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Mendeley readers
Attention Score in Context
| Title |
l1kdeconv: an R package for peak calling analysis with LINCS L1000 data
|
|---|---|
| Published in |
BMC Bioinformatics, July 2017
|
| DOI | 10.1186/s12859-017-1767-9 |
| Pubmed ID | |
| Authors |
Zhao Li, Jin Li, Peng Yu |
| Abstract |
LINCS L1000 is a high-throughput technology that allows gene expression measurement in a large number of assays. However, to fit the measurements of ~1000 genes in the ~500 color channels of LINCS L1000, every two landmark genes are designed to share a single channel. Thus, a deconvolution step is required to infer the expression values of each gene. Any errors in this step can be propagated adversely to the downstream analyses. We presented a LINCS L1000 data peak calling R package l1kdeconv based on a new outlier detection method and an aggregate Gaussian mixture model (AGMM). Upon the remove of outliers and the borrowing information among similar samples, l1kdeconv showed more stable and better performance than methods commonly used in LINCS L1000 data deconvolution. Based on the benchmark using both simulated data and real data, the l1kdeconv package achieved more stable results than the commonly used LINCS L1000 data deconvolution methods. |
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 % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 7 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 43% |
| Student > Bachelor | 2 | 29% |
| Student > Ph. D. Student | 2 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 4 | 57% |
| Biochemistry, Genetics and Molecular Biology | 2 | 29% |
| Mathematics | 1 | 14% |
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 31 August 2017.
All research outputs
#18,565,641
of 22,994,508 outputs
Outputs from BMC Bioinformatics
#6,347
of 7,311 outputs
Outputs of similar age
#243,256
of 317,332 outputs
Outputs of similar age from BMC Bioinformatics
#73
of 90 outputs
Altmetric has tracked 22,994,508 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 7,311 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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We're also able to compare this research output to 90 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.