MSI-data and Matlab Implementation of the method presented in: 
 W. Abdelmoula et al, "Data-Driven Identification of Prognostic Tumor Subpopulations Using
                  Spatially Mapped t-SNE of Mass Spectrometry Imaging Data", PNAS, 2016.
===================== Notes: =============================================
Note: Some parts of this pipeline use R implementation. Please, install R and a
proper toolbox to connect R-and-Matlab.
Note: We use the R Package 'samr' to perform the SAM analysis, and it is integrated
in this Matlab scripts (calling R from Matlab). So  the results may not be 
prinited in a proper readable manner for you (in a Matlab window), then you can find the exciplicit 
R implementation the attached file "SAM_GastricCancer_R.txt" 
(if it is needed, then change the value of parameter delta (here, delta is corresponding to FDR <0.001))
******************** Terms and Conditions ***********************
Please note that by using this implementation you agree on the terms of
use:
  1. The software may be used for research purposes only.
  2. The software is for personal use only, and May not be redistributed.
  3. In no event Shall the LUMC be liable for any direct or indirect damage, 
     Arising in any way out of the use of this software.
  4. Any publication Arising from the use of this implementation should
  cite reference : W. Abdelmoula et al, "Data-Driven Identification of Prognostic Tumor Subpopulations Using
                  Spatially Mapped t-SNE of Mass Spectrometry Imaging Data", PNAS, 2016.
      
=========================================================================
The main Matlab file to be run called:
- for Gastric Cancer case: Run_Gastric.m
- for Breast Cancer case: Run_BreastCancer.m
The variables discription in these main matlab files are explanied in the following section.

-------------------- Variables discription -------------------------------
Clinical_data: contains clinical data for each sample
goodlist: tells for each pixel in the unfolded MSI_data_cube if it is a MSI spectrum or not
HE_image: histological image for each sample
MSI_data_cube: contains the spectral data in the z-dimension; not every pixel is associated to a MSI spectrum (see goodlist)
peak_list: contains information about the peaks (mz value, average intensity, lower boundary, upper boundary)
pixel_to_sample_ID: tells for each pixel in the MSI_data_cube to which sample_ID it belongs; can be linked to sample_ID of Clinical_data table
x: width of dataset in pixels
y: height of dataset in pixels
z: number of peaks (z-dimension of MSI_data_cube)
