Pathways contribute to functional interplay within biological processes through complex but poorly understood interactions. However, limited functional references for global pathway relationships exist. Pathways from databases such as KEGG and Reactome provide discrete annotations of biological processes. Their relationships are currently either inferred from gene set enrichment within specific experiments, or by simple overlap, linking pathway annotations that have genes in common. Here, we provide a unifying interpretation of functional interaction between pathways by systematically quantifying coexpression between 1,330 canonical pathways from the Molecular Signatures Database (MSigDB) to establish the Pathway Coexpression Network (PCxN). We estimated the correlation between canonical pathways valid in a broad context using a curated collection of 3,207 microarrays from 72 normal human tissues. PCxN accounts for shared genes between annotations to estimate significant correlations between pathways with related functions rather than with similar annotations. We demonstrate that PCxN provides novel insight into mechanisms of complex diseases using an Alzheimer’s Disease (AD) case study. PCxN retrieved pathways significantly correlated with an expert curated AD gene list. These pathways have known associations with AD and were significantly enriched for genes independently associated with AD. As a further step, we show how PCxN complements the results of gene set enrichment methods by revealing relationships between enriched pathways, and by identifying additional highly correlated pathways. PCxN revealed that correlated pathways from an AD expression profiling study include functional clusters involved in cell adhesion and oxidative stress. PCxN provides expanded connections to pathways from the extracellular matrix. PCxN provides a powerful new framework for interrogation of global pathway relationships. Comprehensive exploration of PCxN can be performed at  http://pcxn.org/, and as a Bioconductor package at http://bioconductor.org/packages/pcxn/.Genes do not function alone, but interact within pathways to carry out specific biological processes. Pathways, in turn, interact at a higher level to affect major cellular activities such as motility, growth and development. We present a pathway coexpression network (PCxN) that systematically maps and quantifies these high-level interactions and establishes a unifying reference for pathway relationships. The method uses 3,207 human microarrays from 72 normal human tissues and 1,330 of the most well established pathway annotations to describe global relationships between pathways. PCxN accounts for shared genes to estimate correlations between pathways with related functions rather than with redundant pathway definitions. PCxN can be used to discover and explore pathways correlated with a pathway of interest. We applied PCxN to identify key processes related to Alzheimer’s disease (AD), interpreting a mixed genetic association and experimental derived set of disease genes in the context of gene co-expression. We expand the known relationships between pathways identified by gene set enrichment analysis in brain tissues affected with AD. PCxN provides a high-level overview of pathway relationships. PCxN is available as a webtool at  The advancement of high throughput, high dimensional ‘omic’ technology has enabled quantification of a vast array of cellular components. Inducing phenotypic changes, through mutations or perturbations, and observing their impact on genomic, proteomic and metabolomic assays has allowed us to assign roles to sets of genes and gene products –3. We noThe development of databases such as KEGG , ReactomA natural extension to gene-centric analysis is to consider the interactions between biological pathways, taking into account relationships between higher level systemic functions of the cell and the organism , 27. TheSeveral methods for connecting pathways rely solely on annotation, using gene overlap to describe the relationships between gene sets. Methods such as Onto-Express  and BiNGPathway interaction networks can also be defined using distance measures based on aggregating curated gene level connections, such as protein-protein interactions (PPIs) , 42, 43 Systems approaches to interpret the relationships between differentially expressed genes have focused upon development of gene coexpression networks, where these genes are related to each other by known coexpression in extensive large scale assays , 45. TheIn this work, we address the need for a consistent functional map of pathway interactions. A reference network of global relationships between pathways serves two purposes: it allows deeper exploration of basic cell biology, and serves as a tool to discover novel mechanisms and targets in disease while building testable models of pathway interaction. Our aim has been to create a network that delineates the global relationships between canonical pathways in as broad a context as possible. To achieve this goal, we have developed the Pathway Coexpression Network (PCxN). For each experiment from a curated collection of normal human tissue microarrays  from pubhttp://pcxn.org/). In addition, PCxN is available as a Bioconductor package (http://bioconductor.org/packages/pcxn/).Here we report how PCxN effectively captures intra-pathway relationships within known pathways such as the ribosome pathway. Then, we show how PCxN finds pathways associated with a complex disease: Alzheimer’s disease (AD). PCxN determines well known pathways related to AD, including those that influence amyloid pathology and innate immune response. Finally, we show how use of PCxN can complement and expand the results of gene set enrichment analysis within an AD gene expression profiling study. PCxN helps to interpret the results by describing the relationships between the enriched pathways, and provides the opportunity to discover novel relationships by revealing pathways which are highly correlated with the enrichment results. PCxN addresses the need to describe relationships between pathways present across diverse tissues and conditions. These relationships provide a pathway interaction model for a biologically driven phenotype, provide a reference to prioritize targets of biological processes, and provide a powerful enhancement for interpretation of results from gene set enrichment methods. We have built a comprehensive web tool for PCxN to explore novel relationships and to aid with the interpretation of results from gene set enrichment methods   and 3,20Ribosome pathway is a gene set that represents a well characterized, meaningful and ubiquitous biological function [oAB = 0.0469) to high overlap (overlap coefficient oAB = 0.8532).To determine how effectively PCxN captures tightly related biological functions we analysed the ribosome pathway (KEGG accession hsa03010). The KEGG function –63. We cThe correlation estimates from the ribosome gene sets are positive while the estimates for the random gene sets are smaller in magnitude and closer to zero . Under tPathway annotations from different sources present challenges when relating pathways: equivalent pathways with different annotations have similar but not identical names, annotations exist for equivalent but differently constituted pathways in separate databases, and pathways with completely different names share genes , 19. TheCell Cycle and the DNA Replication pathways  for the Wnt signaling pathway. These annotations share genes among each other. Unlike the previous example, the correlation estimates between the Wnt signaling pathways have a small magnitude and most of them are not significant  initiates chromatid separation and entrance into anaphase [E2F Mediated Regulation of DNA Replication pathway is significantly correlated and has a significant overlap with the Mitotic Prometaphase pathway which in turn is significantly correlated and has a significant overlap with the G1/S Transition pathway. The E2F family of transcription factors play a major role during the G1/S transition in mammalian and plant cell cycle [PCxN has an advantage over overlap based approaches when we consider pathways with related functions but without shared genes. For example considering the Reactome pathways, the ith them . On the rrelated . Howeverrrelated . The APCanaphase , and theanaphase , 74. Thell cycle .With the goal of determining the value of our approach in understanding pathway relationships in complex disease, we chose an important disease for which there is abundant transcriptomic data, established genetic associations, and the need for better understanding of the roles of pathways and their relationships is fundamental to the prioritisation of drugs and drug targets. AD is a progressive multifarious neurodegenerative disorder , 77 and GPVI Mediated Activation Cascade, is associated with regulation of Amyloid beta (Aβ). GPVI and FCER1 initiate platelet activation that leads to activation of Syk. Syk enhances the formation of stress granules that are prevalent in AD affected brains. The stress granules produce reactive oxygen and nitrogen species that are toxic to neuronal cells. Downregulation of Syk expression reduces Aβ production and increases the clearance of Aβ across the blood-brain barrier [We used genes within an AD curated list (ADCL) as the disease gene signature. The ADCL is a set of association-derived and experimental-derived genes related to AD. Consisting of 68 genes of which 61 genes were present in the PCxN gene expression background . The ADC barrier . Since PTo explore novel insights resulting from the use of PCxN, and as a complement to enrichment methods based on gene overlap, we compared the top ADCL correlated pathways with pathways significantly enriched for genes in the ADCL. First, we ordered all pathways correlated with the ADCL  by the mp = 7.34 × 10−9).To assess the validity of the ADCL correlation results, we tested the enrichment of genes associated with AD in pathways correlated with the ADCL using independent methods , 89. We Of the top 10 pathways correlated with the ADCL, 6 out of 10 were significantly enriched with genes related to Alzheimer’s found by genetic association. We sorted the ADCL neighbors by the magnitude of their correlation with the ADCL and split them into bins of increasing size . As we mPCxN can be used effectively to determine relationships between pathways as a complement to interpret gene set enrichment (GSE) methods. A typical GSE result is a list of gene sets that are significantly enriched by a list of query genes. PCxN can describe the relationships between the enriched gene sets using the global pathway correlation estimates. To explore correlation between gene sets enriched with a set of query genes, we used Gene Set Enrichment Analysis (GSEA)  to find PS1 Pathway role in AD [Focal Adhesion, A Tetrasaccharide Linker Sequence is Required for GAG Synthesis, Angiopoietin Receptor and SMAD2/3 Nuclear Pathway  share function as they are activated in response to oxidative stress. Elevated Angiopoietin-1 serum levels can be observed in patients with AD [SMAD2/3 Nuclear Pathway contains SMAD3 which regulates expression of angiogenic molecules in tumor cells and vascularization in tumor lesions [β (TGFβ) to the nucleus [β is upregulated. The imbalance between SMAD3 and TGFAβ signalling, shifts the regulatory signalling towards a dysregulated inflammatory activation potentially leading to neurodegenerative changes, such as decreased Aβ clearing [We chose to examine the functional relationships among the top ten enriched pathways identified by GSEA. Functionally, they all appear to be consistently associated with the AD literature (e.g. the le in AD ). We ret Pathway ). These th in AD . As partD brains . FAK alsD brains . Members tissues . Loss of tissues , while Atrocytes . In addi with AD . The clo lesions . SMADs t nucleus . SMAD3,  nucleus , while Tclearing .Dorso-ventral Axis Formation has been suggested as one of the pathways regulated by miRNAs identified in a bioinformatics study of Drosophila AD models [β protein precursor and its paralogues [The other top ten pathways identified in this GSEA have also been associated with AD and some show documented functional relationships. PS1 is well known as