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Gene set analysis methods: a systematic comparison

Overview of attention for article published in BioData Mining, May 2018
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4 tweeters

Citations

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53 Dimensions

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153 Mendeley
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Title
Gene set analysis methods: a systematic comparison
Published in
BioData Mining, May 2018
DOI 10.1186/s13040-018-0166-8
Pubmed ID
Authors

Ravi Mathur, Daniel Rotroff, Jun Ma, Ali Shojaie, Alison Motsinger-Reif

Abstract

Gene set analysis is a valuable tool to summarize high-dimensional gene expression data in terms of biologically relevant sets. This is an active area of research and numerous gene set analysis methods have been developed. Despite this popularity, systematic comparative studies have been limited in scope. In this study we present a semi-synthetic simulation study using real datasets in order to test and compare commonly used methods. A software pipeline, Flexible Algorithm for Novel Gene set Simulation (FANGS) develops simulated data based on a prostate cancer dataset where the KRAS and TGF-β pathways were differentially expressed. The FANGS software is compatible with other datasets and pathways. Comparisons of gene set analysis methods are presented for Gene Set Enrichment Analysis (GSEA), Significance Analysis of Function and Expression (SAFE), sigPathway, and Correlation Adjusted Mean RAnk (CAMERA) methods. All gene set analysis methods are tested using gene sets from the MSigDB knowledge base. The false positive rate and power are estimated and presented for comparison. Recommendations are made for the utility of the default settings of methods and each method's sensitivity towards various effect sizes. The results of this study provide empirical guidance to users of gene set analysis methods. The FANGS software is available for researchers for continued methods comparisons.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 153 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 20%
Researcher 28 18%
Student > Master 23 15%
Student > Bachelor 14 9%
Student > Doctoral Student 6 4%
Other 20 13%
Unknown 32 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 55 36%
Agricultural and Biological Sciences 25 16%
Medicine and Dentistry 11 7%
Computer Science 10 7%
Chemistry 5 3%
Other 15 10%
Unknown 32 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 08 July 2020.
All research outputs
#11,428,325
of 18,187,474 outputs
Outputs from BioData Mining
#196
of 272 outputs
Outputs of similar age
#166,271
of 289,375 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 18,187,474 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 272 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 25th percentile – i.e., 25% of its peers scored the same or lower than it.
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