| Title |
A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies
|
|---|---|
| Published in |
PLoS Computational Biology, May 2010
|
| DOI | 10.1371/journal.pcbi.1000770 |
| Pubmed ID | |
| Authors |
Oliver Stegle, Leopold Parts, Richard Durbin, John Winn |
X Demographics
The data shown below were collected from the profiles of 18 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 | 28% |
| Germany | 4 | 22% |
| Tunisia | 1 | 6% |
| United Kingdom | 1 | 6% |
| Unknown | 7 | 39% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 10 | 56% |
| Members of the public | 7 | 39% |
| Practitioners (doctors, other healthcare professionals) | 1 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 554 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 23 | 4% |
| Germany | 7 | 1% |
| United Kingdom | 3 | <1% |
| France | 2 | <1% |
| Brazil | 2 | <1% |
| Norway | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Netherlands | 1 | <1% |
| New Zealand | 1 | <1% |
| Other | 5 | <1% |
| Unknown | 508 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 178 | 32% |
| Researcher | 134 | 24% |
| Student > Master | 42 | 8% |
| Student > Bachelor | 35 | 6% |
| Professor > Associate Professor | 31 | 6% |
| Other | 83 | 15% |
| Unknown | 51 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 228 | 41% |
| Biochemistry, Genetics and Molecular Biology | 104 | 19% |
| Computer Science | 51 | 9% |
| Mathematics | 36 | 6% |
| Medicine and Dentistry | 18 | 3% |
| Other | 51 | 9% |
| Unknown | 66 | 12% |