a common cause of familial AD . Dorso-vD models . Notch iD models , YAP1 anralogues . Finallyralogues  indicatiralogues .In addition to providing relationships between the GSEA results, PCxN can provide potentially novel relationships by retrieving canonical pathways significantly correlated with the pathways identified as enriched. We retrieved the top 10 canonical pathways which were the most correlated with the AD GSEA enriched gene sets, and clustered the correlated pathways along with the results from GSEA . Most ofhttp://pcxn.org) that can be used to interactively explore and visualise pathway relationships found in PCxN. The tool allows a user to query the various pathway databases using one or more pathways and retrieve correlation estimates, p-values and overlap coefficients. Since the correlations adjusted for shared genes are a complementary perspective to relationships based on gene overlap, the webtool also provides the option to view coexpression networks based on correlation coefficients not adjusted for shared genes in addition to the PCxN coexpression network that is based on the adjusted correlation. The results are presented through heatmaps , interactive networks (with multiple pre-made structures) and data tables. Pathway members are also retrievable along with their descriptions. In addition, PCxN is available as Bioconductor software (http://bioconductor.org/packages/pcxn/) and data (http://bioconductor.org/packages/pcxnData/) packages which contain the same exploratory/visualization functionality and data as the webtool.We created a user-friendly webtool (Ribosome pathway (KEGG) and random gene sets to show that PCxN effectively captures relationships between gene sets with related functions while discarding relationships from random gene sets. The correlation estimates between the ribosome gene set were positive and significant, while the correlation estimates for random gene sets were not significant and with a magnitude close to zero. These results suggest that the correlation between two pathways with related functions is significant.We have developed and described PCxN, a coexpression method to describe global relationships between pathways. PCxN estimates the correlation between 1,330 canonical pathways using a curated collection of 3,207 microarrays in 134 experiments from 72 normal human tissues. We integrated a wide range of experiments by estimating the correlation between summaries of the pathway expression, testing their significance in every experiment, and then aggregating the experiment-level estimates into global estimates. We used gene sets derived from permutations of the Wnt signaling pathway had a small magnitude and were mostly not significant. When pathways share genes due to related functions, the correlations between them might be significant depending on the degree of the overlap. For instance, we found pathways for mitotic cell cycle and related processes that were significantly correlated and had significant overlaps between them. The significant correlations and significant overlaps between these pathways revealed known relationships between ADC/C, CDC20 and the E2F family of transcription factors with the mitotic cell cycle. However, the correlations between a different set of pathways representing other aspects of the mitotic cell cycle, such as the Mitotic Cell Cycle and the G1 Phase pathways and related processes, such as the Recruitment of Mitotic Centromere Proteins and Complexes, were not significant while the overlap was highly significant. PCxN was successful in uncovering relationships between the Mitotic Prometaphase pathway and other cell cycle related pathways such as the G2/M Checkpoints and the S Phase that do not have genes in common.The influence of redundant annotations across pathways databases is often overlooked. Pathway databases often include pathways that share genes with one another to varying degrees. Shared genes between pathways can either be a consequence of closely related functions or redundant annotation from different sources. Ignoring such redundancies during pathway analysis can lead to identifying pathways relationships due to high content-similarity, rather than truly related biological mechanisms. PCxN adjusts the correlation between pathways by conditioning on the shared genes. The correlations between redundant annotations for the PCxN provides powerful means to generate models for complex diseases by providing pathways significantly correlated with an assay-independent disease gene signature. We used PCxN to identify key processes related to Alzheimer’s disease (AD) using an AD curated list (ADCL). The top pathways correlated with the ADCL have known relationships with AD or amyloid pathology. Furthermore, the correlated pathways were significantly enriched for genes associated with AD independently derived from genome wide association studies. These results show the value of PCxN in finding biological processes associated with complex diseases using gene signatures. PCxN provides a powerful contribution to the interpretation of the gene set enrichment methods by describing the relationships between enriched pathways independent of gene overlap. We used PCxN to describe the relationships between pathways identified as enriched by GSEA in a published microarray gene expression experiment profiling the effect of AD in the superior frontal gyrus. We expanded the scope of gene set enrichment results by retrieving pathways correlated with the enriched pathways. The top pathways correlated with the enriched pathways are components of extracellular matrix (ECM) and form a highly correlated cluster. We note that the ECM undergoes significant changes during the early stages of AD, but only a few ECM components have been studied. The relationships between the ECM pathways from PCxN could provide leads to future studies of the individual ECM components.PCxN relies on the completeness and correctness of pathway annotations to relate biological processes. Also, PCxN only considers a pathway as a gene list, omitting any knowledge of the interaction between its members. PCxN is also limited by the gene expression data used to estimate the correlations. The current implementation only uses one microarray platform and a curated expression background. It is widely accepted that pathway activation is phenotype dependent. Using the PCxN approach it will be possible to explore whether pathway-pathway relationships change in relationship to a phenotype, or if consistent functional links prevail irrespective of cell state. Further work is required to investigate how network topology changes with expression background, and in particular into whether pathway networks are significantly disrupted in disease. This implementation of PCxN does not take advantage of the growing number of publicly available RNA-seq data. In future, the method will be expanded to include a wider range of pathway annotations and to use gene expression data from other platforms such as RNA-seq.http://pcxn.org/, as well as a Bioconductor software (http://bioconductor.org/packages/pcxn/) and data (http://bioconductor.org/packages/pcxnData/) package.PCxN establishes the utility of describing relationships between pathways in a broad context. By using a diverse set of gene expression experiments, PCxN leverages correlation estimates across various human tissues effectively capturing relationships regardless of shared genes. We expect that PCxN can serve as a basis for a high-level map of the relationships between biological process. We built an interactive web-tool that provides a user-friendly portal to explore the PCxN at We used 134 experiments with 3,207 Affymetrix Human Genome U133 Plus 2.0 microarrays from 72 normal human tissues manually curated in Barcode 3.0  S1 Tabl. The curWe retrieved the C2: Canonical Pathways collection from MSigDB  v5.1 up.Since the microarrays from the gene expression background belong to different experiments representing different tissues, pooling the microarrays to estimate the correlation between pathways would ignore the underlying structure of the data. Even if the correlations are homogeneous, pooling the data is not a valid procedure in general. The pooled estimates may be severely biased due to the heterogeneity of the experiments , 118. InL samples as the K × L matrix X where K is the total number of genes in the array. Thus, the element xkl of the matrix X corresponds to the expression for gene k in array l. For each array, the genes were ranked by their expression level. Rank normalizations do not depend on the dynamic range of an array and provide a common range. We represent the expression ranks as the K × L matrix S, where L is the total number of arrays and K is the total number of genes in the array. Since within each array the genes are ranked by expression level, from 1 (low expression) to K (high expression), the entries of the matrix S arexkl is the expression level for gene k in array l.We represent an experiment with G = {g1, …, gn}. The pathway expression E is a gene set summary statistic based on the expression ranks of the pathway genes; the pathway expression E is the mean of the expression ranks of the pathway genes. Consider an experiment with L samples, the experiment-level summary for pathway G is given by the L × 1 vector E with entriesE, first we take the rows from S corresponding to the genes {g1, …, gn} to get the matrix of ranks of the pathway constituent genes, and then we take the mean across the columns of this matrix, producing the L × 1 vector E.In this approach pathways are represented as gene sets: groups of functionally related genes. Thus, a pathway is represented by its gene set annotation Compared to other summary statistics, the mean is fast to compute and easy to interpret. We considered several approaches for the pathway summary statistic, but we found that in most cases the mean performed well. For instance, we considered a summary based on principal components analysis (PCA) but the variance explained by the first principal component was less than 50% for all canonical pathways in the majority of the gene expression experiments from the curated collection of normal human tissues .R* is a linear combination of the standard correlation estimator R and a restricted submodel of the correlation matrixR is the empirical correlation matrix and T is identity matrix.We used a shrinkage estimator to compute the experiment-level pathway correlation coefficients. In our setting, a shrinkage estimator will give more reliable experiment-level correlation estimates for experiments with few samples and will set correlation coefficients with a small magnitude to 0 . The shrT assumes that all of the variables are uncorrelated. The optimal λ is found by minimizing the mean squared error L(λ) between the shrinkage estimator R* and the true correlation matrix P.The restricted submodel L(λ). The solution [The analytical solution λ* for the optimal λ λ*=argmisolution  is giveni with gene set Gi and pathway j with gene set Gj there are two possible cases for shared genes: the gene sets overlap or do not overlap.Since genes can be involved in more than one biological process and often pathways share genes, we accounted for the gene overlap between pathways to determine the coexpression between two pathways. Our goal is to describe relationships between patwhays representing related functions rather than pathways with similar annotations. For pathway Ei and Ej for pathways i and j respectively. Then, we estimated the pathway correlation as the Spearman correlation between the two pathway expression summariesFirst we calculated the expression summary Gi and Gj on the summary for the genes common to both pathways (Gi∩j = Gi ∩ Gj).Our approach to deal with overlapping pathway gene sets was to condition the correlation between the summaries for the pathways Ei, Ej, and Ei∩j corresponding to pathway Gi, pathway Gj and the shared genes Gi∩j. Then we estimated the partial correlation between the pathway summaries conditional on the summary for the shared genesFirst, we calculated the summaries We used a t-test to determine which experiment-level correlation coefficients were significantly different from 0.r is the experiment-level correlation estimate.For the