Description Usage Arguments Details Value Author(s) References Examples
A tool for discovering and correcting for batch effect using an approach in Nyamundanda et al (2016), which is based on the PPCCA model.
1 |
D |
Expression dataset in samples by features format. |
batchCL |
A vector identifying batches. |
Conf |
A vector identifying biological classes. |
mindim |
Minimum number of principal components to be considered. Default is 1. |
maxdim |
Maximum number of principal components to be considered. Default is 2. maxdim should be greater or equal to mindim. |
method |
Method for batch correction. Either PPCCA or ComBat. PPCCA is the default. |
It searches for directions in the principal subspace that are associated with the batch effect and correct for batch effect by subtracting this effect only in the affected dimensions. Both the corrected and uncorrected dimensions are then used to predict the data.
The output of this pipeline is saved in your working directory.
Gift Nyamundanda and Anguraj Sadanandam
Nyamundanda, G. and Sadanandam, A., 2016. A novel and robust statistical method to diagnose and correct batch effects in large-scale genomic data.
1 2 3 4 5 | ## help(expBATCH)
## Expression data and batch effect variable is required for this function.
data(Breast)
data(batchBreast)
expBATCH(D= Breast,batchBreast,Conf=NA,mindim=1,maxdim=10,method="ppcca")
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