plot_heatmap_diagnostic {proBatch} | R Documentation

## Plot the heatmap of samples (cols) vs features (rows)

### Description

Plot the heatmap of samples (cols) vs features (rows)

### Usage

    plot_heatmap_diagnostic(
      data_matrix,
      sample_annotation = NULL,
      sample_id_col = "FullRunName",
      factors_to_plot = NULL,
      fill_the_missing = -1,
      color_for_missing = "black",
      heatmap_color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100),
      cluster_rows = TRUE,
      cluster_cols = FALSE,
      color_list = NULL,
      peptide_annotation = NULL,
      feature_id_col = "peptide_group_label",
      factors_of_feature_ann = c("KEGG_pathway", "evolutionary_distance"),
      color_list_features = NULL,
      filename = NULL,
      width = 7,
      height = 7,
      units = c("cm", "in", "mm"),
      plot_title = NULL,
      ...
    )

### Arguments

`data_matrix` | features (in rows) vs samples (in columns) matrix, with feature IDs in rownames and file/sample names as colnames. See "example_proteome_matrix" for more details (to call the description, use `help("example_proteome_matrix")`)
`sample_annotation` | data frame with: `sample_id_col` (this can be repeated as row names) biological covariates technical covariates (batches etc) . See `help("example_sample_annotation")`
`sample_id_col` | name of the column in `sample_annotation` table, where the filenames (colnames of the `data_matrix` are found).
`factors_to_plot` | vector of technical and biological factors to be plotted in this diagnostic plot (assumed to be present in `sample_annotation`)
`fill_the_missing` | numeric value that the missing values are substituted with, or `NULL` if features with missing values are to be excluded.
`color_for_missing` | special color to make missing values. Usually black or white, depending on `heatmap_color`
`heatmap_color` | vector of colors used in heatmap (typicall a gradient)
`cluster_rows` | boolean value determining if rows should be clustered
`cluster_cols` | boolean value determining if columns should be clustered
`color_list` | list, as returned by `sample_annotation_to_colors`, where each item contains a color vector for each factor to be mapped to the color.
`peptide_annotation` | long format data frame with peptide ID and their corresponding protein and/or gene annotations. See `help("example_peptide_annotation")`.
`feature_id_col` | name of the column with feature/gene/peptide/protein ID used in the long format representation `df_long`. In the wide formatted representation `data_matrix` this corresponds to the row names.
`factors_of_feature_ann` | vector of factors that characterize features, as listed in `peptide_annotation`
`color_list_features` | list, as returned by `sample_annotation_to_colors`, but mapping `peptide_annotation` where each item contains a color vector for each factor to be mapped to the color.
`filename` | path where the results are saved. If null the object is returned to the active window; otherwise, the object is save into the file. Currently only pdf and png format is supported
`width` | option determining the output image width
`height` | option determining the output image width
`units` | units: 'cm', 'in' or 'mm'
`plot_title` | title of the plot (e.g., processing step + representation level (fragments, transitions, proteins) + purpose (meanplot/corrplot etc))
`...` | other parameters of `link[pheatmap]{pheatmap}`

### Value

object returned by `link[pheatmap]{pheatmap}`

### See Also

`sample_annotation_to_colors`, `pheatmap`

### Examples

    log_transformed_matrix = log_transform_dm(example_proteome_matrix)
    heatmap_plot <- plot_heatmap_diagnostic(log_transformed_matrix,
    example_sample_annotation,
    factors_to_plot = c("MS_batch",  "digestion_batch", "Diet", 'DateTime'),
    cluster_cols = TRUE, cluster_rows = FALSE,
    show_rownames = FALSE, show_colnames = FALSE)

    color_list <- sample_annotation_to_colors (example_sample_annotation,
    factor_columns = c('MS_batch','EarTag', "Strain",
    "Diet", "digestion_batch", "Sex"),
    numeric_columns = c('DateTime', 'order'))

    log_transformed_matrix = log_transform_dm(example_proteome_matrix)
    heatmap_plot <- plot_heatmap_diagnostic(log_transformed_matrix,
    example_sample_annotation,
    factors_to_plot = c("MS_batch",  "digestion_batch", "Diet", 'DateTime'),
    cluster_cols = TRUE, cluster_rows = FALSE,
    color_list = color_list,
    show_rownames = FALSE, show_colnames = FALSE)

[Package proBatch version 1.10.0 Index]