ARSER is a Python package for identifying periodic expression profiles in analyzing circadian microarray data and has been released under the GPL
Table of Contents
- Input/Output Files
- Additional Tools
- Additional Information
ARSER package is implemented by Python calling R program. Before using the package, please install the following software and packages first:
- Programing environments:
- Python v2.7 or later
- R v3.1 or later
- Packages: 3. scipy v0.7 or later 4. numpy v1.1 or later 5. matplotlib v0.99 or later 6. Rpy2 v2.5.6 or later
- Tips: To avoid wading through all the details (and potential complications) on Installation, the easiest thing for you to do is use one of the pre-packaged python distributions that already provide scipy/numpy/matplotlib built in. The Enthought Python Distribution (EPD) for Windows, OS X or Redhat is an excellent choice. Another alternative for Windows users is Python (x, y).
python arser.py input_file_name output_file_name start(optional) end(optional) default_period(optional)
ARSER searches period in the range [start, end] start: period searching range start, default 20h end: period searching range end, default 28h default_period: default period used by ARSER for searching, default 24h
1. searching circadian rhythm $ python arser.py data.txt output.txt >& log.txt 2. searching ultradian rhythm $ python arser.py data.txt output.txt 10 18 14 >& log.txt 3. searching infradian rhythm $python arser.py data.txt output.txt 30 42 36 >& log.txt
#####Note: If there is any warning message when the program is running, just ignore them. These warning messages come from calling R functions.
Note: Sample input and output files can be found in the examples/ subdirectory.
- Microarray data file with a header line which records the time-points. The 1st column is probesets, other columns are expression values over time. It is assumed that the samples are linearly spaced (e.g., one point every 4 hrs, etc). The current version of ARSER does NOT allow for non-linear sampling.
The 1st column is probesets, other columns are values of parameters as followed:
mean -> mean value for raw y values period -> period identified by ARSER amplitude -> amplitude for single cosine model phase -> phase for single cosine model R2 -> R square of regression curve R2adj -> adjusted R square of regression curve coef_var -> (standard deviation) / mean pvalue -> F test for testing significant regression model FDR_BH -> FDR by BH method filter_type -> filtering for noise by ARSER 0 -- no filtering 1 -- filtering ar_method -> methods for autogressive model fitting 'mle' -- maximum likelihood estimate 'burg' -- burg algorithm 'yule-walker' -- yule-walker equations 'default' -- harmonic regression with 24h period_number -> number of cycles in time series
See the README file in the tools subdirectory.
R implementation of ARSER algorithm
Dr. Gang Wu has implementated a R code for ARSER algorithm. If you are familar with R program, please try it out. https://github.com/gangwug/MetaCycle/blob/master/R/ARS.R
Rendong Yang and Zhen Su, Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation Bioinformatics. 2012 Jun 15;26(12):i168-74.
ARSER website at https://github.com/cauyrd/ARSER/releases
Questions to: Rendong Yang firstname.lastname@example.org