I am confused about when the bicor and its options should be specified.
I have 20 samples, normalised expression data and a traits file containing binary traits (e.g. 0=uninfected, 1=infected). The literature suggests that bicor should be used over pearsons for robustness, but that MaxPOutliers and RobustY=FALSE be used when traits have just two levels.
My question is regarding when such options should be specified.
1 sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5, network="signed", corFnc="bicor", corOptions = list(use ='p', maxPOutliers= 0.05, robustY=FALSE, blocksize=30000)
Is it correct to use robust Y when choosing the threshold?
2. net = blockwiseModules(datExpr, power = 24, TOMType ="signed", type="signed",minModuleSize = 30,maxBlockSize=30000, reassignThreshold = 0, mergeCutHeight = 0.25,numericLabels = TRUE, pamRespectsDendro = FALSE,verbose = 3, corFnc="bicor", corOptions = list(use ='p', maxPOutliers= 0.05, robustY=FALSE,)
This is what I read within the help file:
- Dealing with binary data. When relating high-throughput data
xto binary variable
ysuch as sample traits, one can use argument
robustY = FALSEto turn off the robust treatment for the y argment of
bicor. This results in a hybrid robust-Pearson correlation as described in Langfelder and Horvath (2011). The hybrid correlation can also be used when one of the inputs is numeric but known to not have any outliers.
Sorry for all the questions, I am struggling with there being so many options, as to when they should be opted for if that makes sense. I am confused as to why you would use robustY=FALSE here as these commands do not account for the trait data, they are comparing the expression levels against each other?