OOMPA

OOMPA assesses the quality of Reverse-Phase Protein Array (RPPA) slides by classifying slide data to support reliable high-throughput protein expression measurements.

Key Features:

• Quality Control for Reverse-Phase Protein Array (RPPA) Data: A classifier employing a generalized linear model and a logistic function assesses slide quality for high-throughput protein expression measurements.
• Probabilistic Quality Assessment: The classifier outputs a probability score from 0 to 1 indicating the likelihood that a slide is of good quality.
• Training and Validation: The classifier was trained and validated using two independent datasets to distinguish high-quality and poor-quality slides.

Scientific Applications:

• Normalization and Data Integrity: Identification of good-quality slides prevents erroneous measurements and systematic variation from corrupting normalization schemes and protein expression patterns.
• Advanced Biological Analyses: Providing reliable slide-level quality assessments supports downstream analyses such as pathway exploration and biomarker discovery.

Methodology:

Uses a generalized linear model (GLM) with a logistic function to transform model outputs into probability scores for slide quality.