Add made command

psamm/commands/made.py

@@ -0,0 +1,358 @@
+# This file is part of PSAMM.
+#
+# PSAMM is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# PSAMM is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with PSAMM.  If not, see <http://www.gnu.org/licenses/>.
+#
+# Copyright 2014-2017  Jon Lund Steffensen <[email protected]>
+# Copyright 2016  Keith Dufault-Thompson <[email protected]>
+# Copyright 2016  Julie Cuddigan <[email protected]>
+
+"""Metabolic Adjustment by Differential Expression (MADE) command."""
+
+from __future__ import unicode_literals
+
+import time
+import logging
+import math
+import csv
+
+from six import iteritems
+
+from ..command import SolverCommandMixin, MetabolicMixin, Command
+from ..util import MaybeRelative
+from ..lpsolver import lp
+from ..expression import boolean
+
+# Module-level logging
+logger = logging.getLogger(__name__)
+
+
+class MadeFluxBalance(MetabolicMixin, SolverCommandMixin, Command):
+    """Run MADE flux balance analysis on the model.
+
+    Args:
+        gene_var1 = Dictionary, key:value = gene expression objects:their new
+            variable id, first set
+        gene_var2 = Dictionary; key:value = gene expression objects:their new
+            variable id, second set
+        var_ineqvar1 = xi; Dictionary, key:value = new variable ids:their
+            defined inequality variable, first set
+        var_ineqvar2 = xi+1; Dictionary, key:value = new variable ids:their
+            defined inequality variable, second set
+        gene_pval = Dictionary, key:value = gene ID:gene fold change
+            probability (pvalue)
+        gene_diff = Dictionary, key:value = gene ID: binary up/down/constant
+            regulation values
+        gvdict = Dictionary, key:value = gene ID:defined variable ids from both
+            sets (each key has 2 values)
+        problem = Flux balance problem
+    """
+
+    @classmethod
+    def init_parser(cls, parser):
+        parser.add_argument(
+            '--flux-threshold',
+            help='Enter maximum objective flux as a decimal or percent',
+            type=MaybeRelative, default=MaybeRelative('100%'))
+        parser.add_argument(
+            '--transc-file', help='Enter path to transcriptomic data file',
+            metavar='FILE')
+        parser.add_argument('--fva', help='Enable FVA', action='store_true')
+        super(MadeFluxBalance, cls).init_parser(parser)
+
+    def run(self):
+        """Run MADE implementation."""
+        gene_dict = self.get_gene_dict()
+
+        biomass_fun = self._model.biomass_reaction
+
+        # Create problem instance
+        solver = self._get_solver(integer=True)
+        prob = solver.create_problem()
+        v_0 = prob.namespace()
+        v_1 = prob.namespace()
+
+        # Define flux variables
+        for reaction_id in self._mm.reactions:
+            lower, upper = self._mm.limits[reaction_id]
+            v_0.define([reaction_id], lower=lower, upper=upper)
+            v_1.define([reaction_id], lower=lower, upper=upper)
+
+        # Create mass balance constraints for both conditions
+        massbalance_0_lhs = {compound: 0 for compound in self._mm.compounds}
+        massbalance_1_lhs = {compound: 0 for compound in self._mm.compounds}
+        for (compound, reaction_id), value in iteritems(self._mm.matrix):
+            massbalance_0_lhs[compound] += v_0(reaction_id) * value
+            massbalance_1_lhs[compound] += v_1(reaction_id) * value
+        for _, lhs in iteritems(massbalance_0_lhs):
+            prob.add_linear_constraints(lhs == 0)
+        for _, lhs in iteritems(massbalance_1_lhs):
+            prob.add_linear_constraints(lhs == 0)
+
+        start_time = time.time()
+
+        # Set biomass flux threshold
+        flux_threshold = self._args.flux_threshold
+        if flux_threshold.relative:
+            prob.set_objective(v_0(biomass_fun))
+            result = prob.solve(lp.ObjectiveSense.Maximize)
+            if not result:
+                raise Exception('Failed to solve FBA')
+            flux_threshold.reference = result.get_value(v_0(biomass_fun))
+
+        prob.add_linear_constraints(v_0(biomass_fun) >= float(flux_threshold))
+        prob.add_linear_constraints(v_1(biomass_fun) >= float(flux_threshold))
+
+        gene_term_0 = prob.namespace()
+        gene_term_1 = prob.namespace()
+
+        reaction_0 = prob.namespace(
+            self._mm.reactions, types=lp.VariableType.Binary)
+        reaction_1 = prob.namespace(
+            self._mm.reactions, types=lp.VariableType.Binary)
+
+        gene_term_dict_0 = {}
+        gene_term_dict_1 = {}
+        for rxn_id, exp in sorted(iteritems(gene_dict)):
+            create_gpr_constraints(
+                prob, rxn_id, exp, reaction_0(rxn_id), gene_term_0,
+                gene_term_dict_0)
+            create_gpr_constraints(
+                prob, rxn_id, exp, reaction_1(rxn_id), gene_term_1,
+                gene_term_dict_1)
+
+        if self._args.transc_file is not None:
+            con1, con2, gene_pval, gene_diff = idc(
+                open_file(self._args.transc_file))
+
+        add_final_constraints(self._mm, prob, v_0, reaction_0)
+        add_final_constraints(self._mm, prob, v_1, reaction_1)
+        result = make_obj_fun(
+            prob, gene_diff, gene_pval, gene_term_0, gene_term_1)
+
+        # Run FBA
+        for reaction_id in sorted(self._mm.reactions):
+            print('{}\t{}\t{}\t{}\t{}\t{}\t{}'.format(
+                reaction_id, result.get_value(v_0(reaction_id)),
+                result.get_value(v_1(reaction_id)),
+                result.get_value(reaction_0(reaction_id)) > 0.5,
+                result.get_value(reaction_1(reaction_id)) > 0.5,
+                self._mm.get_reaction(reaction_id),
+                gene_dict.get(reaction_id, '')))
+
+        logger.info('Solving took {:.2f} seconds'.format(
+            time.time() - start_time))
+
+    def get_gene_dict(self):
+        """Using the reaction file called inside of the model file, it returns
+        a dictionary with reaction IDs as keys and their associated
+        gene-protein reaction (GPR) logic (i.e. (gene 1 and gene 2) or gene 3)
+        as values of type str.
