peak-grouping-alignment

This page contains the supporting files for the paper Incorporating peak grouping information for alignment of multiple liquid chromatography-mass spectrometry datasets by Wandy et al. (2014).

• MW contains the main peak alignment (matching) code for the proposed methods (MW/MWG/MWM) method. The implementation is done in Python, and the main executable script is MW.py.
• input_data contains the dataset used in experiments to measure performance in the paper. Further explanation can be found inside that directory.
• experiments contains scripts to run the evaluation pipeline and do parameter scans. More details can be found inside that directory.

Checking out the whole project

Use the following command to clone the whole repository. This will give you all the executables, input_data and evaluation scripts used in the paper.

git clone https://github.com/joewandy/peak-grouping-alignment.git

Required libraries

The program depends on the following Python library:

• Numpy
• Scipy (0.14)
• NetworkX (optional)
• BLAS/OpenBLAS/Atlas for matrix multiplication

In Ubuntu Linux, you can install them easily with the following commands:

sudo apt-get install python-numpy python-scipy python-networkx liblapack3

On Fedora Linux, install the following:

sudo yum install scipy python-networkx lapack-devel blas-devel python-pip

You might need to update scipy to the latest version (0.14) too. Do that from pip:

sudo pip install --upgrade scipy

Example usage

To get help:

MW.py -h

To perform a simple matching using the simple (fast) approximate maximum-weighted matching (MW):

MW.py -i /path/to/input_dir -o /path/to/output_file -dmz 0.025 -drt 100 

To perform a full matching using the (slow) Hungarian maximum-weighted matching (not evaluated in the paper):

MW.py -i /path/to/input_dir -o /path/to/output_file -dmz 0.025 -drt 100 -exact_match

To group related peaks greedily (with 2 seconds grouping tolerance) and perform an approximate maximum-weighted matching incorporating the greedy grouping information for peaks being in the same group (MWG):

MW.py -i /path/to/input_dir -o /path/to/output_file -dmz 0.025 -drt 100 -g -gm greedy -grt 2

To group related peaks using mixture model clustering and perform an approximate maximum-weighted matching incorporating the posterior probabilities of peaks being in the same group (MWM):

MW.py -i /path/to/input_dir -o /path/to/output_file -dmz 0.025 -drt 100 -g -gm posterior -grt 2

Data format

Both the input files and output file follow the format established in SIMA. The input files contain peak features, which are tab-separated files with the following columns:

mass    charge  intensity   rt

E.g.

64.0288984965439	1	254903.00386791077	1438.5521436180882
64.52996790118753	1	14769.037754071698	1446.5771484817171
72.54182358620366	1	218099.4595541399	1442.8666507643893
73.04060164990388	1	2801.21866543637	1456.5747291123264

Only files within with the extension .csv, .txt within input_dir are considered for alignment. The output file establish the correspondence of peaks in the input file. It has the format:

correspondence_id   originating_filename    mass    rt

E.g.

0	synthdata_0.csv	28	93.05494095359997	1552.7644476078922
0	synthdata_1.csv	22	93.05459668295673	1558.3487511435783
1	synthdata_0.csv	74	158.0938159725959	904.2641474422279
1	synthdata_1.csv	70	158.09302110253617	878.1399446798514

In this example, the two features with correspondence_id = 0 are matched together. Similarly, the last two peaks with correspondence_id = 1 are also matched together.

Experiments

The Python scripts are wrapped inside a pipeline that loads the feature data, calls various alignment methods and calculate performance measures. More details, including ready-to-run scripts, can be found inside the experiments folder.