getCovariateMx: Function that returns a matrix of IC coefficients that can be...
In jinhyunju/picaplot: An R package for analyzing gene expression data with Principal / Independent Component Analysis
Description
Usage
Arguments
Value
Examples
View source: R/getCovariateMx.R
Description
Returning a matrix of IC coefficients that are associated with covariates or have multiple clusters.
Usage
1
getCovariateMx(input_list = NULL, idx = NULL)
Arguments
input_list
ICA or PCA object generated by runICA() or runPCA().
idx
Component indexes that the user wishes to recover.
Value
Matrix with component coefficients
Examples
1
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4
5
6
data(expr_data)
ica_object <- runICA(expr_data)
ica_object <- covarAssociationCheck(ica_object, covars = sample_info)
ica_object <- detectClusters(ica_object)
getCovariateMx(ica_object)
jinhyunju/picaplot documentation built on May 19, 2019, 10:35 a.m.
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Description Usage Arguments Value Examples
View source: R/getCovariateMx.R
Description
Returning a matrix of IC coefficients that are associated with covariates or have multiple clusters.
Usage
1 | getCovariateMx(input_list = NULL, idx = NULL)
|
Arguments
input_list |
ICA or PCA object generated by |
idx |
Component indexes that the user wishes to recover. |
Value
Matrix with component coefficients
Examples
1 2 3 4 5 6 | data(expr_data)
ica_object <- runICA(expr_data)
ica_object <- covarAssociationCheck(ica_object, covars = sample_info)
ica_object <- detectClusters(ica_object)
getCovariateMx(ica_object)
|
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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