correlation coefficients between pathways without shared genes, the t-test is given byr is the experiment-level conditional correlation estimate.For the correlation coefficients between pathways with shared genes, the t-test is given byni is the number of samples for experiment i, ri is the correlation estimate for experiment i and N is the total number of experiments [We used the experiment-level correlation estimates to compute the overall correlation between two gene sets with a weighted averageeriments .Since we estimated the correlation coefficients at the experiment level, we first obtained a p-value from each of the experiments by testing if the experiment-level correlation was significant. In order to determine the significance of the overall correlation coefficient we combined the p-values from each experiment using Liptak’s method , 122. Thϕ is the standard normal probability density function, Φ−1 is the standard normal inverse cumulative distribution function, ni is the number of samples for experiment i, pi is the p-value for experiment i and N is the total number of experiments.After aggregating the experiment-level p-values for all pathway pairs, we adjust the combined p-values for multiple comparison using the Benjamini–Hochberg FDR method .G and H, the overlap coefficient is given byoGH ≤ 1. The overlap coefficient is simply the size of the intersection divided by the size of the smaller of the two sets. We chose the overlap coefficient instead of other measures of overlap like the Jaccard index because it highlights whenever a pathway is a fully contained within another pathway. If a set G is a subset of H, the overlap coefficient is always 1. On the other hand, if the sets G and H are disjoint, the overlap coefficient is always 0.The overlap coefficient is a similarity measure for the overlap between two sets. For two sets The annotation for the Ribosome pathway was retrieved from the KEGG REST server using the KEGGREST package (v. 1.10.1) . We ran For the no overlap case, the KEGG Ribosome pathway was split in half. The ribosome pathway annotation, composed of 126 genes, was split into two non overlapping gene sets with 63 genes each with the following stepsPermute indexes of the genes belonging to the ribosome pathwayA and BSplit the gene set into two non overlapping gene sets EA and EB for gene sets A and B respectivelyCalculate the pathway summaries EA and EBCalculate the pathway correlation using the pathway summaries For the random gene set, we sampled 126 genes present in the gene expression background, and split them with the following stepsSample 126 genes from the backgroundArBr with 63 genes eachSplit the genes into two non overlapping gene sets Ar and Br respectivelyCalculate the pathway summaries Calculate the pathway correlation using the pathway summaries s1 and s2 from n distinct elements. In the first step, the two sets s1 and s2 share all but one element. In each consecutive step, we shift the indexes of one of the sets to decrease the number of shared elements between s1 and s2 until the last step when the two sets s1 and s2 do not have any elements in common.We created representative cases of gene overlap between two gene sets. In particular, we created two overlapping sets oAB = 0.0469) to high overlap (oAB = 0.8532).In order to consider different scenarios for the amount of shared genes between pathways, we built 9 different configurations of overlapping gene sets. These 9 overlap cases ranged from low overlap  powered by a Tomcat  HTTP-server. MySQL  was used to manage a relational database containing pathway correlation coefficients. The front-end user interface was developed using HTML and specialized libraries. The Jquery.js library  was used to handle events. The canvasXpress.js library  was used to build heatmaps. The cytoscape.js library  was used to build networks. PCxN is also available through Bioconductor as two distinct but interacting R packages. The pcxn package (http://bioconductor.org/packages/pcxn/) contains exploration and visualization wrapper functions that use data matrices stored in the pcxnData package (http://bioconductor.org/packages/pcxnData/).The PCxN webtool is available at S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 TableDomain expert curated list of genes associated with Alzheimer’s disease identified via genome wide association studies (GWAS).(DOCX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.


Lung cancer is the leading cause of cancer death and its incidence is ranked high in men and women worldwide. Non-small-cell lung cancer (NSCLC) adenocarcinoma is one of the most frequent histological subtypes of lung cancer. The aberration profile and the molecular mechanism driving its progression are the key for precision therapy of lung cancer, while the screening of biomarkers is essential to the precision early diagnosis and treatment of the cancer. In this work, we applied a bioinformatics method to analyze the dysregulated interaction network of microRNA-mRNA in NSCLC, based on both the gene expression data and the microRNA-gene regulation network. Considering the properties of the substructure and their biological functions, we identified the putative diagnostic biomarker microRNAs, some of which have been reported on the PubMed citations while the rest, that is, miR-204-5p, miR-567, miR-454-3p, miR-338-3p, and miR-139-5p, were predicted as the putative novel microRNA biomarker for the diagnosis of NSCLC adenocarcinoma. They were further validated by functional enrichment analysis of their target genes. These findings deserve further experimental validations for future clinical application. Lung cancer is the most death causing cancer for both men and women in the United States, and it is also the most death causing cancer in men and second in women worldwide. The incidence rate is high and ranked second for both men and women in the United States , 2. Non-MicroRNAs are endogenous small noncoding RNAs which regulate many important biological roles and theirs aberrations may have significant effects on the cancer genesis and progression, such as cell proliferation, cell cycle, apoptosis, and tumorigenesis, and therefore are good candidates for cancer diagnosis and therapy biomarkers –16. BiomAt present, more and more computational and bioinformatics models are developing for biomarker discovery; some of them are machine learning based , 20, whiThe data used in our integrative analysis include the gene expression data of NSCLC adenocarcinoma from both microRNA and messenger RNA(mRNA) and the human reference microRNA-mRNA network. We need to first collect the data and reconstruct the NSCLC adenocarcinoma specific network and then to identify putative microRNA biomarkers based on the network structure and their biological functions. The screened microRNAs need to be validated by literature mining, confirmation, and bioinformatics exploration of their associations with NSCLC adenocarcinoma. The pipeline of the whole process of this work could be seen in p value and other parameters. The Benjamini-Hochberg correction was used to adjust the p values. The adjusted p values less than 0.05 were regarded as significant. The human reference microRNA-mRNA interaction network was constructed based mainly on experimentally validated and the consensus predicted microRNA-mRNA interaction pairs as reported in previous studies [The gene expression and microRNA expression data for the NSCLC adenocarcinoma were extracted from the public GEO database . The det studies , 25, 26.p value < 0.05) were detected as our putative biomarkers for diagnosis of NSCLC adenocarcinoma.The basic idea and the methods we used here are based on the models which were developed previously , 40–42, ∗  OR marker∗)”. The related PMIDs, NOGs, and TFPs of these microRNAs were also calculated.To validate the bioinformatics model first, we also collected reported NSCLC adenocarcinoma associated microRNAs from PubMed citations with the searching criteria “(lung adenocarcinoma OR NSCLC adenocarcinoma) AND (miRNA OR microRNA) AND  online tool . The p vThe microRNA biomarkers for diagnosis and prognosis of NSCLC adenocarcinoma reported in PubMed citations were collected and listed in Among the reported microRNA biomarkers for NSCLC adenocarcinoma, many of them played essential roles in lung carcinogenesis and their abnormal expression patterns were highly associated with the occurrence and development of NSCLC adenocarcinoma. For example, serum miR-155 was a sensitive indicator for predicting the initiation of lung adenocarcinoma, especially combining with the index of carbohydrate antigen 125. It altered the expression of downstream proteins and activated the lung carcinogenic signal . Two miRp value < 0.05. These nine microRNAs were predicted to be biomarkers for the diagnosis of NSCLC adenocarcinoma as listed in We performed the predictions according to the pipeline shown in We further performed the functional enrichment analysis to investigate the roles of genes regulated by identified microRNA biomarkers through Database for Annotation, Visualization and Integrated Discovery (DAVID) online tools. This analysis was conducted in two ways: Gene Ontology (GO) analysis and KEGG pathway analysis.γ1 under the situation of growth factor absence. These two proteins will compete for the same protein binding site [In GO analysis, we did this analysis at three levels: biological process (BP), cellular component (CC), and molecular function (MF). The top 10 most significantly enriched items were shown in ing site . FGFR2 eing site , membraning site , positiving site , 48 haveIn KEGG pathway analysis, we totally found 72 significantly enriched pathways. Here we still selected top 10 most significantly enriched pathways for further investigation. These 10 pathways were listed in Here, we investigate the relationship between these 10 pathways and lung adenocarcinoma through literature validation and we found that most of these pathways have been demonstrated to be associated with lung adenocarcinoma. Besides common pathways in lung cancer like cell cycle and MAPK signaling pathways , 50, theIn this research, an integrative bioinformatics model considering the network structure and biological functions of the microRNA targets was used to predict novel biomarker microRNAs for the diagnosis of NSCLC adenocarcinoma. The method was first tested with the reported microRNA biomarkers of NSCLC adenocarcinoma; then we extended the model and applied it to the microRNA and gene expression data. We detected five novel biomarker microRNAs for the diagnosis of NSCLC adenocarcinoma, including miR-204-5p, miR-567, miR-454-3p, miR-338-3p, and miR-139-5p. The novel bioinformatics microRNAs were validated with bioinformatics exploring their functions associated with NSCLC adenocarcinoma.


We would like to thank the editor for providing valuable comments. We also thank Dr. Panse for the comments . The comWe rechecked our manuscript about the two different “tissues” carefully. To overcome this issue, we applied pathway filter analyses instead of using miRNA-mRNA regulatory pairs directly. In our paper, KEGG pathway enrichment analyses were performed for predicted targets of miRNA and mRNA, respectively. Then, pathways enriched for differentially expressed mRNAs were used to filter predicted targets to identify dysfunctional pathways in myasthenia gravis (MG). Interestingly, significant crosstalk was detected between five of these pathways based on a cumulative hypergeometric distribution. Furthermore, we detected some key genes mediated crosstalk between dysfunctional pathways, and these genes were regulated by MG-related miRNAs. Besides, we also reviewed more literatures and found the expression pattern of some genes was consistent in both peripheral blood and thymic tissues in MG patients, such as CXCL13 , 5, CXCRThe current management of MG includes acetylcholinesterase inhibitors, immunomodulatory agents, and thymectomy. Plasma exchange or intravenous immunoglobulins (IVIg) are used for myasthenic crisis . There aOnce again, we thank the editor for editorial efforts regarding our paper and giving us an opportunity to clarify the issues. We also thank Dr. Panse for the helpful comments on our work; this may provide great help to our future research.


