↓ Skip to main content

Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer

Overview of attention for article published in BMC Medical Genomics, March 2017
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#11 of 1,229)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

blogs
2 blogs
twitter
80 tweeters

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
121 Mendeley
citeulike
1 CiteULike
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
Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer
Published in
BMC Medical Genomics, March 2017
DOI 10.1186/s12920-017-0245-6
Pubmed ID
Authors

Shirley Pepke, Greg Ver Steeg

Abstract

De novo inference of clinically relevant gene function relationships from tumor RNA-seq remains a challenging task. Current methods typically either partition patient samples into a few subtypes or rely upon analysis of pairwise gene correlations that will miss some groups in noisy data. Leveraging higher dimensional information can be expected to increase the power to discern targetable pathways, but this is commonly thought to be an intractable computational problem. In this work we adapt a recently developed machine learning algorithm for sensitive detection of complex gene relationships. The algorithm, CorEx, efficiently optimizes over multivariate mutual information and can be iteratively applied to generate a hierarchy of relatively independent latent factors. The learned latent factors are used to stratify patients for survival analysis with respect to both single factors and combinations. These analyses are performed and interpreted in the context of biological function annotations and protein network interactions that might be utilized to match patients to multiple therapies. Analysis of ovarian tumor RNA-seq samples demonstrates the algorithm's power to infer well over one hundred biologically interpretable gene cohorts, several times more than standard methods such as hierarchical clustering and k-means. The CorEx factor hierarchy is also informative, with related but distinct gene clusters grouped by upper nodes. Some latent factors correlate with patient survival, including one for a pathway connected with the epithelial-mesenchymal transition in breast cancer that is regulated by a microRNA that modulates epigenetics. Further, combinations of factors lead to a synergistic survival advantage in some cases. In contrast to studies that attempt to partition patients into a small number of subtypes (typically 4 or fewer) for treatment purposes, our approach utilizes subgroup information for combinatoric transcriptional phenotyping. Considering only the 66 gene expression groups that are found to both have significant Gene Ontology enrichment and are small enough to indicate specific drug targets implies a computational phenotype for ovarian cancer that allows for 3(66) possible patient profiles, enabling truly personalized treatment. The findings here demonstrate a new technique that sheds light on the complexity of gene expression dependencies in tumors and could eventually enable the use of patient RNA-seq profiles for selection of personalized and effective cancer treatments.

Twitter Demographics

The data shown below were collected from the profiles of 80 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 121 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Japan 1 <1%
United States 1 <1%
Sweden 1 <1%
Unknown 117 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 25%
Student > Ph. D. Student 26 21%
Student > Master 16 13%
Student > Bachelor 9 7%
Student > Postgraduate 7 6%
Other 16 13%
Unknown 17 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 21%
Biochemistry, Genetics and Molecular Biology 25 21%
Computer Science 19 16%
Medicine and Dentistry 10 8%
Physics and Astronomy 4 3%
Other 15 12%
Unknown 22 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 57. 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 16 November 2020.
All research outputs
#638,023
of 22,959,818 outputs
Outputs from BMC Medical Genomics
#11
of 1,229 outputs
Outputs of similar age
#14,903
of 308,059 outputs
Outputs of similar age from BMC Medical Genomics
#2
of 13 outputs
Altmetric has tracked 22,959,818 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,229 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 99% 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 308,059 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 95% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.