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Major speed increase for the omics weighting function and edge case bug fix. #142

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merged 3 commits into from
Mar 25, 2024
Merged

Major speed increase for the omics weighting function and edge case bug fix. #142

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cb50084
Update msgapfill.py
jjacobson95 Mar 22, 2024
fc1019b
linted
jjacobson95 Mar 22, 2024
6738874
re-linted msgapfill.py
jjacobson95 Mar 22, 2024
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38 changes: 24 additions & 14 deletions modelseedpy/core/msgapfill.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,8 @@
from modelseedpy.fbapkg.mspackagemanager import MSPackageManager
from modelseedpy.core.msmodelutl import MSModelUtil
from modelseedpy.core.exceptions import GapfillingError
from collections import defaultdict


logger = logging.getLogger(__name__)
logger.setLevel(
Expand Down Expand Up @@ -130,9 +132,9 @@ def test_gapfill_database(self, media, target=None, before_filtering=True):
if before_filtering:
filter_msg = " before filtering "
note = "FBF"
gf_sensitivity[media.id][target][note] = (
self.mdlutl.find_unproducible_biomass_compounds(target)
)
gf_sensitivity[media.id][target][
note
] = self.mdlutl.find_unproducible_biomass_compounds(target)
if target != "rxn00062_c0":
self.mdlutl.save_attributes(gf_sensitivity, "gf_sensitivity")
logger.warning(
Expand Down Expand Up @@ -387,10 +389,10 @@ def integrate_gapfill_solution(
gf_sensitivity[solution["media"].id] = {}
if solution["target"] not in gf_sensitivity[solution["media"].id]:
gf_sensitivity[solution["media"].id][solution["target"]] = {}
gf_sensitivity[solution["media"].id][solution["target"]]["success"] = (
self.mdlutl.find_unproducible_biomass_compounds(
solution["target"], cumulative_solution
)
gf_sensitivity[solution["media"].id][solution["target"]][
"success"
] = self.mdlutl.find_unproducible_biomass_compounds(
solution["target"], cumulative_solution
)
self.mdlutl.save_attributes(gf_sensitivity, "gf_sensitivity")
self.cumulative_gapfilling.extend(cumulative_solution)
Expand Down Expand Up @@ -447,12 +449,21 @@ def compute_reaction_weights_from_expression_data(self, omics_data, annoont):
p = np.zeros(len(restructured_anoot["Reactions"]))
# computed_weights is the rxn_hash ({rxn: weight, ...})
computed_weights = {}

# Precompute gene reaction lookups
gene_reaction_lookup = {}
for idx, row in restructured_anoot.iterrows():
gene = row["Gene"]
reaction = row["Reactions"]
if gene in gene_reaction_lookup:
gene_reaction_lookup[gene].append(reaction)
else:
gene_reaction_lookup[gene] = [reaction]

for rxn in range(0, len(restructured_anoot)):
substr_rxns = [rxn for rxn in restructured_anoot["Reactions"][[rxn]]]
# Get the indices of the rows where the condition is True
mask = restructured_anoot["Reactions"].apply(
lambda x: any(substr in x for substr in substr_rxns)
)
mask = restructured_anoot["Reactions"] == substr_rxns[0]
idx_gene = mask[mask].index
nAG = 0
nMG = 0
Expand All @@ -476,11 +487,10 @@ def compute_reaction_weights_from_expression_data(self, omics_data, annoont):
selected_gene = restructured_anoot["Gene"].iloc[idx_gene[iGene]]

# Finding reactions associated with genes that contain the selected gene
associated_reactions = restructured_anoot["Reactions"][
restructured_anoot["Gene"].str.contains(selected_gene)
]
associated_reactions = gene_reaction_lookup.get(selected_gene, [])

# Checking if there are more than one unique reactions
if len(associated_reactions.unique()) > 1:
if len(associated_reactions) > 1:
nCG += 1

p[rxn] = (nMG / nAG) * (1 / (1 + (nCG / nAG)))
Expand Down
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