The application of pathway and gene-set based analyses to high-throughput data is increasingly common and represents an effort to understand underlying biology where single-gene or single-marker analyses have failed. Many such analyses rely on the a priori identification of genes associated with the trait of interest. In contrast, this variance-component–based approach creates a similarity matrix of individuals based on the expression of genes in each pathway.We compared 16 methods of calculating similarity for positive control matrices based on probes for the genes used to model the simulated Genetic Analysis Workshop phenotypes.A simple correlation matrix outperforms the other methods by identifying pathways associated with the simulated phenotypes at nearly twice the rate expected based on the associations of the component transcripts and an approximate false-positive rate of 0.05.This method has a number of additional advantages compared to single-transcript and pathway overrepresentation analyses, including the ability to estimate the proportion of variation explained by each pathway and the logistical advantage of only calculating the distance matrices once for each messenger RNA data set regardless of the number of phenotypes. Additionally, it offers a significant reduction in the multiple testing burden over individual consideration of each probe. Pathway and gene-set enrichment analyses were developed with several goals, including increasing the biological interpretability of genetic association and RNA expression analyses . Because2 transformed followed by a quantile normalization; we further adjusted the transcript abundance values for sex, age, age^2, and sex*age interaction. The residual values were used for all analyses. Probes were annotated based on their RefSeq IDs and we selected a single, representative probe per gene to avoid upweighting the apparent effect of a gene in the pathway matrix from the inclusion of multiple probes representing a single gene. More than 90 % of genes present in 1 or more pathways are represented by only a single probe. Where there were more than 2 probes mapped to a gene, we compared the expression of each pair of probes using Pearson’s correlation and the probe with the highest average correlation value was considered most representative of the gene as a whole. Where only 2 probes were mapped to a gene, we selected the probe with greater variance. Selected probes were scaled to range between 0 and 1 so that all probes are weighted equally in the similarity calculation; however, weights could be applied at this step to test specific hypotheses or reflect known biological features of the pathway.In the provided Genetic Analysis Workshop 19 data , high-qucis-regulatory and coding variants with a real effect on the messenger RNA (mRNA) probes drawn from the data set [Diastolic blood pressure (DBP) values were simulated based in part on genetic variation in data set . Throughdata set , we measproxy [We generated N × N similarity matrices for the 643 individuals from the probe values in the positive control pathways using 16 methods in the R library proxy : correlaproxy , Bhjattaproxy , Bray/Cuproxy , Chord dproxy , divergeproxy , euclideproxy , geodesiproxy , Hellingproxy , Mahalanproxy , Manhattproxy , Soergelproxy , Tschebyproxy , and Whiproxy . Where dp values derived from the LRT. In these analyses, we used the SOLAR-generated 2φ matrix based on expected kinship from the pedigree; however, an empirical kinship matrix generated from other genetic data or a similarity matrix from the full set of probes can be used in place of or in addition to the 2φ matrix [For each of the 200 replicates, a polygenic null model was generated for simulated DBP and negative-control phenotype Q1 using SOLAR. The Q1 trait, which has a heritability of zero, was modeled independently of transcript and genetic data and should not be associated with any pathway. These polygenic null models include the expected kinship matrix derived from the pedigree with sex and age as covariates and serves as the model to which the similarity matrices are compared. We considered each similarity matrix separately as an additional variance component and applied a likelihood ratio test (LRT) to determine if the positive control pathway explains significantly more of the variation in the phenotype than kinship alone (the null model). For consistency with the individual probe analysis, significance was determined at α = 0.05 for the φ matrix .For the 5 similarity methods showing the largest number of associations in the positive control pathways across the 200 replicates, similarity matrices were calculated for 723 pathways taken from Pathway Studio 8.0 . The phenotypic variation explained by these similarity matrices in the simulated DBP, Q1, and real DBP values was assessed. These 723 pathways represent a wide variety of basic cellular functions, disease-specific gene sets, immune response, and signaling pathways. However, this method is not limited by the choice of pathway or gene set. It can be applied to any set of probes of interest to the researcher.F2RL3 and B3GAT1—are independently associated with DBP in more than half of the 200 simulations. The probes in the top decile are associated with DBP in an average of 59.1 simulations (29.6 %), whereas those in the bottom decile have an average of just 2.04 associations in the 200 simulations (1.0 %). The average number of associations falls off steeply beyond the first decile , only 2 genes—Table In contrast, there is no pattern of associations of the simulated pathways with the negative control Q1 phenotype. Using an α = 0.05 threshold, the number of false-positive associations is approximately what would be expected. The correlation method appears to have a slightly inflated false-positive rate, although this may be the result of kinship detected by the similarities of expression data in the families that is incompletely accounted for by the expected kinship matrix included as the null model.χ2 values for the correlation method for simulated Q1 (λ = 1.01) and DBP (λ = 1.25), as well as the real data. The correlation method produces the best results with 5 Pathway Studio matrices significantly associated with DBP in 20 to 25 % of simulations. The top Pathway Studio pathways are reasonable candidates with 6.7 % of genes in the pathways included as causal probes in the model. One pathway calculated with the correlation method is significantly associated with simulated DBP, despite containing no probes independently associated with the simulated phenotypes. Although this appears to be a false positive, it is possible that the genetic variants underlying these probes are in linkage disequilibrium with 1 or more genetic variants that formed the basis of the simulation.Table p values for the LRT are approximately 5 × 10−3, failing to surpass the Bonferroni-corrected threshold for 723 pathways. This may be a result of the relatively low heritability of DBP (0.33). Twelve pathways show a nominally significant result: Focal junction assembly, cleavage of lamina in apoptosis, systemic lupus erythematosus, glycan catabolism, TGFi, OA transport, fatty acid biosynthesis, NF-1, Myc Mad Max, type 1 diabetes mellitus, and triacylglycerols degradation. The first 3 of these pathways contain probes with previous associations with DBP in this data set. Any analysis of gene expression data must consider the directionality of effect. The appearance of pathways involved in the production and regulation of glycans, triacylglycerols, and fatty acids may be from a phenotypic correlation between individuals with high blood pressure and cardiovascular disease or other outcomes related to the metabolic syndrome. Regardless of the directionality of effect, their appearance among the top pathways is sensible.When the 5 similarity methods are applied to the real DBP data, the minimum The proportion of phenotypic variance explained by each pathway can also be obtained from comparing the null model to one including the matrix-derived variance component. The majority of the nominally significant pathways explain approximately 1 % of the overall variation in DBP. However, 2 large pathways—focal junction assembly and systemic lupus erythematosus—explain 4 and 6 % of the phenotypic variation, respectively. Each of these pathways contains 2 probes independently associated with DBP.χ2 values for the real DBP were plotted against pathway size (Fig. To determine if larger pathways were simply capturing more of the total transcript variation and were therefore more likely to be associated with any phenotype, the The relatively weak relationship between the simulated DBP values and the transcript data makes this a more conservative test than if DBP had been modeled directly from the expression data. Despite this limitation, the use of similarity matrices generated from sets of probes in a variance component-based pathway approach outperformed a single-probe association test. Specifically, the observed number of associations with simulated DBP was higher for the correlation and extended Jaccard similarity matrices for positive control pathways containing probes for genes modeled to be causative than the average for the probes contained in those same control pathways.p values were used across all analyses for similarity of comparison between individual probe associations and pathway similarity matrices, but consideration of the p values illustrates the reduction in the number of tests when the pathway becomes the unit of initial analysis. In the single-probe analyses, none of the 17,265 probes reached a Bonferroni-corrected p value of 2.9 × 10−6. In contrast, multiple pathways surpassed the Bonferroni-corrected critical p value of 6.9 × 10−5 for the simulated phenotypes across all similarity methods. When the correlation method is used to assess the effect of the Pathway Studio pathways on the real DBP phenotype, none of the pathways are significantly associated after multiple testing. However, several of the nominally significant pathways are plausible candidates for contributing to DBP. In addition to significantly reducing the multiple testing burden, this method, like all pathway-based tests, also serves to identify potentially important biological pathways instead of isolated genes.Nominal Although this method should allow for the detection of pathways containing a large number of genes just below the significance threshold, it is difficult to clearly differentiate these associations from false positives based on this simulation. Additionally, the method of equally weighting the probes is problematic with pathways containing large numbers of genes as it may dilute the effect of these genes of moderate effect. Weighting based on additional biological information could improve the performance. Furthermore, the method will likely be more effective where the heritability of the phenotype is higher and expression explains a larger proportion of the variance.The use of a correlation matrix to generate variance components for pathways or gene-sets provides a means for detecting multiple genes that together contribute to phenotypic variation but cannot be detected individually. The correlation matrix is simple to calculate from any type of input and the same matrices can be used to analyze all available phenotypes in a data set, saving computation time. Additionally, the LRT is straightforward to implement in SOLAR with a single additional variance component, but more complex models incorporating multiple pathways or using an empirical kinship matrix as a null model could be incorporated. As data sets grow, this method, applied to transcript or genotypic data, provides a useful method for prioritizing biological pathways for further investigation while avoiding the multiple-testing burden.


