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Mendeley readers
Attention Score in Context
| Title |
ERStruct: a fast Python package for inferring the number of top principal components from whole genome sequencing data
|
|---|---|
| Published in |
BMC Bioinformatics, May 2023
|
| DOI | 10.1186/s12859-023-05305-0 |
| Pubmed ID | |
| Authors |
Jinghan Yang, Yuyang Xu, Minhao Yao, Gao Wang, Zhonghua Liu |
| Abstract |
Large-scale multi-ethnic DNA sequencing data is increasingly available owing to decreasing cost of modern sequencing technologies. Inference of the population structure with such sequencing data is fundamentally important. However, the ultra-dimensionality and complicated linkage disequilibrium patterns across the whole genome make it challenging to infer population structure using traditional principal component analysis based methods and software. We present the ERStruct Python Package, which enables the inference of population structure using whole-genome sequencing data. By leveraging parallel computing and GPU acceleration, our package achieves significant improvements in the speed of matrix operations for large-scale data. Additionally, our package features adaptive data splitting capabilities to facilitate computation on GPUs with limited memory. Our Python package ERStruct is an efficient and user-friendly tool for estimating the number of top informative principal components that capture population structure from whole genome sequencing data. |
X Demographics
The data shown below were collected from the profiles of 14 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 Kingdom | 2 | 14% |
| Spain | 1 | 7% |
| Unknown | 11 | 79% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 71% |
| Scientists | 3 | 21% |
| Practitioners (doctors, other healthcare professionals) | 1 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor > Associate Professor | 1 | 100% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 1 | 100% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 20 June 2023.
All research outputs
#6,201,118
of 24,858,211 outputs
Outputs from BMC Bioinformatics
#2,097
of 7,596 outputs
Outputs of similar age
#104,571
of 391,273 outputs
Outputs of similar age from BMC Bioinformatics
#34
of 137 outputs
Altmetric has tracked 24,858,211 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 7,596 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 71% 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 391,273 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.