| Title |
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis
|
|---|---|
| Published in |
Genome Biology, December 2019
|
| DOI | 10.1186/s13059-019-1898-6 |
| Pubmed ID | |
| Authors |
Shiquan Sun, Jiaqiang Zhu, Ying Ma, Xiang Zhou |
X Demographics
The data shown below were collected from the profiles of 83 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 | 27 | 33% |
| Italy | 6 | 7% |
| United Kingdom | 6 | 7% |
| Australia | 6 | 7% |
| France | 4 | 5% |
| New Zealand | 4 | 5% |
| Czechia | 2 | 2% |
| Germany | 2 | 2% |
| Spain | 2 | 2% |
| Other | 9 | 11% |
| Unknown | 15 | 18% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 54 | 65% |
| Members of the public | 27 | 33% |
| Science communicators (journalists, bloggers, editors) | 2 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 245 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 245 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 52 | 21% |
| Researcher | 35 | 14% |
| Student > Bachelor | 27 | 11% |
| Student > Master | 23 | 9% |
| Student > Doctoral Student | 13 | 5% |
| Other | 31 | 13% |
| Unknown | 64 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 80 | 33% |
| Agricultural and Biological Sciences | 29 | 12% |
| Computer Science | 22 | 9% |
| Medicine and Dentistry | 10 | 4% |
| Mathematics | 5 | 2% |
| Other | 22 | 9% |
| Unknown | 77 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 46. 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 22 January 2024.
All research outputs
#926,794
of 25,758,695 outputs
Outputs from Genome Biology
#624
of 4,514 outputs
Outputs of similar age
#22,309
of 480,625 outputs
Outputs of similar age from Genome Biology
#29
of 96 outputs
Altmetric has tracked 25,758,695 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,514 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.5. This one has done well, scoring higher than 86% 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 480,625 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 96 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.