associateData: Identify associations of trajectories within a data set or...

Description Usage Arguments Details Value References See Also Examples

View source: R/associateData-method.R

Description

Function to estimate differences in expression initation of trajectories to identify associations between time course 'omics' data.

Usage

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associateData(data1,data2,numCores)

Arguments

data1

data.frame or matrix containing the time as rows and features as columns

data2

optional an additional data.frame or matrix containing the time as rows and features as columns

numCores

alternative numeric value indicating the number of CPU cores to be used for parallelization. Default value is automatically estimated.

Details

associateData() takes as input two data sets of interest and performs a pairwise associations comparison between features using a fast Fourier transform approach to detect delays (also called 'associations') between the different features. Note that the argument ‘numCores' indicates the number of CPUs and is detected by default in the function to perform parallelization. The final result is a table with a row for each pairwise comparison. The output presents the dynOmics estimated delay between two features, the p-value ('p') and correlation coefficient ('cor') from a Pearson’s test, before and after the time profiles have been realigned according to the dynOmics estimated delay.

Value

associateData returns an object of class associations containing the following components:

References

Straube J., Bernard A., Huang B.E., Le Cao K.-A.(2017). DynOmics to identify delays and co-expression patterns across time course experiments Scientific Reports

See Also

summary.associations, plot.associations

Examples

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## Not run:  
data(Metabolites)
data(Transcripts)
associations <- associateData(Metabolites[,1],Transcripts[,c(1:50)])
#summary(associations)
#plot(associations,Metabolites,Transcripts,feature1=1)

## End(Not run)

dynOmics documentation built on May 1, 2019, 8:42 p.m.