Unreleased
OutSeekR 1.0.0 - 2024-11-15
Added
- 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.