OmicsQC: Nominating Quality Control Outliers in Genomic Profiling Studies

A method that analyzes quality control metrics from multi-sample genomic sequencing studies and nominates poor quality samples for exclusion. Per sample quality control data are transformed into z-scores and aggregated. The distribution of aggregated z-scores are modelled using parametric distributions. The parameters of the optimal model, selected either by goodness-of-fit statistics or user-designation, are used for outlier nomination. Two implementations of the Cosine Similarity Outlier Detection algorithm are provided with flexible parameters for dataset customization.

Version: 1.1.0
Depends: R (≥ 2.10)
Imports: stats, utils, fitdistrplus, lsa, BoutrosLab.plotting.general
Suggests: knitr, rmarkdown, kableExtra, dplyr, testthat (≥ 3.0.0)
Published: 2024-03-01
Author: Anders Hugo Frelin [aut], Helen Zhu [aut], Paul C. Boutros ORCID iD [aut, cre]
Maintainer: Paul C. Boutros <PBoutros at mednet.ucla.edu>
License: GPL-2
NeedsCompilation: no
Materials: NEWS
CRAN checks: OmicsQC results

Documentation:

Reference manual: OmicsQC.pdf
Vignettes: Introduction to omicsQC

Downloads:

Package source: OmicsQC_1.1.0.tar.gz
Windows binaries: r-prerel: OmicsQC_1.1.0.zip, r-release: OmicsQC_1.1.0.zip, r-oldrel: OmicsQC_1.1.0.zip
macOS binaries: r-prerel (arm64): OmicsQC_1.1.0.tgz, r-release (arm64): OmicsQC_1.1.0.tgz, r-oldrel (arm64): OmicsQC_1.1.0.tgz, r-prerel (x86_64): OmicsQC_1.1.0.tgz, r-release (x86_64): OmicsQC_1.1.0.tgz

Linking:

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