NEWS

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OutSeekR 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.