| Title |
svapls: an R package to correct for hidden factors of variability in gene expression studies
|
|---|---|
| Published in |
BMC Bioinformatics, July 2013
|
| DOI | 10.1186/1471-2105-14-236 |
| Pubmed ID | |
| Authors |
Sutirtha Chakraborty, Somnath Datta, Susmita Datta |
| Abstract |
Hidden variability is a fundamentally important issue in the context of gene expression studies. Collected tissue samples may have a wide variety of hidden effects that may alter their transcriptional landscape significantly. As a result their actual differential expression pattern can be potentially distorted, leading to inaccurate results from a genome-wide testing for the important transcripts. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 5% |
| Belgium | 2 | 5% |
| Sweden | 1 | 3% |
| Malaysia | 1 | 3% |
| Netherlands | 1 | 3% |
| India | 1 | 3% |
| Unknown | 32 | 80% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 35% |
| Student > Ph. D. Student | 9 | 23% |
| Professor | 4 | 10% |
| Other | 3 | 8% |
| Student > Master | 2 | 5% |
| Other | 5 | 13% |
| Unknown | 3 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 43% |
| Biochemistry, Genetics and Molecular Biology | 6 | 15% |
| Computer Science | 5 | 13% |
| Medicine and Dentistry | 4 | 10% |
| Mathematics | 2 | 5% |
| Other | 3 | 8% |
| Unknown | 3 | 8% |
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 05 August 2013.
All research outputs
#14,172,390
of 22,714,025 outputs
Outputs from BMC Bioinformatics
#4,719
of 7,260 outputs
Outputs of similar age
#111,304
of 197,947 outputs
Outputs of similar age from BMC Bioinformatics
#55
of 83 outputs
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