ATHLINKER, a new computational method to reconstruct the interactions in a signaling pathway of interest. PATHLINKER efficiently computes multiple short paths from the receptors to transcriptional regulators (TRs) in a pathway within a background protein interaction network. We use PATHLINKER to accurately reconstruct a comprehensive set of signaling pathways from the NetPath and KEGG databases. We show that PATHLINKER has higher precision and recall than several state-of-the-art algorithms, while also ensuring that the resulting network connects receptor proteins to TRs. PATHLINKER’s reconstruction of the Wnt pathway identified CFTR, an ABC class chloride ion channel transporter, as a novel intermediary that facilitates the signaling of Ryk to Dab2, which are known components of Wnt/β-catenin signaling. In HEK293 cells, we show that the Ryk–CFTR–Dab2 path is a novel amplifier of β-catenin signaling specifically in response to Wnt 1, 2, 3, and 3a of the 11 Wnts tested. PATHLINKER captures the structure of signaling pathways as represented in pathway databases better than existing methods. PATHLINKER’s success in reconstructing pathways from NetPath and KEGG databases point to its applicability for complementing manual curation of these databases. PATHLINKER may serve as a promising approach for prioritizing proteins and interactions for experimental study, as illustrated by its discovery of a novel pathway in Wnt/β-catenin signaling. Our supplementary website at http://bioinformatics.cs.vt.edu/~murali/supplements/2016-sys-bio-applications-pathlinker/ provides links to the PATHLINKER software, input datasets, PATHLINKER reconstructions of NetPath pathways, and links to interactive visualizations of these reconstructions on GraphSpace.Signaling pathways are a cornerstone of systems biology. Several databases store high-quality representations of these pathways that are amenable for automated analyses. Despite painstaking and manual curation, these databases remain incomplete. We present P While these databases have been iteratively improved over the years, they are still largely built through extensive and time-consuming manual curation. Further, the proteins and interactions within the same signaling pathway may vary considerably from one database to another.A major focus in systems biology is the identification of the networks of reactions that guide the propagation of cellular signals from receptors to downstream transcriptional regulators (TRs). Over the past two decades, databases have been developed to store the interactions present in signaling pathways,6–18 However, most of these methods are routinely evaluated on data in budding yeast. To tackle the increased complexity of human signaling pathways, we sought to develop an algorithm with two desirable characteristics. First, the method must be able to compute a reconstruction that captures a large subset of the interactions in the curated signaling pathway. Ideally, it should have a tunable parameter that smoothly determines the size of the solution. Second, to reflect the process of signal transduction, the receptors must be connected to the downstream TRs in the reconstructed pathway.Inspired by these challenges, we sought to develop a computational approach to automatically reconstruct signaling pathways from a background network of molecular interactions (the interactome). We conceptualized the problem as follows : given aATHLINKER, an algorithm that satisfies both criteria. PATHLINKER finds the k highest scoring paths from any receptor to any TR, where k is a user-defined parameter  and its interactions with receptor-like tyrosine kinase (Ryk) and Dab, mitogen-responsive phosphoprotein, homolog 2 (Dab2), both of which are known members of the Wnt pathway, comprise a novel signaling mechanism from Wnts to β-catenin. We experimentally validate this role for CFTR using loss of function short interfering RNA (siRNA)-based silencing.We apply PATHLINKER and other algorithms to reconstruct a diverse collection of 15 signaling pathways in the NetPath database , random walk with restarts (RWR20), network flow (RESPONSENET17), Steiner forest (PCSF15), ANAT,18 and a greedy seed-based method . Brief descriptions of these methods and the user-defined parameters we selected appear in We compared Pgorithms , includiESPONSENET, SHORTESTPATHS, and BOWTIEBUILDER achieved values of recall <0.1. While IPA returned sub-networks with larger recall values, the precision was never above 0.2. RWR achieved the best precision for recall values between 0.05 and 0.13, and PATHLINKER and RWR were comparable for all other values of recall.For each pathway reconstruction, we used the interactions in the NetPath pathway as the set of positives and a subsampled set of interactions not present in the NetPath pathway as the set of negatives . For eacATHLINKER compared with RWR, we asked if the false positives were “close” to the pathway as represented in the NetPath database. First, we recomputed precision of all algorithms after ignoring interactions that involved at least one true positive node in the NetPath pathway (“pathway-adjacent negatives”) before subsampling the negatives  across all three values of recall indicates that PATHLINKER’s false positives were closer to the pathway than RWR’s false positives.To determine the source of the false positive interactions in Pegatives . This monegative . At a reATHLINKER and RWR using the criterion where we required receptors and TRs to be connected in the reconstruction, we assessed how quickly PATHLINKER and RWR recovered the curated receptors and TRs. For PATHLINKER and RWR, we recorded the index of the first interaction that contained each receptor or each TR. ATHLINKER and RWR recovered receptors at about the same rate, although PATHLINKER’s long tail indicated that the last few receptors were difficult for PATHLINKER to retrieve. Conversely, PATHLINKER successfully recovered 90% of the TRs in the pathways in the first 1,000 ranked interactions, compared with only 38% recovered by RWR.To compare PATHLINKER’s performance in several additional ways to investigate its robustness to the inputs and its effectiveness for other pathway databases. First, we added (incorrect) receptors/TRs to the input or removed correct receptors/TRs from the input and compared the resulting reconstructions  and high (>0.4) values of recall.Our analysis thus far relied on 15 pathways from a single database. Our last three assessments estimated the effect of interactions present only in NetPath and extended the scope of the analysis to a larger set of NetPath pathways and to the KEGG database. First, we estimated the reliance of our reconstructions on NetPath-only interactions by applying P NetPath . To evaleractome . Next, wpathways . When weover RWR . Finallyderation . The aggATHLINKER, RWR, and IPA at a recall of 0.20 , a known negative regulator of β-catenin signaling.26 As Wnt signaling is associated with several types of cellular differentiation and specification, the closing of membrane channels to facilitate morphological changes is biologically relevant.27The remaining protein, CFTR, was the highest ranked of all proteins not previously known to be in Wnt pathway in the NetPath or KEGG databases. It appeared in the 59th path computed by PTHLINKER . PATHLINATHLINKER (blue region in 28 to transiently express 11 different secreted Wnt proteins (referred to hereby as Wnt) in HEK293 cells. Transient expression of Wnts has been previously shown to induce the expression of luciferase enzyme driven by a synthetic, tandem TCF/LEF promoter when co-transfected into HEK293 cells.28 We were able to determine and verify the extent of TCF/LEF- promoted luciferase activity by each of the 11 Wnt proteins tested . In the case of IPA, since changing parameters yielded networks with substantially different precision and recall, we present results for this algorithm for nine parameter values. Since the single sub-network approaches had the goal of computing compact sub-networks that connected sources to targets, they were able to reconstruct pathways with high precision but only with low recall. Only the algorithms that offered a ranked list of interactions, PATHLINKER and RWR, reached a recall of ⩾0.6. These results showed that an important component of a pathway reconstruction algorithm was a parameter, such as k, whose increase caused a smooth variation and expansion of the resulting network. While both RWR and PATHLINKER had this property, only PATHLINKER offered an additional guarantee of connecting receptors to TRs . Using 11 of the 19 known Wnts, we further this understanding by showcasing how the tested Wnts differentially activate the TCF/LEF promoter via β-catenin to significantly varying degrees. We show that Wnts 1, 2, 3, and 3a are capable of ≥30-fold activation of the TCF/LEF promoter, and do so in part via a novel Ryk–CFTR–Dab2 pathway that further regulates the cellular levels of β-catenin.Wnt proteins are essential components of higher order eukaryotic development, cellular homeostasis, and wound healing. The canonical Wnt signaling pathway has been shown to be specific for a subset of Wnts, while other Wnts are known to signal through alternate means , an alternate product of certain non-canonical Wnt signaling pathways.34 Dab2 is involved in endosomal recycling and degradation of CFTR and is a well-known regulatory component of receptor-mediated endocytosis.36 Dab2 also functions as a negative regulator of the β-catenin destruction complex.38 Even though prior groups had previously identified these functionalities independently, there was no evidence or speculation for the role of CFTR in Wnt/β-catenin-mediated signaling particularly by Ryk or Dab2.Ryk is a predicted tyrosine-protein kinase containing an extracellular WIF domain that has been previously shown to directly bind to Wnt 1 and Wnt 3a, though its signaling mechanism was unknown.We present a model incorporating the Ryk–CFTR–Dab2 pathway as an amplifier of Wnt 1-, 2-, 3-, and 3a-specific β-catenin signaling . Our resATHLINKER, an algorithm that automates the reconstruction of human signaling pathways by connecting the receptors and TRs for a pathway through a physical and regulatory interaction network. Based on our comprehensive analysis on 15 NetPath pathways, PATHLINKER achieved much higher recall (while maintaining reasonable precision) than several other methods. Furthermore, it was the only method that could control the size of the reconstruction while ensuring that receptors were connected to TRs in the result. PATHLINKER’s reconstruction of the Wnt pathway indicated that CFTR facilitates the signaling from Ryk to Dab2. In HEK293 cells, we validated this path experimentally and showed its specificity for 4 of the 11 Wnts tested . Based on these results, we propose a model that suggests Dab2 is recruited to the Ryk–CFTR membrane complex in response to a defined Wnt stimulus that ultimately amplifies Wnt 1, 2, 3, and 3a canonical signaling. In summary, PATHLINKER provides a promising framework for reconstructing a well-studied signaling pathway given relatively little information about its components. It may serve as a powerful approach for discovering the structure of poorly studied processes and prioritizing both proteins and interactions for experimental study.In conclusion, we have presented PG containing physical and regulatory interactions between pairs of proteins, (ii) the receptors S in a signaling pathway of interest, and (iii) the TRs T in the same pathway. A reconstruction of a pathway P consists of a sub-network of G that connects the receptors in P to the TRs in P using proteins and interactions in G.The problem of pathway reconstruction takes as input (i) a weighted directed interactome G=, where every edge e in E has an associated weight we between 0 and 1, a receptor set S, a TR set T, and a user-defined parameter k, PATHLINKER computes the k highest scoring loopless paths that begin at any receptor in S and terminate at any TR in T. We define the score of a path to be the product of the edge weights along the path. We add an artificial source s with a directed edge  for each node x∈S and an artificial sink t with a directed edge  for each node y∈T. We assign the following cost to each edge :s↝t path is equivalent to the path from S to T that maximizes the path score. PATHLINKER computes the k highest scoring paths in this modified graph by incorporating a novel integration of Yen’s algorithm39 with the A* heuristic  shortest paths, thereby ensuring that our reconstructions vary smoothly with k. For precision and recall calculations, we compute k=20,000 paths and rank each node and edge by the index of the first path in which it appears. This value of k reflects the high degree of redundancy (edge reuse) among paths in signaling networks.We compute a pathway reconstruction 40 The resulting network contained 12,046 nodes and 152,094 directed edges, where multiple types of evidence supported many of the edges. We weighted each edge in the network using a Bayesian approach that computes interaction probabilities based on the sources of evidence.17 We identified sets of signaling receptors and TRs from previously published lists of human receptors41 and TRs.43 We selected 15 NetPath pathways and 32 KEGG pathways that each contained at least one receptor, at least one TR, and were connected by at least three paths (We constructed a directed human protein interactome from numerous protein–protein interaction and signaling pathway databases.ee paths . For mor28 and validated siRNA. We present detailed methods in We conducted experiments in HEK293 cells using the public Wnt plasmid Library


