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UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Overview of attention for article published in BMC Bioinformatics, June 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)

Mentioned by

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1 news outlet
twitter
9 tweeters

Citations

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11 Dimensions

Readers on

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36 Mendeley
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1 CiteULike
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Title
UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets
Published in
BMC Bioinformatics, June 2015
DOI 10.1186/s12859-015-0614-0
Pubmed ID
Authors

Basel Abu-Jamous, Rui Fa, David J. Roberts, Asoke K. Nandi

Abstract

Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 3%
Unknown 35 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 33%
Student > Ph. D. Student 9 25%
Professor > Associate Professor 3 8%
Student > Postgraduate 2 6%
Student > Master 2 6%
Other 4 11%
Unknown 4 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 28%
Biochemistry, Genetics and Molecular Biology 7 19%
Computer Science 6 17%
Neuroscience 3 8%
Medicine and Dentistry 1 3%
Other 3 8%
Unknown 6 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 18 November 2017.
All research outputs
#1,239,188
of 13,520,847 outputs
Outputs from BMC Bioinformatics
#450
of 5,055 outputs
Outputs of similar age
#27,418
of 234,372 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 13,520,847 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,055 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 91% of its peers.
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