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Mosaic autosomal aneuploidies are detectable from single-cell RNAseq data

Overview of attention for article published in BMC Genomics, November 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 (85th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

blogs
1 blog
twitter
9 tweeters

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
52 Mendeley
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2 CiteULike
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Title
Mosaic autosomal aneuploidies are detectable from single-cell RNAseq data
Published in
BMC Genomics, November 2017
DOI 10.1186/s12864-017-4253-x
Pubmed ID
Authors

Jonathan A. Griffiths, Antonio Scialdone, John C. Marioni

Abstract

Aneuploidies are copy number variants that affect entire chromosomes. They are seen commonly in cancer, embryonic stem cells, human embryos, and in various trisomic diseases. Aneuploidies frequently affect only a subset of cells in a sample; this is known as "mosaic" aneuploidy. A cell that harbours an aneuploidy exhibits disrupted gene expression patterns which can alter its behaviour. However, detection of aneuploidies using conventional single-cell DNA-sequencing protocols is slow and expensive. We have developed a method that uses chromosome-wide expression imbalances to identify aneuploidies from single-cell RNA-seq data. The method provides quantitative aneuploidy calls, and is integrated into an R software package available on GitHub and as an Additional file of this manuscript. We validate our approach using data with known copy number, identifying the vast majority of aneuploidies with a low rate of false discovery. We show further support for the method's efficacy by exploiting allele-specific gene expression levels, and differential expression analyses. The method is quick and easy to apply, straightforward to interpret, and represents a substantial cost saving compared to single-cell genome sequencing techniques. However, the method is less well suited to data where gene expression is highly variable. The results obtained from the method can be used to investigate the consequences of aneuploidy itself, or to exclude aneuploidy-affected expression values from conventional scRNA-seq data analysis.

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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 31%
Researcher 11 21%
Student > Bachelor 7 13%
Student > Master 3 6%
Student > Doctoral Student 2 4%
Other 4 8%
Unknown 9 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 40%
Agricultural and Biological Sciences 9 17%
Medicine and Dentistry 6 12%
Chemistry 3 6%
Social Sciences 1 2%
Other 2 4%
Unknown 10 19%

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 14 July 2018.
All research outputs
#1,693,306
of 15,922,434 outputs
Outputs from BMC Genomics
#674
of 8,860 outputs
Outputs of similar age
#59,972
of 412,080 outputs
Outputs of similar age from BMC Genomics
#82
of 828 outputs
Altmetric has tracked 15,922,434 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 8,860 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 92% 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 412,080 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 85% of its contemporaries.
We're also able to compare this research output to 828 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 90% of its contemporaries.