↓ Skip to main content

Tensorial blind source separation for improved analysis of multi-omic data

Overview of attention for article published in Genome Biology (Online Edition), June 2018
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (68th percentile)

Mentioned by

twitter
14 tweeters

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
45 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Tensorial blind source separation for improved analysis of multi-omic data
Published in
Genome Biology (Online Edition), June 2018
DOI 10.1186/s13059-018-1455-8
Pubmed ID
Authors

Andrew E. Teschendorff, Han Jing, Dirk S. Paul, Joni Virta, Klaus Nordhausen

Abstract

There is an increased need for integrative analyses of multi-omic data. We present and benchmark a novel tensorial independent component analysis (tICA) algorithm against current state-of-the-art methods. We find that tICA outperforms competing methods in identifying biological sources of data variation at a reduced computational cost. On epigenetic data, tICA can identify methylation quantitative trait loci at high sensitivity. In the cancer context, tICA identifies gene modules whose expression variation across tumours is driven by copy-number or DNA methylation changes, but whose deregulation relative to normal tissue is independent of such alterations, a result we validate by direct analysis of individual data types.

Twitter Demographics

The data shown below were collected from the profiles of 14 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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 31%
Student > Ph. D. Student 8 18%
Other 4 9%
Student > Master 3 7%
Student > Doctoral Student 3 7%
Other 6 13%
Unknown 7 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 31%
Biochemistry, Genetics and Molecular Biology 13 29%
Computer Science 5 11%
Engineering 2 4%
Mathematics 1 2%
Other 2 4%
Unknown 8 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 25 July 2019.
All research outputs
#4,004,913
of 15,507,566 outputs
Outputs from Genome Biology (Online Edition)
#2,277
of 3,338 outputs
Outputs of similar age
#88,194
of 279,613 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 15,507,566 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 3,338 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.3. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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 279,613 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 68% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them