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A powerful nonparametric method for detecting differentially co-expressed genes: distance correlation screening and edge-count test

Overview of attention for article published in BMC Systems Biology, May 2018
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Title
A powerful nonparametric method for detecting differentially co-expressed genes: distance correlation screening and edge-count test
Published in
BMC Systems Biology, May 2018
DOI 10.1186/s12918-018-0582-x
Pubmed ID
Authors

Qingyang Zhang

Abstract

Differential co-expression analysis, as a complement of differential expression analysis, offers significant insights into the changes in molecular mechanism of different phenotypes. A prevailing approach to detecting differentially co-expressed genes is to compare Pearson's correlation coefficients in two phenotypes. However, due to the limitations of Pearson's correlation measure, this approach lacks the power to detect nonlinear changes in gene co-expression which is common in gene regulatory networks. In this work, a new nonparametric procedure is proposed to search differentially co-expressed gene pairs in different phenotypes from large-scale data. Our computational pipeline consisted of two main steps, a screening step and a testing step. The screening step is to reduce the search space by filtering out all the independent gene pairs using distance correlation measure. In the testing step, we compare the gene co-expression patterns in different phenotypes by a recently developed edge-count test. Both steps are distribution-free and targeting nonlinear relations. We illustrate the promise of the new approach by analyzing the Cancer Genome Atlas data and the METABRIC data for breast cancer subtypes. Compared with some existing methods, the new method is more powerful in detecting nonlinear type of differential co-expressions. The distance correlation screening can greatly improve computational efficiency, facilitating its application to large data sets.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 24%
Student > Bachelor 4 19%
Student > Ph. D. Student 3 14%
Student > Master 3 14%
Librarian 1 5%
Other 1 5%
Unknown 4 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 24%
Mathematics 3 14%
Agricultural and Biological Sciences 2 10%
Engineering 2 10%
Medicine and Dentistry 2 10%
Other 3 14%
Unknown 4 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 May 2018.
All research outputs
#20,512,427
of 23,079,238 outputs
Outputs from BMC Systems Biology
#1,011
of 1,144 outputs
Outputs of similar age
#288,075
of 327,787 outputs
Outputs of similar age from BMC Systems Biology
#29
of 42 outputs
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