checkClusterability: Prepare the data for subsequent clustering

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

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[1:1000]
keepThat <- mvFilterGetIndices(obj, condition = 'WholeMatrix', threshold=ncol(obj))
obj <- mvFilterFromIndices(obj, keepThat)
averaged_means <- averageIntensities(obj)
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)

DAPAR documentation built on April 11, 2021, 6 p.m.