Three methods for optimization of cross-laboratory and cross-platform microarray expression data

Abstract

Microarray gene expression data becomes more valuable as our confidence in the results grows. Guaranteeing data quality becomes increasingly important as microarrays are being used to diagnose and treat patients (1–4). The MAQC Quality Control Consortium, the FDA's Critical Path Initiative, NCI's caBIG and others are implementing procedures that will broadly enhance data quality. As GEO continues to grow, its usefulness is constrained by the level of correlation across experiments and general applicability. Although RNA preparation and array platform play important roles in data accuracy, pre-processing is a user-selected factor that has an enormous effect. Normalization of expression data is necessary, but the methods have specific and pronounced effects on precision, accuracy and historical correlation. As a case study, we present a microarray calibration process using normalization as the adjustable parameter. We examine the impact of eight normalizations across both Agilent and Affymetrix expression platforms on three expression readouts: (1) sensitivity and power, (2) functional/biological interpretation and (3) feature selection and classification error. The reader is encouraged to measure their own discordant data, whether cross-laboratory, cross-platform or across any other variance source, and to use their results to tune the adjustable parameters of their laboratory to ensure increased correlation.

Scalable high-density peptide arrays for comprehensive health monitoring

“…This power plot is reviewed in detail in ref. 30. The blue bars (y axis) represent every peptide's average log2 ratio between 10 oesophageal cancer patients (numerator) and 10 healthy controls (denominator).…”

“…Red circles are the same significant peptides (Po1 Â 10 À 6 ) as in the volcano plot on the left. The power plot and volcano plot represent the general performance of the 330,000 peptide microarray, which far exceeds the precision inherent in modern commercial expression microarrays 30 . There are 23,323 informative peptides on the array that contribute to distinguish oesophageal cancer patients from healthy controls.…”

Development and Evaluation of Normalization Methods for Label-free Relative Quantification of Endogenous Peptides

“…However, in the field of gene expression data, this is a common approach for comparing the performance of different normalization methods (21,40). The largest fraction of differentially expressed peaks for all three data set was found using the method RegrRun.…”

Evaluation of Biological Sample Preparation for Immunosignature-Based Diagnostics

“…Values from triplicate arrays were averaged and used in the analysis. Array-to-array coefficients of variance averaged Ͻ30%; power is 1.3-fold the minimum detectable signal at the 95th percentile per 2 technical replicates (26).…”