golfy

Heuristic solver for peptide pool assignments. Golfy attempts to map peptides to pools with a fixed specified "coverage" (number of pools that each peptide occurs in) while avoiding having any pair of peptides occur in a pool twice.

Installation

pip install golfy

Also, scikit-learn is an requirement for the deconvolution module:

pip install scikit-learn

Usage

High level API

Assignments of peptides to pools are called golfy.Solution objects, which can be constructed and optimized using several different strategies. You can ignore most of the implementation details by just calling find_best_solution, which tries multiple different initialization strategies to create multiple solutions, optimizes each one, and returns the solution which fewest constraint violations and fewest number of total pools.

from golfy import find_best_solution

s = find_best_solution(
    num_peptides=100,
    max_peptides_per_pool=5,
    num_replicates=3,
    invalid_neighbors=[(0,1), (1,2)],
    preferred_neighbors=[(0,3),(1,5)],
    allow_extra_pools=False,
    verbose=False)

A key parameter to find_best_solution is allow_extra_pools, which determines whether Golfy is allowed to expand the number of total pools beyond the minimum by the ceil(num_peptides * num_replicates / max_peptides_per_pool). If Golfy cannot add extra pools then it may not be able to find a valid solution for every combination of parameters (but will still give you the solution with the least constraint violations that it could construct and optimize).

Initialization and optimization of solutions

If you want more control over the way that solutions are initialized and optimized you can use the golfy.init and golfy.optimize functions directly.

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, strategy='random', allow_extra_pools=False)

# 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, allow_extra_pools=False)

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)