generate_synthetic_data {proDA} | R Documentation

## Generate a dataset according to the probabilistic dropout model

### Description

Generate a dataset according to the probabilistic dropout model

### Usage

    generate_synthetic_data(n_proteins, n_conditions = 2, n_replicates = 3,
      frac_changed = 0.1, dropout_curve_position = 18.5,
      dropout_curve_scale = -1.2, location_prior_mean = 20,
      location_prior_scale = 4, variance_prior_scale = 0.05,
      variance_prior_df = 2, effect_size = 2,
      return_summarized_experiment = FALSE)

### Arguments

`n_proteins` | the number of rows in the dataset
`n_conditions` | the number of conditions. Default: 2
`n_replicates` | the number of replicates per condition. Can either be a single number or a vector with `length(n_replicates) == n_conditions`. Default: 3
`frac_changed` | the fraction of proteins that actually differ between the conditions. Default: 0.1
`dropout_curve_position` | the point where the chance to observe a value is 50%. Can be a single number or a vector of `length(dropout_curve_position) == n_conditions * n_replicates`. Default: 18.5
`dropout_curve_scale` | The width of the dropout curve. Negative numbers mean that lower intensities are more likely to be missing. Can be a single number or a vector of `length(dropout_curve_position) == n_conditions * n_replicates`. Default: -1.2
`location_prior_mean, location_prior_scale` | the position and the variance around which the individual condition means (`t_mu`) scatter. Default: 20 and 4
`variance_prior_scale, variance_prior_df` | the scale and the degrees of freedom of the inverse Chi-squared distribution used as a prior for the variances. Default: 0.05 and 2
`effect_size` | the standard deviation that is used to draw different values for the `frac_changed` part of the proteins. Default: 2
`return_summarized_experiment` | a boolean indicator if the method should return a `SummarizedExperiment` object instead of a list. Default: `FALSE`

### Value

a list with the following elements

Y

  the intensity matrix including the missing values

Z

  the intensity matrix before dropping out values

t_mu

  a matrix with `n_proteins` rows and `n_conditions` columns that contains the underlying means for each protein

t_sigma2

  a vector with the true variances for each protein

changed

  a vector with boolean values if the protein is actually changed

group

  the group structure mapping samples to conditions

if `return_summarized_experiment` is `FALSE`. Otherwise returns a `SummarizedExperiment` with the same information.

### Examples

      syn_data <- generate_synthetic_data(n_proteins = 10)
      names(syn_data)
      head(syn_data$Y)

      # Returning a SummarizedExperiment
      se <- generate_synthetic_data(n_proteins = 10, return_summarized_experiment = TRUE)
      se
      head(SummarizedExperiment::assay(se))

[Package proDA version 1.0.0 Index]