plotFactorBeeswarm: Beeswarm plot of latent factors
In MOFA: Multi-Omics Factor Analysis (MOFA)

Description Usage Arguments Details Value Examples

Beeswarm plot of the latent factor values.

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plotFactorBeeswarm(object, factors = "all", color_by = NULL,
  shape_by = NULL, name_color = "", name_shape = "",
  showMissing = FALSE)

`object`	a trained `MOFAmodel` object.
`factors`	character vector with the factor name(s), or numeric vector with the index of the factor(s) to use. Default is 'all'
`color_by`	specifies groups or values used to color the samples. This can be either: a character giving the name of a feature, a character giving the same of a covariate (only if using `MultiAssayExperiment` as input), or a vector of the same length as the number of samples specifying discrete groups or continuous numeric values.
`shape_by`	specifies groups or values used for the shape of samples. See color_by for how this can be specified. A maximum of 6 different values can be specified.
`name_color`	name for color legend (usually only used if color_by is not a character itself)
`name_shape`	name for shape legend (usually only used if shape_by is not a character itself)
`showMissing`	logical indicating whether to remove samples for which `shape_by` or `color_by` is missing.

One of the main steps for the annotation of factors is to visualise and color them using known covariates or phenotypic data.
This function generates a Beeswarm plot of the sample values in a given latent factor.
Similar functions are plotFactorScatter for doing scatter plots and plotFactorHist for doing histogram plots

Returns a ggplot2 object

# Example on the CLL data
filepath <- system.file("extdata", "CLL_model.hdf5", package = "MOFAdata")
MOFA_CLL <- loadModel(filepath)
plotFactorBeeswarm(MOFA_CLL, factors=1:3)
plotFactorBeeswarm(MOFA_CLL, factors=1:2, color_by= "IGHV")

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