replicateOutliers

ReplicateOutliers: Statistical Detection of Outliers in Replicated High-Throughput Data

ReplicateOutliers evaluates technical reproducibility in high-throughput biological datasets by detecting outliers within replicated measurements using statistical modeling and maximum likelihood estimation.

Key Features:

• Flexible Statistical Models: Quantitatively assess reproducibility of technical replicates using model-based approaches.
• Maximum Likelihood Estimation: Estimate model parameters to enable precise and reliable outlier detection.
• Technical Variability Modeling: Account for inherent technical variability and asymmetry in replicate data distributions.
• Replicate-Aware Analysis: Analyze multiple technical replicates within high-throughput datasets.

Scientific Applications:

• High-Throughput Screening and Omics Studies: Detect outliers in compound screening, siRNA screening, next-generation sequencing, and protein expression analyses to improve data reliability and biological interpretation.

Methodology:

ReplicateOutliers computes the absolute difference and coefficient of variation (Zeta) between replicate pairs to assess similarity. It applies maximum likelihood estimation to model replicate variability and identify statistically significant deviations. The function outlier_DZ assigns numeric outlier status categories, including non-outliers, large but non-significant deviations, and true outliers.