Package: scde 2.33.0

Evan Biederstedt

scde: Single Cell Differential Expression

The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734).

Authors:Peter Kharchenko [aut, cre], Jean Fan [aut], Evan Biederstedt [aut]

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scde.pdf |scde.html✨
scde/json (API)

# Install 'scde' in R:
install.packages('scde', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/hms-dbmi/scde/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On BioConductor:scde-2.33.0(bioc 3.20)scde-2.32.0(bioc 3.19)

bioconductor-package

27 exports 2.89 score 39 dependencies 13 mentions

Last updated 2 months agofrom:c4214c1855

Exports:bwpcaclean.countsclean.gosknn.error.modelsmake.pagoda.apppagoda.cluster.cellspagoda.effective.cellspagoda.gene.clusterspagoda.pathway.wPCApagoda.reduce.loading.redundancypagoda.reduce.redundancypagoda.show.pathwayspagoda.subtract.aspectpagoda.top.aspectspagoda.varnormpagoda.view.aspectsscde.browse.diffexpscde.error.modelsscde.expression.differencescde.expression.magnitudescde.expression.priorscde.failure.probabilityscde.fit.models.to.referencescde.posteriorsscde.test.gene.expression.differenceshow.appwinsorize.matrix

Dependencies:BHBiobaseBiocGenericsBiocParallelbrewCairocodetoolscpp11distilleryedgeRextRemesflexmixformatRfutile.loggerfutile.optionslambda.rlatticelimmaLmomentslocfitMASSMatrixMatrixModelsmgcvmodeltoolsnlmennetpcaMethodsquantregRColorBrewerRcppRcppArmadillorjsonRMTstatRooksnowSparseMstatmodsurvival

Readme and manuals

Help Manual

Help pageTopics
Determine principal components of a matrix using per-observation/per-variable weightsbwpca
Filter counts matrixclean.counts
Filter GOs listclean.gos
Sample dataes.mef.small
Sample error modelknn
Build error models for heterogeneous cell populations, based on K-nearest neighbor cells.knn.error.models
Make the PAGODA appmake.pagoda.app
Sample error modelo.ifm
Determine optimal cell clustering based on the genes driving the significant aspectspagoda.cluster.cells
Estimate effective number of cells based on lambda1 of random gene setspagoda.effective.cells
Determine de-novo gene clusters and associated overdispersion infopagoda.gene.clusters
Run weighted PCA analysis on pre-annotated gene setspagoda.pathway.wPCA
Collapse aspects driven by the same combinations of genespagoda.reduce.loading.redundancy
Collapse aspects driven by similar patterns (i.e. separate the same sets of cells)pagoda.reduce.redundancy
View pathway or gene weighted PCApagoda.show.pathways
Control for a particular aspect of expression heterogeneity in a given populationpagoda.subtract.aspect
Score statistical significance of gene set and cluster overdispersionpagoda.top.aspects
Normalize gene expression variance relative to transcriptome-wide expectationspagoda.varnorm
View PAGODA outputpagoda.view.aspects
wrapper around different mclapply mechanismspapply
Sample datapollen
Single-cell Differential Expression (with Pathway And Gene set Overdispersion Analysis)scde-package scde
View differential expression results in a browserscde.browse.diffexp
Internal model datascde.edff
Fit single-cell error/regression modelsscde.error.models
Test for expression differences between two sets of cellsscde.expression.difference
Return scaled expression magnitude estimatesscde.expression.magnitude
Estimate prior distribution for gene expression magnitudesscde.expression.prior
Calculate drop-out probabilities given a set of counts or expression magnitudesscde.failure.probability
Fit scde models relative to provided set of expression magnitudesscde.fit.models.to.reference
Calculate joint expression magnitude posteriors across a set of cellsscde.posteriors
Test differential expression and plot posteriors for a particular genescde.test.gene.expression.difference
View PAGODA applicationshow.app
View heatmapview.aspects
A Reference Class to represent the PAGODA applicationViewPagodaApp ViewPagodaApp-class
Winsorize matrixwinsorize.matrix