qualityAssessment: Remove outlier peaks.
In saezlab/BraDiPluS: BraDiPluS - Braille Display Plugs Data Analysis

Description Usage Arguments Details Value See Also Examples

qualityAssessment removes low quality peaks (i.e. replicates).

1	qualityAssessment(runs)

runs

List with one element for each run. For each run the list elements correspond to the peaks selected for the different samples using selectSamplesPeaks (after defining samples with samplesSelection)

This function allows to remove the peaks that are outliers based on the orange channel. Orange dye is added to the cells suspention in order to monitor mixing of reagents in each plug. This function looks at the distribution of the values of all orange peaks and marks the possible outliers: corresponding peaks are removed from further analysis. It can be done at once for different runs (where we call run a complete cycle of all the samples corresponding to different experimental conditions).

This function returns a list with the same structure of the one used as input but without the outlier peaks.

samplesSelection to define samples, selectSamplesPeaks to select peaks for each sample

data(BxPC3_data,package="BraDiPluS")
res <- samplesSelection(data=MyData, BCchannel="blue",
BCthr=0.01, distThr=300, plotMyData=TRUE)
samples <-res$samples

# select the peaks for each sample
samplesPeaks <- selectSamplesPeaks(samples, channel="green", metric="median", baseThr=0.01, minLength=350, discartPeaks="first", discartPeaksPerc=5)

# remove outliers based on orange channel
runs<-list(run1=samplesPeaks)
runs.qa<-qualityAssessment(runs=runs)