| Title |
Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data
|
|---|---|
| Published in |
Genome Biology, November 2019
|
| DOI | 10.1186/s13059-019-1863-4 |
| Pubmed ID | |
| Authors |
Fenglin Liu, Yuanyuan Zhang, Lei Zhang, Ziyi Li, Qiao Fang, Ranran Gao, Zemin Zhang |
X Demographics
The data shown below were collected from the profiles of 34 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 | 6 | 18% |
| United Kingdom | 5 | 15% |
| France | 4 | 12% |
| Japan | 2 | 6% |
| Hong Kong | 2 | 6% |
| Austria | 1 | 3% |
| China | 1 | 3% |
| Italy | 1 | 3% |
| Switzerland | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 10 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 16 | 47% |
| Scientists | 16 | 47% |
| Science communicators (journalists, bloggers, editors) | 2 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 226 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 226 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 41 | 18% |
| Researcher | 38 | 17% |
| Student > Master | 25 | 11% |
| Student > Bachelor | 22 | 10% |
| Student > Postgraduate | 9 | 4% |
| Other | 26 | 12% |
| Unknown | 65 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 72 | 32% |
| Agricultural and Biological Sciences | 36 | 16% |
| Computer Science | 14 | 6% |
| Medicine and Dentistry | 9 | 4% |
| Engineering | 4 | 2% |
| Other | 15 | 7% |
| Unknown | 76 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 18. 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 10 January 2021.
All research outputs
#2,049,023
of 25,387,668 outputs
Outputs from Genome Biology
#1,733
of 4,470 outputs
Outputs of similar age
#47,906
of 471,675 outputs
Outputs of similar age from Genome Biology
#63
of 100 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,470 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 61% of its peers.
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 471,675 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 100 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.