Summary: With the rapid advances and prevalence of high-throughput genomic technologies, integrating information of multiple relevant genomic studies has brought new challenges. Microarray meta-analysis has become a frequently used tool in biomedical research. Little effort, however, has been made to develop a systematic pipeline and user-friendly software. In this article, we present MetaOmics, a suite of three R packages MetaQC, MetaDE and MetaPath, for quality control, differentially expressed gene identification and enriched pathway detection for microarray meta-analysis. MetaQC provides a quantitative and objective tool to assist study inclusion/exclusion criteria for meta-analysis. MetaDE and MetaPath were developed for candidate marker and pathway detection, which provide choices of marker detection, meta-analysis and pathway analysis methods. The system allows flexible input of experimental data, clinical outcome (case–control, multi-class, continuous or survival) and pathway databases. It allows missing values in experimental data and utilizes multi-core parallel computing for fast implementation. It generates informative summary output and visualization plots, operates on different operation systems and can be expanded to include new algorithms or combine different types of genomic data. This software suite provides a comprehensive tool to conveniently implement and compare various genomic meta-analysis pipelines. Availability:  http://www.biostat.pitt.edu/bioinfo/software.htm Contact:  ctseng@pitt.edu Supplementary Information:  Supplementary data are available at Bioinformatics online.

Schizophrenia is associated with alterations in working memory that reflect dysfunction of dorsolateral prefrontal cortex (DLPFC) circuitry. Working memory depends on the activity of excitatory pyramidal cells in DLPFC layer 3, and to a lesser extent in layer 5. Although many studies have profiled gene expression in DLPFC gray matter in schizophrenia, little is known about cell type-specific transcript expression in these two populations of pyramidal cells. We hypothesized that interrogating gene expression specifically in DLPFC layer 3 or 5 pyramidal cells would reveal new and/or more robust schizophrenia-associated differences that would provide new insights into the nature of pyramidal cell dysfunction in the illness. We also sought to determine the impact of other variables, such as a diagnosis of schizoaffective disorder or medication use at time of death, on the patterns of gene expression in pyramidal neurons.Individual pyramidal cells in DLPFC layers 3 or 5 were captured by laser microdissection from 36 subjects with schizophrenia or schizoaffective disorder and matched normal comparison subjects. The mRNA from cell collections was subjected to transcriptome profiling by microarray followed by qPCR validation.Expression of genes involved in mitochondrial (MT) or ubiquitin-proteasome system (UPS) functions were markedly down-regulated in the patient group (p values for MT-related and UPS-related pathways were <10−7 and <10−5 respectively). MT-related gene alterations were more prominent in layer 3 pyramidal cells, whereas UPS-related gene alterations were more prominent in layer 5 pyramidal cells. Many of these alterations were not present, or found to a lesser degree, in samples of DLPFC gray matter from the same subjects, suggesting that they are pyramidal cell-specific. Furthermore, these findings principally reflected alterations in the schizophrenia subjects, were not present or present to a lesser degree in the schizoaffective disorder subjects (diagnosis of schizoaffective disorder was the most significant covariate, p<10−6), and were not attributable to factors frequently comorbid with schizophrenia.In summary, our findings reveal expression deficits in MT- and UPS-related genes specific to layer 3 and/or layer 5 pyramidal cells in the DLPFC of schizophrenia subjects. These cell type-specific transcriptome signatures are not characteristic of schizoaffective disorder, providing a potential molecular-cellular basis of differences in clinical phenotypes.