Analysis of experimental crosses to identify genetic markers associated with correlation changes in quantitative traits (CTL). The additional correlation information obtained can be combined with QTL information to perform de novo reconstruction of interaction networks.
For more background information about the method we refer to the methodology article published in XX (201X).
The R package is a basic iomplementation and it includes the following core functionality:
CTLscan - Main function to scan for CTL.
CTLsignificant - Significant interactions from a CTLscan.
CTLnetwork - Create a CTL network from a CTLscan.
image.CTLobject - Heatmap overview of a CTLscan.
plot.CTLscan - Plot the CTL curve for a single trait.
ctl.circle - Circle plot CTLs on single and multiple traits.
ctl.lineplot - Line plot CTLs on single and multiple traits.
CTLprofiles - Extract CTL interaction profiles.
For all these functions we also provide examples and demonstrations on real genetical genomics data. We thank all contributors for publishing their data online and will accept submissions of intrestion datasets, currently ctl provides:
ath.metabolites - Metabolite expression data from Arabidopsis Thaliana
ath.churchill - Metabolite expression data from Arabidopsis Thaliana
yeast.brem - Gene expression data from Saccharomyces cerevisiae
Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com
Contributions from: Bruno Tesson, Pjotr Prins and Ritsert C. Jansen
More detailed information and/or examples are given per function as needed. Some additional functionality:
basic.qc - Some basic quality checks for phenotype and genotype data
CTLscan.cross - Use an R/qtl cross object with CTLscan
TODO
CTLscan - Scan for CTL
CTLscan.cross - Use an R/qtl cross object with CTLscan