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Comprehensive evaluation of RNA-seq quantification methods for linearity

Overview of attention for article published in BMC Bioinformatics, March 2017
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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 (90th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

blogs
2 blogs
twitter
19 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
44 Dimensions

Readers on

mendeley
172 Mendeley
citeulike
1 CiteULike
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Title
Comprehensive evaluation of RNA-seq quantification methods for linearity
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1526-y
Pubmed ID
Authors

Haijing Jin, Ying-Wooi Wan, Zhandong Liu

Abstract

Deconvolution is a mathematical process of resolving an observed function into its constituent elements. In the field of biomedical research, deconvolution analysis is applied to obtain single cell-type or tissue specific signatures from a mixed signal and most of them follow the linearity assumption. Although recent development of next generation sequencing technology suggests RNA-seq as a fast and accurate method for obtaining transcriptomic profiles, few studies have been conducted to investigate best RNA-seq quantification methods that yield the optimum linear space for deconvolution analysis. Using a benchmark RNA-seq dataset, we investigated the linearity of abundance estimated from seven most popular RNA-seq quantification methods both at the gene and isoform levels. Linearity is evaluated through parameter estimation, concordance analysis and residual analysis based on a multiple linear regression model. Results show that count data gives poor parameter estimations, large intercepts and high inter-sample variability; while TPM value from Kallisto and Salmon shows high linearity in all analyses. Salmon and Kallisto TPM data gives the best fit to the linear model studied. This suggests that TPM values estimated from Salmon and Kallisto are the ideal RNA-seq measurements for deconvolution studies.

Twitter Demographics

The data shown below were collected from the profiles of 19 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 172 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
China 1 <1%
Czechia 1 <1%
Australia 1 <1%
Unknown 168 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 40 23%
Researcher 39 23%
Student > Master 27 16%
Student > Bachelor 15 9%
Student > Doctoral Student 11 6%
Other 21 12%
Unknown 19 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 59 34%
Agricultural and Biological Sciences 49 28%
Computer Science 11 6%
Medicine and Dentistry 9 5%
Mathematics 7 4%
Other 16 9%
Unknown 21 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 24. 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 02 August 2017.
All research outputs
#1,024,658
of 17,812,415 outputs
Outputs from BMC Bioinformatics
#174
of 6,271 outputs
Outputs of similar age
#26,147
of 274,630 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 23 outputs
Altmetric has tracked 17,812,415 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,271 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 97% 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 274,630 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 23 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 95% of its contemporaries.