Package: spqrp 0.1.0

spqrp: Sample Provenance Quality Resolver in Proteomics

Detect sample-provenance inconsistencies and potential mix-ups in mass-spectrometry-based plasma-proteome cohorts. Provides a clustering-based approach (build a nearest-neighbour graph in a dimensionality-reduced space and iteratively split large components by edge weight), a threshold-based approach (classify sample pairs as belonging or not-belonging from a pairwise distance cutoff), parameter optimization over distance metrics and cutoffs, and a pairwise random-forest classifier for protein importance ranking. This is a native R port of the author's Python package 'spqrp' (<https://github.com/fhradilak/spqrp>), implementing methods from an associated manuscript currently in preparation.

Authors:Franziska Hradilak [aut, cre]

spqrp_0.1.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
spqrp/json (API)

# Install 'spqrp' in R:

install.packages('spqrp', repos = c('https://fhradilak.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/fhradilak/spqrp_r/issues

Datasets:

cohort_a_ranking - Protein-importance ranking for plasma cohort "A"

On CRAN:

4.00 score 3 scripts 210 downloads 15 exports 47 dependencies

Last updated from:724422e653. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	154
source / vignettes	OK	462
linux-release	OK	217
macos-release	OK	209
macos-oldrel	OK	183
windows-devel	OK	114
windows-release	OK	142
windows-oldrel	OK	123
wasm-release	OK	159

Exports:by_isolation_forest check_input_data_format filter_by_occurrence log_transform normalize_medianintensity optimize_parameters perform_distance_evaluation_on_ranked_proteins plate_correct_residuals_by_protein remove_outlier_samples retrieve_ranking run_clustering spqrp_example_data spqrp_example_path train_pairwise_balanced_rand_forest train_with_normalise

Dependencies:cli codetools cpp11 data.table digest dplyr farver future future.apply generics ggplot2 globals glue gtable igraph isoband labeling lattice lgr lifecycle listenv magrittr Matrix parallelly pillar pkgconfig pROC purrr R6 randomForest ranger RColorBrewer Rcpp RcppEigen rlang S7 scales solitude stringi stringr tibble tidyr tidyselect utf8 vctrs viridisLite withr

spqrp on mock data

Rendered fromspqrp-mock-data.Rmdusingknitr::rmarkdown

Last update: 2026-06-09
Started: 2026-06-09

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

Package: spqrp 0.1.0

spqrp: Sample Provenance Quality Resolver in Proteomics

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