| Title |
Comparison of Scanpy-based algorithms to remove the batch effect from single-cell RNA-seq data
|
|---|---|
| Published in |
Cell Regeneration, July 2020
|
| DOI | 10.1186/s13619-020-00041-9 |
| Pubmed ID | |
| Authors |
Jiaqi Li, Chengxuan Yu, Lifeng Ma, Jingjing Wang, Guoji Guo |
| Abstract |
With the development of single-cell RNA sequencing (scRNA-seq) technology, analysts need to integrate hundreds of thousands of cells with multiple experimental batches. It is becoming increasingly difficult for users to select the best integration methods to remove batch effects. Here, we compared the advantages and limitations of four commonly used Scanpy-based batch-correction methods using two representative and large-scale scRNA-seq datasets. We quantitatively evaluated batch-correction performance and efficiency. Furthermore, we discussed the performance differences among the evaluated methods at the algorithm level. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 20% |
| Germany | 1 | 20% |
| Italy | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Science communicators (journalists, bloggers, editors) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 38 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 16% |
| Student > Master | 5 | 13% |
| Student > Bachelor | 3 | 8% |
| Researcher | 3 | 8% |
| Other | 2 | 5% |
| Other | 4 | 11% |
| Unknown | 15 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 8 | 21% |
| Immunology and Microbiology | 3 | 8% |
| Agricultural and Biological Sciences | 2 | 5% |
| Engineering | 2 | 5% |
| Neuroscience | 2 | 5% |
| Other | 5 | 13% |
| Unknown | 16 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 July 2022.
All research outputs
#7,724,716
of 24,022,746 outputs
Outputs from Cell Regeneration
#39
of 174 outputs
Outputs of similar age
#159,296
of 400,511 outputs
Outputs of similar age from Cell Regeneration
#1
of 13 outputs
Altmetric has tracked 24,022,746 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 174 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 77% 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 400,511 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 59% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.