The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies
Open accessAbstract
Spatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies. SPARK-X not only produces effective type I error control and high power but also brings orders of magnitude computational savings. We apply SPARK-X to analyze three large datasets, one of which is only analyzable by SPARK-X. In these data, SPARK-X identifies many spatially expressed genes including those that are spatially expressed within the same cell type, revealing new biological insights.
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Metadata
- Authors
- Zhu, J., Sun, S., & Zhou, X.
- Venue
- Genome Biology
- Published
- 2021-06-21
- Volume
- 22
- Issue
- 1
- Pages
- 184-184
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- OpenAlex
- W3173882906
- Cited by
- 253
- References
- 77
- FWCI
- 10.33
- Type
- journal article
- Language
- en
- Open access
- gold
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- Zhu, J., Sun, S., & Zhou, X. (2021). SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies. Genome Biology. https://doi.org/10.1186/s13059-021-02404-0
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