Preprocessing and Quality Control Strategies for Illumina DASL Assay-Based Brain Gene Expression Studies with Semi-Degraded Samples - Chow, Maggie L.; Winn, Mary E.; Li, Hai-Ri; April, Craig; Wynshaw-Boris, Anthony; Fan, Jian-Bing; Fu, Xiang-Dong; Courchesne, Eric; Schork, Nicholas (Frontiers in Genetics, 2012)
Available statistical preprocessing or quality control analysis tools for gene expression microarray datasets are known to greatly affect downstream data analysis, especially when degraded samples, unique tissue samples or novel expression assays are used. It is therefore important to assess the validity and impact of the assumptions built in to preprocessing schemes for a dataset. We developed and assessed a data preprocessing strategy for use with the Illumina DASL-based gene expression assay with partially degraded postmortem prefrontal cortex samples. The samples were obtained from individuals with autism as part of an investigation of the pathogenic factors contributing to autism. Using statistical analysis methods and metrics such as those associated with multivariate distance matrix regression (MDMR) and mean inter-array correlation, we developed a DASL-based assay gene expression preprocessing pipeline to accommodate and detect problems with microarray-based gene expression values obtained with degraded brain samples. Key steps in the pipeline included outlier exclusion, data transformation and normalization, and batch effect and covariate corrections. Our goal was to produce a clean dataset for subsequent downstream differential expression analysis. We ultimately settled on available transformation and normalization algorithms in the R/Bioconductor package lumi based on an assessment of their use in various combinations. A log2-transformed, quantile-normalized, and batch and seizure-corrected procedure was likely the most appropriate for our data. We empirically tested different components of our proposed preprocessing strategy and believe that our results suggest that a preprocessing strategy that effectively identifies outliers, normalizes the data, and corrects for batch effects, like ours can be applied to all studies, even those pursued with degraded samples.