MOTIVATION: Complex diseases are often caused and characterized by misregulation of multiple biological pathways. Differential network analysis aims to detect significant rewiring of biological network structures under different conditions and has become an important tool for understanding the molecular etiology of disease progression and therapeutic response. With few exceptions, most existing differential network analysis tools perform differential tests on separately learned network structures that are computationally expensive and prone to collapse when grouped samples are limited or less consistent. RESULTS: We previously developed an accurate differential network analysis method-differential dependency networks (DDN), that enables joint learning of common and rewired network structures under different conditions. We now introduce the DDN3.0 tool that improves this framework with three new and highly efficient algorithms, namely, unbiased model estimation with a weighted error measure applicable to imbalance sample groups, multiple acceleration strategies to improve learning efficiency, and data-driven determination of proper hyperparameters. The comparative experimental results obtained from both realistic simulations and case studies show that DDN3.0 can help biologists more accurately identify, in a study-specific and often unknown conserved regulatory circuitry, a network of significantly rewired molecular players potentially responsible for phenotypic transitions. AVAILABILITY AND IMPLEMENTATION: The Python package of DDN3.0 is freely available at https://github.com/cbil-vt/DDN3. A user's guide and a vignette are provided at https://ddn-30.readthedocs.io/.

MOTIVATION: Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments. RESULTS: We previously developed a fully unsupervised deconvolution method-Convex Analysis of Mixtures (CAM), that enables estimation of cell type composition and expression from bulk tissues. We now introduce CAM3.0 tool that improves this framework with three new and highly efficient algorithms, namely, radius-fixed clustering to identify reliable markers, linear programming to detect an initial scatter simplex, and a smart floating search for the optimum latent variable model. The comparative experimental results obtained from both realistic simulations and case studies show that the CAM3.0 tool can help biologists more accurately identify known or novel cell markers, determine cell proportions, and estimate cell-specific expressions, complementing the existing tools particularly when study- or datatype-specific references are unreliable or unavailable. AVAILABILITY AND IMPLEMENTATION: The open-source R Scripts of CAM3.0 is freely available at https://github.com/ChiungTingWu/CAM3/(https://github.com/Bioconductor/Contributions/issues/3205). A user's guide and a vignette are provided.

MOTIVATION: Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific (STS) expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and STS expressions. Existing deconvolution methods can only estimate averaged STS expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations. RESULTS: We report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and STS expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and ℓ2,1-norm regularized matrix factorization problem. We determine hyperparameter values using cross-validation with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. Experimental results on realistic simulation data show that swCAM can accurately estimate STS expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk data. In two real-world applications, swCAM analysis of bulk RNASeq data from brain tissue of cases and controls with bipolar disorder or Alzheimer's disease identified significant changes in cell proportion, expression pattern and co-expression module in patient neurons. Comparative evaluation of swCAM versus peer methods is also provided. AVAILABILITY AND IMPLEMENTATION: The R Scripts of swCAM are freely available at https://github.com/Lululuella/swCAM. A user's guide and a vignette are provided. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Studies of complex disorders benefit from integrative analyses of multiple omics data. Yet, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking false findings. Accurately aligning sample information, genotype, and corresponding omics data is critical for integrative analyses. We developed DRAMS (https://github.com/Yi-Jiang/DRAMS) to Detect and Re-Align Mixed-up Samples to address the sample mix-up problem. It uses a logistic regression model followed by a modified topological sorting algorithm to identify the potential true IDs based on data relationships of multi-omics. According to tests using simulated data, the more types of omics data used or the smaller the proportion of mix-ups, the better that DRAMS performs. Applying DRAMS to real data from the PsychENCODE BrainGVEX project, we detected and corrected 201 (12.5% of total data generated) mix-ups. Of the 21 mix-ups involving errors of racial identity, DRAMS re-assigned all data to the correct racial group in the 1000 Genomes project. In doing so, quantitative trait loci (QTL) (FDR<0.01) increased by an average of 1.62-fold. The use of DRAMS in multi-omics studies will strengthen statistical power of the study and improve quality of the results. Even though very limited studies have multi-omics data in place, we expect such data will increase quickly with the needs of DRAMS.

