Based on the useful notebook runner tool by https://github.com/tritemio/nbrun (requires nbrun.py)
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%reset -f
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## import all required dependencies
from nbrun import run_notebook
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## hardcoded results from http://localhost:8889/notebooks/notebooks/zscore_highbinders_for_galectin.ipynb#
# filtered by p-value then rank
results = {'average': {547,
549,
550,
551,
565,
566,
569,
577,
578,
579,
580,
581,
582,
587,
588,
589},
'fivemg_filter50': {543,
547,
549,
550,
551,
565,
566,
569,
577,
578,
579,
580,
581,
582,
585,
586,
587,
588,
589},
'fivemg_filter75': {543,
547,
549,
550,
551,
565,
566,
569,
577,
578,
579,
580,
581,
582,
585,
586,
587,
588,
589},
'fivemg_topten_nofilter': {550, 551, 565, 566, 577, 579, 582, 585, 588, 589},
'tenmg_filter50': {75,
372,
543,
545,
547,
549,
550,
551,
552,
565,
566,
569,
572,
576,
577,
578,
579,
580,
581,
582,
583,
585,
586,
587,
588,
589},
'tenmg_filter75': {545,
549,
550,
551,
565,
566,
578,
579,
580,
581,
582,
586,
587,
588,
589},
'tenmg_topten_nofilter': {545, 550, 551, 565, 566, 578, 580, 581, 582, 589},
'twomg_filter50': {547,
549,
551,
565,
566,
569,
577,
578,
579,
580,
581,
582,
588,
589},
'twomg_filter75': {547,
549,
551,
565,
566,
569,
578,
579,
581,
582,
588,
589},
'twomg_topten_nofilter': {547, 549, 551, 565, 566, 569, 578, 581, 582, 589}}
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# loop over highbinders and run MCAW analysis
dataframefile = "../results/galectin-3/dataframes_galectin.pkl"
nb_name = 'run_mcaw_analysis'
for samplename in results:
nb_kwargs = {'samplename': samplename, 'highbinding_glycans':results[samplename],'dataframefile':dataframefile}
run_notebook(nb_name, nb_suffix='-out_%s' % (samplename), nb_kwargs=nb_kwargs)
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