chowCor: Runs the superNOVA pipeline directly on one or two feature...
In ryananeff/superNOVA: Differential coexpression within multiple subgroups of data
chowCor R Documentation
Runs the superNOVA pipeline directly on one or two feature matrices.
Description
Evaluates differential coexpression between two or more subgroups of samples in the data versus the global model.
Usage
chowCor(design_mat, matA, matB = NULL, compare = NULL,
corrType = "pearson")
Arguments
design_mat
A model.matrix type design matrix, denoting which samples are in which subgroups of the dataset. Required.
matA
A features (rows) by samples (columns) data.frame of feature values, such as a gene expression matrix. Required.
matB
A second features (rows) by samples (columns) data.frame of feature values, such as a gene expression matrix. Values in this matrix will be compared to matA if provided. Default=NULL.
compare
Which groups of samples in the design matrix should we evaluate? Otherwise, all groups in the design matrix will be evaluated. Default=NULL.
corrType
The base correlation metric to evaluate coexpression. One of "pearson","spearman", or "bicor". Default=pearson.
Value
Returns a list of matrices, includes pvalues for the superNOVA test (pvalues) and their classes (classes), correlation values for each subgroup model (corrs) and their pvalues (corrsP), correlation values for the global model (globalCor) and its pvalues (globalCorP), and a flag to indicate whether a second matrix was used (secondMat).
ryananeff/superNOVA documentation built on March 29, 2024, 5:31 p.m.
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| chowCor | R Documentation |
Runs the superNOVA pipeline directly on one or two feature matrices.
Description
Evaluates differential coexpression between two or more subgroups of samples in the data versus the global model.
Usage
chowCor(design_mat, matA, matB = NULL, compare = NULL,
corrType = "pearson")
Arguments
design_mat |
A model.matrix type design matrix, denoting which samples are in which subgroups of the dataset. Required. |
matA |
A features (rows) by samples (columns) data.frame of feature values, such as a gene expression matrix. Required. |
matB |
A second features (rows) by samples (columns) data.frame of feature values, such as a gene expression matrix. Values in this matrix will be compared to matA if provided. Default=NULL. |
compare |
Which groups of samples in the design matrix should we evaluate? Otherwise, all groups in the design matrix will be evaluated. Default=NULL. |
corrType |
The base correlation metric to evaluate coexpression. One of "pearson","spearman", or "bicor". Default=pearson. |
Value
Returns a list of matrices, includes pvalues for the superNOVA test (pvalues) and their classes (classes), correlation values for each subgroup model (corrs) and their pvalues (corrsP), correlation values for the global model (globalCor) and its pvalues (globalCorP), and a flag to indicate whether a second matrix was used (secondMat).
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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