Description Usage Arguments Details Value References See Also Examples
Function to remove non-informative trajectories
1 2 3 4 5 6 7 8 9 10 11 12 | filterNoise(data, noise, RTCutoff, RICutoff, propMissingCutoff, fcCutoff)
## S4 method for signature
## 'matrixOrframe,
## noise,
## missingOrnumeric,
## missingOrnumeric,
## missingOrnumeric,
## missingOrnumeric'
filterNoise(data,
noise, RTCutoff, RICutoff, propMissingCutoff, fcCutoff)
|
data |
|
noise |
an object of class |
RTCutoff |
|
RICutoff |
|
propMissingCutoff |
|
fcCutoff |
|
filterNoise removes noisy or non-informative profiles based on selected theresholds R_I, R_T (Straube et al. 2015), maximum foldchanges and/or missing values.
filterNoise returns an object of class list
containing the following components:
data |
|
removedIndices |
|
Straube J., Gorse A.-D., Huang B.E., Le Cao K.-A. (2015). A linear mixed model spline framework for analyzing time course 'omics' data PLOSONE, 10(8), e0134540.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ## Not run:
data(kidneySimTimeGroup)
G1 <- kidneySimTimeGroup$group=="G1"
noiseTest <-investNoise(data=kidneySimTimeGroup$data[G1,],time=kidneySimTimeGroup$time[G1],
sampleID=kidneySimTimeGroup$sampleID[G1])
data <-filterNoise(data=kidneySimTimeGroup$data[G1,],noise=noiseTest,RTCutoff=0.9,
RICutoff=0.3,propMissingCutoff=0.5)$data
#Alternatively model-based clustering can be used for filtering
library(mclust)
clusterFilter <- Mclust(cbind(noiseTest@RT,noiseTest@RI),G=2)
plot(clusterFilter,what = "classification")
meanRTCluster <-tapply(noiseTest@RT,clusterFilter$classification,mean)
bestCluster <- names(meanRTCluster[which.min(meanRTCluster)])
filterdata <- kidneySimTimeGroup$data[G1,clusterFilter$classification==bestCluster]
## End(Not run)
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