Batch correction evaluation framework using a-priori gene-gene associations: applied to the GTEx dataset

Somekh, Judith; Shen-Orr, Shai S.; Kohane, Isaac S.

doi:10.1186/s12859-019-2855-9

Cited by 31 publications

(42 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…circumstances) is based on a four-point Hardy Scale. The ventilator death group samples were analysed since it had significantly shorter ischemic times, which preserve sample quality 41 . Our previous work 41 showed that using some common methods for adjusting the heterogenous GTEx expression data for hidden confounding factors (e.g., using principle components) filters out many of the biological signals-which is relevant here.…”

Section: Confounding Factors Adjustment the Type Of Death Classificamentioning

confidence: 99%

“…The ventilator death group samples were analysed since it had significantly shorter ischemic times, which preserve sample quality 41 . Our previous work 41 showed that using some common methods for adjusting the heterogenous GTEx expression data for hidden confounding factors (e.g., using principle components) filters out many of the biological signals-which is relevant here. Thus ComBat 42 from the R/Bioconductor package sva 42 was used to adjust for known confounding factors, which has been shown to outperform other methods 41 .…”

Section: Confounding Factors Adjustment the Type Of Death Classificamentioning

confidence: 99%

“…Our previous work 41 showed that using some common methods for adjusting the heterogenous GTEx expression data for hidden confounding factors (e.g., using principle components) filters out many of the biological signals-which is relevant here. Thus ComBat 42 from the R/Bioconductor package sva 42 was used to adjust for known confounding factors, which has been shown to outperform other methods 41 . ComBat was applied to adjust for experimental batch, ischemic time (time that elapsed between actual death and sample extraction), gender and age.…”

Section: Confounding Factors Adjustment the Type Of Death Classificamentioning

confidence: 99%

See 2 more Smart Citations

A methodology for predicting tissue-specific metabolic roles of receptors applied to subcutaneous adipose

Somekh

2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

The human biological system uses ‘inter-organ’ communication to achieve a state of homeostasis. This communication occurs through the response of receptors, located on target organs, to the binding of secreted ligands from source organs. Albeit years of research, the roles these receptors play in tissues is only partially understood. This work presents a new methodology based on the enrichment analysis scores of co-expression networks fed into support vector machines (SVMs) and k-NN classifiers to predict the tissue-specific metabolic roles of receptors. The approach is primarily based on the detection of coordination patterns of receptors expression. These patterns and the enrichment analysis scores of their co-expression networks were used to analyse ~ 700 receptors and predict metabolic roles of receptors in subcutaneous adipose. To facilitate supervised learning, a list of known metabolic and non-metabolic receptors was constructed using a semi-supervised approach following literature-based verification. Our approach confirms that pathway enrichment scores are good signatures for correctly classifying the metabolic receptors in adipose. We also show that the k-NN method outperforms the SVM method in classifying metabolic receptors. Finally, we predict novel metabolic roles of receptors. These predictions can enhance biological understanding and the development of new receptor-targeting metabolic drugs.

show abstract

Section: Confounding Factors Adjustment the Type Of Death Classificamentioning

confidence: 99%

Section: Confounding Factors Adjustment the Type Of Death Classificamentioning

confidence: 99%

Section: Confounding Factors Adjustment the Type Of Death Classificamentioning

confidence: 99%

See 1 more Smart Citation

A methodology for predicting tissue-specific metabolic roles of receptors applied to subcutaneous adipose

Somekh

2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…As GTEx data is known to be subject to multiple confounding factors (batch effect, experimental bias, read contamination, etc.) [43,44,25], a partial PCcorrection [25] was applied to correct the data (Additional file 1.3). To investigate the aging process two subsets representing contrasting age classes were selected from the corrected data set: 73 samples between 20 and 30 years old (referred as young in this report), and 292 samples between 60 and 70 years old (referred as old in this report).…”

Section: Resultsmentioning

confidence: 99%

GWENA: Gene Co-expression Networks Analysis and Extended Modules Characterization in a Single Bioconductor Package