Summary: Gene expression changes over the lifespan and varies among different tissues or cell types. Gene co-expression also changes by sex, age, different tissues or cell types. However, gene expression under the normal state and gene co-expression in the human brain has not been fully defined and quantified. Here we present a database named Brain EXPression Database (BrainEXP) which provides spatiotemporal expression of individual genes and co-expression in normal human brains. BrainEXP consists of 4567 samples from 2863 healthy individuals gathered from existing public databases and our own data, in either microarray or RNA-Seq library types. We mainly provide two analysis results based on the large dataset: (i) basic gene expression across specific brain regions, age ranges and sexes; (ii) co-expression analysis from different platforms. Availability and implementation: http://www.brainexp.org/. Supplementary information: Supplementary data are available at Bioinformatics online.

Human diseases involve a sequence of complex interactions between multiple biological processes. In particular, multiple genomic data such as Single Nucleotide Polymorphism (SNP), Copy Number Variation (CNV), DNA Methylation (DM), and their interactions simultaneously play an important role in human diseases. However, despite the widely known complex multi-layer biological processes and increased availability of the heterogeneous genomic data, most research has considered only a single type of genomic data. Furthermore, recent integrative genomic studies for the multiple genomic data have also been facing difficulties due to the high-dimensionality and complexity, especially when considering their intra- and inter-block interactions. In this paper, we introduce a novel multi-block bipartite graph and its inference methods, MB2I and sMB2I, for the integrative genomic study. The proposed methods not only integrate multiple genomic data but also incorporate intra/inter-block interactions by using a multi-block bipartite graph. In addition, the methods can be used to predict quantitative traits (e.g., gene expression, survival time) from the multi-block genomic data. The performance was assessed by simulation experiments that implement practical situations. We also applied the method to the human brain data of psychiatric disorders. The experimental results were analyzed by maximum edge biclique and biclustering, and biological findings were discussed.

Biological network inference is of importance to understand underlying biological mechanisms. Gene regulatory networks describe molecular interactions of complex biological processes. Graph models are mainly used for gene regulatory networks, where nodes and edges represent genes and their regulations respectively. In the most research, the molecular interactions (edges) of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data. However, gene expression is a product of sequential interactions of DNA sequence variations, single nucleotide polymorphism, copy number variation, histone modifications, transcription factor, DNA methylation, and many other factors. There are high-throughput genomic data that measure the various biological processes. We call the multiple types of genomics data as ‘multi-omics data’. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that can incorporate multi-omics data and their interactions in the graph model of gene regulatory network. Copy number variation and DNA methylation were considered for multi-omics data in this paper. The proposed method, iGRN, was applied to the human brain data of psychiatric disorder. Through the experiments, iGRN showed its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference.

Human diseases involve a sequence of complex interactions in multiple biological processes. In particular, multiple genomic data such as Single Nucleotide Polymorphism (SNP), Copy Number Variation (CNV), and DNA Methylation (DM) and their interactions simultaneously play an important role in the variation of mRNA transcription in human diseases. However, despite of the widely known complex multi-layer biological processes and increased availability of the heterogeneous genomic data, most research has considered only a single type of the genomic data. Furthermore, recent integrative genomic studies for the multiple genomic data have also been facing difficulties due to the high-dimensionality and complexity, especially when considering their intra- and inter-block interactions. In this paper, we introduce a novel multi-block bipartite graph and its inference methods, MB2I and sMB2I, for the integrative genomic study. The proposed methods not only integrate the multiple genomic data but also incorporate their intra/inter-block interactions by using a multi-block bipartite graph. In addition, the methods can be used to predict quantitative traits (e.g. gene expression, survival time) from the multi-block genomic data. The outstanding performance was assessed by simulation experiments that implement practical situations.

Inferring gene regulatory networks is one of the most interesting research areas in the systems biology. Many inference methods have been developed by using a variety of computational models and approaches. In this paper, we propose two network inference methods based on a lasso-based random feature selection algorithm (LARF). There are three main contributions. First, our z score-based method to measure gene expression variations from knockout data is more effective than similar criteria of related works. Second, we confirmed that the true regulator selection can be effectively improved by LARF. Lastly, we verified that an integrative approach can clearly outperform a single method when two different methods are effectively jointed. In the experiments, our method outperformed state of the art methods on simulated data, and LARF also was applied to the inference of gene regulatory networks associated with Psychiatric disorders.