The problem of discovering genetic markers as disease signatures is of great significance for the successful diagnosis, treatment, and prognosis of complex diseases. Even if many earlier studies worked on identifying disease markers from a variety of biological resources, they mostly focused on the markers of genes or gene-sets . However, these markers may not be enough to explain biological interactions between genetic variables that are related to diseases. Thus, in this study, our aim is to investigate distinctive associations among active pathways  shown each in case and control samples which can be observed from gene expression and/or methylation data.The pathway-sets are obtained by identifying a set of associated pathways that are often active together over a significant number of class samples. For this purpose, gene expression or methylation profiles are first analyzed to identify significant (active) pathways via gene-set enrichment analysis. Then, regarding these active pathways, an association rule mining approach is applied to examine interesting pathway-sets in each class of samples (case or control). By doing so, the sets of associated pathways often working together in activity profiles are finally chosen as our distinctive signature of each class. The identified pathway-sets are aggregated into a pathway activity network (PAN), which facilitates the visualization of differential pathway associations between case and control samples. From our experiments with two publicly available datasets, we could find interesting PAN structures as the distinctive signatures of breast cancer and uterine leiomyoma cancer, respectively.Our pathway-set markers were shown to be superior or very comparable to other genetic markers (such as genes or gene-sets) in disease classification. Furthermore, the PAN structure, which can be constructed from the identified markers of pathway-sets, could provide deeper insights into distinctive associations between pathway activities in case and control samples.The online version of this article (doi:10.1186/s13040-017-0127-7) contains supplementary material, which is available to authorized users. The problem of finding effective disease markers or signatures is very important for the successful diagnosis, treatment, and prognosis of complex diseases such as cancers. So far, many studies have worked on identifying disease-related markers with a variety of biological resources, such as gene expression profiles –8, proteAlthough such methods were shown to be very useful in many applications, they may not be enough to fully understand disease mechanisms. Moreover, multiple candidate pathways often share some genes, so it leads to forming complex interconnections among pathways while making analysis and interpretation complicated. For example, when there are common genes in multiple candidate pathways, they might be considered to be associated with each other. However, even when multiple candidate pathways do not share genes, they may be mutually associated or active together. Thus, it seems worthwhile to investigate the associations among the activities of significant pathways differentially shown in case and control samples, as potential disease signatures.In this study, our aim is to identify the sets of pathways  having associations in the activities observed from gene expression profiles (or DNA methylation data), especially shown differentially in case and control samples. For this purpose, we utilized an association rule mining approach to find active pathway-sets that are shown distinctively and frequently in each class of samples (case or control). This was done based on the assumption that the set of pathways often being active together in gene expression (or methylation) profiles could have some interesting biological relationships among them. For the discovery of interesting pathway-sets, we conducted gene-set enrichment analysis (GSEA)  to identFor experiments, we utilized two publicly available cancer datasets in this study. GSE15852  (DatasetIn breast cancer dataset Dataset 1), we first identified 36 significant pathways by GSEA with FDR q-value < 0.05. Then, we produced the activity profiles of these pathways and utilized them to produce pathway-sets satisfying minimum support of 25%. , we first identified 24 pathways as differentially expressed (DE) pathways from gene expression profiles and 25 pathways as differentially methylated (DM) pathways from methylation data, by using GSEA. Then, we chose the union of all these pathways (48 pathways) as significant pathways and used them to detect interesting pathway-sets from gene expression data and methylation data, respectively. The details of 48 pathways are given in Additional file , we firsp-value < 0.05, whereas 36 pathway markers were found by GSEA. As pathway-set markers, we used 500 pathway-sets chosen in earlier stage. For classification with pathway-set markers, we derived the activities of pathway-sets from pathway activity profiles in the similar way to define pathway activities from gene expression profiles. Table For disease classification, three different types of genetic markers (including the markers of genes/pathways/pathway-sets) were used and compared in terms of the sensitivity, specificity, and accuracy. In the breast cancer dataset Dataset 1), 67 differentially expressed genes were identified as gene markers using Limma with , 67 diffp-value <0.001, respectively. Among them, we selected 28 common genes between 276 DEGs and 1370 DMGs as gene markers. For pathway and pathway-set markers, we used the 48 pathways and 496 pathway-sets identified earlier, respectively. Table For uterine leiomyoma cancer dataset Dataset 2), we identified 276 differentially expressed genes (DEGs) from gene expression and 1370 differentially methylated genes (DMGs) from methylation data using Limma with , we ident-test to support the claim that the difference in classification accuracies between biomarkers is significant, as in Table In addition, we performed paired To have better understanding of the identified pathway-set markers, we constructed the pathway activity network (PAN) (See the Method section for details). Based on our pathway-sets, two different PANs were generated that demonstrate differential pathway associations appeared in case and control samples, respectively. By doing so, we could easily capture the distinctive features of pathway activities and their associations shown in class samples. In the PAN, the thickness of an edge is proportional to the relative frequency of pathway associations shown in pathway-set markers of class samples. The size of a node is proportional to the relative significance of corresponding pathway in the sense of how much distinguishable its activity level is between case and control samples. Thus, as pathway is more significant, its node size would be larger. Also, the color of a node indicates the activity type of its corresponding pathway in terms of expression changes, i.e., the node is displayed in red when its corresponding pathway is up-regulated under the class condition (case/control) or in blue when it is down-regulated.For Dataset 1, we obtained the two PANs shown in case and control samples, as in Fig. For Dataset 2, we obtained the PANs shown in case and control samples of gene expression data, which are given in Fig. n most significant pathway-sets. The chosen pathway-sets performed superior or very comparable to other biomarkers in disease classification. Moreover, by constructing pathway activity network from the identified pathway-sets, we could gain deeper insights into pathway associations differentiating between case and control samples. However, since our experiments were conducted with only one disease and one kind of control types, the results we obtained in this study may have some limitation. To strengthen the prominence of our approach, it will be necessary to perform additional experiments with wide selection of data, including multiple cancer and tissue types. Although our work may not cover all aspects of biomarker identification issues, the methodology used in this study could be still applicable to discover interesting biomarkers for other diseases, as well as to underpin the biological mechanisms of target diseases. As future works, we plan to perform pathway association studies with multiple cancer types. We also plan to work on identifying disease-specific markers using normal samples of multiple tissue types in near future.In this study, we have focused on identifying interesting pathway-sets that indicate the distinctive features of the activities of significant pathways and their associations shown in case and control samples. These pathway-sets were found by considering sets of significant pathways frequently activated together over class samples. For this purpose, we utilized an association rule mining approach that employs the BiMax biclustering method to detect pathway associations with discretized pathway activity profiles. Even if the BiMax method is relatively faster and more time-efficient than traditional approaches like Apriori algorithm that often create too many rules and is time-consuming, it still has the practical problem such that the number of clusters tends to increase drastically as the number of input features  increases, leading to the radical increase of corresponding pathway-sets. To handle this situation, we prioritized the identified pathway-sets using rule interestingness measures and selected top-t-test) were used to find differentially expressed genes. From two GSEA results obtained by SNR and t-test, respectively, common pathways were considered as significant pathways.In the gene expression (or methylation) data, when there are multiple probes corresponding to gene, we used their averaged expression values as an expression value of the gene. Also, such genes that the variances of their expression values are lower than a certain threshold were excluded from the data, and zero-mean normalization was applied to adjust different scales of genes into a common scale. We used MSigDB C2 curated data to define candidate pathways and found significant (active) pathways among the candidates by performing GSEA with gene expression or methylation data, in which two different methods , since it contains gene expression profiles and DNA methylation data, significant pathways were finally chosen by taking the union of significant pathways found from gene expression data and from DNA methylation data, respectively.n-dimensional pathway activity vector of +1’s and/or -1’s, we produced a 2n-dimensional binary vector of 1’s and 0’s by assigning first n bits to the binarization of