results: results

In scDD: Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions

Description

extract results objects after running scDD analysis

Usage

results(SCdat, type = c("Genes", "Zhat.c1", "Zhat.c2", "Zhat.combined"))

Arguments

SCdat

An object of class SingleCellExperiment that contains normalized single-cell expression and metadata, and the output of the scDD function.

type

A character variable specifying which output is desired, with possible values "Genes", "Zhat.c1", "Zhat.c2", and "Zhat.overall". The default value is "Genes", which contains a a data frame with nine columns: gene name (matches rownames of SCdat), permutation p-value for testing of independence of condition membership with clustering, Benjamini-Hochberg adjusted version of the previous column, p-value for test of difference in dropout rate (only for non-DD genes), Benjamini-Hochberg adjusted version of the previous column, name of the DD (DE, DP, DM, DB) pattern or DZ (otherwise NS = not significant), the number of clusters identified overall, the number of clusters identified in condition 1 alone, and the number of clusters identified in condition 2 alone. If type is "Zhat.c1", then a matrix is returned that contains the fitted cluster memberships (partition estimates Z) for each sample (cluster number given by 1,2,3,...) in columns and gene in rows only for condition 1. The same information is returned only for condition 2, and for the overall clustering, when type is set to "Zhat.c2" or "Zhat.overall", respectively. Zeroes, which are not involved in the clustering, are labeled as zero.

Details

Convenient helper function to extract the results (gene classifications, pvalues, and clustering information). Results data.frames/matrices are stored in the metadata slot and can also be accessed without the help of this convenience function by calling metadata(SCdat).

Value

A data.frame which contains either the gene classification and p-value results, or cluster membership information, as detailed in the description of the type input parameter.

Examples

 
# load toy simulated example SingleCellExperiment object to find DD genes  
data(scDatExSim)

# set arguments to pass to scDD function

prior_param=list(alpha=0.01, mu0=0, s0=0.01, a0=0.01, b0=0.01)

# call the scDD function to perform permutations and classify DD genes

scDatExSim <- scDD(scDatExSim, prior_param=prior_param, testZeroes=FALSE)

# extract main results object

RES <- results(scDatExSim)

scDD documentation built on Nov. 8, 2020, 7:10 p.m.