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Workflow to build cohort templates based on multiple participants_tsv files
This workflow uses greedy instead of ANTS, for the sake of efficiency, in fact, the registrations as compared to
ants_build_template
seem to be more accurate (though that of course is likely just due to parameter selection, not an inherent limitation of ANTS).. There is ~20-30x speedup for a single pairwise registration compared to ANTS, making template-generation for hundreds of subjects a job that can be completed in around a day with modest resources (32cores). The 4-core 16gb memory greedy registration jobs take <30mins.
Running with a cluster profile (e.g. cc-slurm)
This option will be the most # of jobs (
num_iters * num_subjects * num_cohorts
), but will maximize parallelization on a cluster
snakemake --profile cc-slurm
Running with cluster profile, but grouping registration jobs together in chunks of 8
This will reduce the number of jobs by factor of 8 by grouping the 8 4core jobs to fill the 32-core nodes
HOWEVER -- if you are using the --group-components option to group the registrations in chunks of 8, you
MUST
run each iteration separately, using the
--config run_iter=#
option, where # is the iteration to run -- this seems to be a bug/limitation of the group-components option when a job has recursively-defined rules. (open issue as of Oct 3 2020: https://github.com/snakemake/snakemake/issues/656)
snakemake --config run_iter=2 --profile cc-slurm --group-components reg=8 composite=100
Note: composite=100 is for the composing warps to another ref space (e.g. mni) to combine 100 subjects in a single job (each is relatively quick and disk-bound)..
Running as a single job
This is the most frugal for resources (32-cores max), but is simple in that only a single job is spawned.. This is the method to use if you are running this on a single machine. Four iterations of building a single 1mm template with 100 subjects takes < 24hrs on a 32core system.
regularSubmit -j Fat snakemake --use-singularity -j32
Running each cohort in parallel with single jobs and the --nolock flag
This runs each cohort separately with single-node jobs.. This is a happy medium for running on a cluster while not needing hundreds (or thousands) of short jobs..
WARNING: make sure your cohorts are mutually-exclusive when using this method, as it is running snakemake in parallel on the same directory with the --nolock option -- if cohorts are not mutually-exclusive, you can still use this method, but only after all the pre-processing (e.g. T2/T1 registration, is completed)
for cohort in young middle1 middle2 old1 old2; do regularSubmit -j Fat snakemake --use-singularity -j32 --nolock --config run_cohort=$cohort; done
Code Snippets
12 13 | shell: 'AverageImages {params.dim} {output} {params.use_n4} {input} &> {log}' |
20 | shell: 'cp -v {input} {output} &> {log}' |
30 | shell: 'cp -v {input} {output}' |
75 76 | shell: 'AverageImages {params.dim} {output} {params.use_n4} {input} &> {log}' |
89 90 | shell: 'AverageImages {params.dim} {output} {params.use_n4} {input} &> {log}' |
101 102 | shell: 'MultiplyImages {params.dim} {input} {params.gradient_step} {output} &> {log}' |
114 115 | shell: 'AverageAffineTransformNoRigid {params.dim} {output} {input} &> {log}' |
129 130 | shell: 'antsApplyTransforms {params.dim} -e vector -i {input.invwarp} -o {output} -t [{input.affine},1] -r {input.ref} --verbose 1 &> {log}' |
144 145 146 147 148 149 150 151 152 | shell: 'antsApplyTransforms {params.dim} --float 1 --verbose 1 -i {input.template} -o {output.template} -t [{input.affine},1] ' ' -t {input.invwarp} -t {input.invwarp} -t {input.invwarp} -t {input.invwarp} -r {input.template} &> {log}' #apply warp 4 times #resample the template to a new resolution, based on the schedule in config rule resample_template: input: template = 'results/cohort-{cohort}/iter_{iteration}/templateupd_{channel}.nii.gz' |
158 159 | shell: 'c3d {input} -resample {params.resample} {output}' |
67 68 69 70 | shell: 'greedy -d 3 -rf {input.ref_std} ' ' -r {input.cohort2std_warp} {input.cohort2std_affine_xfm_ras} ' ' {input.subj2cohort_warp} {input.subj2cohort_affine_xfm_ras} ' ' -rc {output.subj2std_warp}' |
103 104 105 106 107 108 | shell: 'greedy -d 3 -rf {input.ref_subj} -r ' ' {input.subj2cohort_affine_xfm_ras},-1 ' ' {input.subj2cohort_invwarp}' ' {input.cohort2std_affine_xfm_ras},-1 ' ' {input.cohort2std_invwarp} ' ' -rc {output.subj2std_invwarp}' |
118 119 | shell: 'greedy -d 3 -rf {input.ref} -r {input.subj2std_invwarp} -rm {input.seg} {output.seg}' |
128 129 | shell: 'greedy -d 3 -rf {input.ref} -r {input.subj2std_invwarp} -rm {input.seg} {output.seg}' |
138 139 | shell: 'greedy -d 3 -rf {input.ref} -r {input.subj2std_invwarp} -rm {input.seg} {output.seg} -ri NN' |
10 11 12 | shell: 'ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS={threads} ' 'N4BiasFieldCorrection -d 3 -i {input.img} -o {output}' |
27 28 | shell: 'c3d {input} -resample {params.resample} {output}' |
43 44 | shell: 'c3d {input} -pad-to {params.pad} 0 {output}' |
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