Lemoine

Scott‐Boyer

Ambroise

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Network-based analysis of gene expression through co-expression networks can be used to investigate modular interactions occurring between genes toward diﬀerent biological functions. An extended description of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline.Results: Here we present GWENA, a new R package that integrates gene co-expression network construction and a whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and diﬀerential co-expression. To demonstrate its performances, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. We successfully prioritized a gene whose involvement was unknown in the muscle development and growth. We also gave new insight about the variations in patterns of co-expression as the already known age-dependent loss of connectivity was found coupled to a genes interactions reorganization leading to the expression of other functions involved in aging. Conclusion: GWENA is an R package available through Bioconductor (https://bioconductor.org/packages/release/bioc/html/GWENA.html) developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as conditions comparison, it eases the understanding of genes interactions involved in diseases or phenotypes. Going beyond actual packages to perform co-expression analysis, GWENA includes new tools to fully characterize modules, such as diﬀerential co-expression, additional enrichment databases, and network visualization.

show abstract

“…This observation is consistent with recent results that caution against the use of latent factor correction when applying coexpression analysis in highly heterogeneous tissues such as brain. 31 To ensure modules are not driven by brain-involved diseases or atypical sample outliers, we exclude individuals on the basis of their known medical conditions at time of death, principal-component outliers, and sample-sample connectivity outliers (Methods, Suppl. Table 1).…”

Section: Building Robust Human Co-expression Networkmentioning

confidence: 99%

The architecture of brain co-expression reveals the brain-wide basis of disease susceptibility

Hartl¹,

Ramaswami²,

Pembroke³

et al. 2020

Preprint

View full text Add to dashboard Cite

Gene networks have proven their utility for elucidating transcriptome structure in the brain, yielding numerous biological insights. Most analyses have focused on expression relationships within a circumspect number of regions -how these relationships vary across a broad array of brain regions is largely unknown. By leveraging RNAsequencing in 864 samples representing 12 brain regions in a cohort of 131 phenotypically normal individuals, we identify 12 brain-wide, 114 region-specific, and 50 cross-regional co-expression modules. We replicate the majority (81%) of modules in regional microarray datasets. Nearly 40% of expressed genes fall into brain-wide modules corresponding to major cell classes and conserved biological processes.Region-specific modules comprise 25% of expressed genes and correspond to regionspecific cell types and processes, such as oxytocin signaling in the hypothalamus, or addiction pathways in the nucleus accumbens. We further leverage these modules to capture cell-type-specific lncRNA and gene isoforms, both of which contribute substantially to regional synaptic diversity. We identify enrichment of neuropsychiatric disease risk variants in brain wide and multi-regional modules, consistent with their broad impact on cell classes, and highlight specific roles in neuronal proliferation and activity-dependent processes. Finally, we examine the manner in which gene coexpression and gene regulatory networks reflect genetic risk, including the recently framed omnigenic model of disease architecture.Using this hierarchy, we form a tree of consensus co-expression networks for each split, thereby generating co-expression modules for 20 hierarchical expression categories: 12 brain region specific categories (corresponding to each sampled region), 7 multiregional categories (corresponding to multiple, structurally-linked regions, figure 1b), and a brain-wide category. The majority of the resulting modules are highly overlapping, therefore we group these modules hierarchically into groups of highly similar modules which we term "module sets" (Methods). In total, we identify 311 modules at all levels, of which 173/199 (87%) of the tissue-level modules are replicated with strong support in at least one other independent dataset (figure 1c; Methods). Finally, by using network preservation statistics on all samples within regions and meta regions, we verify that module sets are supported by strong evidence within their own regions and little evidence outside of them (figure 1d).To test whether co-expression modules vary substantially by the method used for co-expression network construction, we build modules at the tissue level using three alternative approaches: ARACNe, 34 PAM-guided graphical LASSO, 35 and Fisher-von-Mises mixture modeling. 36 We find that all methods show high pairwise clustering coefficients (figure S1e), and differ predominantly by module splitting (figure 1e). For instance, most of the differences between ARACNe clusters and WGCNA clusters come from large ARACNe modules represented as...

show abstract

Batch correction evaluation framework using a-priori gene-gene associations: applied to the GTEx dataset

Cited by 31 publications

References 28 publications

A methodology for predicting tissue-specific metabolic roles of receptors applied to subcutaneous adipose

A methodology for predicting tissue-specific metabolic roles of receptors applied to subcutaneous adipose

GWENA: Gene Co-expression Networks Analysis and Extended Modules Characterization in a Single Bioconductor Package

The architecture of brain co-expression reveals the brain-wide basis of disease susceptibility

Contact Info

Product

Resources

About