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Differential correlation for sequencing data

Overview of attention for article published in BMC Research Notes, January 2017
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  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

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Differential correlation for sequencing data
Published in
BMC Research Notes, January 2017
DOI 10.1186/s13104-016-2331-9
Pubmed ID

Charlotte Siska, Katerina Kechris


Several methods have been developed to identify differential correlation (DC) between pairs of molecular features from -omics studies. Most DC methods have only been tested with microarrays and other platforms producing continuous and Gaussian-like data. Sequencing data is in the form of counts, often modeled with a negative binomial distribution making it difficult to apply standard correlation metrics. We have developed an R package for identifying DC called Discordant which uses mixture models for correlations between features and the Expectation Maximization (EM) algorithm for fitting parameters of the mixture model. Several correlation metrics for sequencing data are provided and tested using simulations. Other extensions in the Discordant package include additional modeling for different types of differential correlation, and faster implementation, using a subsampling routine to reduce run-time and address the assumption of independence between molecular feature pairs. With simulations and breast cancer miRNA-Seq and RNA-Seq data, we find that Spearman's correlation has the best performance among the tested correlation methods for identifying differential correlation. Application of Spearman's correlation in the Discordant method demonstrated the most power in ROC curves and sensitivity/specificity plots, and improved ability to identify experimentally validated breast cancer miRNA. We also considered including additional types of differential correlation, which showed a slight reduction in power due to the additional parameters that need to be estimated, but more versatility in applications. Finally, subsampling within the EM algorithm considerably decreased run-time with negligible effect on performance. A new method and R package called Discordant is presented for identifying differential correlation with sequencing data. Based on comparisons with different correlation metrics, this study suggests Spearman's correlation is appropriate for sequencing data, but other correlation metrics are available to the user depending on the application and data type. The Discordant method can also be extended to investigate additional DC types and subsampling with the EM algorithm is now available for reduced run-time. These extensions to the R package make Discordant more robust and versatile for multiple -omics studies.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Luxembourg 1 2%
Unknown 57 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 24%
Student > Master 10 17%
Researcher 10 17%
Student > Bachelor 6 10%
Student > Postgraduate 3 5%
Other 3 5%
Unknown 12 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 26%
Biochemistry, Genetics and Molecular Biology 14 24%
Mathematics 3 5%
Computer Science 2 3%
Medicine and Dentistry 2 3%
Other 8 14%
Unknown 14 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 March 2017.
All research outputs
of 22,959,818 outputs
Outputs from BMC Research Notes
of 4,282 outputs
Outputs of similar age
of 417,757 outputs
Outputs of similar age from BMC Research Notes
of 65 outputs
Altmetric has tracked 22,959,818 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 4,282 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 well, scoring higher than 80% 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 417,757 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 65 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.