I'm in a startup working with a research institution and they have given me .D folders, which is the 'raw data' from an Agilent GC-MS machines.
I want to run Deep learning models on the raw data. First, I will take the 2D vector and create a 3D plot (RT, M/Z, intensity). Then, I will use image classification techniques to try to classify the sample.
I have a few questions:
- Do .D folders contain the raw GC-MS data (RT, M/Z), also known as an abundance matrix?
- If so, which sub-folder is it?
- I want the raw 2D vector of data.
- How do I import these files into python?
- Could you direct me to any resources or explain how to extract the data from the .D file and bring it into python?
- I want to run Deep Learning models on
