filterNoise-methods: Filter non-informative trajectories

In lmms: Linear Mixed Effect Model Splines for Modelling and Analysis of Time Course Data

Description

Function to remove non-informative trajectories

Usage

filterNoise(data, noise, RTCutoff, RICutoff, propMissingCutoff, fcCutoff)


  ## S4 method for signature 
## 'matrixOrframe,
##   noise,
##   missingOrnumeric,
##   missingOrnumeric,
##   missingOrnumeric,
##   missingOrnumeric'
filterNoise(data,
  noise, RTCutoff, RICutoff, propMissingCutoff, fcCutoff)

Arguments

data	data.frame or matrix containing the samples as rows and features as columns.
noise	an object of class noise containing time and individual to molecule sd ratios number of missing values and maximum fold changes.
RTCutoff	numeric the R_T cutoff to remove non-informative trajectories.
RICutoff	numeric the R_I to remove non-informative trajectories.
propMissingCutoff	numeric maximum proportion of missing values in trajectories.
fcCutoff	numeric the minimum fold change observed between the mean of any two time points.

Details

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.

Value

filterNoise returns an object of class list containing the following components:

data	numeric filtered data.
removedIndices	numeric removed indices

References

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.

See Also

investNoise

Examples

## 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)

lmms documentation built on May 2, 2019, 6:53 a.m.