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 
[aut, cre] |
| Maintainer: |
Paul C. Boutros <PBoutros at mednet.ucla.edu> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
OmicsQC results |
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