Brain disease studies based on experiments using genome-wide measurements with microarrays are traditionally challenging as compared to other disease areas. The biological results are often hindered by statistical issues of small sample sizes, small effect sizes, and patient-to-patient variability [1
]. Also, clinical information for patients is typically sparse, such that unknown clinical covariates can either confound or confuse many of the gene expression patterns and trends, as opposed to the primary disease. Corrections using such clinical information can greatly improve inference in determining markers for disease, as well as elucidating patterns within the disease.
Technical problems in microarray data can also affect the analyses. Meaningful results are often limited by array platform-to-platform comparisons and overall organization/presentation of large data sets/results. Studies conducted on disparate platforms are inherently more difficult to analyze than those conducted on the same platform [4
]. Cross-platform comparisons present analysis challenges due to differences in scaling and sensitivity (to name a few) which introduce inconsistencies in reproducibility [5
]. Large data sets and comprehensive results summaries present another challenge that requires good organization of both analytical and bioinformatics information (e.g. expression profiles, gene summary information, pathway diagrams, fold change value comparisons, etc.) into a user-friendly format to facilitate efficient data mining. A relational web-based tool that logically combines all of these factors can enhance researchers' ability to determine the underlying genomic patterns in brain disease.
The SMRIDB is an online data warehouse and analytical system designed to aid researchers in understanding the biological associations both between and within the brain disorders of schizophrenia, bipolar, and major depression. This open source database combines genomic patterns of brain disease with patient clinical metadata into a user-friendly query interface to enable efficient data mining for purposes of biomarker discovery and elucidating biological mechanisms of brain disease. The metadata includes a full summary of clinical history for each patient with hyperlinks to disease-level information, such that demographic- and lifestyle-associated effects can be determined as they relate to brain disorders. The genomic data has been compiled from 12 separate labs (identified as studies), each data set generated from brain tissue isolated from two controlled populations of 165 patients, diagnosed with one of the three brain disorders (plus unaffected control brain tissue). This genomic data has been generated across 6 separate human array platforms (Affymetrix: hgu133a, hgu133plus, hgu95av2, Agilent, Codelink, and cDNA custom array) providing patterns/trends and analytical inferences that are not limited by platform dependencies.