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Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings.

ABSTRACT: It is a basic task in high-throughput gene expression profiling studies to identify differentially expressed genes (DEGs) between two phenotypes. But the weakly differential expression signals between two phenotypes are hardly detectable with limited sample sizes. To solve this problem, many researchers tried to combine multiple independent datasets using meta-analysis or batch effect adjustment algorithms. However, these algorithms may distort true biological differences between two phenotypes and introduce unacceptable high false rates, as demonstrated in this study. These problems pose critical obstacles for analyzing the transcriptional data in The Cancer Genome Atlas where there are many small-scale batches of data. Previously, we developed RankComp to detect DEGs for individual disease samples through exploiting the incongruous relative expression orderings between two phenotypes and further improved it here to identify DEGs using multiple independent datasets. We demonstrated the improved RankComp can directly analyze integrated cross-site data to detect DEGs between two phenotypes without the need of batch effect adjustments. Its usage was illustrated in detecting weak differential expression signals of breast cancer drug-response data using combined datasets from multiple experiments.

SUBMITTER: Cai H

PROVIDER: S-EPMC6036750 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings.

Cai Hao H Li Xiangyu X Li Jing J Liang Qirui Q Zheng Weicheng W Guan Qingzhou Q Guo Zheng Z Wang Xianlong X

International journal of biological sciences 20180522 8

It is a basic task in high-throughput gene expression profiling studies to identify differentially expressed genes (DEGs) between two phenotypes. But the weakly differential expression signals between two phenotypes are hardly detectable with limited sample sizes. To solve this problem, many researchers tried to combine multiple independent datasets using meta-analysis or batch effect adjustment algorithms. However, these algorithms may distort true biological differences between two phenotypes ...[more]

PMID: 29989020

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Project description:ObjectiveWound healing is a complex process involving the coordinated interaction of various genes and molecularpathways. The study aimed to uncover novel therapeutic targets, biomarkers and candidate genes for drug developmentto improve successful wound repair interventions.Materials and Methods: This study is a network-meta analysis study. Nine wound healing microarray datasets obtainedfrom the Gene Expression Omnibus (GEO) database were used for this study. Differentially expressed genes (DEGs)were described using the Limma package and shared genes were used as input for weighted gene co-expressionnetwork analysis. The Gene Ontology analysis was performed using the EnrichR web server, and construction of aprotein-protein interaction (PPI) network was achieved by the STRING and Cytoscape.Results: A total of 424 DEGs were determined. A co-expression network was constructed using 7692 shared genesbetween nine data sets, resulting in the identification of seven modules. Among these modules, those with the top 20genes of up and down-regulation were selected. The top down-regulated genes, including TJP1, SEC61A1, PLEK,ATP5B, PDIA6, PIK3R1, SRGN, SDC2, and RBBP7, and the top up-regulated genes including RPS27A, EEF1A1,HNRNPA1, CTNNB1, POLR2A, CFL1, CSNk1E, HSPD1, FN1, and AURKB, which can potentially serve as therapeutictargets were identified. The KEGG pathway analysis found that the majority of the genes are enriched in the "Wntsignaling pathway".Conclusion: In our study of nine wound healing microarray datasets, we identified DEGs and co-expressed modulesusing WGCNA. These genes are involved in important cellular processes such as transcription, translation, and posttranslationalmodifications. We found nine down-regulated genes and ten up-regulated genes, which could serve aspotential therapeutic targets for further experimental validation. Targeting pathways related to protein synthesis and celladhesion and migration may enhance wound healing, but additional experimental validation is needed to confirm theeffectiveness and safety of targeted interventions.

Dataset Information

Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings.

Publications

Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings.

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