checkClusterability: xxx

In prostarproteomics/DAPAR: Tools for the Differential Analysis of Proteins Abundance with R

xxx

Description

The first step is to standardize the data (with the Mfuzz package). Then the function checks that these data are clusterizable or not (use of [diptest::dip.test()] to determine whether the distribution is unimodal or multimodal). Finally, it determines the "optimal" k by the Gap statistic approach.

Usage

checkClusterability(standards, b = 500)

Arguments

standards	a matrix or dataframe containing only the standardized mean intensities returned by the function [standardiseMeanIntensities()]
b	Parameter B of the function [gap_cluster()]

Value

a list of 2 elements: * dip_test: the result of the clusterability of the data * gap_cluster: the gap statistic obtained with the function [cluster::clusGap()].

Author(s)

Helene Borges

Examples

data(Exp1_R25_prot, package="DAPARdata")
obj <- Exp1_R25_prot[seq_len(100)]
level <- 'protein'
metacell.mask <- match.metacell(GetMetacell(obj), c("Missing POV", "Missing MEC"), level)
indices <- GetIndices_WholeMatrix(metacell.mask, op = ">=", th = 1)
obj <- MetaCellFiltering(obj, indices, cmd = "delete")
averaged_means <- averageIntensities(obj$new)
only_means <- dplyr::select_if(averaged_means, is.numeric)
only_features <- dplyr::select_if(averaged_means, is.character)
means <- purrr::map(purrr::array_branch(as.matrix(only_means), 1), mean)
centered <- only_means - unlist(means)
centered_means <- dplyr::bind_cols(
feature = dplyr::as_tibble(only_features),
dplyr::as_tibble(centered))
checkClust <- checkClusterability(centered_means, b = 100)

prostarproteomics/DAPAR documentation built on March 28, 2024, 4:44 a.m.