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Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods
- Source :
- Genome Biology, Vol 24, Iss 1, Pp 1-12 (2023)
- Publication Year :
- 2023
- Publisher :
- BMC, 2023.
-
Abstract
- Abstract Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.
- Subjects :
- Biology (General)
QH301-705.5
Genetics
QH426-470
Subjects
Details
- Language :
- English
- ISSN :
- 1474760X
- Volume :
- 24
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Genome Biology
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.1b4f8dd834724091bdaa2bce84d5993b
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s13059-023-03045-1