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).

AuthorPeter Kharchenko [aut, cre], Jean Fan [aut]
Date of publicationNone
MaintainerJean Fan <jeanfan@fas.harvard.edu>
LicenseGPL-2
Version2.2.0
http://pklab.med.harvard.edu/scde

View on Bioconductor

Man pages

bwpca: Determine principal components of a matrix using...

clean.counts: Filter counts matrix

clean.gos: Filter GOs list

es.mef.small: Sample data

knn: Sample error model

knn.error.models: Build error models for heterogeneous cell populations, based...

make.pagoda.app: Make the PAGODA app

o.ifm: Sample error model

pagoda.cluster.cells: Determine optimal cell clustering based on the genes driving...

pagoda.effective.cells: Estimate effective number of cells based on lambda1 of random...

pagoda.gene.clusters: Determine de-novo gene clusters and associated overdispersion...

pagoda.pathway.wPCA: Run weighted PCA analysis on pre-annotated gene sets

pagoda.reduce.loading.redundancy: Collapse aspects driven by the same combinations of genes

pagoda.reduce.redundancy: Collapse aspects driven by similar patterns (i.e. separate...

pagoda.show.pathways: View pathway or gene weighted PCA

pagoda.subtract.aspect: Control for a particular aspect of expression heterogeneity...

pagoda.top.aspects: Score statistical significance of gene set and cluster...

pagoda.varnorm: Normalize gene expression variance relative to...

pagoda.view.aspects: View PAGODA output

papply: wrapper around different mclapply mechanisms

pollen: Sample data

scde: Single-cell Differential Expression (with Pathway And Gene...

scde.browse.diffexp: View differential expression results in a browser

scde.edff: Internal model data

scde.error.models: Fit single-cell error/regression models

scde.expression.difference: Test for expression differences between two sets of cells

scde.expression.magnitude: Return scaled expression magnitude estimates

scde.expression.prior: Estimate prior distribution for gene expression magnitudes

scde.failure.probability: Calculate drop-out probabilities given a set of counts or...

scde.fit.models.to.reference: Fit scde models relative to provided set of expression...

scde.posteriors: Calculate joint expression magnitude posteriors across a set...

scde.test.gene.expression.difference: Test differential expression and plot posteriors for a...

show.app: View PAGODA application

view.aspects: View heatmap

ViewPagodaApp-class: A Reference Class to represent the PAGODA application

winsorize.matrix: Winsorize matrix

Files in this package

scde/DESCRIPTION
scde/NAMESPACE
scde/R
scde/R/functions.R
scde/data
scde/data/es.mef.small.rda
scde/data/knn.rda
scde/data/o.ifm.rda
scde/data/pollen.rda
scde/data/scde.edff.rda
scde/man
scde/man/ViewPagodaApp-class.Rd scde/man/bwpca.Rd scde/man/clean.counts.Rd scde/man/clean.gos.Rd scde/man/es.mef.small.Rd scde/man/knn.Rd scde/man/knn.error.models.Rd scde/man/make.pagoda.app.Rd scde/man/o.ifm.Rd scde/man/pagoda.cluster.cells.Rd scde/man/pagoda.effective.cells.Rd scde/man/pagoda.gene.clusters.Rd scde/man/pagoda.pathway.wPCA.Rd scde/man/pagoda.reduce.loading.redundancy.Rd scde/man/pagoda.reduce.redundancy.Rd scde/man/pagoda.show.pathways.Rd scde/man/pagoda.subtract.aspect.Rd scde/man/pagoda.top.aspects.Rd scde/man/pagoda.varnorm.Rd scde/man/pagoda.view.aspects.Rd scde/man/papply.Rd scde/man/pollen.Rd scde/man/scde.Rd scde/man/scde.browse.diffexp.Rd scde/man/scde.edff.Rd scde/man/scde.error.models.Rd scde/man/scde.expression.difference.Rd scde/man/scde.expression.magnitude.Rd scde/man/scde.expression.prior.Rd scde/man/scde.failure.probability.Rd scde/man/scde.fit.models.to.reference.Rd scde/man/scde.posteriors.Rd scde/man/scde.test.gene.expression.difference.Rd scde/man/show.app.Rd scde/man/view.aspects.Rd scde/man/winsorize.matrix.Rd
scde/src
scde/src/Makevars
scde/src/Makevars.win
scde/src/bwpca.cpp
scde/src/bwpca.h
scde/src/jpmatLogBoot.cpp
scde/src/jpmatLogBoot.h
scde/src/matSlideMult.cpp
scde/src/matSlideMult.h
scde/src/pagoda.cpp
scde/src/pagoda.h
scde/vignettes
scde/vignettes/diffexp.Rmd
scde/vignettes/pagoda.Rmd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.