plotDataHeatmap {MOFA} | R Documentation

## Plot heatmap of relevant features

### Description

Function to plot a heatmap of the input data for relevant features, usually the ones with highest loadings in a given factor.

### Usage

    plotDataHeatmap(object, view, factor, features = 50,
      includeWeights = FALSE, transpose = FALSE, imputed = FALSE, ...)

### Arguments

`object` | a `MOFAmodel` object.
`view` | character vector with the view name, or numeric vector with the index of the view.
`factor` | character vector with the factor name, or numeric vector with the index of the factor.
`features` | if an integer, the total number of top features to plot, based on the absolute value of the loading. If a character vector, a set of manually-defined features. Default is 50.
`includeWeights` | logical indicating whether to include the weight of each feature as an extra annotation in the heatmap. Default is FALSE.
`transpose` | logical indicating whether to transpose the output heatmap. Default corresponds to features as rows and samples as columns.
`imputed` | logical indicating whether to plot the imputed data instead of the original data. Default is FALSE.
`...` | further arguments that can be passed to `pheatmap`

### Details

One of the first steps for the annotation of a given factor is to visualise the corresponding loadings, using for example `plotWeights` or `plotTopWeights`. These functions display the top features that are driving the heterogeneity captured by a factor.
However, one might also be interested in visualising the coordinated heterogeneity in the input data, rather than looking at "abstract" weights.
This function extracts the top features for a given factor and view, and generates a heatmap with dimensions (samples,features). This should reveal the underlying heterogeneity that is captured by the latent factor.
A similar function for doing scatterplots rather than heatmaps is `plotDataScatter`.

### Value

plots a heatmap of the data for the top features for a given factor and views

### Examples

    # Load CLL data
    filepath <- system.file("extdata", "CLL_model.hdf5", package = "MOFAdata")
    MOFA_CLL <- loadModel(filepath)
    # plot top 30 features on Factor 1 in the mRNA view
    plotDataHeatmap(MOFA_CLL, view="mRNA", factor=1, features=30)
    # without column names (extra arguments passed to pheatmap)
    plotDataHeatmap(MOFA_CLL, view="mRNA", factor=1, features=30, show_colnames = FALSE)
    # transpose the heatmap
    plotDataHeatmap(MOFA_CLL, view="mRNA", factor=1, features=30, transpose=TRUE)
    # do not cluster rows (extra arguments passed to pheatmap)
    plotDataHeatmap(MOFA_CLL, view="mRNA", factor=1, features=30, cluster_rows=FALSE)

[Package MOFA version 1.0.0 Index]