Twelve-hour rhythms in transcript expression within the human dorsolateral prefrontal cortex are altered in schizophrenia.

Twelve-hour (12 h) ultradian rhythms are a well-known phenomenon in coastal marine organisms. While 12 h cycles are observed in human behavior and physiology, no study has measured 12 h rhythms in the human brain. Here, we identify 12 h rhythms in transcripts that either peak at sleep/wake transitions (approximately 9 AM/PM) or static times (approximately 3 PM/AM) in the dorsolateral prefrontal cortex, a region involved in cognition. Subjects with schizophrenia (SZ) lose 12 h rhythms in genes associated with the unfolded protein response and neuronal structural maintenance. Moreover, genes involved in mitochondrial function and protein translation, which normally peak at sleep/wake transitions, peak instead at static times in SZ, suggesting suboptimal timing of these essential processes.

Transcriptomic congruence analysis for evaluating model organisms.

Model organisms are instrumental substitutes for human studies to expedite basic, translational, and clinical research. Despite their indispensable role in mechanistic investigation and drug development, molecular congruence of animal models to humans has long been questioned and debated. Little effort has been made for an objective quantification and mechanistic exploration of a model organism's resemblance to humans in terms of molecular response under disease or drug treatment. We hereby propose a framework, namely Congruence Analysis for Model Organisms (CAMO), for transcriptomic response analysis by developing threshold-free differential expression analysis, quantitative concordance/discordance scores incorporating data variabilities, pathway-centric downstream investigation, knowledge retrieval by text mining, and topological gene module detection for hypothesis generation. Instead of a genome-wide vague and dichotomous answer of "poorly" or "greatly" mimicking humans, CAMO assists researchers to numerically quantify congruence, to dissect true cross-species differences from unwanted biological or cohort variabilities, and to visually identify molecular mechanisms and pathway subnetworks that are best or least mimicked by model organisms, which altogether provides foundations for hypothesis generation and subsequent translational decisions.

Cell-type and sex-specific rhythmic gene expression in the nucleus accumbens.

Circadian rhythms are critical for human health and are highly conserved across species. Disruptions in these rhythms contribute to many diseases, including psychiatric disorders. Previous results suggest that circadian genes modulate behavior through specific cell types in the nucleus accumbens (NAc), particularly dopamine D1-expressing medium spiny neurons (MSNs). However, diurnal rhythms in transcript expression have not been investigated in NAc MSNs. In this study we identified and characterized rhythmic transcripts in D1- and D2-expressing neurons and compared rhythmicity results to homogenate as well as astrocyte samples taken from the NAc of male and female mice. We find that all cell types have transcripts with diurnal rhythms and that top rhythmic transcripts are largely core clock genes, which peak at approximately the same time of day in each cell type and sex. While clock-controlled rhythmic transcripts are enriched for protein regulation pathways across cell type, cell signaling and signal transduction related processes are most commonly enriched in MSNs. In contrast to core clock genes, these clock-controlled rhythmic transcripts tend to reach their peak in expression about 2-h later in females than males, suggesting diurnal rhythms in reward may be delayed in females. We also find sex differences in pathway enrichment for rhythmic transcripts peaking at different times of day. Protein folding and immune responses are enriched in transcripts that peak in the dark phase, while metabolic processes are primarily enriched in transcripts that peak in the light phase. Importantly, we also find that several classic markers used to categorize MSNs are rhythmic in the NAc. This is critical since the use of rhythmic markers could lead to over- or under-enrichment of targeted cell types depending on the time at which they are sampled. This study greatly expands our knowledge of how individual cell types contribute to rhythms in the NAc.

Systems approach for congruence and selection of cancer models towards precision medicine.

Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine.

Circadian rhythm disruptions associated with opioid use disorder in synaptic proteomes of human dorsolateral prefrontal cortex and nucleus accumbens.

