Heuristic peptide pool optimization for ELISpot and other immunological assays
Project description
golfy
Heuristic solver for peptide pool assingments
Installation
pip install golfy
Also, scikit-learn is an requirement for the deconvolution module:
pip install scikit-learn
Usage
Peptide to pool assignment
from golfy import init, is_valid, optimize
# create a random initial assignment of peptides to pools
s = init(num_peptides = 100, peptides_per_pool = 5, num_replicates = 3)
# the random assignment probably isn't yet a valid solution
assert not is_valid(s)
# iteratively swap peptides which violate constraints until
# a valid configuration is achieved
optimize(s)
assert is_valid(s)
Deconvolution of hit peptides from ELISpot counts
from golfy.deconvolution import create_linear_system, solve_linear_system
# s is a golfy.Solution object containing the mapping of peptides to pools
# counts is a dictionary from (replicate, pool) index pairs to ELISpot counts or activity values
linear_system = create_linear_system(s, counts)
# result type has an array of individual peptide activity estimates (result.activity_per_peptide)
# and a set of high confidence hit peptides (result.high_confidence_peptides)
result = solve_linear_system(linear_system)
print(result.high_confidence_hits)
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