PyMSI

Collection of python tools for the analysis of Mass Spectrometry Imaging datasets

Accepted File Formats

The module provides function to load the data stored in the following formats.

Analyze7.5 The data are stored in three different files:

• .hdr : the header file contains informations about the image spatial properties
• .t2m : this file contains the m/z scale of the mass spectra. Note: the mass scale is the same for all the spectra (32 bit Long)
• .img : this file contains the actual intensity values recorded at each position for each m/z value.

Module Structure

The module is organized around the following classes:

1. spectrum: low level class which contains mz and I for a single spectrum.
2. speclist: this class represent a collection of spectra with the associated x y coordinates. The constructor requires:
   • a list of mass spectra
   • an optional [x,y] dimensions of the raster
   • a string representing the geometry of the acquisition. either "S" for meandering or "N" for simple row-wise acquisition

Usage

Creating ion images

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Input required : Folder path, mass range

Output : Ion intensity image, Image and segmentation map matrix in csv file

Suppose for folder named Images, containing multiple image dataset folder such as A1, A2, A3. command line argument will be:

/Documents/MSimaging/Python/PyMSI$ python CreateIonintensityImage.py --file '~/Documents/MSimaging/Images/' -f 284.2 284.3

Calculate first-order statistics based texture features

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Input required : image matrix csv file path

Output : .csv file contains FOS based features value

~/Documents/MSimaging$ python Features_Firstorderstatistics.py -f '~/Documents/Msimaging/Images/A1_image.csv'

Calculate gray-level co-occurence matrix based texture features

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Input required : image matrix csv file path, distance parameter value

Output : csv file contains GLCM based features value

~/Documents/MSimaging$ python Features_Coocurrencematrix.py -f '~/Documents/Msimaging/Images/A1_image.csv -d 1'

Calculate size-zone matrix based texture features

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Input required : image matrix csv file path

Output : csv file contains SZM based features value

Dependency : rpy2 module, and r radiomics library

~/Documents/MSimaging$ python Features_SZMbased.py -f '~/Documents/Msimaging/Images/A1_image.csv'

Calculate shape factors

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Input required : mask_matrix csv file path

Output : csv file contains shape factors value

Dependency : python cv2 module

~/Documents/MSimaging$ python Features_Coocurrencematrix.py -f '~/Documents/MSimaging/Images/A1_maski.csv'