Sample Provenance Quality Resolver in Proteomics

Documentation for package ‘spqrp’ version 0.1.0

by_isolation_forest	Isolation Forest outlier detection
check_input_data_format	Validate required columns of a cohort and (optionally) a ranking
cohort_a_ranking	Protein-importance ranking for plasma cohort "A"
filter_by_occurrence	Keep proteins present in at least a given fraction of samples
log_transform	Log2-transform the intensity column in place (long format)
normalize_medianintensity	Per-sample median normalisation (log-space subtraction)
optimize_parameters	Grid-search the cutoff that optimises a chosen performance metric
perform_distance_evaluation_on_ranked_proteins	Threshold-based pairwise distance evaluation
plate_correct_residuals_by_protein	Regress intensity on plate and replace it with OLS residuals
print.spqrp_train	Print a one-line summary of an spqrp_train object
remove_outlier_samples	Remove samples flagged as outliers by Isolation Forest
retrieve_ranking	Convert classifier output to a Protein / Importance ranking
run_clustering	End-to-end clustering pipeline
spqrp_example_data	Load a bundled example data file as a tibble
spqrp_example_path	Filesystem path to a bundled example CSV
train_pairwise_balanced_rand_forest	Pairwise balanced random-forest classifier
train_with_normalise	End-to-end ranking pipeline: filter, normalise, optionally plate-correct, train RF