For more details on the methods and further information, please have a look at our paper: Schefzik, R., Flesch, J., and Goncalves, A. (2021). Fast identification of differential distributions in single-cell RNA-sequencing data with waddR. Bioinformatics, 37, 3204-3211. DOI: https://doi.org/10.1093/bioinformatics/btab226
waddR package offers statistical tests based on the 2-Wasserstein distance for detecting and characterizing differences between two distributions given in the form of samples. Functions for calculating the 2-Wasserstein distance and testing for differential distributions are provided, as well as specifically tailored test for differential expression in single-cell RNA sequencing data.
The package provides tools to address the following tasks: 1. Computation of the 2-Wasserstein distance 2. Two-sample tests to check for differences between two distributions 3. Detection of differential gene expression distributions in single-cell RNA sequencing data
Available on Bioconductor:
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("waddR")
The latest package version can be installed from Github using
if (!requireNamespace("BiocManager")) install.packages("BiocManager") BiocManager::install("goncalves-lab/waddR")
Tests can be run by calling
test() from the
All tests are implemented using the
testthat package and reside in
The 2-Wasserstein distance is a metric to quantify the distance between two
distributions, representing two different conditions A and B. The
specifically considers the squared 2-Wasserstein distance, which
offers a decomposition into location, size, and shape terms, thus providing a characterization of potential differences.
waddR package offers three functions to calculate the 2-Wasserstein
distance, which are implemented in C++ and exported to R with Rcpp for
wasserstein_metric is a C++ reimplementation of the
wasserstein1d from the R package
approximations of the squared 2-Wasserstein distance, with
also returning the decomposition terms for location, size, and shape.
?squared_wass_decomp, as well as the accompanying paper Schefzik et al. (2021).
waddR package provides two testing procedures using the 2-Wasserstein distance
to test whether two distributions given in the form of samples are
different by specifically testing the null hypothesis of no difference against the
alternative hypothesis that the two distributions are different.
The first, semi-parametric (SP), procedure uses a permutation-based test combined with a generalized Pareto distribution approximation to estimate small p-values accurately.
The second procedure uses a test based on asymptotic theory (ASY) which is valid only if the samples can be assumed to come from continuous distributions.
?wasserstein.test for more details.
waddR package provides an adaptation of the
semi-parametric testing procedure based on the 2-Wasserstein distance
which is specifically tailored to identify differential distributions in
single-cell RNA-seqencing (scRNA-seq) data. In particular, a two-stage
(TS) approach is implemented that takes account of the specific
nature of scRNA-seq data by separately testing for differential
proportions of zero gene expression (using a logistic regression model)
and differences in non-zero gene expression (using the semi-parametric
2-Wasserstein distance-based test) between two conditions.
Note that as input for scRNA-seq analysis,
waddR expects a table of pre-filtered and normalised count data. As filtering and normalisation are important steps that can have a profound impact in a scRNA-seq workflow (Cole et al., 2019), these should be tailored to the specific question of interest before applying
waddR is applicable to data from any scRNA-seq platform (demonstrated in our paper for 10x Genomics and Fluidigm C1 Smart-Seq2) normalised using most common methods, such as those implemented in the
Seurat (Butler et al., 2018) or
scran (Lun et al., 2016) packages. Normalisation approaches that change the shape of the gene distributions (such as quantile normalisation) and gene-wise scaling or standardizing should be avoided when using
?testZeroes for more details.
We have included detailed examples of how to use the functions provided with
waddR in our vignettes.
They are available online here
(update this link once it is final) or from an R session with the
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., and Satija, R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology, 36, 411–420.
Cole, M. B., Risso, D., Wagner, A., De Tomaso, D., Ngai, J., Purdom, E., Dudoit, S., and Yosef, N. (2019). Performance assessment and selection of normalization procedures for single-cell RNA-seq. Cell Systems, 8, 315–328.
Lun, A. T. L., Bach, K., and Marioni, J. C. (2016). Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biology, 17, 75.
Schefzik, R., Flesch, J., and Goncalves, A. (2021). Fast identification of differential distributions in single-cell RNA-sequencing data with waddR. To appear in Bioinformatics. DOI: https://doi.org/10.1093/bioinformatics/btab226
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.