The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis

Abstract

Background: The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses.

An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients

“…Then, only probes measured on both GPL96 and GPL570 were retained (n=22,277). At this stage, we performed a second scaling normalization to set the average expression on each chip to 1000 to avoid batch effects [14].…”

Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients

“…At this stage, we performed a second scaling normalization to set the average expression on each chip to 1000. Although this technique cannot remove all, but it can significantly reduce batch effects (Sims et al 2008). We integrated the gene expression and clinical data using PostgreSQL, an open-source object-relational database system (www.postgresql.…”

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis

“…org/packages/release/bioc/html/annotate.html), and the probe was discarded if it did not match any genes. The two expression datasets were merged and synthetically analyzed using Batch Mean-centering, a merged data method (19), following adaptation according to Support Vector Machines, through the inSilicoMerging package (20).…”