Placental ion channels: potential target of chemical exposure

Zhao, Yang; Pasanen, Markku; Rysä, Jaana

doi:10.1093/biolre/ioac186

Cited by 4 publications

(2 citation statements)

References 155 publications

(88 reference statements)

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“…Meanwhile, CACNA1S was the top gene mediating the causal direction from ME3 to preeclampsia. This gene had expression in the placenta ( 29 , 30 ), and encoded a calcium channel responsible for blood vascular contraction. Given that preeclampsia was a pregnancy hypertension disease, the driving effect of CACNA1S on it was clear.…”

Section: Resultsmentioning

confidence: 99%

CWGCNA: an R package to perform causal inference from the WGCNA framework

Liu

2024

NAR Genomics and Bioinformatics

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WGCNA (weighted gene co-expression network analysis) is a very useful tool for identifying co-expressed gene modules and detecting their correlations to phenotypic traits. Here, we explored more possibilities about it and developed the R package CWGCNA (causal WGCNA), which works from the traditional WGCNA pipeline but mines more information. It couples a mediation model with WGCNA, so the causal relationships among WGCNA modules, module features, and phenotypes can be found, demonstrating whether the module change causes the phenotype change or vice versa. After that, when annotating the module gene set functions, it uses a novel network-based method, considering the modules' topological structures and capturing their influence on the gene set functions. In addition to conducting these biological explorations, CWGCNA also contains a machine learning section to perform clustering and classification on multi-omics data, given the increasing popularity of this data type. Some basic functions, such as differential feature identification, are also available in our package. Its effectiveness is proved by the performance on three single or multi-omics datasets, showing better performance than existing methods. CWGCNA is available at: https://github.com/yuabrahamliu/CWGCNA.

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Section: Resultsmentioning

confidence: 99%

CWGCNA: an R package to perform causal inference from the WGCNA framework

Liu

2024

NAR Genomics and Bioinformatics

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“…Meanwhile, CACNA1S and C8orf46 (VXN) were the only 2 genes mediating the causal direction from ME3 to preeclampsia. CACNA1S had expression in the placenta (14,15), and encoded the α-1S subunit of the dihydropyridine receptor, a calcium channel responsible for vascular contraction. Given that preeclampsia was a pregnancy hypertension disease, the causal effect of CACNA1S on it was clear.…”

Section: The Package Performs Well On Placenta Dna Methylation Datamentioning

confidence: 99%

eOmics: an R package for improved omics data analysis

Liu

2023

Preprint

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Many computational tools have been developed for high-throughput omics data, and some are very popular, such as limma, WGCNA, and EnrichR. However, they also exhibit disadvantages in some special cases, such as imbalanced data analysis, causal inference, gene network functional enrichment, etc. Hence, we developed the R package eOmics to provide a comprehensive pipeline with these problems addressed. It combines an ensemble framework with limma, improving its performance on imbalanced data. Moreover, it couples a mediation model with WGCNA, so the causal relationship among WGCNA modules, module features, and phenotypes can be found, and this model is also used to explore the relationship between different omics. In addition, our package has some novel functional enrichment methods, capturing the influence of topological structure on gene set functions. Finally, it contains multi-omics clustering and classification functions to facilitate machine-learning tasks. Some basic functions, such as ANOVA analysis, are also available in it. The effectiveness of our package is proved by its performance on the three single or multi-omics datasets here. eOmics is available at: https://github.com/yuabrahamliu/eOmics.

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