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Attention Score in Context
| Title |
Effect of Normalization on Statistical and Biological Interpretation of Gene Expression Profiles
|
|---|---|
| Published in |
Frontiers in Genetics, January 2012
|
| DOI | 10.3389/fgene.2012.00160 |
| Pubmed ID | |
| Authors |
Shaopu Qin, Jinhee Kim, Dalia Arafat, Greg Gibson |
| Abstract |
An under-appreciated aspect of the genetic analysis of gene expression is the impact of post-probe level normalization on biological inference. Here we contrast nine different methods for normalization of an Illumina bead-array gene expression profiling dataset consisting of peripheral blood samples from 189 individual participants in the Center for Health Discovery and Well Being study in Atlanta, quantifying differences in the inference of global variance components and covariance of gene expression, as well as the detection of variants that affect transcript abundance (eSNPs). The normalization strategies, all relative to raw log2 measures, include simple mean centering, two modes of transcript-level linear adjustment for technical factors, and for differential immune cell counts, variance normalization by interquartile range and by quantile, fitting the first 16 Principal Components, and supervised normalization using the SNM procedure with adjustment for cell counts. Robustness of genetic associations as a consequence of Pearson and Spearman rank correlation is also reported for each method, and it is shown that the normalization strategy has a far greater impact than correlation method. We describe similarities among methods, discuss the impact on biological interpretation, and make recommendations regarding appropriate strategies. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 1% |
| Denmark | 1 | 1% |
| Germany | 1 | 1% |
| Unknown | 93 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 26 | 27% |
| Student > Ph. D. Student | 25 | 26% |
| Student > Master | 8 | 8% |
| Student > Bachelor | 8 | 8% |
| Student > Doctoral Student | 7 | 7% |
| Other | 16 | 17% |
| Unknown | 6 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 47 | 49% |
| Biochemistry, Genetics and Molecular Biology | 17 | 18% |
| Medicine and Dentistry | 8 | 8% |
| Computer Science | 6 | 6% |
| Engineering | 4 | 4% |
| Other | 5 | 5% |
| Unknown | 9 | 9% |
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 29 August 2012.
All research outputs
#20,165,369
of 22,675,759 outputs
Outputs from Frontiers in Genetics
#8,510
of 11,737 outputs
Outputs of similar age
#221,176
of 244,088 outputs
Outputs of similar age from Frontiers in Genetics
#195
of 255 outputs
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So far Altmetric has tracked 11,737 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 255 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.