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

In dynOmics: Fast Fourier Transform to Identify Associations Between Time Course Omics Data

Description

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

Usage

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:

• Feature1 character the colnames or the index of data1.

• Feature2 character the colnames or the index of data2.

• delay numeric estimated delay between feature1 and feature2.

• pBefore numeric p-value of the test for association before applying the predicted time shift.

• pAfter numeric p-value of the test for association after applying the predicted time shift.

• corBefore numeric Pearson correlation before applying the predicted time shift.

• corAfter numeric Pearson correlation after applying the predicted time shift.

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

## 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.