Statistical calibration of qRT-PCR, microarray and RNA-Seq gene expression data with measurement error models

TL;DR

This paper introduces a measurement error model-based calibration method for gene expression data from microarray, RNA-Seq, and qRT-PCR platforms, improving accuracy and consistency in transcriptome analysis.

Contribution

It develops a two-step calibration approach using measurement error models to correct biases and errors across platforms, enhancing gene expression quantification.

Findings

Calibrated estimates reduce measurement bias.

The method reveals platform-specific error characteristics.

Application to real datasets demonstrates improved accuracy.

Abstract

The accurate quantification of gene expression levels is crucial for transcriptome study. Microarray platforms are commonly used for simultaneously interrogating thousands of genes in the past decade, and recently RNA-Seq has emerged as a promising alternative. The gene expression measurements obtained by microarray and RNA-Seq are, however, subject to various measurement errors. A third platform called qRT-PCR is acknowledged to provide more accurate quantification of gene expression levels than microarray and RNA-Seq, but it has limited throughput capacity. In this article, we propose to use a system of functional measurement error models to model gene expression measurements and calibrate the microarray and RNA-Seq platforms with qRT-PCR. Based on the system, a two-step approach was developed to estimate the biases and error variance components of the three platforms and calculate calibrated estimates of gene expression levels. The estimated biases and variance components shed light on the relative strengths and weaknesses of the three platforms and the calibrated estimates provide a more accurate and consistent quantification of gene expression levels. Theoretical and simulation studies were conducted to establish the properties of those estimates. The system was applied to analyze two gene expression data sets from the Microarray Quality Control (MAQC) and Sequencing Quality Control (SEQC) projects.