Removing intensity effects and identifying significant genes for Affymetrix arrays in macrophage migration inhibitory factor-suppressed neuroblastoma cells

Abstract

A semilinear in-slide model is introduced to remove the intensity effect in the scanning process. It is demonstrated that the intensity effect can be estimated accurately and removed effectively. This normalization step is vital for Affymetrix arrays to reveal relevant biological results when comparing gene expression in multiple arrays. The normalized expression ratios are analyzed further by a modified two-sample t test along with a sieved permutation scheme for computing P values. The improved specificity and sensitivity are demonstrated by using a study on the impact of macrophage migration inhibitory factor (MIF) reduction in neuroblastoma cells. With semilinear in-slide model analysis, expression of 166 genes was altered with a P value no greater than 0.001. Among those genes, 44 were altered >2-fold. MIF-regulated genes associated with tumor development including IL-8 and C-met, which are overexpressed in many tumors, were down-regulated in MIF-reduced cells. On the other hand, some tumor-suppressor genes such as EPHB6, visinin-like protein 1 (VSNL-1), and BLU were up-regulated in MIF-reduced cells. In addition, we demonstrated that down-regulation of MIF expression could result in a reduction in cell proliferation and tumor growth in vitro and in vivo. Our data not only demonstrate that targeting MIF expression is a promising therapeutic strategy in human neuroblastoma therapy but also indicate the MIF target genes for additional study.MIF ͉ normalization ͉ signficance analysis ͉ SLIM A ffymetrix GeneChip arrays (1, 2) have been widely used for monitoring mRNA expression in many areas of biomedical research. The high-density oligonucleotide array technology allows researchers to monitor tens of thousands of genes in a single hybridization experiment as they are expressed differently in tissues and cells. The expression profile of an mRNA molecule of a gene is obtained by the combined intensity information from probes in a probe set, which consists of 11-20 probe pairs of oligonucleotides of 25 bp in length, interrogating a different part of the sequence of a gene. Several techniques have been proposed for extracting expression profiles from the information at probe level. These techniques include the detection signals that are prominently featured in Affymetrix Micorarray Suite 5.0 (MAS 5.0) software, the model-based expression index (3), and the robust multichip average (4, 5). A comprehensive review and comparisons of various expression measures are given in refs. 6 and 7.To account for the overall brightness of scanned images and other experimental variations such as differences in sample preparation and array production, normalization is needed for direct array-to-array comparisons. Several methods (7-10) have been proposed for normalizing data at probe level, and their impacts on the analysis of gene expressions have been examined. However, most researchers use detection signals from MAS 5.0 as starting points of their investigation. Yet, as reported below, the techniques for probe-level normalizati...

Statistical Analysis of DNA Microarray Data in Cancer Research

“…Replications (at least two) should also be made for control sample to reduce measurement errors and to improve the sensitivity and specificity in the downstream statistical analysis. With the average expression profiles of control arrays, log ratios of expression profiles between the test and reference arrays for all genes can be computed (11). The downstream statistical analysis, such as tumor classification and biochemical expression pathways, can be analogously analyzed in the same way as the cDNA microarray.…”

“…The essential difference between data from cDNA and Affymetrix GeneChip is that the test and reference expression profiles are paired in cDNA arrays but not in Affymetrix arrays. Thus, two-sample test statistics should be used for selecting significantly expressed genes using Affymetrix arrays (11). Replications (at least two) should also be made for control sample to reduce measurement errors and to improve the sensitivity and specificity in the downstream statistical analysis.…”

“…The techniques for probe level normalization are not effective for data at detection signal level. Semilinear inslide model has been effectively extended to normalize detection signals to account of intensity effect (11).…”

“…After data have been properly normalized, statistical techniques can be applied to select significant genes (11). The essential difference between data from cDNA and Affymetrix GeneChip is that the test and reference expression profiles are paired in cDNA arrays but not in Affymetrix arrays.…”

Introduction to Microarray‐Based Detection Methods

“…For gene expression studies one approach is to determine a set of invariant genes for normalization [151,177]. Another approach recommends replicating genes on the array and using this within-array replication for normalization [53][54][55]. Standard normalization protocols rely on the assumption that the majority of genes on the microarray are not differentially expressed between samples [194].…”