Single Cell Differential Expression

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Documentation for package ‘scde’ version 2.30.0

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scde-package Single-cell Differential Expression (with Pathway And Gene set Overdispersion Analysis)
bwpca Determine principal components of a matrix using per-observation/per-variable weights
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 on K-nearest neighbor cells.
make.pagoda.app Make the PAGODA app
o.ifm Sample error model
pagoda.cluster.cells Determine optimal cell clustering based on the genes driving the significant aspects
pagoda.effective.cells Estimate effective number of cells based on lambda1 of random gene sets
pagoda.gene.clusters Determine de-novo gene clusters and associated overdispersion info
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 the same sets of cells)
pagoda.show.pathways View pathway or gene weighted PCA
pagoda.subtract.aspect Control for a particular aspect of expression heterogeneity in a given population
pagoda.top.aspects Score statistical significance of gene set and cluster overdispersion
pagoda.varnorm Normalize gene expression variance relative to transcriptome-wide expectations
pagoda.view.aspects View PAGODA output
papply wrapper around different mclapply mechanisms
pollen Sample data
scde Single-cell Differential Expression (with Pathway And Gene set Overdispersion Analysis)
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 expression magnitudes
scde.fit.models.to.reference Fit scde models relative to provided set of expression magnitudes
scde.posteriors Calculate joint expression magnitude posteriors across a set of cells
scde.test.gene.expression.difference Test differential expression and plot posteriors for a particular gene
show.app View PAGODA application
view.aspects View heatmap
ViewPagodaApp A Reference Class to represent the PAGODA application
ViewPagodaApp-class A Reference Class to represent the PAGODA application
winsorize.matrix Winsorize matrix