README.md
In chansigit/scFly: Mapping cells with single-cell transcriptome data
scFly
Single-cell sequencing methods enables measurement of cells' molecular states at single cell resolution. One problem arised along with the new data is mapping common/similar cells across different measurements. Our scFly efficiently maps cells across different datasets with locality sensitive hashing, which is resistent to common single-cell omics noises or biases like dropout events and batch effects.
Installation
devtools::install_github("chansigit/scFly")
Tutorial
- MNIST Demo
We use MNIST data to illustrate how DenseFly algorithm performs similarity searching. Please see the Jupyter Notebook file
DenseFly Tutorial - MNIST demo.ipynb
chansigit/scFly documentation built on Nov. 10, 2020, 12:33 a.m.
R Package Documentation
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scFly
Single-cell sequencing methods enables measurement of cells' molecular states at single cell resolution. One problem arised along with the new data is mapping common/similar cells across different measurements. Our scFly efficiently maps cells across different datasets with locality sensitive hashing, which is resistent to common single-cell omics noises or biases like dropout events and batch effects.
Installation
devtools::install_github("chansigit/scFly")
Tutorial
- MNIST Demo
We use MNIST data to illustrate how DenseFly algorithm performs similarity searching. Please see the Jupyter Notebook file
DenseFly Tutorial - MNIST demo.ipynb
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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