Human Gene Coexpression Landscape: Confident Network Derived from Tissue Transcriptomic Profiles

Abstract

BackgroundAnalysis of gene expression data using genome-wide microarrays is a technique often used in genomic studies to find coexpression patterns and locate groups of co-transcribed genes. However, most studies done at global “omic” scale are not focused on human samples and when they correspond to human very often include heterogeneous datasets, mixing normal with disease-altered samples. Moreover, the technical noise present in genome-wide expression microarrays is another well reported problem that many times is not addressed with robust statistical methods, and the estimation of errors in the data is not provided.Methodology/Principal FindingsHuman genome-wide expression data from a controlled set of normal-healthy tissues is used to build a confident human gene coexpression network avoiding both pathological and technical noise. To achieve this we describe a new method that combines several statistical and computational strategies: robust normalization and expression signal calculation; correlation coefficients obtained by parametric and non-parametric methods; random cross-validations; and estimation of the statistical accuracy and coverage of the data. All these methods provide a series of coexpression datasets where the level of error is measured and can be tuned. To define the errors, the rates of true positives are calculated by assignment to biological pathways. The results provide a confident human gene coexpression network that includes 3327 gene-nodes and 15841 coexpression-links and a comparative analysis shows good improvement over previously published datasets. Further functional analysis of a subset core network, validated by two independent methods, shows coherent biological modules that share common transcription factors. The network reveals a map of coexpression clusters organized in well defined functional constellations. Two major regions in this network correspond to genes involved in nuclear and mitochondrial metabolism and investigations on their functional assignment indicate that more than 60% are house-keeping and essential genes. The network displays new non-described gene associations and it allows the placement in a functional context of some unknown non-assigned genes based on their interactions with known gene families.Conclusions/SignificanceThe identification of stable and reliable human gene to gene coexpression networks is essential to unravel the interactions and functional correlations between human genes at an omic scale. This work contributes to this aim, and we are making available for the scientific community the validated human gene coexpression networks obtained, to allow further analyses on the network or on some specific gene associations.The data are available free online at http://bioinfow.dep.usal.es/coexpression/.

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

“…Genes with similar function or that participate in the same pathway often have correlated patterns of gene expression (Prieto et al 2008;Roider et al 2009;Khatri et al 2012;Hormozdiari et al 2015). In the GTEx TWNs, we observed enrichment of edges between transcription factors and known target genes (Supplemental Methods; Supplemental Fig.…”

Assembly of an Interactive Correlation Network for the Arabidopsis Genome Using a Novel Heuristic Clustering Algorithm

“…To estimate the quality of the clustering solution obtained by HCCA, we clustered the HRR network using the MCL algorithm with a range of different inflation values (Supplemental Table S1). In addition, we included clustering solutions for MCODE (Bader and Hogue, 2003;Prieto et al, 2008), performed clustering using k-means with different settings (Hartigan and Wong, 1979), and then compared the results obtained from the HCCA with the different clustering solutions for the other algorithms ( Fig. 3; Supplemental Table S1).…”

Knockdown of metallopanstimulin‐1 inhibits NF‐κB signaling at different levels: The role of apoptosis induction of gastric cancer cells

“…15 Some other ribosomal proteins have also been shown to be involved in the apoptotic process, including RPS19, RPS3a and RPS29, via Bcl2 or p53 related mechanisms. 16 In this work, directed by the cDNA microarray analysis, pathway, Gene Ontology (GO) and Gene-RegulatoryNetwork (GenRelNet) analyses, [17][18][19] we demonstrated that the inhibition of NF-jB activity and down-regulation of its target gene Gadd45b contributed to MPS-1 knockdowninduced apoptosis of gastric cancer cells.…”