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Run the Ordinal SuStaIn Algorithm (OSA)

Source: R/run_OSA.R
run_OSA.Rd

Run the Ordinal SuStaIn Algorithm (OSA)

Usage

run_OSA(
  patient_data,
  prob_correct,
  prob_score = compute_prob_scores(dataset = patient_data, prob_correct = prob_correct,
    biomarker_levels = biomarker_levels),
  score_vals = build_score_vals(biomarker_levels),
  biomarker_levels = get_levels(patient_data, varnames = SuStaInLabels),
  SuStaInLabels = names(biomarker_levels),
  N_startpoints = 25,
  N_S_max = 8,
  N_iterations_MCMC = 100000L,
  output_folder = "output",
  dataset_name = "sample_data",
  use_parallel_startpoints = FALSE,
  seed = 1L,
  plot = FALSE,
  N_CV_folds = 0,
  CV_fold_nums = 1:N_CV_folds,
  verbose = TRUE,
  keep_data = TRUE,
  fig_size = c(10, 10),
  ...
)

Arguments

patient_data

patient biomarker data

prob_correct

the probability of correctly classifying the underlying biomarker level: p(obs = true)

prob_score

array probability of each score for all subjects across all biomarkers

  • dim = number of subjects x number of biomarkers x number of scores

score_vals

a matrix specifying the scores for each biomarker

  • dim: number of biomarkers x number of scores

biomarker_levels

a list containing the levels for each biomarker

SuStaInLabels

the names of the biomarkers as a list of strings

N_startpoints

number of startpoints to use in maximum likelihood step of SuStaIn, typically 25

N_S_max

maximum number of subtypes, should be 1 or more

N_iterations_MCMC

number of MCMC iterations, typically 1e5 or 1e6 but can be lower for debugging

output_folder

where to save pickle files, etc.

dataset_name

for naming pickle files

use_parallel_startpoints

boolean for whether or not to parallelize the maximum likelihood loop

seed

random number seed for python code

plot

logical() indicating whether to construct PVD plots via python subroutines

N_CV_folds

number of cross-validation folds to create

CV_fold_nums

which CV folds to run (for parallel processing)

verbose

logical() indicating whether to print debugging information

keep_data

logical() indicating whether to include the ata in the return object

fig_size

python figure size, in inches (width, height)

...

additional arguments passed to the Python function pySuStaIn$OrdinalSustain()

Value

a list()