MOTIVATION: Epistasis is the interactions among multiple genetic variants. It has emerged to explain the 'missing heritability' that a marginal genetic effect does not account for by genome-wide association studies, and also to understand the hierarchical relationships between genes in the genetic pathways. The Fisher's geometric model is common in detecting the epistatic effects. However, despite the substantial successes of many studies with the model, it often fails to discover the functional dependence between genes in an epistasis study, which is an important role in inferring hierarchical relationships of genes in the biological pathway. RESULTS: We justify the imperfectness of Fisher's model in the simulation study and its application to the biological data. Then, we propose a novel generic epistasis model that provides a flexible solution for various biological putative epistatic models in practice. The proposed method enables one to efficiently characterize the functional dependence between genes. Moreover, we suggest a statistical strategy for determining a recessive or dominant link among epistatic expression quantitative trait locus to enable the ability to infer the hierarchical relationships. The proposed method is assessed by simulation experiments of various settings and is applied to human brain data regarding schizophrenia. AVAILABILITY AND IMPLEMENTATION: The MATLAB source codes are publicly available at: http://biomecis.uta.edu/epistasis.

The importance of an integrative genomic study is steadily increasing in an emerging era of various high-throughput genomic data. Mechanisms of human diseases consist of complex interactions of multiple biological processes such as genetic, epigenetic, and transcriptional regulation. The collection of the multiple genomic data that represents the multiple processes is called 'multi-block data'. The multi-block data profiled from human disease samples provide comprehensive global snapshots of the diseases. Due to the rapid development of high-throughput technologies, the integrative genomic study using the multi-block data has been more highlighted than ever. However, in spite of its importance, there are only a few methodologies that can analyze such data. In this paper, we propose a novel Multi-Block and Multi-Task Learning (MBMTL) method for the integrative genomic study. We consider Single Nucleotide Polymorphism (SNP), Copy Number Variation (CNV), DNA methylation, and gene expression data as the multi-block data from four group samples of three major psychiatric disorders as well as data from a normal control. MBMTL identifies biomarkers that play important roles in explaining mechanisms of the human diseases from the multi-block data. We also take a multi-task problem into account so that we can identify different functions of the mechanisms. The performance of the proposed MBMTL was assessed by comparing it to a number of existing multi-block methods through simulation studies. We applied MBMTL to the multi-block data of the major psychiatric disorder samples.

Biological network inference is a crucial problem to solve in Bioinformatics as most of biological process are based on bio molecular interactions. Many researchers have worked on especially the inference of gene regulatory networks where a node and edge represent a gene and regulation relationship respectively assuming that a gene can regulate another gene indirectly. However, a gene expression level can be influenced by not only genes and proteins but also other biological factors. Therefore, the inference could be more effective if those factors are considered in gene regulatory network inferences. In this paper, we propose an integrative approach to infer gene regulatory networks where a gene can be regulated by not only gene and but also DNA Methylation and copy number variation. It is assumed that a gene can be directly regulated by a single DNA Methylation and copy number variation at most. The simulation results show that our method outperforms popular and state-of-the-art methods of biological network inference. In addition, we applied the proposed method to psychiatric disorder data. The inferred networks provide the relationships within a set of genes that are more likely to be regulated by DNA Methylation and copy number variation of the genes.

The identification of expression quantitative trait loci (eQTL) epistasis, which is a non-linear interaction effect between two or more genetic loci that control quantitative traits, play an essential role in understanding the mechanisms of gene interactions in the complex diseases. However, many studies have ignored the possibility of the epistasis in spite of the countless empirical evidence of epistasis in Genetics. Thus, the epistasis research is still in its infancy. Furthermore, most eQTL epistasis studies have involved only the interaction model with additive effects, which lacks the power to represent either 1) other biologically putative interaction models or 2) the genetic dominance that is a superior relationship of an allele against another one at the same locus. To tackle the problems, we propose a general eQTL epistasis model that provides the capability to incorporate diverse interaction models so that it can be widely utilized as a generic equation of epistasis in the multiple testing of eQTL studies. The extensibility of the eQTL epistasis model into multivariate methods is also proposed to reduce the computational burden of the multiple testing and to consider group effects. As the example of the extensibility, we provide the methodology that embeds the eQTL epistasis model into the sparse canonical correlation analysis (SCCA) method. A globally optimal solution is provided and the performance was assessed by realistic simulation experiments. A study of psychiatric disorder diseases with the method was conducted as a target application.

