NEWS.md
In OutSeekR: Statistical Approach to Outlier Detection in RNA-Seq and Related Data

Unreleased

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.apply to enable parallelization, compatible with various computing environments.
Sample outliers data and usage demonstration.