Opioid craving and relapse vulnerability is associated with severe and persistent sleep and circadian rhythm disruptions. Understanding the neurobiological underpinnings of circadian rhythms and opioid use disorder (OUD) may prove valuable for developing new treatments for opioid addiction. Previous work indicated molecular rhythm disruptions in the human brain associated with OUD, highlighting synaptic alterations in the dorsolateral prefrontal cortex (DLPFC) and nucleus accumbens (NAc)-key brain regions involved in cognition and reward, and heavily implicated in the pathophysiology of OUD. To provide further insights into the synaptic alterations in OUD, we used mass-spectrometry based proteomics to deeply profile protein expression alterations in bulk tissue and synaptosome preparations from DLPFC and NAc of unaffected and OUD subjects. We identified 55 differentially expressed (DE) proteins in DLPFC homogenates, and 44 DE proteins in NAc homogenates, between unaffected and OUD subjects. In synaptosomes, we identified 161 and 56 DE proteins in DLPFC and NAc, respectively, of OUD subjects. By comparing homogenate and synaptosome protein expression, we identified proteins enriched specifically in synapses that were significantly altered in both DLPFC and NAc of OUD subjects. Across brain regions, synaptic protein alterations in OUD subjects were primarily identified in glutamate, GABA, and circadian rhythm signaling. Using time-of-death (TOD) analyses, where the TOD of each subject is used as a time-point across a 24-h cycle, we were able to map circadian-related changes associated with OUD in synaptic proteomes associated with vesicle-mediated transport and membrane trafficking in the NAc and platelet-derived growth factor receptor beta signaling in DLPFC. Collectively, our findings lend further support for molecular rhythm disruptions in synaptic signaling in the human brain as a key factor in opioid addiction.

Diurnal rhythms across the human dorsal and ventral striatum.

The human striatum can be subdivided into the caudate, putamen, and nucleus accumbens (NAc). Each of these structures have some overlapping and some distinct functions related to motor control, cognitive processing, motivation, and reward. Previously, we used a "time-of-death" approach to identify diurnal rhythms in RNA transcripts in human cortical regions. Here, we identify molecular rhythms across the three striatal subregions collected from postmortem human brain tissue in subjects without psychiatric or neurological disorders. Core circadian clock genes are rhythmic across all three regions and show strong phase concordance across regions. However, the putamen contains a much larger number of significantly rhythmic transcripts than the other two regions. Moreover, there are many differences in pathways that are rhythmic across regions. Strikingly, the top rhythmic transcripts in NAc (but not the other regions) are predominantly small nucleolar RNAs and long noncoding RNAs, suggesting that a completely different mechanism might be used for the regulation of diurnal rhythms in translation and/or RNA processing in the NAc versus the other regions. Further, although the NAc and putamen are generally in phase with regard to timing of expression rhythms, the NAc and caudate, and caudate and putamen, have several clusters of discordant rhythmic transcripts, suggesting a temporal wave of specific cellular processes across the striatum. Taken together, these studies reveal distinct transcriptome rhythms across the human striatum and are an important step in helping to understand the normal function of diurnal rhythms in these regions and how disruption could lead to pathology.

Differential Effects of Cocaine and Morphine on the Diurnal Regulation of the Mouse Nucleus Accumbens Proteome.

Substance use disorders are associated with disruptions in sleep and circadian rhythms that persist during abstinence and may contribute to relapse risk. Repeated use of substances such as psychostimulants and opioids may lead to significant alterations in molecular rhythms in the nucleus accumbens (NAc), a brain region central to reward and motivation. Previous studies have identified rhythm alterations in the transcriptome of the NAc and other brain regions following the administration of psychostimulants or opioids. However, little is known about the impact of substance use on the diurnal rhythms of the proteome in the NAc. We used liquid chromatography coupled to tandem mass spectrometry-based quantitative proteomics, along with a data-independent acquisition analysis pipeline, to investigate the effects of cocaine or morphine administration on diurnal rhythms of proteome in the mouse NAc. Overall, our data reveal cocaine and morphine differentially alter diurnal rhythms of the proteome in the NAc, with largely independent differentially expressed proteins dependent on time-of-day. Pathways enriched from cocaine altered protein rhythms were primarily associated with glucocorticoid signaling and metabolism, whereas morphine was associated with neuroinflammation. Collectively, these findings are the first to characterize the diurnal regulation of the NAc proteome and demonstrate a novel relationship between the phase-dependent regulation of protein expression and the differential effects of cocaine and morphine on the NAc proteome. The proteomics data in this study are available via ProteomeXchange with identifier PXD042043.

A sparse negative binomial mixture model for clustering RNA-seq count data.

