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X Demographics
Mendeley readers
Attention Score in Context
| Title |
GEN3VA: aggregation and analysis of gene expression signatures from related studies
|
|---|---|
| Published in |
BMC Bioinformatics, November 2016
|
| DOI | 10.1186/s12859-016-1321-1 |
| Pubmed ID | |
| Authors |
Gregory W. Gundersen, Kathleen M. Jagodnik, Holly Woodland, Nicholas F. Fernandez, Kevin Sani, Anders B. Dohlman, Peter Man-Un Ung, Caroline D. Monteiro, Avner Schlessinger, Avi Ma’ayan |
| Abstract |
Genome-wide gene expression profiling of mammalian cells is becoming a staple of many published biomedical and biological research studies. Such data is deposited into data repositories such as the Gene Expression Omnibus (GEO) for potential reuse. However, these repositories currently do not provide simple interfaces to systematically analyze collections of related studies. Here we present GENE Expression and Enrichment Vector Analyzer (GEN3VA), a web-based system that enables the integrative analysis of aggregated collections of tagged gene expression signatures identified and extracted from GEO. Each tagged collection of signatures is presented in a report that consists of heatmaps of the differentially expressed genes; principal component analysis of all signatures; enrichment analysis with several gene set libraries across all signatures, which we term enrichment vector analysis; and global mapping of small molecules that are predicted to reverse or mimic each signature in the aggregate. We demonstrate how GEN3VA can be used to identify common molecular mechanisms of aging by analyzing tagged signatures from 244 studies that compared young vs. old tissues in mammalian systems. In a second case study, we collected 86 signatures from treatment of human cells with dexamethasone, a glucocorticoid receptor (GR) agonist. Our analysis confirms consensus GR target genes and predicts potential drug mimickers. GEN3VA can be used to identify, aggregate, and analyze themed collections of gene expression signatures from diverse but related studies. Such integrative analyses can be used to address concerns about data reproducibility, confirm results across labs, and discover new collective knowledge by data reuse. GEN3VA is an open-source web-based system that is freely available at: http://amp.pharm.mssm.edu/gen3va . |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 71% |
| Germany | 1 | 14% |
| United Kingdom | 1 | 14% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 57% |
| Scientists | 3 | 43% |
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 % |
|---|---|---|
| United States | 2 | 4% |
| United Kingdom | 1 | 2% |
| France | 1 | 2% |
| Unknown | 41 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 27% |
| Researcher | 10 | 22% |
| Student > Bachelor | 5 | 11% |
| Other | 3 | 7% |
| Student > Master | 3 | 7% |
| Other | 5 | 11% |
| Unknown | 7 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 12 | 27% |
| Computer Science | 8 | 18% |
| Medicine and Dentistry | 7 | 16% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 4% |
| Agricultural and Biological Sciences | 2 | 4% |
| Other | 4 | 9% |
| Unknown | 10 | 22% |
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 19 December 2016.
All research outputs
#6,174,192
of 22,901,818 outputs
Outputs from BMC Bioinformatics
#2,331
of 7,302 outputs
Outputs of similar age
#92,164
of 306,451 outputs
Outputs of similar age from BMC Bioinformatics
#26
of 123 outputs
Altmetric has tracked 22,901,818 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,302 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 67% 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 306,451 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 69% of its contemporaries.
We're also able to compare this research output to 123 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.