 stars 5.82 score 8 scripts

rezakj

iCellR:Analyzing High-Throughput Single Cell Sequencing Data

A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550> and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.

Maintained by Alireza Khodadadi-Jamayran. Last updated 9 months ago.

10xgenomics 3d batch-normalization cell-type-classification cite-seq clustering clustering-algorithm diffusion-maps dropout icellr imputation intractive-graph normalization pseudotime scrna-seq scvdj-seq singel-cell-sequencing umap cpp

121 stars 5.56 score 7 scripts 1 dependents

bioc

BgeeCall:Automatic RNA-Seq present/absent gene expression calls generation

BgeeCall allows to generate present/absent gene expression calls without using an arbitrary cutoff like TPM<1. Calls are generated based on reference intergenic sequences. These sequences are generated based on expression of all RNA-Seq libraries of each species integrated in Bgee (https://bgee.org).

Maintained by Julien Wollbrett. Last updated 5 months ago.

software geneexpression rnaseq biology gene-expression gene-level intergenic-regions present-absent-calls rna-seq rna-seq-libraries scrna-seq

3 stars 5.56 score 9 scripts

igordot

clustermole:Unbiased Single-Cell Transcriptomic Data Cell Type Identification

Assignment of cell type labels to single-cell RNA sequencing (scRNA-seq) clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging when unexpected or poorly described populations are present. The clustermole R package provides methods to query thousands of human and mouse cell identity markers sourced from a variety of databases.

Maintained by Igor Dolgalev. Last updated 1 years ago.

cell-type cell-type-annotation cell-type-classification cell-type-identification cell-type-matching gene-expression-signatures scrna-seq single-cell

13 stars 5.37 score 36 scripts

bioc

NewWave:Negative binomial model for scRNA-seq

A model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise.

Maintained by Federico Agostinis. Last updated 5 months ago.

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

4 stars 5.33 score 27 scripts

jiang-junyao

CACIMAR:cross-species analysis of cell identities, markers and regulations

A toolkit to perform cross-species analysis based on scRNA-seq data. CACIMAR contains 5 main features. (1) identify Markers in each cluster. (2) Cell type annotaion (3) identify conserved markers. (4) identify conserved cell types. (5) identify conserved modules of regulatory networks.

Maintained by Junyao Jiang. Last updated 4 months ago.

cross-species-analysis scrna-seq

12 stars 5.26 score 6 scripts

bioc

TREG:Tools for finding Total RNA Expression Genes in single nucleus RNA-seq data

RNA abundance and cell size parameters could improve RNA-seq deconvolution algorithms to more accurately estimate cell type proportions given the different cell type transcription activity levels. A Total RNA Expression Gene (TREG) can facilitate estimating total RNA content using single molecule fluorescent in situ hybridization (smFISH). We developed a data-driven approach using a measure of expression invariance to find candidate TREGs in postmortem human brain single nucleus RNA-seq. This R package implements the method for identifying candidate TREGs from snRNA-seq data.

Maintained by Louise Huuki-Myers. Last updated 4 months ago.

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

4 stars 5.20 score 5 scripts

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

12 stars 4.38 score 5 scripts

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

zellkonverter:Conversion Between scRNA-seq Objects

BASiCS:Bayesian Analysis of Single-Cell Sequencing data

splatter:Simple Simulation of Single-cell RNA Sequencing Data

BgeeDB:Annotation and gene expression data retrieval from Bgee database. TopAnat, an anatomical entities Enrichment Analysis tool for UBERON ontology

scCustomize:Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing

pagoda2:Single Cell Analysis and Differential Expression

conos:Clustering on Network of Samples

cardelino:Clone Identification from Single Cell Data

APackOfTheClones:Visualization of Clonal Expansion for Single Cell Immune Profiles

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

MuData:Serialization for MultiAssayExperiment Objects

scBubbletree:Quantitative visual exploration of scRNA-seq data

iCellR:Analyzing High-Throughput Single Cell Sequencing Data

BgeeCall:Automatic RNA-Seq present/absent gene expression calls generation

clustermole:Unbiased Single-Cell Transcriptomic Data Cell Type Identification

NewWave:Negative binomial model for scRNA-seq

CACIMAR:cross-species analysis of cell identities, markers and regulations

TREG:Tools for finding Total RNA Expression Genes in single nucleus RNA-seq data

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

scINSIGHT:Interpretation of Heterogeneous Single-Cell Gene Expression Data

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