Rendong Yang and Zhen Su,

A Presentation for ARSER at ISMB2010 could be found here:)

**Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation***Bioinformatics*. 2010 Jun 15;26(12):i168-74. [link]A Presentation for ARSER at ISMB2010 could be found here:)

### Introduction

ARSER is a Python package for identifying periodic expression profiles in analyzing circadian microarray data and has been released under the GPL.### 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 version 2.5 or later

2. R version 2.7 or later

- Packages:

3. scipy version 0.7 or later

4. numpy version 1.1 or later

5. matplotlib version 0.99 or later

6. Rpy

- 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 that “just works” out of the box. Another excellent alternative for Windows users is Python (x, y).*### Usage

- Command-line running:usage: python arser.py input_file_name output_file_name

example: $ python arser.py data.txt output.txt >& log.txt

### 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>:Analytical results.

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

qvalue -> FDR by stroey method

FDR_BH -> FDR by Benjamini-Hochberg 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

### Download

Source codes are available at here.### Web Server

Additional Tools are avialble in the tools subdirectory of the source code.A webserver of ARSER algorithm can be found at:

http://bioinfo.cau.edu.cn/BioClock

### Contact

Please contact me if you have suggestions for improvement or if any problem arises in the use of the program, or the interpretation of the results.Rendong Yang

College of Biological Science

China Agricultural University

P.O.Box B1061

100193, Beijing

China.

tel.: +86-10-62734385

email: cauyrd@gmail.com

GOOD LUCK AND HAVE FUN !