generate_synthetic_data {proDA} | R Documentation |
Generate a dataset according to the probabilistic dropout model
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)
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
|
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 |
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
|
location_prior_mean, location_prior_scale |
the position and the variance around which the individual
condition means ( |
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 |
return_summarized_experiment |
a boolean indicator if
the method should return a |
a list with the following elements
the intensity matrix including the missing values
the intensity matrix before dropping out values
a matrix with n_proteins
rows and
n_conditions
columns that contains the underlying
means for each protein
a vector with the true variances for each protein
a vector with boolean values if the protein is actually changed
the group structure mapping samples to conditions
if return_summarized_experiment
is FALSE
. Otherwise
returns a SummarizedExperiment
with the same information.
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))