Dataset Information

DECODE: an integrated differential co-expression and differential expression analysis of gene expression data.

ABSTRACT: Both differential expression (DE) and differential co-expression (DC) analyses are appreciated as useful tools in understanding gene regulation related to complex diseases. The performance of integrating DE and DC, however, remains unexplored.In this study, we proposed a novel analytical approach called DECODE (Differential Co-expression and Differential Expression) to integrate DC and DE analyses of gene expression data. DECODE allows one to study the combined features of DC and DE of each transcript between two conditions. By incorporating information of the dependency between DC and DE variables, two optimal thresholds for defining substantial change in expression and co-expression are systematically defined for each gene based on chi-square maximization. By using these thresholds, genes can be categorized into four groups with either high or low DC and DE characteristics. In this study, DECODE was applied to a large breast cancer microarray data set consisted of two thousand tumor samples. By identifying genes with high DE and high DC, we demonstrated that DECODE could improve the detection of some functional gene sets such as those related to immune system, metastasis, lipid and glucose metabolism. Further investigation on the identified genes and the associated functional pathways would provide an additional level of understanding of complex disease mechanism.By complementing the recent DC and the traditional DE analyses, DECODE is a valuable methodology for investigating biological functions of genes exhibiting disease-associated DE and DC combined characteristics, which may not be easily revealed through DC or DE approach alone. DECODE is available at the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/decode/index.html .

SUBMITTER: Lui TW

PROVIDER: S-EPMC4449974 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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DECODE: an integrated differential co-expression and differential expression analysis of gene expression data.

Lui Thomas W H TW Tsui Nancy B Y NB Chan Lawrence W C LW Wong Cesar S C CS Siu Parco M F PM Yung Benjamin Y M BY

BMC bioinformatics 20150531

<h4>Background</h4>Both differential expression (DE) and differential co-expression (DC) analyses are appreciated as useful tools in understanding gene regulation related to complex diseases. The performance of integrating DE and DC, however, remains unexplored.<h4>Results</h4>In this study, we proposed a novel analytical approach called DECODE (Differential Co-expression and Differential Expression) to integrate DC and DE analyses of gene expression data. DECODE allows one to study the combined ...[more]

PMID: 26026612

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Project description:Differential expression (DE) analysis is commonly used to identify biomarker candidates that have significant changes in their expression levels between distinct biological groups. One drawback of DE analysis is that it only considers the changes on single biomolecule level. Recently, differential network (DN) analysis has become popular due to its capability to measure the changes on biomolecular pair level. In DN analysis, network is typically built based on correlation and biomarker candidates are selected by investigating the network topology. However, correlation tends to generate over-complicated networks and the selection of biomarker candidates purely based on network topology ignores the changes on single biomolecule level. In this paper, we propose a novel approach, INDEED, that builds sparse differential network based on partial correlation and integrates DE and DN analyses for biomarker discovery. We applied this approach on real proteomic and glycomic data generated by liquid chromatography coupled with mass spectrometry for hepatocellular carcinoma (HCC) biomarker discovery study. For each omic data, we used one dataset to select biomarker candidates, built a disease classifier and evaluated the performance of the classifier on an independent dataset. The biomarker candidates, selected by INDEED, were more reproducible across independent datasets, and led to a higher classification accuracy in predicting HCC cases and cirrhotic controls compared with those selected by separate DE and DN analyses. INDEED also identified some candidates previously reported to be relevant to HCC, such as intercellular adhesion molecule 2 (ICAM2) and c4b-binding protein alpha chain (C4BPA), which were missed by both DE and DN analyses. In addition, we applied INDEED for survival time prediction based on transcriptomic data acquired by analysis of samples from breast cancer patients. We selected biomarker candidates and built a regression model for survival time prediction based on a gene expression dataset and patients' survival records. We evaluated the performance of the regression model on an independent dataset. Compared with the biomarker candidates selected by DE and DN analyses, those selected through INDEED led to more accurate survival time prediction.

Project description:BackgroundUse of next-generation sequencing technologies to transcriptomics (RNA-seq) for gene expression profiling has found widespread application in studying different biological conditions including cancers. However, RNA-seq experiments are still small sample size experiments due to the cost. Recently, an increased focus has been on meta-analysis methods for integrated differential expression analysis for exploration of potential biomarkers. In this study, we propose a p-value combination method for meta-analysis of multiple independent but related RNA-seq studies that accounts for sample size of a study and direction of expression of genes in individual studies.ResultsThe proposed method generalizes the inverse-normal method without an increase in statistical or computational complexity and does not pre- or post-hoc filter genes that have conflicting direction of expression in different studies. Thus, the proposed method, as compared to the inverse-normal, has better potential for the discovery of differentially expressed genes (DEGs) with potentially conflicting differential signals from multiple studies related to disease. We demonstrated the use of the proposed method in detection of biologically relevant DEGs in glioblastoma (GBM), the most aggressive brain cancer. Our approach notably enabled the identification of over-expressed tumour suppressor gene RAD51 in GBM compared to healthy controls, which has recently been shown to be a target for inhibition to enhance radiosensitivity of GBM cells during treatment. Pathway analysis identified multiple aberrant GBM related pathways as well as novel regulators such as TCF7L2 and MAPT as important upstream regulators in GBM.ConclusionsThe proposed meta-analysis method generalizes the existing inverse-normal method by providing a way to establish differential expression status for genes with conflicting direction of expression in individual RNA-seq studies. Hence, leading to further exploration of them as potential biomarkers for the disease.

Project description:Gene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data.Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server ( http://adage.greenelab.com ) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and ?anr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and Anr.We designed ADAGE signature analysis to perform gene set analysis using data-defined functional gene signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE signature analysis to the community.

Dataset Information

DECODE: an integrated differential co-expression and differential expression analysis of gene expression data.

Publications

DECODE: an integrated differential co-expression and differential expression analysis of gene expression data.

Similar Datasets

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