clean.counts

read count matrix. The rows correspond to genes, columns correspond to individual cells

counts

Minimum number of genes detected in a cell. Cells with fewer genes will be removed (default: 1.8e3)

min.lib.size

Minimum number of reads per gene. Genes with fewer reads will be removed (default: 10)

min.reads

Minimum number of cells a gene must be seen in. Genes not seen in a sufficient number of cells will be removed (default: 5)

min.detected

Filter counts matrix based on gene and cell requirements

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).

clean.counts: Filter counts matrix

Description

Usage

Arguments

Value

Examples