Running brainmapper

brainmapper runs with a single command, with various arguments that are detailed in the 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

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.

Putting this all together into a single command gives:

brainmapper -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

You’ll know brainmapper 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

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

brainmapper -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

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 brainmapper to make sure that your GPU is set up properly.

Once brainmapper has run, you can go onto Visualising the results.