Showing 10 of total 10 results (show query)
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-atlassingle-cell-genomicssingle-cell-rna-seqcpp
2.4k stars 16.86 score 50k scripts 73 dependentsbioc
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.
immunooncologynormalizationsequencingrnaseqsoftwaregeneexpressiontranscriptomicssinglecellclusteringbioconductor-packagehuman-cell-atlassingle-cell-rna-seqopenblascpp
41 stars 13.05 score 7.6k scripts 37 dependentsbioc
iSEE:Interactive SummarizedExperiment Explorer
Create an interactive Shiny-based graphical user interface for exploring data stored in SummarizedExperiment objects, including row- and column-level metadata. The interface supports transmission of selections between plots and tables, code tracking, interactive tours, interactive or programmatic initialization, preservation of app state, and extensibility to new panel types via S4 classes. Special attention is given to single-cell data in a SingleCellExperiment object with visualization of dimensionality reduction results.
Maintained by Kevin Rue-Albrecht. Last updated 24 days ago.
cellbasedassaysclusteringdimensionreductionfeatureextractiongeneexpressionguiimmunooncologyshinyappssinglecelltranscriptiontranscriptomicsvisualizationdimension-reductionfeature-extractiongene-expressionhacktoberfesthuman-cell-atlasshinysingle-cell
225 stars 12.86 score 380 scripts 9 dependentsbioc
BiocSingular:Singular Value Decomposition for Bioconductor Packages
Implements exact and approximate methods for singular value decomposition and principal components analysis, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Where possible, parallelization is achieved using the BiocParallel framework.
Maintained by Aaron Lun. Last updated 5 months ago.
softwaredimensionreductionprincipalcomponentbioconductor-packagehuman-cell-atlassingular-value-decompositioncpp
7 stars 12.10 score 1.2k scripts 103 dependentsbioc
beachmat:Compiling Bioconductor to Handle Each Matrix Type
Provides a consistent C++ class interface for reading from a variety of commonly used matrix types. Ordinary matrices and several sparse/dense Matrix classes are directly supported, along with a subset of the delayed operations implemented in the DelayedArray package. All other matrix-like objects are supported by calling back into R.
Maintained by Aaron Lun. Last updated 16 days ago.
datarepresentationdataimportinfrastructurebioconductor-packagehuman-cell-atlasmatrix-librarycpp
4 stars 11.09 score 21 scripts 142 dependentsbioc
SC3:Single-Cell Consensus Clustering
A tool for unsupervised clustering and analysis of single cell RNA-Seq data.
Maintained by Vladimir Kiselev. Last updated 5 months ago.
immunooncologysinglecellsoftwareclassificationclusteringdimensionreductionsupportvectormachinernaseqvisualizationtranscriptomicsdatarepresentationguidifferentialexpressiontranscriptionbioconductor-packagehuman-cell-atlassingle-cell-rna-seqopenblascpp
125 stars 10.10 score 374 scripts 1 dependentsbioc
scmap:A tool for unsupervised projection of single cell RNA-seq data
Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types or individual cells identified in a different experiment.
Maintained by Vladimir Kiselev. Last updated 5 months ago.
immunooncologysinglecellsoftwareclassificationsupportvectormachinernaseqvisualizationtranscriptomicsdatarepresentationtranscriptionsequencingpreprocessinggeneexpressiondataimportbioconductor-packagehuman-cell-atlasprojection-mappingsingle-cell-rna-seqopenblascpp
95 stars 8.82 score 172 scriptsbioc
M3Drop:Michaelis-Menten Modelling of Dropouts in single-cell RNASeq
This package fits a model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. Also includes an method for calculating exact Pearson residuals in UMI-tagged data using a library-size aware negative binomial model.
Maintained by Tallulah Andrews. Last updated 5 months ago.
rnaseqsequencingtranscriptomicsgeneexpressionsoftwaredifferentialexpressiondimensionreductionfeatureextractionhuman-cell-atlasrna-seqsingle-cellsingle-cell-rna-seq
29 stars 8.53 score 119 scripts 2 dependentsbioc
mbkmeans:Mini-batch K-means Clustering for Single-Cell RNA-seq
Implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation.
Maintained by Davide Risso. Last updated 5 months ago.
clusteringgeneexpressionrnaseqsoftwaretranscriptomicssequencingsinglecellhuman-cell-atlascpp
10 stars 7.41 score 54 scripts 2 dependentsbioc
CuratedAtlasQueryR:Queries the Human Cell Atlas
Provides access to a copy of the Human Cell Atlas, but with harmonised metadata. This allows for uniform querying across numerous datasets within the Atlas using common fields such as cell type, tissue type, and patient ethnicity. Usage involves first querying the metadata table for cells of interest, and then downloading the corresponding cells into a SingleCellExperiment object.
Maintained by Stefano Mangiola. Last updated 5 months ago.
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicsdatabaseduckdbhdf5human-cell-atlassingle-cellsinglecellexperimenttidyverse
90 stars 7.04 score 41 scripts