plotFactorHist {MOFA} | R Documentation

## Plot histogram of latent factor values

### 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)

[Package MOFA version 1.0.0 Index]