pagoda.effective.cells

result of the pagoda.pathway.wPCA() call with n.randomizations > 1

pwpca

optional starting value for the optimization (if the NLS breaks, trying high starting values usually fixed the local gradient problem)

start


Examines the dependency between the amount of variance explained by the first principal component
of a gene set and the number of genes in a gene set to determine the effective number of cells
for the Tracy-Widom distribution


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).

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

Description

Usage

Arguments

Value

Examples