CoExp: A Web Tool for the Exploitation of Co-expression Networks

García-Ruiz, Sonia; Gil-Martínez, Ana Luisa; Cisterna, Alejandro; Jurado-Ruiz, Federico; Reynolds, Regina H.; Nabec,; Cookson, Mark; Hardy, John; Ryten, Mina; Botía, Juan A.

doi:10.3389/fgene.2021.630187

Cited by 20 publications

(17 citation statements)

References 42 publications

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“…Majority of existing computational methodologies to construct gene co-expression networks in standard single-cell studies (Crow et al, 2016;Wang et al, 2016;García-Ruiz et al, 2021), intrinsically involve a dimension reduction step which achieves two goals: one is to avoid curse of dimensionality and aid computational feasibility; second is to preserve the intrinsic dimensionality while reducing the noise. The existing network methods, however, do not incorporate spatial information which are critical in spatial transcriptomics.…”

Section: Introductionmentioning

confidence: 99%

SpaceX: gene co-expression network estimation for spatial transcriptomics

2022

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Motivation The analysis of spatially-resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene-gene co-expression at distinct spatial locations or cell types in the tissue enables delineation of spatial co-regulatory patterns as opposed to standard differential single gene analyses. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop a statistical framework to detect gene co-expression patterns in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains. Results We develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for (increasing) spatial correlation and appropriate noise distributions. In-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using SpaceX, detected multiple hub genes which are related to cognitive abilities for the hypothalamus data and multiple cancer genes (e.g. collagen family) from the tumor region for the breast cancer data. Availability and implementation The SpaceX R-package is available at github.com/bayesrx/SpaceX. Supplementary information Supplementary data are available at bookdown.org/satwik91/SpaceX_supplementary/.

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Section: Introductionmentioning

confidence: 99%

SpaceX: gene co-expression network estimation for spatial transcriptomics

2022

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“…To functionally characterize the top associated interactions, we carried out loci connectivity analyses across gene-expression datasets from GTEX v.8 27 , gene-ontologies and molecular pathways using FUMA 28 , phenotype enrichment using PhenoExam 29 , functional gene interaction networks using STRING 30 , and gene co-expression network analysis using CoExp Web 31 . SNP lists from significant interactions were extracted for significant eQTL associations in PD-relevant tissues (caudate, putamen, nucleus accumbens and substantia nigra) in the GTEX v.8 data to obtain significant eQTL genes (eGenes).…”

Section: Functional Enrichment Analysis Methodsmentioning

confidence: 99%

Genome-wide epistasis analysis in Parkinson’s disease between populations with different genetic ancestry reveals significant variant-variant interactions

Cisterna-García

Bustos²,

Bandrés‐Ciga

et al. 2022

Preprint

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Genome-wide association studies (GWAS) have increased our understanding of Parkinson's disease (PD) genetics through the identification of common disease-associated variants. However, much of the heritability remains unaccounted for and we hypothesized that this could be partly explained by epistasis. Here, we developed a genome-wide non-exhaustive epistasis screening pipeline called Variant-variant interaction through variable thresholds (VARI3) and applied it to diverse PD GWAS cohorts. First, as a discovery cohort, we used 14 cohorts of European ancestry (14,671 cases and 17,667 controls) to identify candidate variant-variant interactions. Next, we replicated significant results in a cohort with a predominately Latino genetic ancestry (807 cases and 690 controls). We identified 14 significant epistatic signals in the discovery stage, with genes showing enrichment in PD-relevant ontologies and pathways. Next, we successfully replicated two of the 14 interactions, where the signals were located nearby SNCA and within MAPT and WNT3. Finally, we determined that the epistatic effect on PD of those variants was similar between populations. In brief, we identified several epistatic signals associated with PD and replicated associations despite differences in the genetic ancestry between cohorts. We also observed their biological relevance and effect on the phenotype using in silico analysis.

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“…CoExp is a resource developed to exploit gene co-expression networks (GCNs) to build gene groupings based on transcriptomic profiling 7 . The CoExp tool is an online web application consisting of a collection of 109 networks, powered by the CoExpNets suite of R packages.…”

Section: Co-expression Network Analysismentioning

confidence: 99%

“…7 . The CoExp tool is an online web application consisting of a collection of 109 networks, powered by the CoExpNets suite of R packages.…”

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confidence: 99%

Transcriptomic analyses reveal neuronal specificity of Leigh syndrome associated genes

Wahedi

Soondram²,

Murphy

et al. 2022

Preprint

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Leigh syndrome (subacute necrotising encephalomyelopathy) is a rare inherited, complex, and typically early onset mitochondrial disorder with clinical and genetic heterogeneity. It owes its heterogeneous nature to the complex nature of mitochondrial genetics and the significant interactions that occur between the mitochondrial and nuclear genomes. Stepwise developmental regression is a classical feature of patients with Leigh syndrome, but other neurological features include ataxia, tremor, seizures, and occasionally psychiatric manifestations. To date the involvement of more than 100 genes has been identified in patients with Leigh syndrome. Despite this, the pathophysiology of Leigh syndrome remains unknown, although it is thought to be due to primary mitochondrial dysfunction. Here, we sought to determine the cellular specificity of all Leigh syndrome associated genes within the brain and investigate a potential common genetic mechanism between Leigh syndrome and other disorders with overlapping clinical features. We utilized co-expression network analyses constructed using existing UK Brain Expression Consortium (UKBEC) data from 10 brain regions to identify regions of the brain where our genes of interest were enriched. Next, expression weighted cell type enrichment (EWCE) was employed to determine the cellular specificity of Leigh syndrome associated genes. Association with pre- or post- synaptic structures was identified using synaptic gene ontology (SynGO) analysis. Finally, heritability analysis was performed on the co-expression network modules demonstrating enrichment of Leigh syndrome associated genes to investigate potential genetic relationships with Parkinson disease, epilepsy, and schizophrenia. Co-expression network analyses reveal that genes encoding oxidative phosphorylation subunits and assembly factors exhibit the highest levels of expression in brain regions most affected on MRI and neuropathology in Leigh syndrome. Further, over two thirds of the genes were found to be enriched within the substantia nigra and 19 genes appeared to cluster, with selective enrichment in the putamen, substantia nigra, medulla and thalamus. EWCE analyses of single cell RNA-Seq data from mouse brain revealed significant enrichment of Leigh syndrome associated genes in hippocampal and somatosensory pyramidal neurones and interneurons of the brain. SynGO analysis demonstrated that expression of these genes is preferentially associated with pre-synaptic structures. Heritability studies suggested that some modules in which Leigh syndrome associated genes were enriched were also enriched for Parkinson disease and epilepsy. In conclusion, our findings suggest a primary mitochondrial dysfunction as the underlying basis of Leigh syndrome with causative genes primarily expressed in neuronal cells, explaining the relative sparing of glial cells observed in neuropathological studies.

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CoExp: A Web Tool for the Exploitation of Co-expression Networks

Cited by 20 publications

References 42 publications

SpaceX: gene co-expression network estimation for spatial transcriptomics

SpaceX: gene co-expression network estimation for spatial transcriptomics

Genome-wide epistasis analysis in Parkinson’s disease between populations with different genetic ancestry reveals significant variant-variant interactions

Transcriptomic analyses reveal neuronal specificity of Leigh syndrome associated genes

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