Clustering with variable selection is a challenging yet critical task for modern small-n-large-p data. Existing methods based on sparse Gaussian mixture models or sparse $K$-means provide solutions to continuous data. With the prevalence of RNA-seq technology and lack of count data modeling for clustering, the current practice is to normalize count expression data into continuous measures and apply existing models with a Gaussian assumption. In this article, we develop a negative binomial mixture model with lasso or fused lasso gene regularization to cluster samples (small $n$) with high-dimensional gene features (large $p$). A modified EM algorithm and Bayesian information criterion are used for inference and determining tuning parameters. The method is compared with existing methods using extensive simulations and two real transcriptomic applications in rat brain and breast cancer studies. The result shows the superior performance of the proposed count data model in clustering accuracy, feature selection, and biological interpretation in pathways.

The microRNA target site landscape is a novel molecular feature associating alternative polyadenylation with immune evasion activity in breast cancer.

Alternative polyadenylation (APA) in breast tumor samples results in the removal/addition of cis-regulatory elements such as microRNA (miRNA) target sites in the 3'-untranslated region (3'-UTRs) of genes. Although previous computational APA studies focused on a subset of genes strongly affected by APA (APA genes), we identify miRNAs of which widespread APA events collectively increase or decrease the number of target sites [probabilistic inference of microRNA target site modification through APA (PRIMATA-APA)]. Using PRIMATA-APA on the cancer genome atlas (TCGA) breast cancer data, we found that the global APA events change the number of the target sites of particular microRNAs [target sites modified miRNA (tamoMiRNA)] enriched for cancer development and treatments. We also found that when knockdown (KD) of NUDT21 in HeLa cells induces a different set of widespread 3'-UTR shortening than TCGA breast cancer data, it changes the target sites of the common tamoMiRNAs. Since the NUDT21 KD experiment previously demonstrated the tumorigenic role of APA events in a miRNA dependent fashion, this result suggests that the APA-initiated tumorigenesis is attributable to the miRNA target site changes, not the APA events themselves. Further, we found that the miRNA target site changes identify tumor cell proliferation and immune cell infiltration to the tumor microenvironment better than the miRNA expression levels or the APA events themselves. Altogether, our computational analyses provide a proof-of-concept demonstration that the miRNA target site information indicates the effect of global APA events with a potential as predictive biomarker.

Likelihood-based tests for detecting circadian rhythmicity and differential circadian patterns in transcriptomic applications.

Circadian rhythmicity in transcriptomic profiles has been shown in many physiological processes, and the disruption of circadian patterns has been found to associate with several diseases. In this paper, we developed a series of likelihood-based methods to detect (i) circadian rhythmicity (denoted as LR_rhythmicity) and (ii) differential circadian patterns comparing two experimental conditions (denoted as LR_diff). In terms of circadian rhythmicity detection, we demonstrated that our proposed LR_rhythmicity could better control the type I error rate compared to existing methods under a wide variety of simulation settings. In terms of differential circadian patterns, we developed methods in detecting differential amplitude, differential phase, differential basal level and differential fit, which also successfully controlled the type I error rate. In addition, we demonstrated that the proposed LR_diff could achieve higher statistical power in detecting differential fit, compared to existing methods. The superior performance of LR_rhythmicity and LR_diff was demonstrated in four real data applications, including a brain aging data (gene expression microarray data of human postmortem brain), a time-restricted feeding data (RNA sequencing data of human skeletal muscles) and a scRNAseq data (single cell RNA sequencing data of mouse suprachiasmatic nucleus). An R package for our methods is publicly available on GitHub https://github.com/diffCircadian/diffCircadian.

High-dimension to high-dimension screening for detecting genome-wide epigenetic and noncoding RNA regulators of gene expression.

MOTIVATION: The advancement of high-throughput technology characterizes a wide variety of epigenetic modifications and noncoding RNAs across the genome involved in disease pathogenesis via regulating gene expression. The high dimensionality of both epigenetic/noncoding RNA and gene expression data make it challenging to identify the important regulators of genes. Conducting univariate test for each possible regulator-gene pair is subject to serious multiple comparison burden, and direct application of regularization methods to select regulator-gene pairs is computationally infeasible. Applying fast screening to reduce dimension first before regularization is more efficient and stable than applying regularization methods alone. RESULTS: We propose a novel screening method based on robust partial correlation to detect epigenetic and noncoding RNA regulators of gene expression over the whole genome, a problem that includes both high-dimensional predictors and high-dimensional responses. Compared to existing screening methods, our method is conceptually innovative that it reduces the dimension of both predictor and response, and screens at both node (regulators or genes) and edge (regulator-gene pairs) levels. We develop data-driven procedures to determine the conditional sets and the optimal screening threshold, and implement a fast iterative algorithm. Simulations and applications to long noncoding RNA and microRNA regulation in Kidney cancer and DNA methylation regulation in Glioblastoma Multiforme illustrate the validity and advantage of our method. AVAILABILITY AND IMPLEMENTATION: The R package, related source codes and real datasets used in this article are provided at https://github.com/kehongjie/rPCor. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Experimental design and power calculation in omics circadian rhythmicity detection using the cosinor model.

