visualizeClusters {DAPAR} | R Documentation

## Visualize the clusters according to pvalue thresholds

### Description

Visualize the clusters according to pvalue thresholds

### Usage

    visualizeClusters(
      dat,
      clust_model,
      adjusted_pValues,
      FDR_th = NULL,
      ttl = "",
      subttl = ""
    )

### Arguments

`dat` | the standardize data returned by the function [checkClusterability()]
`clust_model` | the clustering model obtained with dat.
`adjusted_pValues` | vector of the adjusted pvalues obtained for each protein with a 1-way ANOVA (for example obtained with the function [wrapperClassic1wayAnova()]).
`FDR_th` | the thresholds of FDR pvalues for the coloring of the profiles. The default (NULL) creates 4 thresholds: 0.001, 0.005, 0.01, 0.05 For the sake of readability, a maximum of 4 values can be specified.
`ttl` | title for the plot.
`subttl` | subtitle for the plot.

### Value

a ggplot object

### Author(s)

Helene Borges

### Examples

    library(dplyr)
    utils::data(Exp1_R25_prot, package='DAPARdata')
    obj <- Exp1_R25_prot[1:1000]
    level <- obj@experimentData@other$typeOfData
    metacell.mask <- match.metacell(GetMetacell(obj), 'missing', level)
    indices <- GetIndices_WholeMatrix(metacell.mask, op='>=', th=1)
    obj <- MetaCellFiltering(obj, indices, cmd='delete')
    expR25_ttest <- compute_t_tests(obj$new)
    averaged_means <- averageIntensities(obj$new)
    only_means <- dplyr::select_if(averaged_means, is.numeric)
    only_features <- dplyr::select_if(averaged_means, is.character)
    means <- purrr::map(purrr::array_branch(as.matrix(only_means), 1),mean)
    centered <- only_means - unlist(means)
    centered_means <- dplyr::bind_cols(feature = dplyr::as_tibble(only_features),
    dplyr::as_tibble(centered))
    difference <- only_means[,1] - only_means[,2]
    clusters <- as.data.frame(difference) %>%
        dplyr::mutate(cluster = dplyr::if_else(difference > 0, 1,2))
    vizu <- visualizeClusters(dat = centered_means,
                              clust_model = as.factor(clusters$cluster),
             adjusted_pValues = expR25_ttest$P_Value$`25fmol_vs_10fmol_pval`,
                              FDR_th = c(0.001,0.005,0.01,0.05),
                              ttl = "Clustering of protein profiles")

[Package DAPAR version 1.24.8 Index]