dot-testWass: Check for differential distributions in single-cell RNA...

In goncalves-lab/diffexpR: Statistical tests for detecting differential distributions based on the 2-Wasserstein distance

Check for differential distributions in single-cell RNA sequencing data via a semi-paramteric test using the 2-Wasserstein distance

Description

Two-sample test for single-cell RNA-sequencing data to check for differences between two distributions using the 2-Wasserstein distance: Semi-parametric implementation using a permutation test with a generalized Pareto distribution (GPD) approximation to estimate small p-values accurately

Usage

.testWass(dat, condition, permnum, inclZero = TRUE, seed = NULL)

Arguments

dat	matrix of single-cell RNA-sequencing expression data, with rowas corresponding to genes and columns corresponding to cells (samples)
condition	vector of condition labels
permnum	number of permutations used in the permutation testing procedure
inclZero	logical; if TRUE, a one-stage method is performed, i.e. the semi-parametric test based on the 2-Wasserstein distance is applied to all (zero and non-zero) expression values; if FALSE, a two-stage method is performed, i.e. the semi-parametric test based on the 2-Wasserstein distance is applied to non-zero expression values only, and a separate test for differential proportions of zero expression using logistic regression is conducted; default is TRUE
seed	Number to be used as a L'Ecuyer-CMRG seed, which itself seeds the generation of an nextRNGStream() for each gene. Internally, when this argument is given, a seed is specified by calling ‘RNGkind("L’Ecuyer-CMRG")' followed by 'set.seed(seed)'. The 'RNGkind' and '.Random.seed' will be reset on termination of this function. Default is NULL, and no seed is set.

Details

Details concerning the testing procedure for single-cell RNA-sequencing data can be found in Schefzik et al. (2021) and in the description of the details of the function wasserstein.sc.

Value

Matrix, where each row contains the testing results of the respective gene from dat. For the corresponding values of each row (gene), see the description of the function wasserstein.sc, where the argument inclZero=TRUE in .testWass has to be identified with the argument method="OS", and the argument inclZero=FALSE with the argument method="TS".

References

Schefzik, R., Flesch, J., and Goncalves, A. (2021). Fast identification of differential distributions in single-cell RNA-sequencing data with waddR.

goncalves-lab/diffexpR documentation built on June 5, 2023, 10:18 p.m.