To elucidate the overall relationships between gene expressions and genetic perturbations, many approaches for SNPs to be involved in gene regulatory network (GRNs) inference have been suggested. In the most of the inferences of networks named as SNP-Gene Regulatory Networks (SGRNs) inference, pairs of SNP-gene are still separately given by performing eQTL mappings but eQTLs are not identified during network constructions. In order to build a SGRN for a given set of genes and SNPs without pre-defined eQTL information, we propose a method that is based on a structural equation model. The method consists of three steps: (i) ridge regression, (ii) elastic net regression, and (iii) iterative adaptive lasso regression. In the inference, it is assumed that each gene has a single unknown eQTL. The first two steps are to remove false positive edges keeping as many true positive edges as possible, and then lastly, final edges are selected by iterative adaptive lasso regression that iteratively gives more weight to the edge whose coefficient value is relatively high. To evaluate the performance, the method is applied to data that is randomly generated from the simulated networks and parameters. The method is also applied to psychiatric disorder data. There are three main contributions in this work. First, the proposed method provides both the gene regulatory inference and the identification of eQTL that affect genes in the network. Second, the simulation result proves that an integrative approach of multiple regression methods can effectively detect true edges as well as filter false positive edges. Lastly, it is demonstrated by applying it to psychiatric disorder data that our inference without eQTL information can discover the SGRNs that partially confirms eQTLs identified by our previous work.

While genome-wide association studies (GWAS) have focused on discovering genetic loci mapped to a disease, expression quantitative trait loci (eQTL) studies combine micro array data and provide a powerful approach. Micro arrays allow one to measure thousands of gene expressions simultaneously and the advances in eQTL studies enable one to capture the insight of the genetic architecture of gene expression. A number of multivariate methods have been recently proposed to identify genetic loci which are linked to gene expression taking into account joint effects and relationships between the units rather than the single locus alone independently. However, the previous research has limitations, such as the lack of supporting the cis/tran-eQTL model into being accepted as a general genetics model. We propose a novel regularized eQTL association mapping detection (Reg-AMADE) method. We have focused on the following three problems. First, we need to take into account co-expressed genes without using clustering or partitioning techniques, as well as detecting linkage disequilibrium and the joint effect of multiple genetic markers. Secondly, we need to build a regularized model to support the cis- and trans-eQTL model observed in most association studies. Lastly, we need to discover the significant genes underlying within diseases rather than a common component. We also propose a new simulation experiment method that implements practical situations so that the results can be evaluated in the true sense instead of the assessment with random samples generated from multivariate normal distributions that most research has mainly used. The power to detect both the joint effect and grouping effect of SNPs and gene expressions is assessed in the simulation study.

Publicly available single nucleotide polymorphism (SNP) allele frequencies are an important resource for the selection of genetic markers that may be most useful for gene mapping and association studies. Data mining these allele frequencies through disparate public databases and Websites is time consuming and can result in inconsistent findings. We have developed a web-based software tool, Frequency Finder, to acquire SNP allele frequencies from multiple public data sources and return a summarized result to the user. Our software optimizes and automates the search of candidate markers, decreasing the amount of time it would take to extract pertinent data manually. We have included several methods to output the data, including on-screen and as a compressed text file. We show that Frequency Finder accurately retrieves available frequency data from the available sources. Using this tool, we detect significant differences between Asian, African and Caucasian populations in the allele frequency spectra of 246 097 SNPs. While limited to public databases that provide web-based access to allele frequencies, Frequency Finder provides a single, user-friendly interface for retrieving allele frequencies for large batches of SNPs from multiple data sources.