Introduction | Setup | Generation and aggregation of z-scores | Calculation of z-scores | Adjusting metric directionality | Calculating total quality score | Outlier detection using cosine similarity | Finding the best fitting distribution | The iterative method for outlier nomination using cosine similarity | The cutoff method for outlier nomination using cosine similarity | Data visualisation | Quality score barplot | Z-score heatmap | Aggregating the plots
Package: OmicsQC 1.1.1
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.
Authors:
OmicsQC_1.1.1.tar.gz
OmicsQC_1.1.1.zip(r-4.7-any)OmicsQC_1.1.1.zip(r-4.6-any)OmicsQC_1.1.1.zip(r-4.5-any)
OmicsQC_1.1.1.tgz(r-4.6-any)OmicsQC_1.1.1.tgz(r-4.5-any)
OmicsQC_1.1.1.tar.gz(r-4.7-any)OmicsQC_1.1.1.tar.gz(r-4.6-any)
OmicsQC_1.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
OmicsQC/json (API)
| # Install 'OmicsQC' in R: |
| install.packages('OmicsQC', repos = c('https://pboutros.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- example.qc.dataframe - QC metrics across 100 samples
- sign.correction - Directionality of QC metrics
- ylabels - Formatted QC metrics labels
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:b26f8975fe. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 138 | ||
| source / vignettes | OK | 196 | ||
| linux-release-x86_64 | OK | 139 | ||
| macos-release-arm64 | OK | 170 | ||
| macos-oldrel-arm64 | OK | 213 | ||
| windows-devel | OK | 102 | ||
| windows-release | OK | 87 | ||
| windows-oldrel | OK | 84 | ||
| wasm-release | OK | 153 |
Exports:accumulate.zscorescorrect.zscore.signscosine.similarity.cutoffcosine.similarity.iterativefit.and.evaluateget.qc.barplotget.qc.heatmapget.qc.multipanelplotzscores.from.metrics
Dependencies:BoutrosLab.plotting.generalclasscliclusterdeldire1071fitdistrplusgluegridExtragtablehexbininterpjpeglatticelatticeExtralifecyclelsaMASSMatrixpngproxyRColorBrewerRcppRcppEigenrlangSnowballCsurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Sum across sign corrected z-scores for total sample quality score | accumulate.zscores |
| Corrects the z-scores signs according to the metrics | correct.zscore.signs |
| Calculate an outlier cutoff using cosine similarity | cosine.similarity.cutoff |
| Tests the accumulated quality scores for outliers using cosine similarity | cosine.similarity.iterative |
| QC metrics across 100 samples | example.qc.dataframe |
| Fits the QC data to distributions and returns the KS test result and BIC score | fit.and.evaluate |
| Generates the standard barplot of scores for each sample | get.qc.barplot |
| Generates the standard heatmap of scores for each sample. | get.qc.heatmap |
| Generates the multipanel plot of heatmap and barplot | get.qc.multipanelplot |
| Directionality of QC metrics | sign.correction |
| Formatted QC metrics labels | ylabels |
| Calculate z-scores for each metric across each sample | zscores.from.metrics |
