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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Online Quantitative Proteomics p‑Value Calculator for Permutation-Based Statistical Testing of Peptide Ratios
|
|---|---|
| Published in |
Journal of Proteome Research, July 2014
|
| DOI | 10.1021/pr500525e |
| Pubmed ID | |
| Authors |
David Chen, Anup Shah, Hien Nguyen, Dorothy Loo, Kerry L. Inder, Michelle M. Hill |
| Abstract |
The utility of high-throughput quantitative proteomics to identify differentially abundant proteins en-masse relies on suitable and accessible statistical methodology, which remains mostly an unmet need. We present a free web-based tool, called Quantitative Proteomics p-value Calculator (QPPC), designed for accessibility and usability by proteomics scientists and biologists. Being an online tool, there is no requirement for software installation. Furthermore, QPPC accepts generic peptide ratio data generated by any mass spectrometer and database search engine. Importantly, QPPC utilizes the permutation test that we recently found to be superior to other methods for analysis of peptide ratios because it does not assume normal distributions.1 QPPC assists the user in selecting significantly altered proteins based on numerical fold change, or standard deviation from the mean or median, together with the permutation p-value. Output is in the form of comma separated values files, along with graphical visualization using volcano plots and histograms. We evaluate the optimal parameters for use of QPPC, including the permutation level and the effect of outlier and contaminant peptides on p-value variability. The optimal parameters defined are deployed as default for the web-tool at http://qppc.di.uq.edu.au/ . |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 2% |
| Russia | 1 | 2% |
| Germany | 1 | 2% |
| Unknown | 51 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 31% |
| Student > Ph. D. Student | 16 | 30% |
| Student > Master | 5 | 9% |
| Student > Bachelor | 3 | 6% |
| Professor > Associate Professor | 3 | 6% |
| Other | 8 | 15% |
| Unknown | 2 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 20 | 37% |
| Biochemistry, Genetics and Molecular Biology | 17 | 31% |
| Computer Science | 4 | 7% |
| Chemistry | 3 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 6% |
| Other | 3 | 6% |
| Unknown | 4 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 20 May 2015.
All research outputs
#18,805,293
of 23,305,591 outputs
Outputs from Journal of Proteome Research
#5,253
of 6,106 outputs
Outputs of similar age
#164,740
of 229,969 outputs
Outputs of similar age from Journal of Proteome Research
#83
of 126 outputs
Altmetric has tracked 23,305,591 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,106 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one is in the 5th percentile – i.e., 5% 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 229,969 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 126 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.