- Implementation of core Outlier Detection Algorithm, a statistical approach for detecting transcript-level outliers in RNA-seq or related data types, leveraging normalized data (e.g., FPKM) and several statistical metrics.
- Five distinct statistics for robustly assessing outliers:
- Z-scores using mean and standard deviation.
- Z-scores using median and median absolute deviation.
- Z-scores with 5%-trimmed mean and standard deviation.
- Fraction of observations in the smaller cluster from K-means (K=2).
- Cosine similarity between extreme observed values and theoretical distribution quantiles.
- Comprehensive null simulation functionality. Generates null datasets mimicking the observed data distribution (without outliers) through generalized additive modeling of four potential distributions.
- Outlier p-value calculation by comparing rank products from observed and null data across multiple rounds, refining the detection by iteratively removing the most extreme outliers.
- Support for false discovery rate (FDR) correction to control for multiple testing.
- Optimization for high-performance analysis using
future.applyto enable parallelization, compatible with various computing environments. - Sample
outliersdata and usage demonstration.