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citcdf

CRAN status R-CMD-check

Overview

citcdf is a package to perform conditional independence testing using empirical conditional cumulative distribution function estimations.

The package has two main entry points: cit_multi() for gene-wise conditional independence testing across many outcomes, and cit_gsa() for gene-set analysis. Both accept test = "asymptotic" (large samples) or test = "permutation" (small samples). The single-outcome workhorses cit_asymp() and cit_perm(), the CCDF estimator ccdf(), and the plotting functions plot_compare_ccdf(), plot.cit_multi() and plot.cit_gsa() are also exported.

The approach implemented in this package is detailed in the following article:

Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2021). Distribution-free complex hypothesis testing for single-cell RNA-seq differential expression analysis, bioRxiv doi:10.1101/2021.05.21.445165

Installation

citcdf is available on CRAN:

install.packages("citcdf")

The development version is available from GitHub:

# install.packages("remotes")
remotes::install_github("sistm/citcdf")

Example

Here is a basic example which shows how to use citcdf with simple generated data.

library(citcdf)
## Data Generation
set.seed(123)
n <- 100
X <- data.frame(X1 = as.factor(rbinom(n = n, size = 1, prob = 0.5)))
Y <- replicate(10, (X$X1 == 1) * rnorm(n) + (X$X1 == 0) * rnorm(n, mean = 0.5))
# Hypothesis testing
res_asymp <- cit_multi(M = data.frame(Y = Y), X = X,
                       test = "asymptotic", parallel = FALSE) # asymptotic test
res_asymp$pvals
#>          raw_pval    adj_pval test_statistic
#> Y.1  0.0003601504 0.003601504      30.418136
#> Y.2  0.3282920437 0.328292044       4.115804
#> Y.3  0.3225103024 0.328292044       4.372046
#> Y.4  0.0384303687 0.085257670      11.802747
#> Y.5  0.0678438958 0.096919851      10.451354
#> Y.6  0.0502973835 0.085257670      12.780768
#> Y.7  0.0158684748 0.072020162      15.581473
#> Y.8  0.0216060487 0.072020162      14.080686
#> Y.9  0.0511546022 0.085257670       8.920593
#> Y.10 0.1628874626 0.203609328       6.991416
plot(res_asymp)

plot_compare_ccdf(Y=Y[, 1, drop=FALSE], X=X)

– Marine Gauthier, Denis Agniel, Sara Fallet, Kalidou Ba, Rodolphe Thiébaut & Boris Hejblum

hex illustration by Jérôme Dubois.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.