checkClusterability: Prepare the data for subsequent clustering

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

Prepare the data for subsequent clustering

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

utils::data(Exp1_R25_prot, package='DAPARdata')
obj <- Exp1_R25_prot[1:100]
level <- obj@experimentData@other$typeOfData
metacell.mask <- match.metacell(GetMetacell(obj), 'missing', level)
indices <- GetIndices_WholeMatrix(metacell.mask, op='>=', th=1)
obj <- MetaCellFiltering(obj, indices, cmd='delete')
averaged_means <- averageIntensities(obj$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)

samWieczorek/DAPAR documentation built on May 6, 2022, 5:30 p.m.