ArticleProceedings of the National Academy of Sciences of the United States of America2014
Gene coexpression measures in large heterogeneous samples using count statistics.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Cell type heterogeneity in gene co-expression networks: implications for toxicological research.Briefings in bioinformatics · 2025Review
- EfficientNet-resDDSC: A Hybrid Deep Learning Model Integrating Residual Blocks and Dilated Convolutions for Inferring Gene Causality in Single-Cell Data.Interdisciplinary sciences, computational life sciences · 2025Article
- Article
- Dissecting gene expression heterogeneity: generalized Pearson correlation squares and theJournal of the American Statistical Association · 2024Article
- Design, execution, and interpretation of plant RNA-seq analyses.Frontiers in plant science · 2023Review
- Gene Co-Expression Network Tools and Databases for Crop Improvement.Plants (Basel, Switzerland) · 2022Review
- Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.Briefings in bioinformatics · 2022Article
- Network Modeling in Biology: Statistical Methods for Gene and Brain Networks.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- GeneFishing to reconstruct context specific portraits of biological processes.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Using Machine Learning to Measure Relatedness Between Genes: A Multi-Features Model.Scientific reports · 2019Article
- Generalized correlation measure using count statistics for gene expression data with ordered samples.Bioinformatics (Oxford, England) · 2018Article
- An improved Bayesian network method for reconstructing gene regulatory network based on candidate auto selection.BMC genomics · 2017Article
- Brain transcriptome atlases: a computational perspective.Brain structure & function · 2017Review
- Quantifying Gene Regulatory Relationships with Association Measures: A Comparative Study.Frontiers in genetics · 2017Article
- Article
- Part mutual information for quantifying direct associations in networks.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Putting things in order.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
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3 authors.
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Abstract
With the advent of high-throughput technologies making large-scale gene expression data readily available, developing appropriate computational tools to process these data and distill insights into systems biology has been an important part of the "big data" challenge. Gene coexpression is one of the earliest techniques developed that is still widely in use for functional annotation, pathway analysis, and, most importantly, the reconstruction of gene regulatory networks, based on gene expression data. However, most coexpression measures do not specifically account for local features in expression profiles. For example, it is very likely that the patterns of gene association may change or only exist in a subset of the samples, especially when the samples are pooled from a range of experiments. We propose two new gene coexpression statistics based on counting local patterns of gene expression ranks to take into account the potentially diverse nature of gene interactions. In particular, one of our statistics is designed for time-course data with local dependence structures, such as time series coupled over a subregion of the time domain. We provide asymptotic analysis of their distributions and power, and evaluate their performance against a wide range of existing coexpression measures on simulated and real data. Our new statistics are fast to compute, robust against outliers, and show comparable and often better general performance.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.