OUTRIDER

OUTRIDER identifies aberrantly expressed genes in RNA-seq data by modeling read count expectations with an autoencoder and detecting significant deviations from a negative binomial distribution.

Key Features:

• Autoencoder-based confounder control: Models read count expectations according to gene covariation resulting from technical, environmental, or common genetic variations.
• Negative binomial distribution modeling: Assumes RNA-seq read counts follow a negative binomial distribution with gene-specific dispersion.
• Significance-based outlier detection: Identifies outliers as read counts that significantly deviate from the expected distribution using false-discovery-rate-adjusted p values.
• Automatic model fitting: Fits the model automatically to achieve optimal recall of artificially corrupted data.
• Gene filtering: Filters out genes not expressed in a dataset.
• Sample quality control: Identifies outlier samples with too many aberrantly expressed genes.
• Visualization functions: Provides plotting functions to analyze and visualize results.

Scientific Applications:

• Rare disease diagnosis: Identifies aberrant gene expression levels as potential pathogenic events in rare-disorder diagnostic platforms.
• Complementary analysis to genome sequencing: Serves as a complementary assay to genome sequencing for precisely identifying molecular causes of rare disorders.
• Precision-recall optimization: Demonstrates improved detection of simulated outlier read counts through covariation control and significance-based thresholds.

Methodology:

OUTRIDER uses an autoencoder to model read-count expectations according to gene covariation. RNA-seq read counts are assumed to follow a negative binomial distribution with gene-specific dispersion, and outliers are identified as read counts that significantly deviate from this distribution.