A New Procedure for Microarray Experiments to Account for Experimental Noise and the Uncertainty of Probe Response

TL;DR

This paper introduces a new procedure for microarray experiments that reduces experimental noise and assesses probe performance, leading to more reliable signal measurement and potential for absolute quantification without normalization.

Contribution

The study presents a novel method combining noise control and probe performance assessment to improve microarray data reliability.

Findings

Variance can be reduced by averaging probes and removing nonresponsive ones.

Proper calibration enables straightforward absolute quantification.

The new procedure outperforms conventional approaches in reliability.

Abstract

Although microarrays are routine analysis tools in biomedical research, they still yield noisy output that often requires experimental confirmation. Many studies have aimed at optimizing probe design and statistical analysis to tackle this problem. However, less emphasis has been placed on controlling the noise inherent to the experimental approach. To address this problem, we investigate here a procedure that controls for such experimental variance and combine it with an assessment of probe performance. Two custom arrays were used to evaluate the procedure: one based on 25mer probes from an Affymetrix design and the other based on 60mer probes from an Agilent design. To assess experimental variance, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an absorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive. Thus, a more reliable signal could be obtained using our procedure than conventional approaches. We suggest that once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations.