• Overview
• Installation
• Docker
• Data
• Tutorials
• License
• References

The R-package tigaRcycle performs integrative detection of circadian signals in time series omics data, sequencing counts RNA-seq, and continuous microarray data. The package offers a broad range of functions for the visualization of rhythmic signals and comparison between different diets and time points. Pathway enrichment analysis and visualization using different repositories can also be performed by comparing different conditions.

Note: If you can choose to use Windows or Unix/Linux, opt for the latter. tigaRcycle runs more efficiently under Unix/Linux than Windows. NOTE: When running tigaRcycle you may see *** WARNINGS *** from INLA (e.g. on eigenvalues, or convergence, or even something like 18500 Aborted...). They can currently not be suppressed, because C-code produces them. Please ignore them.

The package tigaRcycle depends on tigaR and on R >= 3.0.0 and is available from GitHub. This requires the package devtools:

devtools::install_github("viktormiok/tigaRcycle", build_vignettes=TRUE)

Please restart R before loading the package and its documentation:

library(tigaRcycle)
utils::help(tigaRcycle)
utils::vignette("tigaRcycle")

If your system configuration makes installing tigaRcycle natively difficult, a docker container is an alternative way to get tigaRcycle running.

Note: Docker Machine has Memory and CPU limits on Mac OS X. To control it, please check instructions either for CLI or for Docker Desktop.

For building a Docker image from the Dockerfile, download the Dockerfile (available in this repo) and run the following command to make it:

docker build -t tigaRcycle.

This will create a tigaRcycle docker image on your system (please be patient, as the build could take approximately 30-50 minutes to finish). You can then run it using the following command:

docker run -d -p 8787:8787 -e PASSWORD=pass --name tigaRcycle -it tigaRcycle

Data required for identifying circadian rhythms in transcriptomics data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO). They are accessible through the GEO Series accession numbers:

To access one of the data sets for instance GSE138079 you need to run the code below. Unpacking the data requires tar and gunzip, which should already be available on most systems.

cd ../  #To get to the main GitHub repo folder
mkdir -p data/tigaRcycle_data_analysis/
cd data/tigaRcycle_data_analysis/
wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE13nnn/GSE138079/suppl/GSE138079_RAW.tar
mkdir GSE138079_RAW
tar -C GSE138079_RAW -xvf GSE138079_RAW.tar
gunzip GSE138079_RAW/*_Regional_*

Please see the following tutorials for detailed examples of how to use tigaRcycle:

• Notebook

tigaRcycle is distributed under the MIT license. Please read the license before using tigaRcycle, distributed in the LICENSE file.

Publication related to tigaRcycle include:

• Miok, V., Yi, C., Gonzalez Garcia, I., Lutter, D., García-Cáceres, C., "Circadian transcriptomics of different brain regions reveals system-wide coordination and communication between clocks", In preparation

• Miok, V., Wilting, S.M., Van de Wiel, M.A., Jaspers, A., van Noort, P.I., Brakenhoff, R.H., Snijders, P.J.F., Steenbergen, R.D.M., Van Wieringen, W.N. (2014), "tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities", BMC Bioinformatics, 15, 327.

Please cite the relevant publication if you use tigaRcycle.