SPARK-package: Spatial Pattern Recognition via Kernels
In xzhoulab/SPARK: Spatial Pattern Recognition via Kernels
SPARK-package R Documentation
Spatial Pattern Recognition via Kernels
Description
SPARK is an efficient tool for identifying spatial expression patterns. SPARK directly models raw count data generated from various spatial resolved transcriptomic techniques. With a new efficient penalized quasi-likelihood based algorithm, SPARK is scalable to data sets with tens of thousands of genes measured on thousands of samples. Build upon a non-parametric framework, SPARK-X is scalable to large-scale data sets with tens of thousands of genes measured on hundred thousands of samples.
Package Content
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Maintainer
Jiaqiang Zhu <jiaqiang@umich.edu>
Author(s)
Shiquan Sun, Jiaqiang Zhu, and Xiang Zhou
xzhoulab/SPARK documentation built on Nov. 20, 2022, 2:54 p.m.
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| SPARK-package | R Documentation |
Spatial Pattern Recognition via Kernels
Description
SPARK is an efficient tool for identifying spatial expression patterns. SPARK directly models raw count data generated from various spatial resolved transcriptomic techniques. With a new efficient penalized quasi-likelihood based algorithm, SPARK is scalable to data sets with tens of thousands of genes measured on thousands of samples. Build upon a non-parametric framework, SPARK-X is scalable to large-scale data sets with tens of thousands of genes measured on hundred thousands of samples.
Package Content
Index: This package was not yet installed at build time.
Maintainer
Jiaqiang Zhu <jiaqiang@umich.edu>
Author(s)
Shiquan Sun, Jiaqiang Zhu, and Xiang Zhou
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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