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CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses

Overview of attention for article published in BMC Bioinformatics, February 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

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1 blog
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20 X users
patent
1 patent

Citations

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224 Dimensions

Readers on

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386 Mendeley
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Title
CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses
Published in
BMC Bioinformatics, February 2018
DOI 10.1186/s12859-018-2053-1
Pubmed ID
Authors

Pedro S. T. Russo, Gustavo R. Ferreira, Lucas E. Cardozo, Matheus C. Bürger, Raul Arias-Carrasco, Sandra R. Maruyama, Thiago D. C. Hirata, Diógenes S. Lima, Fernando M. Passos, Kiyoshi F. Fukutani, Melissa Lever, João S. Silva, Vinicius Maracaja-Coutinho, Helder I. Nakaya

Abstract

The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions. In this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, our tool evaluates whether modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group, as well as it integrates transcriptomic data with interactome information, identifying the potential hubs on each network. We successfully applied CEMiTool to over 1000 transcriptome datasets, and to a new RNA-seq dataset of patients infected with Leishmania, revealing novel insights of the disease's physiopathology. The CEMiTool R package provides users with an easy-to-use method to automatically implement gene co-expression network analyses, obtain key information about the discovered gene modules using additional downstream analyses and retrieve publication-ready results via a high-quality interactive report.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 386 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 386 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 77 20%
Researcher 73 19%
Student > Bachelor 54 14%
Student > Master 45 12%
Student > Postgraduate 14 4%
Other 37 10%
Unknown 86 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 106 27%
Agricultural and Biological Sciences 78 20%
Medicine and Dentistry 23 6%
Immunology and Microbiology 20 5%
Computer Science 18 5%
Other 42 11%
Unknown 99 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 14 June 2022.
All research outputs
#1,446,478
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#251
of 7,387 outputs
Outputs of similar age
#34,030
of 331,910 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 92 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,387 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,910 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 92 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.