Zhiguang Huo

An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection

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.

Comprehensive evaluation of fusion transcript detection algorithms and a meta-caller to combine top performing methods in paired-end RNA-seq data

Background: Fusion transcripts are formed by either fusion genes (DNA level) or trans-splicing events (RNA level). They have been recognized as a promising tool for diagnosing, subtyping and treating cancers. RNA-seq has become a precise and efficient standard for genome-wide screening of such aberration events. Many fusion transcript detection algorithms have been developed for paired-end RNA-seq data but their performance has not been comprehensively evaluated to guide practitioners. In this paper, we evaluated 15 popular algorithms by their precision and recall trade-off, accuracy of supporting reads and computational cost. We further combine top-performing methods for improved ensemble detection. Results: Fifteen fusion transcript detection tools were compared using three synthetic data sets under different coverage, read length, insert size and background noise, and three real data sets with selected experimental validations. No single method dominantly performed the best but SOAPfuse generally performed well, followed by FusionCatcher and JAFFA. We further demonstrated the potential of a meta-caller algorithm by combining top performing methods to re-prioritize candidate fusion transcripts with high confidence that can be followed by experimental validation. Conclusion: Our result provides insightful recommendations when applying individual tool or combining top performers to identify fusion transcript candidates.

Targeted DNA Methylation Screen in the Mouse Mammary Genome Reveals a Parity-Induced Hypermethylation of Igf1r That Persists Long after Parturition

The most effective natural prevention against breast cancer is an early first full-term pregnancy. Understanding how the protective effect is elicited will inform the development of new prevention strategies. To better understand the role of epigenetics in long-term protection, we investigated parity-induced DNA methylation in the mammary gland. FVB mice were bred or remained nulliparous and mammary glands harvested immediately after involution (early) or 6.5 months following involution (late), allowing identification of both transient and persistent changes. Targeted DNA methylation (109 Mb of Ensemble regulatory features) analysis was performed using the SureSelectXT Mouse Methyl-seq assay and massively parallel sequencing. Two hundred sixty-nine genes were hypermethylated and 128 hypomethylated persistently at both the early and late time points. Pathway analysis of the persistently differentially methylated genes revealed Igf1r to be central to one of the top identified signaling networks, and Igf1r itself was one of the most significantly hypermethylated genes. Hypermethylation of Igf1r in the parous mammary gland was associated with a reduction of Igf1r mRNA expression. These data suggest that the IGF pathway is regulated at multiple levels during pregnancy and that its modification might be critical in the protective role of pregnancy. This supports the approach of lowering IGF action for prevention of breast cancer, a concept that is currently being tested clinically. Cancer Prev Res; 8(10); 1000–9. ©2015 AACR.

Transcriptome alterations in prefrontal pyramidal cells distinguish schizophrenia from bipolar and major depressive disorders

BACKGROUND Impairments in certain cognitive processes (e.g., working memory) are typically most pronounced in schizophrenia (SZ), intermediate in bipolar disorder, and least in major depressive disorder. Given that working memory depends, in part, on neural circuitry that includes pyramidal cells in layer 3 (L3) and layer 5 (L5) of the dorsolateral prefrontal cortex (DLPFC), we sought to determine if transcriptome alterations in these neurons were shared or distinctive for each diagnosis. METHODS Pools of L3 and L5 pyramidal cells in the DLPFC were individually captured by laser microdissection from 19 matched tetrads of unaffected comparison subjects and subjects with SZ, bipolar disorder, and major depressive disorder, and the messenger RNA was subjected to transcriptome profiling by microarray. RESULTS In DLPFC L3 and L5 pyramidal cells, transcriptome alterations were numerous in subjects with SZ, but rare in subjects with bipolar disorder and major depressive disorder. The leading molecular pathways altered in subjects with SZ involved mitochondrial energy production and the regulation of protein translation. In addition, we did not find any significant transcriptome signatures related to psychosis or suicide. CONCLUSIONS In concert, these findings suggest that molecular alterations in DLPFC L3 and L5 pyramidal cells might be characteristic of the disease processes operative in individuals diagnosed with SZ and thus might contribute to the circuitry alterations underlying cognitive dysfunction in individuals with SZ.

Meta-Analytic Framework for Sparse K-Means to Identify Disease Subtypes in Multiple Transcriptomic Studies

Disease phenotyping by omics data has become a popular approach that potentially can lead to better personalized treatment. Identifying disease subtypes via unsupervised machine learning is the first step toward this goal. In this article, we extend a sparse K-means method toward a meta-analytic framework to identify novel disease subtypes when expression profiles of multiple cohorts are available. The lasso regularization and meta-analysis identify a unique set of gene features for subtype characterization. An additional pattern matching reward function guarantees consistent subtype signatures across studies. The method was evaluated by simulations and leukemia and breast cancer datasets. The identified disease subtypes from meta-analysis were characterized with improved accuracy and stability compared to single study analysis. The breast cancer model was applied to an independent METABRIC dataset and generated improved survival difference between subtypes. These results provide a basis for diagnosis and development of targeted treatments for disease subgroups. Supplementary materials for this article are available online.

