knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
The waddR
package provides two testing procedures using the 2-Wasserstein
distance to test whether two distributions $F_A$ and $F_B$ given in the form of
samples are different by specifically testing the null hypothesis
$H_0: F_A = F_B$ against the alternative
$H_1: F_A \neq F_B$.
The first, semi-parametric (SP), procedure uses a test based on permutations combined with a generalized Pareto distribution approximation to estimate small p-values accurately.
The second procedure (ASY) uses a test based on asymptotic theory which is valid only if the samples can be assumed to come from continuous distributions.
To demonstrate the capabilities of the testing procedures, we consider models based on normal distributions here. We exemplarily construct three cases in which two distributions (samples) differ with respect to location, size, and shape, respectively, and one case without a difference. For convenience, we focus on the p-value, the value of the (squared) 2-Wasserstein distance, and the fractions of the location, size, and shape terms (in \%) with respect to the (squared) 2-Wasserstein distance here, while the functions also provide additional output.
library(waddR) spec.output<-c("pval","d.wass^2","perc.loc","perc.size","perc.shape")
We start with an example, in which the two distributions (samples) are supposed to only differ with respect to the location, and show the results for the two testing procedures (SP and ASY).
Note that the semi-parametric method "SP" with a permutation number of 10000 is used by default in
wasserstein.test
if nothing else is specified. The permutation procedure in the "SP" method uses a random group assignment step, and to obtain reproducible results, a seed can be set from the user environment before calling wasserstein.test
.
set.seed(24) ctrl<-rnorm(300,0,1) dd1<-rnorm(300,1,1) set.seed(32) wasserstein.test(ctrl,dd1,method="SP",permnum=10000)[spec.output] wasserstein.test(ctrl,dd1,method="ASY")[spec.output]
We obtain a very low p-value, clearly pointing at the existence of a difference, and see that differences with respect to location make up by far the major part of the 2-Wasserstein distance.
Analogously, we look at a case in which the two distributions (samples) are supposed to only differ with respect to the size.
set.seed(24) ctrl<-rnorm(300,0,1) dd2<-rnorm(300,0,2) set.seed(32) wasserstein.test(ctrl,dd2)[spec.output] wasserstein.test(ctrl,dd2,method="ASY")[spec.output]
Similarly, we consider an example in which the two distributions (samples) are supposed to only differ with respect to the shape.
set.seed(24) ctrl<-rnorm(300,6.5,sqrt(13.25)) sam1<-rnorm(300,3,1) sam2<-rnorm(300,10,1) dd3<-sapply(1:300, function(n) { sample(c(sam1[n],sam2[n]),1,prob=c(0.5, 0.5))}) set.seed(32) wasserstein.test(ctrl,dd3)[spec.output] wasserstein.test(ctrl,dd3,method="ASY")[spec.output]
Finally, we show an example in which the two distributions (samples) are supposed to be equal. We obtain a high p-value, indicating that the null hypothesis cannot be rejected.
set.seed(24) ctrl<-rnorm(300,0,1) nodd<-rnorm(300,0,1) set.seed(32) wasserstein.test(ctrl,nodd)[spec.output] wasserstein.test(ctrl,nodd,method="ASY")[spec.output]
The waddR
package
Detection of differential gene expression distributions in scRNAseq data
sessionInfo()
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