A statistical framework for the analysis of microarray probe-level data

TL;DR

This paper introduces a comprehensive statistical framework that integrates preprocessing steps into the analysis of microarray probe-level data, enhancing the reliability and interpretability of results across various platforms.

Contribution

It presents a novel framework that incorporates preprocessing into the statistical analysis pipeline for microarray data, addressing a gap in current practices.

Findings

Framework applicable to multiple microarray platforms

Improves accuracy of statistical summaries

Demonstrated in three different applications

Abstract

In microarray technology, a number of critical steps are required to convert the raw measurements into the data relied upon by biologists and clinicians. These data manipulations, referred to as preprocessing, influence the quality of the ultimate measurements and studies that rely upon them. Standard operating procedure for microarray researchers is to use preprocessed data as the starting point for the statistical analyses that produce reported results. This has prevented many researchers from carefully considering their choice of preprocessing methodology. Furthermore, the fact that the preprocessing step affects the stochastic properties of the final statistical summaries is often ignored. In this paper we propose a statistical framework that permits the integration of preprocessing into the standard statistical analysis flow of microarray data. This general framework is relevant in many microarray platforms and motivates targeted analysis methods for specific applications. We demonstrate its usefulness by applying the idea in three different applications of the technology.