ArticleJournal of molecular biology2017
BFDCA: A Comprehensive Tool of Using Bayes Factor for Differential Co-Expression Analysis.
Article in Journal of molecular biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Flexibility in Gene Coexpression at Developmental and Evolutionary Timescales.Molecular biology and evolution · 2025Article
- Functional convergence of genomic and transcriptomic architecture underlies schooling behaviour in a live-bearing fish.Nature ecology & evolution · 2024Article
- Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression.International journal of molecular sciences · 2020Review
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Authors and funding
5 authors.
Funding
Abstract
Comparing the gene-expression profiles between biological conditions is useful for understanding gene regulation underlying complex phenotypes. Along this line, analysis of differential co-expression (DC) has gained attention in the recent years, where genes under one condition have different co-expression patterns compared with another. We developed an R package Bayes Factor approach for Differential Co-expression Analysis (BFDCA) for DC analysis. BFDCA is unique in integrating various aspects of DC patterns (including Shift, Cross, and Re-wiring) into one uniform Bayes factor. We tested BFDCA using simulation data and experimental data. Simulation results indicate that BFDCA outperforms existing methods in accuracy and robustness of detecting DC pairs and DC modules. Results of using experimental data suggest that BFDCA can cluster disease-related genes into functional DC subunits and estimate the regulatory impact of disease-related genes well. BFDCA also achieves high accuracy in predicting case-control phenotypes by using significant DC gene pairs as markers. BFDCA is publicly available at http://dx.doi.org/10.17632/jdz4vtvnm3.1.
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