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running-cellfinder.md

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How to set the analysis running

Running cellfinder

cellfinder runs with a single command, with various arguments that are detailed in Command line options. To analyse the example data, the flags we need are:

  • -s The primary signal channel: test_brain/ch00
  • -b The secondary autofluorescence channel (or background): test_brain/ch01
  • -o The output directory : test_brain/output
  • --orientation The data orientation: psl
  • -v The voxel spacing in the same order as the data orientation (psl): 5 2 2
  • --atlas The atlas we want to use: allen_mouse_10um

{% hint style="warning" %} If your machine has less than 32GB of RAM, you should use the allen_mouse_25um atlas either way, as registration with the high-resolution atlas requires about 30GB for this image. {% endhint %}

Putting this all together into a single command gives:

cellfinder -s test_brain/ch00 -b test_brain/ch01 -o test_brain/output -v 5 2 2 --orientation psl --atlas allen_mouse_10um

This command will take quite a long time (anywhere from 2-10 hours) to run, depending on:

  • The speed of the disk the data is stored on
  • The CPU speed and number of cores
  • The GPU you have

{% hint style="info" %} You'll know cellfinder has finished when you see something like this:
2020-10-14 00:07:20 AM - INFO - MainProcess main.py:86 - Finished. Total time taken: 3:22:42 {% endhint %}

If you just want to check that everything is working, we can speed everything up by:

  • Only analysing part of the brain using the flags: --start-plane 1500 --end-plane 1550
  • Using a lower-resolution atlas, using the flag: --atlas allen_mouse_25um
cellfinder -s test_brain/ch00 -b test_brain/ch01 -o test_brain/output -v 5 2 2 --orientation psl --atlas allen_mouse_25um --start-plane 1500 --end-plane 1550

{% hint style="warning" %} If your machine has less than 32GB of RAM, you should use the allen_mouse_25um atlas either way, as registration with the high-resolution atlas requires about 30GB for this image. {% endhint %}

{% hint style="info" %} If the cell classification step takes a (very) long time, it may not be using the GPU. If you have an NVIDIA GPU, see Speeding up cellfinder to make sure that your GPU is set up properly. {% endhint %}

Once cellfinder has run, you can go onto Visualising the results