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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

  • Pre-installation
  • Usage
  • Examples
  • Input/Output Files
  • Additional Tools
  • Citations
  • Additional Information

Pre-installation

ARSER package is implemented by Python calling R program. Before using the package, please install the following software and packages first:

  • Programing environments:
    1. Python v2.7 or later
    2. 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).

Usage

Command-line running:

python arser.py input_file_name output_file_name start(optional) end(optional) default_period(optional)

Options:

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

example:

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.

Input/Output Files

Note: Sample input and output files can be found in the examples/ subdirectory.

  • Input:

    • 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.
  • Output:

    • 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
      

Additional Tools

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

Citations

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.

Additional Information

ARSER website at https://github.com/cauyrd/ARSER/releases

Questions to: Rendong Yang cauyrd@gmail.com

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Periodicity detection algorithm for evenly spaced circadian gene expression data

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