Circadian clocks are 24-h endogenous oscillators in physiological and behavioral processes. Though recent transcriptomic studies have been successful in revealing the circadian rhythmicity in gene expression, the power calculation for omics circadian analysis have not been fully explored. In this paper, we develop a statistical method, namely CircaPower, to perform power calculation for circadian pattern detection. Our theoretical framework is determined by three key factors in circadian gene detection: sample size, intrinsic effect size and sampling design. Via simulations, we systematically investigate the impact of these key factors on circadian power calculation. We not only demonstrate that CircaPower is fast and accurate, but also show its underlying cosinor model is robust against variety of violations of model assumptions. In real applications, we demonstrate the performance of CircaPower using mouse pan-tissue data and human post-mortem brain data, and illustrate how to perform circadian power calculation using mouse skeleton muscle RNA-Seq pilot as case study. Our method CircaPower has been implemented in an R package, which is made publicly available on GitHub ( https://github.com/circaPower/circaPower).

Circadian-Dependent and Sex-Dependent Increases in Intravenous Cocaine Self-Administration in Npas2 Mutant Mice.

Substance use disorder (SUD) is associated with disruptions in circadian rhythms. The circadian transcription factor neuronal PAS domain protein 2 (NPAS2) is enriched in reward-related brain regions and regulates reward, but its role in SU is unclear. To examine the role of NPAS2 in drug taking, we measured intravenous cocaine self-administration (acquisition, dose-response, progressive ratio, extinction, cue-induced reinstatement) in wild-type (WT) and Npas2 mutant mice at different times of day. In the light (inactive) phase, cocaine self-administration, reinforcement, motivation and extinction responding were increased in all Npas2 mutants. Sex differences emerged during the dark (active) phase with Npas2 mutation increasing self-administration, extinction responding, and reinstatement only in females as well as reinforcement and motivation in males and females. To determine whether circulating hormones are driving these sex differences, we ovariectomized WT and Npas2 mutant females and confirmed that unlike sham controls, ovariectomized mutant mice showed no increase in self-administration. To identify whether striatal brain regions are activated in Npas2 mutant females, we measured cocaine-induced ΔFosB expression. Relative to WT, ΔFosB expression was increased in D1+ neurons in the nucleus accumbens (NAc) core and dorsolateral (DLS) striatum in Npas2 mutant females after dark phase self-administration. We also identified potential target genes that may underlie the behavioral responses to cocaine in Npas2 mutant females. These results suggest NPAS2 regulates reward and activity in specific striatal regions in a sex and time of day (TOD)-specific manner. Striatal activation could be augmented by circulating sex hormones, leading to an increased effect of Npas2 mutation in females.SIGNIFICANCE STATEMENT Circadian disruptions are a common symptom of substance use disorders (SUDs) and chronic exposure to drugs of abuse alters circadian rhythms, which may contribute to subsequent SU. Diurnal rhythms are commonly found in behavioral responses to drugs of abuse with drug sensitivity and motivation peaking during the dark (active) phase in nocturnal rodents. Emerging evidence links disrupted circadian genes to SU vulnerability and drug-induced alterations to these genes may augment drug-seeking. The circadian transcription factor neuronal PAS domain protein 2 (NPAS2) is enriched in reward-related brain regions and regulates reward, but its role in SU is unclear. To examine the role of NPAS2 in drug taking, we measured intravenous cocaine self-administration in wild-type (WT) and Npas2 mutant mice at different times of day.

Unpaired data empowers association tests.

