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A multiple testing procedure for multi-dimensional pairwise comparisons with application to gene expression studies

Overview of attention for article published in BMC Bioinformatics, February 2016
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Mentioned by

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7 tweeters

Citations

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5 Dimensions

Readers on

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21 Mendeley
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Title
A multiple testing procedure for multi-dimensional pairwise comparisons with application to gene expression studies
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0937-5
Pubmed ID
Authors

Anjana Grandhi, Wenge Guo, Shyamal D. Peddada

Abstract

Often researchers are interested in comparing multiple experimental groups (e.g. tumor size) with a reference group (e.g. normal tissue) on the basis of thousands of features (e.g. genes) and determine if a differentially expressed feature is up or down regulated in a pairwise comparison. There are two sources of false discoveries, one due to multiple testing involving several pairwise comparisons and the second due to falsely declaring a feature to be up (or down) regulated when it is not (known as directional error). Together, the total error rate is called the mixed directional false discovery rate (mdFDR). We develop a general powerful mdFDR controlling testing procedure and illustrate the methodology by analyzing uterine fibroid gene expression data (PLoS ONE 8:63909, 2013). We identify several differentially expressed genes (DEGs) and pathways that are specifically enriched according to the size of a uterine fibroid. The proposed general procedure strongly controls mdFDR. Several specific methodologies can be derived from this general methodology by using appropriate testing procedures at different steps of the general procedure. Thus we are providing a general framework for making multiple pairwise comparisons. Our analysis of the uterine fibroid growth gene expression data suggests that molecular characteristics of a fibroid changes with size. Our powerful methodology allowed us to draw several interesting conclusions regarding the molecular characteristics of uterine fibroids. For example, IL-1 signaling pathway (Sci STKE 2003:3, 2003), associated with inflammation and known to activate prostaglandins that are implicated in the progression of fibroids, is significantly enriched only in small tumors (volume < 5.7 cm (3)). It appears that the molecular apparatus necessary for fibroid growth and development is established during tumor development. A complete list of all DEGs and the corresponding enriched pathways according to tumor size is provided for researchers to mine these data. Identification of these DEGs and the pathways may potentially have clinical implications.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 5%
Unknown 20 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 33%
Student > Ph. D. Student 6 29%
Student > Postgraduate 2 10%
Student > Bachelor 1 5%
Student > Doctoral Student 1 5%
Other 2 10%
Unknown 2 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 43%
Biochemistry, Genetics and Molecular Biology 5 24%
Computer Science 1 5%
Business, Management and Accounting 1 5%
Immunology and Microbiology 1 5%
Other 2 10%
Unknown 2 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 11 March 2016.
All research outputs
#5,941,789
of 11,293,566 outputs
Outputs from BMC Bioinformatics
#2,226
of 4,195 outputs
Outputs of similar age
#114,389
of 293,075 outputs
Outputs of similar age from BMC Bioinformatics
#83
of 141 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,195 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 45th percentile – i.e., 45% 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 293,075 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 59% of its contemporaries.
We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.