classifyDD: classifyDD

View source: R/classify.dd.R

classifyDDR Documentation

classifyDD

Description

Classify significantly DD genes into the four categories (DE, DP, DM or DB) based on posterior distributions of cluster mean parameters

Usage

classifyDD(pe_mat, condition, sig_genes, oa, c1, c2, alpha, m0, s0, a0, b0,
  log.nonzero = TRUE, adjust.perms = FALSE, ref, min.size = 3)

Arguments

pe_mat

Matrix with genes in rows and samples in columns. Column names indicate condition.

condition

Vector of condition indicators (with two possible values).

sig_genes

Vector of the indices of significantly DD genes (indicating the row number of pe_mat)

oa

List item with one item for each gene where the first element contains the cluster membership for each nonzero sample in the overall (pooled) fit.

c1

List item with one item for each gene where the first element contains the cluster membership for each nonzero sample in condition 1 only fit

c2

List item with one item for each gene where the first element contains the cluster membership for each nonzero sample in condition 2 only fit

alpha

Value for the Dirichlet concentration parameter

m0

Prior mean value for generating distribution of cluster means

s0

Prior precision value for generating distribution of cluster means

a0

Prior shape parameter value for the generating distribution of cluster precision

b0

Prior scale parameter value for the generating distribution of cluster precision

log.nonzero

Logical indicating whether to perform log transformation of nonzero values.

adjust.perms

Logical indicating whether or not to adjust the permutation tests for the sample detection rate (proportion of nonzero values). If true, the residuals of a linear model adjusted for detection rate are permuted, and new fitted values are obtained using these residuals.

ref

one of two possible values in condition; represents the referent category.

min.size

a positive integer that specifies the minimum size of a cluster (number of cells) for it to be used during the classification step. Any clusters containing fewer than min.size cells will be considered an outlier cluster and ignored in the classfication algorithm. The default value is three.

Value

cat Character vector of the same length as sig_genes that indicates which category of DD each significant gene belongs to (DE, DP, DM, DB, or NC (no call))

References

Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y


kdkorthauer/scDD documentation built on March 27, 2022, 5:11 a.m.