up-regulated data (or hyper-methylation data) and other n bits to the binarization of down-regulated data (or hypo-methylation data), as in Fig. For pathway association analysis, we first obtained pathway activity profiles from gene expression (or methylation) data by adapting the method used in  and discOnce all clusters were found for case and control samples, respectively, we examined each of them to generate association rules. Prior to rule generation, we calculated the confidence and the lift of each cluster for evaluation, and eventually produced the association rules only for the clusters that satisfy the thresholds of confidence ≥0.8 and lift >1. If a certain cluster were found in the both classes, two association rules are considered; one for the case and the other for the control. Out of these two rules, the less-significant rules are eliminated. Then, from all the produced association rules, we extracted interesting pathway-sets. Figure From the pathway-set markers, we obtained pathway-set activity profiles by applying the method adapted from Lee et al.  to pathwt-test between the accuracies of biomarkers  and examined if the difference between classification accuracies is statistically significant.For classification with pathway-set markers, we employed four classification methods, including k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and naïve Bayesian classifiers. This task is to predict a target class (control or case) of a given sample from the distinctive signature shown in identified pathway-set markers. For performance evaluation, we used 4-fold cross-validation with 8 repetitions in which each repetition is for random selection in data partitioning, and compared the averaged classification results over 8 repetitions in terms of sensitivity, specificity, and accuracy. We also conducted paired p-value) when p-value is a statistical significance of the difference in its activity level between case and control classes. The size of a node represents the relative significance of corresponding pathway in the sense of how much distinguishable its activity level is between case and control samples. The PAN can be drawn separately for each class (case or control), which shows distinctive features of pathway activities and their associations in case or control samples.For better understanding of the identified pathway-set markers, we constructed pathway activity network (PAN) in which the nodes represent significant (active) pathways, the edges represent the associations between active pathways. The thickness of an edge represents the relative frequency of pathway associations shown in the identified pathway-set markers. Also, the size of a node represents the relative significance of pathway that is defined as –log2 (


Osteoporosis is a prevalent bone metabolic disease and peripheral blood monocytes represent a major systemic cell type for bone metabolism. To identify the key dysfunctional pathways in osteoporosis, we performed pathway analyses on microarray data of monocytes from subjects with extremely high/low hip bone mineral density. We first performed a traditional pathway analysis for which different pathways were treated as independent. However, genes overlap among pathways will lead to “crosstalk” phenomenon, which may lead to false positive/negative results. Therefore, we applied correction techniques including a novel approach that considers the correlation among genes to adjust the crosstalk effects in the analysis. In traditional analysis, 10 pathways were found to be significantly associated with BMD variation. After correction for crosstalk effects, three of them remained significant. Moreover, the MAPK signaling pathway, which has been shown to be important for osteoclastogenesis, became significant only after the correction for crosstalk effects. We also identified a new module mainly consisting of genes present in mitochondria to be significant. In summary, we describe a novel method to correct the crosstalk effect in pathway analysis and found five key independent pathways involved in BMD regulation, which may provide a better understanding of biological functional networks in osteoporosis. Osteoporosis is the most common metabolic bone disease, mainly manifested as low bone mineral density (BMD). One of the key pathophysiological mechanisms of this disease is excessive bone resorption (by osteoclasts) over bone formation (by osteoblasts).1. First, PBMs may act as precursors of osteoclasts5, the bone resorption cells. Particularly for the adult peripheral skeleton , circulating monocytes provide the sole source of osteoclast precursors6. Second, PBMs can secrete a number of potent cytokines important for osteoclast differentiation, activation, and apoptosis10. Reduced production of PBM cytokines represents a major mechanism for the inhibitory effects of sex hormones on osteoclastogenesis and bone resorption13. With their abundance and diverse roles in bone metabolism, PBMs may thus represent a highly valuable and unique working cell model for dissecting some of the important pathogenesis mechanisms underlying various skeletal disorders. In fact, abnormalities in PBMs, not only by their percentage in circulation but also by their functional activities, have been linked to a variety of skeletal disorders and traits, such as osteoporosis14, rheumatoid arthritis15 and alcoholism16. Therefore, our study will use PBMs as a cell model to investigate the pathways associated with osteoporosis.Peripheral blood monocytes (PBMs) are an appropriate cell model for studying osteoporosis19. The majority of these analyses applied typical approaches to identify the pathways related to BMD variation, such as KEGG and Gene Ontology analysis, which treat the pathways as independent. However, because pathways may have regulatory interactions, or some genes may overlap with each other in different pathways, the derivation of p-values which aim to quantify the significance of the involvement of each pathway in a given phenotype will be affected, which may lead to both false positive and false negative results. The term “crosstalk” was coined by Donato et al.20 to represent the effect that pathways influence each other via overlapping genes. By simulation test, they found that three major pathway analysis methods  produced a significant number of false positives due to crosstalk effects, and that crosstalk could be explained by the presence of overlapping genes among pathways20. Thus, they proposed a method called Maximum Impact Estimation based on maximum likelihood (ML) to correct the crosstalk effects by reassigning each gene to only one of the pathways which it originally belongs to.In recent years, taking advantage of high-throughput technologies, pathway analyses have been performed as a crucial step in expression profiling studies for osteoporosis, e.g.Inspired by Donato’s study, the goal of this work was to apply crosstalk correction methods to identify the pathways associated with BMD variations. Toward this goal, we detected the existence of crosstalk effects by classical overrepresentation analysis (ORA) and then applied the ML method to correct these crosstalk effects. However, the ML method did not consider correlation among genes. Based on biological perspectives, expression levels of genes in the same pathway most likely are associated and thus correlated with each other. Here, we further propose a novel improved correction approach based on correlation among genes to improve pathway analyses, then compare the results from all the three methods. ML method corrects the crosstalk effects by reassigning the overlap genes to a unique pathway, but it may generate false positive results because it is mainly based on mathematical not biological prediction. Our approach focuses on the interaction between the genes in the same pathway and could further improve the correction for crosstalk effects. With the application of the methods to osteoporosis research, we identified an independent functional module which may play a different role from the pathways they conventionally and originally belong to.After gene expression data processing, 591 genes were identified as nominal DE genes (p < 0.05) in the “core set genes” (n = 22011). Among the core set genes, 4801 genes were present in at least one KEGG pathway and 103 of them were nominal DE genes. Using the classical ORA methods, ten pathways were significantly associated with BMD variation (p < 0.05)  Intersection of Alzheimer’s, Parkinson’s, Huntington’s disease and oxidative phosphorylation pathway (Intersection_Pak_Oxi_Hun_Alz); and 2) Intersection of Rig-I-like receptor signaling, cytosolic DNA sensing and regulation of autophagy pathway (Intersection_Rig_Cyt_Auto). A total of 53 genes were included in the module, Intersection_Pak_Oxi_Hun_Alz  was 5.0%. For ML method, type I error was 4.1% and type II error was 15.6%. For the PCA method, type I error was 1.9% and type II error was 19.4%.In this study, we aimed to identify the important pathways involved in osteoporosis mechanisms by analyzing transcriptome-wide gene expression of monocytes in 73 Caucasian females with extremely high or low hip BMD. Unlike traditional pathway analysis studies, we adopted a detection and correction approach for crosstalk effects among pathways during the analysis process. Furthermore, we proposed and employed a novel method (PCA) to correct the crosstalk effects based on the correlation of experimental expression data within pathways or the new modules detected and constructed. Since the PCA considered interaction among genes in the same pathway, it included more information from the experiment, especially the regulatory networks in the pathways, and generated biologically more meaningful results for a better understanding of the pathophysiological mechanisms. Using a module detection algorithm and the correction methods described in Methods section, we found that two pathways were persistently significant in all the results, and importantly, we identified a new independent functional module underlying BMD variation.20. However, pathways in the majority of pathway databases may share genes with each other. Genes may participate in different pathways, leading to non-independence among pathways; this is essentially a mathematical/statistical problem that will lead to undesired false positive/negative results. Donato et al.20 have shown that the traditional pathway analysis approaches produced a significant number of false positives due to crosstalk effects. In our ORA results, RIG I like receptor signaling pathway was identified as the second most significant pathway underlying osteoporosis. However, there is no literature showing the relationship between them. Through the heat map, we observed that four pathways  lost their significance when the overlapping genes between any two of them were eliminated. This result suggested that the intersection of these pathways determined their significance. Our follow-up analysis further showed that these genes should be considered as an independent functional module.The classical ORA and other prevalent pathway analyses treat pathways as independentet al.20 provided an approach using maximum impact estimation based on maximum likelihood. We also applied this approach to analyze our dataset. However, the results were not as good as its applications in other experiments20, yielding results that did not make sense biologically. For example, Leishmania infection describes the pathway underlying a disease spread by the bite of certain types of sandflies. It is unlikely to be related to osteoporosis and no study found any connection between them. However, it became the second most significant pathway after the crosstalk correction using ML.To correct the crosstalk effects, Donato 22. Eugenol, another compound which can mediate attenuation of RANKL-induced NF-κB and MAPK pathways, could synergistically contribute to the inhibition of osteoclast formation23. Two pathways, fatty acid metabolism and glycosphingolipid biosynthesis lacto and neolacto series, were still significant. Essential fatty acid (EFA)-deficient animals have been shown to develop severe osteoporosis24, while