A Critical Evaluation of Analytic Aspects of Gene Expression Profiling in Lymphoid Leukemias with Broad Applications to Cancer Genomics

In cancer research, transcriptional aberrations are often deduced from mRNA-based gene expression profiling (GEP). Although transcriptome sequencing (RNA-seq) has gained ground in the recent past, mRNA-based microarrays remain a useful asset for high-throughput experiments in many laboratories. Possible reasons are the lower per-sample costs and the opportunity to analyze obtained GEP data in association with published data sets. There are established and widely used methods for the analysis of microarray data, which increase the comparability of different GEP data sets and facilitate data-mining approaches. However, analytic pitfalls, such as batch effects and issues of sample purity, e.g. by complex tissue composition, are often not properly addressed by these standard approaches. Moreover, most of these tools do not capitalize on the full range of public data sources or do not take advantage of the analytic possibilities for functional interpretation or of comprehensive meta-analyses. We present an overview of the most critical steps in the analysis of microarray-based GEP data. We discuss software and database query solutions that may be useful for