investNoise-methods: Quality control for time course profiles

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

Description

Function to calculate filter ratios of trajectories.

Usage

investNoise(data, time, sampleID, log, numCores)

Arguments

data	data.frame or matrix containing the samples as rows and features as columns
time	numeric vector containing the sample time point information.
sampleID	character, numeric or factor vector containing information about the unique identity of each sample
log	logical indicating log transformation of the data. Default value is TRUE
numCores	alternative numeric value indicating the number of CPU cores to be used for parallelization. Default value is automatically estimated.

Details

investNoise calculates filter ratios R_T and R_I based on the time, individual and overall standard deviation as proposed by Straube et al. 2015.

Value

investNoise returns an object of class noise containing the following components:

name	character the colnames or the index.
RT	numeric the time to molecule sd ratio of each trajectory.
RI	numeric the individual to molecule sd ratio of each trajectory.
propMissing	numeric Proportion of missing values for each trajectory.
foldChange	numeric the maximum absolute fold change (either for log transformed data max(time)-min(time) or not log transformed data max(time)/min(time)) observed between the mean of any two time points.

References

Straube J., Gorse 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

summary.noise, plot.noise, filterNoise

Examples

## Not run: 
data(kidneySimTimeGroup)
G1 <- kidneySimTimeGroup$group=="G1"
noiseTest <-investNoise(data=kidneySimTimeGroup$data[G1,],time=kidneySimTimeGroup$time[G1],
            sampleID=kidneySimTimeGroup$sampleID[G1])
summary(noiseTest)
plot(noiseTest,colorBy="propMissing")
## End(Not run)

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