plot_factors_vs_cov {MOFA2} | R Documentation

## Scatterplots of a factor's values againt the sample covariates

### Description

Scatterplots of a factor's values againt the sample covariates

### Usage

    plot_factors_vs_cov(
      object,
      factors = "all",
      covariates = NULL,
      warped = TRUE,
      show_missing = TRUE,
      scale = FALSE,
      color_by = NULL,
      shape_by = NULL,
      color_name = NULL,
      shape_name = NULL,
      dot_size = 1.5,
      alpha = 1,
      stroke = NULL,
      legend = TRUE,
      rotate_x = FALSE,
      rotate_y = FALSE,
      return_data = FALSE,
      show_variance = FALSE
    )

### Arguments

`object` | a trained `MOFA` object using MEFISTO.
`factors` | character or numeric specifying the factor(s) to plot, default is "all"
`covariates` | specifies sample covariate(s) to plot against: (1) a character giving the name of a column present in the sample covariates or sample metadata. (2) a character giving the name of a feature present in the training data. (3) a vector of the same length as the number of samples specifying continuous numeric values per sample. Default is the first sample covariates in covariates slot
`warped` | logical indicating whether to show the aligned covariate (default: TRUE), only relevant if warping has been used to align multiple sample groups
`show_missing` | (for 1-dim covariates) logical indicating whether to include samples for which `shape_by` or `color_by` is missing
`scale` | logical indicating whether to scale factor values.
`color_by` | (for 1-dim covariates) specifies groups or values used to color the samples. This can be either: (1) a character giving the name of a feature present in the training data. (2) a character giving the same of a column present in the sample metadata. (3) a vector of the same length as the number of samples specifying discrete groups or continuous numeric values.
`shape_by` | (for 1-dim covariates) specifies groups or values used to shape the samples. This can be either: (1) a character giving the name of a feature present in the training data, (2) a character giving the same of a column present in the sample metadata. (3) a vector of the same length as the number of samples specifying discrete groups.
`color_name` | (for 1-dim covariates) name for color legend.
`shape_name` | (for 1-dim covariates) name for shape legend.
`dot_size` | (for 1-dim covariates) numeric indicating dot size.
`alpha` | (for 1-dim covariates) numeric indicating dot transparency.
`stroke` | (for 1-dim covariates) numeric indicating the stroke size
`legend` | (for 1-dim covariates) logical indicating whether to add legend.
`rotate_x` | (for spatial, 2-dim covariates) Rotate covariate on x-axis
`rotate_y` | (for spatial, 2-dim covariates) Rotate covariate on y-axis
`return_data` | logical indicating whether to return the data frame to plot instead of plotting
`show_variance` | (for 1-dim covariates) logical indicating whether to show the marginal variance of inferred factor values (only relevant for 1-dimensional covariates)

### Details

To investigate the factors pattern along the covariates (such as time or a spatial coordinate) this function an be used to plot a scatterplot of the factor againt the values of each covariate

### Value

Returns a `ggplot2` object

### Examples

    # Using an existing trained model
    file <- system.file("extdata", "MEFISTO_model.hdf5", package = "MOFA2")
    model <- load_model(file)
    plot_factors_vs_cov(model)

[Package MOFA2 version 1.4.0 Index]