COEXPEDIA: exploring biomedical hypotheses via co-expressions associated with medical subject headings (MeSH)

Abstract

The use of high-throughput array and sequencing technologies has produced unprecedented amounts of gene expression data in central public depositories, including the Gene Expression Omnibus (GEO). The immense amount of expression data in GEO provides both vast research opportunities and data analysis challenges. Co-expression analysis of high-dimensional expression data has proven effective for the study of gene functions, and several co-expression databases have been developed. Here, we present a new co-expression database, COEXPEDIA (www.coexpedia.org), which is distinctive from other co-expression databases in three aspects: (i) it contains only co-functional co-expressions that passed a rigorous statistical assessment for functional association, (ii) the co-expressions were inferred from individual studies, each of which was designed to investigate gene functions with respect to a particular biomedical context such as a disease and (iii) the co-expressions are associated with medical subject headings (MeSH) that provide biomedical information for anatomical, disease, and chemical relevance. COEXPEDIA currently contains approximately eight million co-expressions inferred from 384 and 248 GEO series for humans and mice, respectively. We describe how these MeSH-associated co-expressions enable the identification of diseases and drugs previously unknown to be related to a gene or a gene group of interest.

netNMF-sc: Leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis

“…First, netNMF-sc relies on existing gene-gene networks. While we have demonstrated that generic gene-gene co-expression networks [Yang et al, 2016] can improve clustering performance of human and mouse scRNA-seq data, netNMF-sc may not offer substantial improvements over existing methods on tissues or organisms where high-quality gene-gene networks are not available. In the future, other prior knowledge could be incorporated into netNMF-sc, such as cell-cell correlations, which might be obtained from underlying knowledge of cell types or from spatial or temporal information.…”

“…To quantify the contribution of the network information to the performance of netNMF-sc, we ran netNMF-sc with three additional networks: a generic gene-gene co-expression network from Coexpedia [Yang et al, 2016], a K-nearest neighbors network (KNN), and a random network with the same degree distribution as the ESCAPE network. The K-nearest neighbors network was constructed by placing an edge between the ten nearest neighbors of each gene in the input data matrix X, based on Euclidean distance.…”

“…We used the simulator SPLATTER [Zappia et al, 2017] to generate complete transcript count data, estimating the parameters of the model from mouse embryonic stem cell scRNA-seq data [Buettner et al, 2015] using the SplatEstimate command. We modified SPLATTER to introduce correlations between genes that are differentially expressed in each cluster using a gene coexpression network from Yang et al [2016]. See Supplemental Section S3 for further details.…”

“…The resulting matrix factors can be used to cluster cells and impute values for dropout events. While netNMF-sc may use any type of network as prior information, a particularly promising approach is to leverage tissue-specific gene co-expression networks derived from the vast repository of RNA-seq and microarray studies of bulk tissue, and recorded in large databases such as Coexpressdb [Okamura et al, 2014], Coexpedia [Yang et al, 2016], GeneSigDB [Culhane et al, 2009], and others [Lee et al, 2004, Wu et al, 2010.…”

Investigating Research Hotspots and Publication Trends of Spinal Stenosis: A Bibliometric Analysis during 2000-2018

“…5606 literatures were exported and the document types involved in ARTICLE and REVIEW, improving the comprehensive feature of our research. Medical subjects Headings (MeSH) terms express the main idea of a literature, identified for the following co-word associated analysis [9].…”