MOTIVATION: There is growing interest in the biomedical research community to incorporate retrospective data, available in healthcare systems, to shed light on associations between different biomarkers. Understanding the association between various types of biomedical data, such as genetic, blood biomarkers, imaging, etc. can provide a holistic understanding of human diseases. To formally test a hypothesized association between two types of data in Electronic Health Records (EHRs), one requires a substantial sample size with both data modalities to achieve a reasonable power. Current association test methods only allow using data from individuals who have both data modalities. Hence, researchers cannot take advantage of much larger EHR samples that includes individuals with at least one of the data types, which limits the power of the association test. RESULTS: We present a new method called the Semi-paired Association Test (SAT) that makes use of both paired and unpaired data. In contrast to classical approaches, incorporating unpaired data allows SAT to produce better control of false discovery and to improve the power of the association test. We study the properties of the new test theoretically and empirically, through a series of simulations and by applying our method on real studies in the context of Chronic Obstructive Pulmonary Disease. We are able to identify an association between the high-dimensional characterization of Computed Tomography chest images and several blood biomarkers as well as the expression of dozens of genes involved in the immune system. AVAILABILITY AND IMPLEMENTATION: Code is available on https://github.com/batmanlab/Semi-paired-Association-Test. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cocaine-induced neural adaptations in the lateral hypothalamic melanin-concentrating hormone neurons and the role in regulating rapid eye movement sleep after withdrawal.

Sleep abnormalities are often a prominent contributor to withdrawal symptoms following chronic drug use. Notably, rapid eye movement (REM) sleep regulates emotional memory, and persistent REM sleep impairment after cocaine withdrawal negatively impacts relapse-like behaviors in rats. However, it is not understood how cocaine experience may alter REM sleep regulatory machinery, and what may serve to improve REM sleep after withdrawal. Here, we focus on the melanin-concentrating hormone (MCH) neurons in the lateral hypothalamus (LH), which regulate REM sleep initiation and maintenance. Using adult male Sprague-Dawley rats trained to self-administer intravenous cocaine, we did transcriptome profiling of LH MCH neurons after long-term withdrawal using RNA-sequencing, and performed functional assessment using slice electrophysiology. We found that 3 weeks after withdrawal from cocaine, LH MCH neurons exhibit a wide range of gene expression changes tapping into cell membrane signaling, intracellular signaling, and transcriptional regulations. Functionally, they show reduced membrane excitability and decreased glutamatergic receptor activity, consistent with increased expression of voltage-gated potassium channel gene Kcna1 and decreased expression of metabotropic glutamate receptor gene Grm5. Finally, chemogenetic or optogenetic stimulations of LH MCH neural activity increase REM sleep after long-term withdrawal with important differences. Whereas chemogenetic stimulation promotes both wakefulness and REM sleep, optogenetic stimulation of these neurons in sleep selectively promotes REM sleep. In summary, cocaine exposure persistently alters gene expression profiles and electrophysiological properties of LH MCH neurons. Counteracting cocaine-induced hypoactivity of these neurons selectively in sleep enhances REM sleep quality and quantity after long-term withdrawal.

Older molecular brain age in severe mental illness.

Psychiatric disorders are associated with accelerated aging and enhanced risk for neurodegenerative disorders. Brain aging is associated with molecular, cellular, and structural changes that are robust on the group level, yet show substantial inter-individual variability. Here we assessed deviations in gene expression from normal age-dependent trajectories, and tested their validity as predictors of risk for major mental illnesses and neurodegenerative disorders. We performed large-scale gene expression and genotype analyses in postmortem samples of two frontal cortical brain regions from 214 control subjects aged 20-90 years. Individual estimates of "molecular age" were derived from age-dependent genes, identified by robust regression analysis. Deviation from chronological age was defined as "delta age". Genetic variants associated with deviations from normal gene expression patterns were identified by expression quantitative trait loci (cis-eQTL) of age-dependent genes or genome-wide association study (GWAS) on delta age, combined into distinct polygenic risk scores (PRScis-eQTL and PRSGWAS), and tested for predicting brain disorders or pathology in independent postmortem expression datasets and clinical cohorts. In these validation datasets, molecular ages, defined by 68 and 76 age-related genes for two brain regions respectively, were positively correlated with chronological ages (r = 0.88/0.91), elevated in bipolar disorder (BP) and schizophrenia (SCZ), and unchanged in major depressive disorder (MDD). Exploratory analyses in independent clinical datasets show that PRSs were associated with SCZ and MDD diagnostics, and with cognition in SCZ and pathology in Alzheimer's disease (AD). These results suggest that older molecular brain aging is a common feature of severe mental illnesses and neurodegeneration.

Valproate reverses mania-like behaviors in mice via preferential targeting of HDAC2.

