erccdashboard

erccdashboard assesses technical performance in genome-scale differential gene expression experiments by analyzing External RNA Controls Consortium (ERCC) spike-in ratio mixtures to compute diagnostic metrics, limits of detection (LOD), and measurement bias.

Key Features:

• Standardized Metrics Suite: Computes a standardized set of performance metrics derived from ERCC external spike-in ratio mixtures for assessing diagnostic power, LOD, and measurement bias.
• Defined Abundance Ratios: Uses control mixtures with predefined abundance ratios to benchmark accuracy and reliability of differentially expressed transcript detection.
• Diagnostic Performance Assessment: Quantifies diagnostic power to evaluate how well experiments detect true differences in gene expression levels.
• Limit of Detection (LOD) Estimates: Provides estimates of the limit of detection to assess sensitivity and the lowest reliably detectable expression levels.
• Expression Ratio Variability and Bias Measurement: Quantifies variability in expression ratios and identifies biases introduced by experimental protocols, including differing mRNA-enrichment methods.

Scientific Applications:

• Protocol validation: Validate experimental protocols by comparing diagnostic power, LOD, and measurement biases using ERCC spike-ins.
• Experimental design optimization: Identify sources of variability and bias to inform optimization of sequencing and sample-preparation strategies.
• Reproducibility assessment: Enable comparison of technical performance across experiments and laboratories to improve reproducibility and transparency in differential gene expression studies.

Methodology:

Analyzes ERCC external spike-in RNA controls mixed at known ratios and derives metrics for diagnostic accuracy, sensitivity (LOD), and measurement bias; includes interlaboratory comparisons demonstrating metric consistency across identical samples and varying measurement protocols.