plotFactorHist: Plot histogram of latent factor values

In MOFA: Multi-Omics Factor Analysis (MOFA)

Description

Plot a histogram of latent factor values.

Usage

plotFactorHist(object, factor, group_by = NULL, group_names = "",
  alpha = 0.5, binwidth = NULL, showMissing = FALSE)

Arguments

object	a trained MOFAmodel object.
factor	character vector with the factor name or numeric vector with the index of the factor.
group_by	specifies groups used to color the samples of the histogram. This can be either: a character giving the name of a feature, the name of a covariate (only if using a MultiAssayExperiment as input), or a vector of the same length as the number of samples.
group_names	names for the groups.
alpha	transparency parameter. Default is 0.5
binwidth	binwidth for histogram. Default is NULL, which uses ggplot2 default calculation.
showMissing	boolean indicating whether to remove sample for which group_by is missing (default is FALSE)

Details

One of the first steps for the annotation of factors is to visualise and color them using known covariates such as phenotypic or clinical data.
This method generates a histogram of the sample values in a given latent factor.
Similar functions are plotFactorScatter for doing scatter plots between pairs of factors and plotFactorBeeswarm for doing Beeswarm plots of single factors.

Value

Returns a ggplot2 object

Examples

# Example on the CLL data
filepath <- system.file("extdata", "CLL_model.hdf5", package = "MOFAdata")
MOFA_CLL <- loadModel(filepath)
plotFactorHist(MOFA_CLL, factor=1)
plotFactorHist(MOFA_CLL, factor=1, group_by= "IGHV")

# Example on the scMT data
filepath <- system.file("extdata", "scMT_model.hdf5", package = "MOFAdata")
MOFA_scMT <- loadModel(filepath)
plotFactorHist(MOFA_scMT, factor=2)

MOFA documentation built on Feb. 11, 2021, 2:01 a.m.