checkClusterability {DAPAR} | R Documentation

## 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)

[Package DAPAR version 1.24.8 Index]