Learn R Programming

scde (version 2.0.1)

Single Cell Differential Expression

Description

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).

Copy Link

Version

Version

2.0.1

License

GPL-2

Maintainer

Peter Kharchenko

Last Published

February 15th, 2017

Functions in scde (2.0.1)

ViewPagodaApp-class

A Reference Class to represent the PAGODA application
pagoda.effective.cells

Estimate effective number of cells based on lambda1 of random gene sets
scde

Single-cell Differential Expression (with Pathway And Gene set Overdispersion Analysis)
pagoda.show.pathways

View pathway or gene weighted PCA
scde.expression.difference

Test for expression differences between two sets of cells
pagoda.top.aspects

Score statistical significance of gene set and cluster overdispersion
scde.error.models

Fit single-cell error/regression models
scde.expression.prior

Estimate prior distribution for gene expression magnitudes
es.mef.small

Sample data
clean.gos

Filter GOs list
papply

wrapper around different mclapply mechanisms
scde.edff

Internal model data
make.pagoda.app

Make the PAGODA app
pagoda.cluster.cells

Determine optimal cell clustering based on the genes driving the significant aspects
pagoda.reduce.redundancy

Collapse aspects driven by similar patterns (i.e. separate the same sets of cells)
pagoda.reduce.loading.redundancy

Collapse aspects driven by the same combinations of genes
bwpca

Determine principal components of a matrix using per-observation/per-variable weights
scde.posteriors

Calculate joint expression magnitude posteriors across a set of cells
knn.error.models

Build error models for heterogeneous cell populations, based on K-nearest neighbor cells.
pagoda.pathway.wPCA

Run weighted PCA analysis on pre-annotated gene sets
o.ifm

Sample error model
view.aspects

View heatmap
scde.test.gene.expression.difference

Test differential expression and plot posteriors for a particular gene
pagoda.view.aspects

View PAGODA output
scde.browse.diffexp

View differential expression results in a browser
scde.failure.probability

Calculate drop-out probabilities given a set of counts or expression magnitudes
pagoda.varnorm

Normalize gene expression variance relative to transcriptome-wide expectations
scde.expression.magnitude

Return scaled expression magnitude estimates
clean.counts

Filter counts matrix
pagoda.subtract.aspect

Control for a particular aspect of expression heterogeneity in a given population
knn

Sample error model
show.app

View PAGODA application
pagoda.gene.clusters

Determine de-novo gene clusters and associated overdispersion info
winsorize.matrix

Winsorize matrix
pollen

Sample data
scde.fit.models.to.reference

Fit scde models relative to provided set of expression magnitudes