Xin Zhang
Department of Computer Science
Courant Institute of Mathematical Sciences
New York University

Dennis Shasha
Department of Computer Science
Courant Institute of Mathematical Sciences
New York University

Yang Song
Department of Computer Science
New Jersey Institute of Technology

Jason Wang
Department of Computer Science
New Jersey Institute of Technology
wangj@njit.edu

Introduction

We present here a software tool, called PeakID, for fast elastic peak detection in 2D liquid chromatographic-mass spectrometry (LC-MS) data. PeakID takes 2D LC-MS data as input and locates all peaks across multiple window sizes of interest in the input data.

Installation

The programs of PeakID are written in C++. They were compiled and tested on a Dell PC under the Microsoft Windows XP operating system.
Here, we post the source code of the software and provide instructions to compile and run the programs.

• Click here to download the software in a zip package, called sat.zip.
• After you download and unzip the package, you will find a file, called SAT.dsw.
• To compile the program, use Microsoft Visual C++ 6.0 to open SAT.dsw.
• After you compile and build the program in Visual Studio, a sub-directory named "Release" will be created. In this sub-directory, an executable file, called SAT.exe, will be created.
• For your convenience, we have included a copy of SAT.exe in this package so that you can run the program directly in the Microsoft XP Windows environment.

Input

All the input files are text files. In the training phase, we use a state-space algorithm to find the topology and structure of an efficient Shifted Aggregation Tree to be used by PeakID. To train the software tool, you need to provide two input files: a sample data file (sample.txt) and a threshold file (thresh.txt). In addition, you need to provide the name of a file (tree.txt), in which the topology and structure, i.e., the shift, shadow size and degree of each level, of the efficient Shifted Aggregation Tree will be computed and stored.

To detect peaks, you need to provide three input files: an input data file (input.txt), a threshold file (thresh.txt), and the Shifted Aggregation Tree structure file (tree.txt). The sample data file (sample.txt) and the input data file (input.txt) have exactly the same format.

• Click here to download the sample data file (sample.txt).
  Each line in this file has an intensity value associated with one time point.
• Click here to download the threshold file (thresh.txt).
  Each line in this file begins with a window size, followed by a tab, followed by the threshold value associated with the window size.
• Click here to download the Shifted Aggregation Tree structure file (tree.txt).
  The first line in this file begins with the total number of levels in the Shifted Aggregation Tree, followed by the shadow size of the top level of the Shifted Aggregation Tree. The ith line in this file corresponds to the (i-1)th level of the Shifted Aggregation Tree. The ith line begins with the shift of level (i-1), followed by the shadow size of level (i-1), followed by d columns of numeric values where d is the degree of level (i-1) of the Shifted Aggregation Tree.
• Click here to download the input data file (input.txt).
  

For your convenience, we have included a copy of each of the above text files in this package.

Output

The software tool displays the output on the terminal. You can redirect the output to a file. The output comprises a list of tuples as follows:
     Starting_position, Window_size, Sum_of_intensity_values
Each tuple represents that there is a peak in the time window beginning at Starting_position with Window_size time points and the sum of the intensity values occurring within this time window equals Sum_of_intensity_values. The Sum_of_intensity_values must be greater than or equal to the threshold associated with the Window_size.

Usage

To run SAT.exe, you must be in the DOS environment.

• First, in the training phase, type
       SAT   1   thresh.txt   sample.txt   tree.txt   Max_num_states
  Max_num_states is optional; the default value is 500.

  The following screenshot shows how the training step works:

  

• Next, to detect the peaks in the input LC-MS data, type:
       SAT   2   thresh.txt   input.txt   tree.txt
  

  The following screenshot shows how the peak detection works:

  

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