BatchSVG: identifying batch-biased genes in the application of spatially variable gene detection
Clicks: 2
ID: 321317
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
0.3
/100
2 views
1 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #807 of 835 articles by views in BMC Bioinformatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 835 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Abstract Summary A standard task in the analysis of spatially resolved transcriptomics data is to identify spatially variable genes (SVGs). This is most commonly done within one tissue section at a time because the spatial relationships between the tissue sections are typically unknown. However, large-scale spatial atlases are being generated, for example across hundreds of donors, where the goal is to identify a common set of SVGs to use for downstream analyses. One challenge is how to identify and remove SVGs that are associated with a known bias or technical artifact, such as the slide, which can lead to poor performance in downstream analyses, such as spatial domain detection. Here, we introduce BatchSVG, a tool to identify batch-biased genes SVGs. Our approach compares the rank of per-gene deviance under a binomial model (i) with and (ii) without including a covariate in the model that is associated with the known bias or technical artifact. If the rank of a gene changes significantly between these, then we infer that this gene is likely associated with the bias or technical artifact and should be removed from the downstream analyses. We consider two SRT datasets and show how our model can improve the results of downstream analysis. Availability and Implementation The BatchSVG package is freely available at https://bioconductor.org/packages/BatchSVG, and the code to reproduce the figures is publicly available at https://github.com/kinnaryshah/BatchSVG-analyses. Supplementary information Supplementary data are available at Bioinformatics online.
| Reference Key |
openalex_W7169224552
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Kinnary Shah, Christine Hou, Jacqueline R. Thompson, Stephanie C. Hicks |
| Journal | BMC Bioinformatics |
| Year | 2026 |
| DOI |
10.1093/bioinformatics/btag522
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.