`waddR`

packageThe `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 a specifically tailored test for differential expression in single-cell RNA sequencing data.

`waddR`

provides tools to address the following tasks, each described in a separate vignette:

Two-sample tests to check for differences between two distributions,

Detection of differential gene expression distributions in single-cell RNA sequencing (scRNAseq) data.

These are bundled into one package, because they are internally dependent: The procedure for detecting differential distributions in scRNAseq data is an adaptation of the general two-sample test, which itself uses the 2-Wasserstein distance to compare two distributions.

The 2-Wasserstein distance is a metric to describe the distance between two distributions, representing e.g. two diferent conditions \(A\) and \(B\). The `waddR`

package specifically considers the squared 2-Wasserstein distance which can be decomposed into location, size, and shape terms, thus providing a characterization of potential differences.

The `waddR`

package offers three functions to calculate the (squared) 2-Wasserstein distance, which are implemented in C++ and exported to R with Rcpp for faster computation. The function `wasserstein_metric`

is a Cpp reimplementation of the `wasserstein1d`

function from the R package `transport`

. The functions `squared_wass_approx`

and `squared_wass_decomp`

compute approximations of the squared 2-Wasserstein distance, with `squared_wass_decomp`

also returning the decomposition terms for location, size, and shape.

See `?wasserstein_metric`

, `?squared_wass_aprox`

, and `?squared_wass_decomp`

for more details.

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 testing the null hypothesis \(H_0: F_A = F_B\) against the alternative hypothesis \(H_1: F_A != F_B\).

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.

See `?wasserstein.test`

for more details.

The `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 scRNAseq data. In particular, a two-stage (TS) approach is implemented that takes account of the specific nature of scRNAseq 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 semiparametric 2-Wasserstein distance-based test) between two conditions.

See `?wasserstein.sc`

and `?testZeroes`

for more details.

To install `waddR`

from Bioconductor, use `BiocManager`

with the following commands:

Using `BiocManager`

, the package can also be installed from GitHub directly:

The package `waddR`

can then be used in R:

```
sessionInfo()
#> R version 4.4.0 RC (2024-04-16 r86468)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] waddR_1.19.0
#>
#> loaded via a namespace (and not attached):
#> [1] SummarizedExperiment_1.35.0 xfun_0.43
#> [3] bslib_0.7.0 Biobase_2.65.0
#> [5] lattice_0.22-6 vctrs_0.6.5
#> [7] tools_4.4.0 generics_0.1.3
#> [9] stats4_4.4.0 curl_5.2.1
#> [11] parallel_4.4.0 tibble_3.2.1
#> [13] fansi_1.0.6 RSQLite_2.3.6
#> [15] blob_1.2.4 pkgconfig_2.0.3
#> [17] Matrix_1.7-0 arm_1.14-4
#> [19] dbplyr_2.5.0 S4Vectors_0.43.0
#> [21] lifecycle_1.0.4 GenomeInfoDbData_1.2.12
#> [23] compiler_4.4.0 codetools_0.2-20
#> [25] eva_0.2.6 GenomeInfoDb_1.41.0
#> [27] htmltools_0.5.8.1 sass_0.4.9
#> [29] yaml_2.3.8 nloptr_2.0.3
#> [31] pillar_1.9.0 crayon_1.5.2
#> [33] jquerylib_0.1.4 MASS_7.3-60.2
#> [35] BiocParallel_1.39.0 SingleCellExperiment_1.27.0
#> [37] DelayedArray_0.31.0 cachem_1.0.8
#> [39] boot_1.3-30 abind_1.4-5
#> [41] nlme_3.1-164 tidyselect_1.2.1
#> [43] digest_0.6.35 purrr_1.0.2
#> [45] dplyr_1.1.4 splines_4.4.0
#> [47] fastmap_1.1.1 grid_4.4.0
#> [49] SparseArray_1.5.0 cli_3.6.2
#> [51] magrittr_2.0.3 S4Arrays_1.5.0
#> [53] utf8_1.2.4 withr_3.0.0
#> [55] filelock_1.0.3 UCSC.utils_1.1.0
#> [57] bit64_4.0.5 rmarkdown_2.26
#> [59] XVector_0.45.0 httr_1.4.7
#> [61] matrixStats_1.3.0 lme4_1.1-35.3
#> [63] bit_4.0.5 coda_0.19-4.1
#> [65] memoise_2.0.1 evaluate_0.23
#> [67] knitr_1.46 GenomicRanges_1.57.0
#> [69] IRanges_2.39.0 BiocFileCache_2.13.0
#> [71] rlang_1.1.3 Rcpp_1.0.12
#> [73] glue_1.7.0 DBI_1.2.2
#> [75] BiocGenerics_0.51.0 minqa_1.2.6
#> [77] jsonlite_1.8.8 R6_2.5.1
#> [79] MatrixGenerics_1.17.0 zlibbioc_1.51.0
```