regressConfoundingFactors: regressConfoundingFactors
In IMB-Computational-Genomics-Lab/ascend: ascend - Analysis of Single Cell Expression, Normalisation, and Differential expression
Description
Usage
Arguments
Value
Examples
View source: R/ascend_confoundingfactors.R
Description
This function generates a scaled regression matrix based on candidate genes
supplied by the user. This function should be used after normalisation.
Usage
1
regressConfoundingFactors(object, candidate.genes = c())
Arguments
object
An EMSet that has been normalised.
candidate.genes
A list of genes you wish to regress from the dataset.
Refer to the vignette on how to choose genes for regression.
Value
An EMSet with confounding factors regressed
from the expression values.
Examples
1
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3
4
5
6
7
# Load example EMSet
em_set <- ascend::analyzed_set
# Define genes to regress
genes <- c("CDK4","CCND1")
regressedSet <- regressConfoundingFactors(em_set, candidate.genes = genes)
IMB-Computational-Genomics-Lab/ascend documentation built on Aug. 29, 2019, 4:10 a.m.
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Description Usage Arguments Value Examples
View source: R/ascend_confoundingfactors.R
Description
This function generates a scaled regression matrix based on candidate genes supplied by the user. This function should be used after normalisation.
Usage
1 | regressConfoundingFactors(object, candidate.genes = c())
|
Arguments
object |
An |
candidate.genes |
A list of genes you wish to regress from the dataset. Refer to the vignette on how to choose genes for regression. |
Value
An EMSet with confounding factors regressed
from the expression values.
Examples
1 2 3 4 5 6 7 | # Load example EMSet
em_set <- ascend::analyzed_set
# Define genes to regress
genes <- c("CDK4","CCND1")
regressedSet <- regressConfoundingFactors(em_set, candidate.genes = genes)
|
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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