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 |
| DOI: | 10.32614/CRAN.package.OmicsQC |
| Author: | Anders Hugo Frelin [aut], Helen Zhu [aut], Paul C. Boutros [aut, cre] |
| Maintainer: | Paul C. Boutros <PBoutros at mednet.ucla.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | AnomalyDetection |
| CRAN checks: | OmicsQC results |
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