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Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data

Overview of attention for article published in BMC Bioinformatics, June 2018
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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 (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

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23 tweeters

Citations

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54 Dimensions

Readers on

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187 Mendeley
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Title
Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data
Published in
BMC Bioinformatics, June 2018
DOI 10.1186/s12859-018-2217-z
Pubmed ID
Authors

Shuonan Chen, Jessica C. Mar

Abstract

A fundamental fact in biology states that genes do not operate in isolation, and yet, methods that infer regulatory networks for single cell gene expression data have been slow to emerge. With single cell sequencing methods now becoming accessible, general network inference algorithms that were initially developed for data collected from bulk samples may not be suitable for single cells. Meanwhile, although methods that are specific for single cell data are now emerging, whether they have improved performance over general methods is unknown. In this study, we evaluate the applicability of five general methods and three single cell methods for inferring gene regulatory networks from both experimental single cell gene expression data and in silico simulated data. Standard evaluation metrics using ROC curves and Precision-Recall curves against reference sets sourced from the literature demonstrated that most of the methods performed poorly when they were applied to either experimental single cell data, or simulated single cell data, which demonstrates their lack of performance for this task. Using default settings, network methods were applied to the same datasets. Comparisons of the learned networks highlighted the uniqueness of some predicted edges for each method. The fact that different methods infer networks that vary substantially reflects the underlying mathematical rationale and assumptions that distinguish network methods from each other. This study provides a comprehensive evaluation of network modeling algorithms applied to experimental single cell gene expression data and in silico simulated datasets where the network structure is known. Comparisons demonstrate that most of these assessed network methods are not able to predict network structures from single cell expression data accurately, even if they are specifically developed for single cell methods. Also, single cell methods, which usually depend on more elaborative algorithms, in general have less similarity to each other in the sets of edges detected. The results from this study emphasize the importance for developing more accurate optimized network modeling methods that are compatible for single cell data. Newly-developed single cell methods may uniquely capture particular features of potential gene-gene relationships, and caution should be taken when we interpret these results.

Twitter Demographics

The data shown below were collected from the profiles of 23 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 187 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 187 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 53 28%
Researcher 37 20%
Student > Master 29 16%
Student > Bachelor 15 8%
Student > Doctoral Student 6 3%
Other 17 9%
Unknown 30 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 66 35%
Agricultural and Biological Sciences 39 21%
Computer Science 13 7%
Medicine and Dentistry 7 4%
Engineering 7 4%
Other 24 13%
Unknown 31 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 10 February 2020.
All research outputs
#1,679,756
of 15,625,699 outputs
Outputs from BMC Bioinformatics
#613
of 5,680 outputs
Outputs of similar age
#47,049
of 277,260 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 24 outputs
Altmetric has tracked 15,625,699 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,680 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done well, scoring higher than 89% 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 277,260 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 82% of its contemporaries.
We're also able to compare this research output to 24 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 91% of its contemporaries.