Dataset Information

Improved gene co-expression network quality through expression dataset down-sampling and network aggregation.

ABSTRACT: Large-scale gene co-expression networks are an effective methodology to analyze sets of co-expressed genes and discover new gene functions or associations. Distances between genes are estimated according to their expression profiles and are visualized in networks that may be further partitioned to reveal communities of co-expressed genes. Creating expression profiles is now eased by the large amounts of publicly available expression data (microarrays and RNA-seq). Although many distance calculation methods have been intensively compared and reviewed in the past, it is unclear how to proceed when many samples reflecting a wide range of different conditions are available. Should as many samples as possible be integrated into network construction or be partitioned into smaller sets of more related samples? Previous studies have indicated a saturation in network performances to capture known associations once a certain number of samples is included in distance calculations. Here, we examined the influence of sample size on co-expression network construction using microarray and RNA-seq expression data from three plant species. We tested different down-sampling methods and compared network performances in recovering known gene associations to networks obtained from full datasets. We further examined how aggregating networks may help increase this performance by testing six aggregation methods.

SUBMITTER: Liesecke F

PROVIDER: S-EPMC6783424 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Improved gene co-expression network quality through expression dataset down-sampling and network aggregation.

Liesecke Franziska F De Craene Johan-Owen JO Besseau Sébastien S Courdavault Vincent V Clastre Marc M Vergès Valentin V Papon Nicolas N Giglioli-Guivarc'h Nathalie N Glévarec Gaëlle G Pichon Olivier O Dugé de Bernonville Thomas T

Scientific reports 20191008 1

Large-scale gene co-expression networks are an effective methodology to analyze sets of co-expressed genes and discover new gene functions or associations. Distances between genes are estimated according to their expression profiles and are visualized in networks that may be further partitioned to reveal communities of co-expressed genes. Creating expression profiles is now eased by the large amounts of publicly available expression data (microarrays and RNA-seq). Although many distance calculat ...[more]

PMID: 31594989

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Project description:BackgroundMelanoma is one of the most malignant and aggressive skin tumors, and its incidence is increasing worldwide. However, few studies have investigated the process of tumorigenesis from normal skin to melanoma.MethodsSeveral bioinformatics analyses, including GEO databases, Oncomine database, TCGA database, STRING, MCODE and cytoHubba plug-ins, GEPIA, TIMER and TRRUST and DGIdb, were performed to disclose the hub genes and immunology implicated in primary melanoma tumorigenesis. Finally, immunohistochemistry (IHC) and quantitative real-time PCR (qPCR) were used to validate the results of bioinformatics analysis in vitro.ResultsA total of 295 overlapping DEGs (ODEGs) (157 upregulated and 138 downregulated) and 9 hub genes were identified between primary melanoma and normal skin tissues. Functional analysis of these 9 hub genes indicated that the genes were primarily enriched in cell chemotaxis, the chemokine-mediated signaling pathway, the extracellular region, the extracellular space, chemokine activity and CXCR3 chemokine receptor binding. KEGG pathway enrichment showed that these genes were primarily involved in the chemokine signaling pathway, cytokine-cytokine receptor interaction, the toll-like receptor signaling pathway, the cytosolic DNA-sensing pathway and the TNF signaling pathway. Upregulated CCL5, CCL4, CXCL9 and CXCL10 demonstrated good overall survival (OS), and most of them have a higher expression in stage 0 and 1 of melanoma. Moreover, immune infiltration analysis showed that the above hub genes showed a strong positive correlation between their expression and infiltration of the six immune cell subsets. Transcription factor regulation network suggested that RELA and NFKB1 are the transcription factors of CCL4, CCL5, CXCL10 and CXCL2, while IRF7, IRF3 and IRF1 are the transcription factors of CCL5 and CXCL10. Drug-gene interaction analysis identified 46 drug-gene interactions. In vitro data demonstrated that the level of CCL4, CCL5, CXCL9 and CXCL10 is higher in melanoma than that in normal skin tissues, either at tissue or cell lines level.ConclusionIn summary, we identified 4 key chemokine members related to tumorigenesis and progression in primary melanoma, and these results may help to elucidate melanoma tumorigenesis and facilitate its treatment.

Dataset Information

Improved gene co-expression network quality through expression dataset down-sampling and network aggregation.

Publications

Improved gene co-expression network quality through expression dataset down-sampling and network aggregation.

Similar Datasets

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