plot_sample_corr_heatmap {proBatch} | R Documentation

## Sample correlation matrix (heatmap)

### Description

Plot correlation of selected samples

### Usage

    plot_sample_corr_heatmap(data_matrix, samples_to_plot = NULL,
      sample_annotation = NULL, sample_id_col = "FullRunName",
      factors_to_plot = NULL, cluster_rows = FALSE, cluster_cols = FALSE,
      heatmap_color = colorRampPalette(rev(brewer.pal(n = 7, name =
      "RdYlBu")))(100), color_list = NULL, filename = NULL, width = NA,
      height = NA, units = c("cm", "in", "mm"),
      plot_title = sprintf("Correlation matrix of%s samples",
      ifelse(is.null(samples_to_plot), "", " selected")), ...)

### 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")`)
`samples_to_plot` | string vector of samples in `data_matrix` to be used in the plot
`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 covariates to be plotted in this diagnostic plot (assumed to be present in `sample_annotation`)
`cluster_rows` | boolean values determining if rows should be clustered or `hclust` object
`cluster_cols` | boolean values determining if columns should be clustered or `hclust` object
`heatmap_color` | vector of colors used in heatmap.
`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.
`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))
`...` | parameters for the `pheatmap` visualisation, for details see examples and help to corresponding functions

### Value

`pheatmap` object

### See Also

`pheatmap`

### Examples

    specified_samples = example_sample_annotation$FullRunName[
    which(example_sample_annotation$order %in% 110:115)]

    sample_corr_heatmap <- plot_sample_corr_heatmap(example_proteome_matrix,
    samples_to_plot = specified_samples,
    factors_to_plot = c('MS_batch','Diet', 'DateTime', 'digestion_batch'),
     cluster_rows= FALSE, cluster_cols=FALSE,
     annotation_names_col = TRUE, annotation_legend = 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'))
     sample_corr_heatmap_annotated <- plot_sample_corr_heatmap(log_transform_dm(example_proteome_matrix),
     sample_annotation = example_sample_annotation,
     factors_to_plot = c('MS_batch','Diet', 'DateTime', 'digestion_batch'),
     cluster_rows= FALSE, cluster_cols=FALSE,
     annotation_names_col = TRUE,
     show_colnames = FALSE, color_list = color_list)

[Package proBatch version 1.3.0 Index]