classifyDD: classifyDD

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

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.