76 Gigs

Here's where it starts! My first day on the job and a 76 GB data file to munge! I can't load it into a Jupyter Notebook:-D

It starts from this paper: "Internal state dynamics shape brainwide activity and foraging behaviour", Marques et al, Nature, 2020. My first task is to replicate the methods section. The methods section says: 

"Registration to a common reference brain across animals 

We used Advanced Normalization Tools (ANTs, v.2.2.0, running on an Ubuntu 16.04 Linux server)21,29–31 and ITK-SNAP (v.3.6.0, running on a Windows desktop) to spatially map live fluorescent brain volumes from different animals into a common reference space (Z-Brain23). The Z-Brain atlas (Elavl3:H2B-RFP line) served as the fixed image, and live fluorescent brain volumes were used as the moving images. We first aligned the moving images to the fixed image by using ITK-SNAP to match the centre, orientation and scale of the brain. The aligned moving and fixed images were then saved in the NII file format. By combining linear (translation, rigid, similarity and affine, with 3, 6, 7 and 12 degrees of freedom, respectively) and nonlinear (diffeomorphic symmetric normalization algorithm, SyN) ANTs registration, we optimized the compromise between preservation of cell morphology and global alignment and achieved reasonably precise registration (within 1–2 cell diameters) of all live fluorescent brain volumes to the common reference. After testing a range of values for each of the ANTs registration parameters, we finally chose the following parameters: antsRegistration -verbose 1–dimensionality 3–float 1–output [moving_image, moving _image _Warped.nii.gz, moving _image_InverseWarped.nii.gz]–interpolation WelchWindowedSinc– use-histogram-matching 0–winsorize-image-intensities [0.005,0.995]– initial-moving-transform [fixed_image.nii, moving_image.nii, 1]–transform Rigid[0.1]–metric MI [fixed_image.nii, moving_image. nii, 1,32,Regular,0.25]–convergence [1000x500x250x0,1e-8,10]– shrink-factors 12x8x4x2–smoothing-sigmas 4x3x2x1vox–transform Affine[0.1]–metric MI [fixed_image.nii, moving_image.nii, 1,32,Regular,0.25]–convergence [1000x500x250x0,1e-8,10]–shrink-factors 12x8x4x2–smoothing-sigmas 4x3x2x1vox–transform SyN[0.1,6,0]– metric CC [fixed_image.nii, moving_image.nii, 1,2]–convergence [100x100x70x50x0,1e-7,10]–shrink-factors 12x8x4x2x1–smoothingsigmas 5x3x2x1x0vox."