plotDataHeatmap {MOFA} | R Documentation |
Function to plot a heatmap of the input data for relevant features, usually the ones with highest loadings in a given factor.
plotDataHeatmap(object, view, factor, features = 50, includeWeights = FALSE, transpose = FALSE, imputed = FALSE, ...)
object |
a |
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 |
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
.
plots a heatmap of the data for the top features for a given factor and views
# 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)