inhibition of glycosphingolipid synthesis has been proven to affect osteoclastogenesis and reduce osteoclast activation25.When a gene is involved in a pathway, its expression levels may affect the expression levels of other genes or be affected by other genes in the same pathway. Based on this biologically realistic consideration, our PCA method reassigned a given gene to the pathway where it has the strongest connection with the rest of the genes. Compared with the ORA or ML results, all the biologically unlikely pathways were excluded from the significant list via PCA correction based on the gene expression correlations, while the mitogen-activated protein kinase (MAPK) signaling pathway was identified as significantly contributing to BMD variation. The MAPK signaling pathway plays important roles in both osteoblast and osteoclast biology. In particular, for osteoclast differentiation from PBM, several studies have confirmed that a p38 MAPK inhibitor, SB203580, could also inhibit RANKL-induced osteoclast differentiation26, Parkinson’s27 and Huntington’s28 disease have been reported, the underlying molecular mechanisms are still unclear. Our new module, which mainly consisted of protein families present in mitochondria, was consistently significant for both correction methods. This result suggests that mitochondrial activity may play a key role in the relevance of these diseases to osteoporosis. A recent study indicated that deletion of NDUFS4 may promote osteoclast differentiation and bone resorption via both cell-autonomous and systemic regulation29.Although links between osteoporosis and Alzheimer’s20. Compared with the ORA and ML methods, the PCA method has the smallest type I error (1.9%). It indicates that PCA method could significantly reduce the false positive rate. In simulation or real situation, the number of true non-significant pathways would be much larger than the number of true significant pathways. So the small increase of false positive rate will remarkably amplify the number of falsely detected significant pathways. Although the PCA method had higher type II error than ML method (19.4% vs 15.6%), the commonly used threshold of type II error used in randomized clinical trials was 20%32, which was higher than the PCA results. Therefore, the type II error rate of the PCA method is still reasonable and acceptable.We performed the simulation by the process reported in Donato’s paperThere are several limitations of this study. First, PBMs are not equal to osteoclasts. Our results could imply the pathophysiological mechanism of osteoporosis, but the findings need further direct validation. Second, the current knowledge about signaling pathways are still lacking. In our case, only ~4800 genes out of 22,000 genes (microarray data) were able to be annotated to the KEGG pathways. Third, in our method, the construction of new modules was based on the Jaccard Similarities between the new modules. In current study, the Jaccard Similarities were either 0 or greater than 0.8. Therefore, we merged two modules which have a non-zero Jaccard Similarity into a new module. But the Jaccard Similarities could be more various in other studies. Then, the threshold should be set to identify the “similar” new modules. The new modules will be merged in to a new module when their Jaccard Similarity is greater than the threshold, if not, they will be considered as separate ones in the following analysis processes.In summary, we performed pathway analysis on gene expression data of monocytes for osteoporosis and detected the crosstalk effects among pathways. To correct the crosstalk effects, we applied a novel method based on the correlation of gene expression levels to reduce false positive results and obtained a better understanding of biological networks underlying osteoporosis.All the methods were conducted in accordance with the rules and guidelines of the Institutional Review Boards of University of Missouri Kansas City and Tulane University. The Institutional Review Boards of University of Missouri Kansas City and Tulane University approved the study. Written informed consent was obtained from all participants before inclusion in the study.33. . Strict exclusion criteria were used to exclude individuals with diseases that might affect bone metabolism.Subjects for the study came from our microarray-based transcriptome-wide profiling research of PBMs in 73 Caucasian females with extremely high vs. low hip BMD2) of each subject was measured using a Hologic dual energy x-ray absorptiometer (DXA) scanner, Hologic QDR-4500 . The machine was calibrated daily. The coefficient of variation of the DXA measurements for BMD was 0.9%. The obtained BMD value was then transformed into a Z score, which is the number of standard deviations a subject’s BMD differs from the mean BMD of a healthy, ethnic-, gender-, and age-matched reference population. The detailed characteristics of subjects are shown in Table 33.The hip BMD  following the manufacturer’s recommendation. Then, total RNA from monocytes was extracted using Qiagen RNeasy Mini kit  and we used Agilent Bioanalyzer  to control the RNA quality before each array experiment, where RNA integrity number (RIN) should be no less than 7.0. Preparation of cDNA, hybridization, and scanning of the mRNA expression levels by the GeneChip Human Exon 1.0 ST Array  were performed according to the manufacturer’s protocol. The raw microarray data for this cohort have been submitted to GEO (Gene Expression Omnibus) under the accession number GSE56814.34 was applied to normalize the array signals35 and differential expression analysis was performed using Student’s t-test through the Bioconductor’s (version 2.7) LIMMA (linear models for microarray data) package (version 3.6.9)37. Because the sample size was limited , we used raw p-value < 0.05 as threshold for nominally significant differential expression  in R (version 2.3.0). The probe IDs were annotated with their corresponding official gene symbols via annotation file (pd.huex.1.0.st.v2). Due to the higher level of evidence supporting the existence of a particular exon, we only analyzed “core” (annotated by RefSeq) probesets and we excluded the probesets without gene symbol annotation. When multiple probe IDs were matched to the same gene symbol, the probe ID with the lowest p-value was selected to represent that gene. Robust multiarray average method.14.0 in In this study, we constructed a dataset which represents the intersection of genes present in at least one KEGG pathway and the genes measured by the GeneChip Human Exon 1.0 ST Array. In total, we obtained 186 pathways and 4801 genes that overlapped between KEGG dataset (c2.cp.kegg.v4.0.symbols.gmt) and our microarray dataset for further pathway analysis. In these genes, 103 were identified as differentially expressed (DE) genes  by the methods described above.m + n)*k membership matrix X  and rows represent genes . In the matrix, genes are ranked in ascending order of p-values from the differential expression analysis. The top n (n = 103) rows represent DE genes with p-values < 0.05. The m rows represent non-DE (NDE) genes. So for each cell Xi,j of matrix X, if gene i is included in pathway j, Xi,j = 1; otherwise Xi,j = 0.With the information from the KEGG database, we established a ; bi represents the number of NDE  genes in pathway Pi (count of 1 s in column i from row 104 to row 4801). The result generated by ORA indicates the probability of the number of DE genes contained in pathway Pi being equal to or higher than expected by chance.We performed classical overrepresentation analysis (ORA) using Fisher’s exact test to assess the significance of each pathway. For example, Table Pi shares genes with another pathway Pj, we removed the intersection part from Pi and recalculated the significance of the remaining element Pi\j in Pi via ORA. We then used the p-value of Pi\j for each pair of pathways  to establish a k*k matrix, where k was equal to the number of pathways and the diagonal cells were the original p-values of Pi. Both rows and columns were ordered ascendingly by the original p-values of Pi. The crosstalk effects were intuitively shown , which have large Jaccard Similarity, we merged the two modules into a new module. Jaccard Similarity is defined as equation  and zeros elsewhere. It represented that gene i had the strongest influence on pathway j than on other pathways which also included gene i. If there were no crosstalk effect, the matrix X and the matrix Z would be equal.Donato Z and observing membership matrix X. They provided an expectation maximization approach to maximize the similarity by an iterative algorithm. The details of this method were shown in the ref.20. Finally, ORA was conducted on maximum impact matrix Z instead of membership X after matrix Z was established.The authors used a likelihood-based estimation to calculate the similarity between the matrix Instead of calculating the maximum likelihood of the observed membership matrix, our PCA method takes into account the association among mRNA expression levels based on real experimental data, which are more likely to be biologically meaningful and realistic.j , the following process was conducted:j and construct a new matrix Ej.Select all the rows of the genes in pathway (m + n)*k matrix C (with the same size as the membership matrix after module selection) to record the correlation coefficients. Each cell of matrix C, Ci,j is computed as follows:i is not included in pathway j, Ci,j = 0; otherwise:If gene i is included in pathway j, remove gene i from matrix Ej. Use Ej/i to denote the rest of the matrix.If gene Ej/i to compute first principle component (PC1) of Ej/i, whose length should be equal to the column number of Ej/i.Conduct PCA on matrix Ej/i and each row of gene i, which was removed from Ej previously. Assign the greatest correlation coefficient to Ci,j.Compute the Pearson correlation coefficient between PC1 of Create a C, we create the maximum impact matrix Z by PCA method. We assume the correlation coefficients between gene i and matrix Ej/i should reflect the contribution of gene i to pathway p. Therefore, Zi,p = 1 if Ci,p = max{Ci,j│1 ≤ j ≤ k}, otherwise Zi,p = 0.After obtaining matrix Z.Conduct ORA on matrix We established a matrix of mRNA expression data, where columns represent subjects and rows represent genes. For each pathway j, we used the first principal component Ej\i to represent pathway j without gene i. When the P value for correlation between Ej\i and expression level of gene i is the lowest, we assumed that it means gene i has the highest association with pathway j and assigned gene i to pathway j.In this method, via PCA on expression data of the rest genes in pathway 38. All the parameters were set to the default values and the interaction between genes were only sourced from experiments.To explore and visualize the biological relationships among genes in the new module detected, STRING v10.0 software was used to build the topological structure of the new module20. Briefly, for each pathway Pi, we calculated the number ni of DE genes that would make Pi significant by Fisher Exact Test. We simulated a situation as following. There are 100 DE genes in the reference set and ni of them belong to Pi. So pathway i should be significant in Fisher Exact Test. In this case, we used the reference set to randomly pick ni genes from Pi and 100- ni genes that are not in Pi, and calculated the Fisher Exact Test significance of all other pathways. Since the 100- ni genes that are not in Pi are randomly chosen from the reference set, no other pathway should be significant. In this simulation, the hypothesis is true for the Pi, while the null hypothesis is true for all other pathways. We repeated this simulation 1,000 times for each pathway Pi, and each time we applied the ORA, ML and PCA methods to calculate the significance of all the pathways.Because PCA considered the correlation between genes, we used real data described above in membership matrix preparation as the reference set (including 4801 genes) for simulation analysis. We conducted the simulation according to that described in Donato’s papersupplementary table 1