Opposite Molecular Signatures of Depression in Men and Women

BACKGROUND Major depressive disorder (MDD) affects women approximately twice as often as men. Women are three times as likely to have atypical depression, with hypersomnia and weight gain. This suggests that the molecular mechanisms of MDD may differ by sex. METHODS To test this hypothesis, we performed a large-scale gene expression meta-analysis across three corticolimbic brain regions: the dorsolateral prefrontal cortex, subgenual anterior cingulate cortex, and basolateral amygdala (26 men, 24 women with MDD and sex-matched control subjects). Results were further analyzed using a threshold-free approach, Gene Ontology, and cell type-specific analyses. A separate dataset was used for independent validation (13 MDD subjects/sex and 22 control subjects [13 men, 9 women]). RESULTS Of the 706 genes differentially expressed in men with MDD and 882 genes differentially expressed in women with MDD, only 21 were changed in the same direction in both sexes. Notably, 52 genes displayed expression changes in opposite directions between men and women with MDD. Similar results were obtained using a threshold-free approach, in which the overall transcriptional profile of MDD was opposite in men and women. Gene Ontology indicated that men with MDD had decreases in synapse-related genes, whereas women with MDD exhibited transcriptional increases in this pathway. Cell type-specific analysis indicated that men with MDD exhibited increases in oligodendrocyte- and microglia-related genes, while women with MDD had decreases in markers of these cell types. CONCLUSIONS The brain transcriptional profile of MDD differs greatly by sex, with multiple transcriptional changes in opposite directions between men and women with MDD.

Integrative sparse

Cancer subtypes discovery is the first step to deliver personalized medicine to cancer patients. With the accumulation of massive multi-level omics datasets and established biological knowledge databases, omics data integration with incorporation of rich existing biological knowledge is essential for deciphering a biological mechanism behind the complex diseases. In this manuscript, we propose an integrative sparse K-means (is-K means) approach to discover disease subtypes with the guidance of prior biological knowledge via sparse overlapping group lasso. An algorithm using an alternating direction method of multiplier (ADMM) will be applied for fast optimization. Simulation and three real applications in breast cancer and leukemia will be used to compare is-K means with existing methods and demonstrate its superior clustering accuracy, feature selection, functional annotation of detected molecular features and computing efficiency.

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Schizophrenia (SZ) is associated with dysfunction of the dorsolateral prefrontal cortex (DLPFC). This dysfunction is manifest as cognitive deficits that appear to arise from disturbances in gamma frequency oscillations. These oscillations are generated in DLPFC layer 3 (L3) via reciprocal connections between pyramidal cells (PCs) and parvalbumin (PV)-containing interneurons. The density of cortical PV neurons is not altered in SZ, but expression levels of several transcripts involved in PV cell function, including PV, are lower in the disease. However, the transcriptome of PV cells has not been comprehensively assessed in a large cohort of subjects with SZ. In this study, we combined an immunohistochemical approach, laser microdissection, and microarray profiling to analyze the transcriptome of DLPFC L3 PV cells in 36 matched pairs of SZ and unaffected comparison subjects. Over 800 transcripts in PV neurons were identified as differentially expressed in SZ subjects; most of these alterations have not previously been reported. The altered transcripts were enriched for pathways involved in mitochondrial function and tight junction signaling. Comparison with the transcriptome of L3 PCs from the same subjects revealed both shared and distinct disease-related effects on gene expression between cell types. Furthermore, network structures of gene pathways differed across cell types and subject groups. These findings provide new insights into cell type-specific molecular alterations in SZ which may point toward novel strategies for identifying therapeutic targets.

-means with overlapping group lasso in genomic applications for disease subtype discovery

Accurate prediction of prostate cancer clinical courses remains elusive. In this study, we performed whole genome copy number analysis on leukocytes of 273 prostate cancer patients using Affymetrix SNP6.0 chip. Copy number variations (CNV) were found across all chromosomes of the human genome. An average of 152 CNV fragments per genome was identified in the leukocytes from prostate cancer patients. The size distributions of CNV in the genome of leukocytes were highly correlative with prostate cancer aggressiveness. A prostate cancer outcome prediction model was developed based on large size ratio of CNV from the leukocyte genomes. This prediction model generated an average prediction rate of 75.2%, with sensitivity of 77.3% and specificity of 69.0% for prostate cancer recurrence. When combined with Nomogram and the status of fusion transcripts, the average prediction rate was improved to 82.5% with sensitivity of 84.8% and specificity of 78.2%. In addition, the leukocyte prediction model was 62.6% accurate in predicting short prostate specific antigen doubling time. When combined with Gleason’s grade, Nomogram and the status of fusion transcripts, the prediction model generated a correct prediction rate of 77.5% with 73.7% sensitivity and 80.1% specificity. To our knowledge, this is the first study showing that CNVs in leukocyte genomes are predictive of clinical outcomes of a human malignancy.

Transcriptome alterations of prefrontal cortical parvalbumin neurons in schizophrenia

Motivation With the prevalent usage of microarray and massively parallel sequencing, numerous high‐throughput omics datasets have become available in the public domain. Integrating abundant information among omics datasets is critical to elucidate biological mechanisms. Due to the high‐dimensional nature of the data, methods such as principal component analysis (PCA) have been widely applied, aiming at effective dimension reduction and exploratory visualization. Results In this article, we combine multiple omics datasets of identical or similar biological hypothesis and introduce two variations of meta‐analytic framework of PCA, namely MetaPCA. Regularization is further incorporated to facilitate sparse feature selection in MetaPCA. We apply MetaPCA and sparse MetaPCA to simulations, three transcriptomic meta‐analysis studies in yeast cell cycle, prostate cancer, mouse metabolism and a TCGA pan‐cancer methylation study. The result shows improved accuracy, robustness and exploratory visualization of the proposed framework. Availability and implementation An R package MetaPCA is available online. (http://tsenglab.biostat.pitt.edu/software.htm). Supplementary information Supplementary data are available at Bioinformatics online.