Valproate (VPA) has been used in the treatment of bipolar disorder since the 1990s. However, the therapeutic targets of VPA have remained elusive. Here we employ a preclinical model to identify the therapeutic targets of VPA. We find compounds that inhibit histone deacetylase proteins (HDACs) are effective in normalizing manic-like behavior, and that class I HDACs (e.g., HDAC1 and HDAC2) are most important in this response. Using an RNAi approach, we find that HDAC2, but not HDAC1, inhibition in the ventral tegmental area (VTA) is sufficient to normalize behavior. Furthermore, HDAC2 overexpression in the VTA prevents the actions of VPA. We used RNA sequencing in both mice and human induced pluripotent stem cells (iPSCs) derived from bipolar patients to further identify important molecular targets. Together, these studies identify HDAC2 and downstream targets for the development of novel therapeutics for bipolar mania.

Simultaneous estimation of cluster number and feature sparsity in high-dimensional cluster analysis.

Estimating the number of clusters (K) is a critical and often difficult task in cluster analysis. Many methods have been proposed to estimate K, including some top performers using resampling approach. When performing cluster analysis in high-dimensional data, simultaneous clustering and feature selection is needed for improved interpretation and performance. To our knowledge, little has been studied for simultaneous estimation of K and feature sparsity parameter in a high-dimensional exploratory cluster analysis. In this paper, we propose a resampling method to bridge this gap and evaluate its performance under the sparse K-means clustering framework. The proposed target function balances between sensitivity and specificity of clustering evaluation of pairwise subjects from clustering of full and subsampled data. Through extensive simulations, the method performs among the best over classical methods in estimating K in low-dimensional data. For high-dimensional simulation data, it also shows superior performance to simultaneously estimate K and feature sparsity parameter. Finally, we evaluated the methods in four microarray, two RNA-seq, one SNP, and two nonomics datasets. The proposed method achieves better clustering accuracy with fewer selected predictive genes in almost all real applications.

MetaOmics: analysis pipeline and browser-based software suite for transcriptomic meta-analysis.

SUMMARY: The rapid advances of omics technologies have generated abundant genomic data in public repositories and effective analytical approaches are critical to fully decipher biological knowledge inside these data. Meta-analysis combines multiple studies of a related hypothesis to improve statistical power, accuracy and reproducibility beyond individual study analysis. To date, many transcriptomic meta-analysis methods have been developed, yet few thoughtful guidelines exist. Here, we introduce a comprehensive analytical pipeline and browser-based software suite, called MetaOmics, to meta-analyze multiple transcriptomic studies for various biological purposes, including quality control, differential expression analysis, pathway enrichment analysis, differential co-expression network analysis, prediction, clustering and dimension reduction. The pipeline includes many public as well as >10 in-house transcriptomic meta-analytic methods with data-driven and biological-aim-driven strategies, hands-on protocols, an intuitive user interface and step-by-step instructions. AVAILABILITY AND IMPLEMENTATION: MetaOmics is freely available at https://github.com/metaOmics/metaOmics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Meta-analytic framework for modeling genetic coexpression dynamics.

Methods for exploring genetic interactions have been developed in an attempt to move beyond single gene analyses. Because biological molecules frequently participate in different processes under various cellular conditions, investigating the changes in gene coexpression patterns under various biological conditions could reveal important regulatory mechanisms. One of the methods for capturing gene coexpression dynamics, named liquid association (LA), quantifies the relationship where the coexpression between two genes is modulated by a third "coordinator" gene. This LA measure offers a natural framework for studying gene coexpression changes and has been applied increasingly to study regulatory networks among genes. With a wealth of publicly available gene expression data, there is a need to develop a meta-analytic framework for LA analysis. In this paper, we incorporated mixed effects when modeling correlation to account for between-studies heterogeneity. For statistical inference about LA, we developed a Markov chain Monte Carlo (MCMC) estimation procedure through a Bayesian hierarchical framework. We evaluated the proposed methods in a set of simulations and illustrated their use in two collections of experimental data sets. The first data set combined 10 pancreatic ductal adenocarcinoma gene expression studies to determine the role of possible coordinator gene USP9X in the Hippo pathway. The second experimental data set consisted of 907 gene expression microarray Escherichia coli experiments from multiple studies publicly available through the Many Microbe Microarray Database website (http://m3d.bu.edu/) and examined genes that coexpress with serA in the presence of coordinator gene Lrp.