EucDist: Compute Euclidean similarity
In jeonglab/prime:
Description
Usage
Arguments
Value
Author(s)
References
Examples
Description
Compute Euclidean similarity using a Gaussian kernel.
First, it normalize the input
then it transforms the distance to similarity using a Gaussian kernel
so that similar objects have larger values
Usage
1
Arguments
data
(M x N) dimensional inputmatrix. N is the number of objects M is the number of features describing each object.
Value
(N x N) dimensional normalized Euclidean similarity matrix.
Author(s)
Hyundoo Jeong
References
Hyundoo Jeong and Zhandong Liu
PRIME: a probabilistic imputation method to reduce dropout effects in single cell RNA sequencing
Examples
1
jeonglab/prime documentation built on May 7, 2019, 6:58 p.m.
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.
Description Usage Arguments Value Author(s) References Examples
Description
Compute Euclidean similarity using a Gaussian kernel. First, it normalize the input then it transforms the distance to similarity using a Gaussian kernel so that similar objects have larger values
Usage
1 |
Arguments
data |
(M x N) dimensional inputmatrix. N is the number of objects M is the number of features describing each object. |
Value
(N x N) dimensional normalized Euclidean similarity matrix.
Author(s)
Hyundoo Jeong
References
Hyundoo Jeong and Zhandong Liu
PRIME: a probabilistic imputation method to reduce dropout effects in single cell RNA sequencing
Examples
1 |
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.