+        """
+        gene_dict = {}
+        for reaction in self._model.parse_reactions():
+            if reaction.genes is not None:
+                gene_dict[reaction.id] = boolean.Expression(reaction.genes)
+
+        return gene_dict
+
+
+def make_obj_fun(prob, gene_diff, gene_pval, gene_term_0, gene_term_1):
+    """Constructs the MADE objective funtion from dictionaries of LP variables.
+
+    Objective function consists of the summation of three functions dependent
+    on the up/down regulation of gene expression between conditions. The
+    functions contain a weighting function, and the difference between the
+    binary representations of condition 1 and condition 2.
+    """
+    i_vars = 0.0  # Increasing gene expression
+    d_vars = 0.0  # Decreasing gene expression
+    c_vars = 0.0  # Constant gene expression
+
+    def weight(p):
+        return -math.log10(p)
+
+    for gene in gene_pval:
+        # Comment by Julie/Matt?: Limitation of math.log()
+        wp = max(2.2204460492e-16, gene_pval[gene])
+
+        x_0 = gene_term_0(boolean.Variable(gene))
+        x_1 = gene_term_1(boolean.Variable(gene))
+        # x_delta = x_0 XOR X_1
+        prob.define(('xor', gene), types=lp.VariableType.Binary)
+        x_delta = prob.var(('xor', gene))
+        prob.add_linear_constraints(
+            x_delta <= x_0 + x_1,
+            x_delta >= x_0 - x_1, x_delta >= x_1 - x_0,
+            x_delta <= 2 - x_0 - x_1)
+
+        if gene_diff[gene] == 1:
+            i_vars += weight(wp) * (x_1 - x_0)
+        elif gene_diff[gene] == -1:
+            d_vars += weight(wp) * (x_0 - x_1)
+        elif gene_diff[gene] == 0:
+            c_vars += weight(wp) * x_delta
+
+    objective = i_vars + d_vars - c_vars
+
+    prob.set_objective(objective)
+    result = prob.solve(lp.ObjectiveSense.Maximize)
+    if not result:
+        raise Exception('Unable to solve: ' + result.status)
+
+    obj_value = result.get_value(objective)
+    logger.info('Objective: {}'.format(obj_value))
+    # prob.add_linear_constraints(objective == obj_value)
+
+    return result
+
+
+def create_gpr_constraints(prob, rxn_id, exp, reaction_var, gene_term,
+                           gene_term_dict):
+    """Opens all gene-logic containers, defines content, outputs the linear
+    inequalities by calling bool_ineqs().  Sorts data into dictionaries that
+    are used in other functions.  Is recursive. No output.
+
+    Args:
+        exp_obj: All of the expression objects (genes, AND, OR)
+        var_gen: Counter used for relabeling the genes and arguments as
+            variables
+        new_var_id: Variable ID, also includes original reaction ID for first
+            layer
+    """
+    next_terms = iter([exp.root])
+    current_type = None
+    variable = reaction_var
+    arguments = []
+    stack = []
+    while True:
+        try:
+            term = next(next_terms)
+        except StopIteration:
+            term = None
+
+        if term in gene_term_dict:
+            arguments.append(gene_term_dict[term])
+        elif isinstance(term, boolean.Variable):
+            gene_term.define([term], types=lp.VariableType.Binary)
+            term_var = gene_term(term)
+            gene_term_dict[term] = term_var
+            arguments.append(term_var)
+        elif isinstance(term, (boolean.And, boolean.Or)):
+            stack.append((current_type, next_terms, variable, arguments))
+            current_type = term.__class__
+            next_terms = iter(term)
+            arguments = []
+            gene_term.define([term], types=lp.VariableType.Binary)
+            variable = gene_term(term)
+            gene_term_dict[term] = variable
+        else:
+            # End of term
+            if current_type is None:
+                prob.add_linear_constraints(variable == arguments[0])
+                break
+            elif current_type == boolean.And:
+                prob.add_linear_constraints(
+                    *and_constraints(variable, arguments))
+            elif current_type == boolean.Or:
+                prob.add_linear_constraints(
+                    *or_constraints(variable, arguments))
+
+            term_var = variable
+            current_type, next_terms, variable, arguments = stack.pop()
+            arguments.append(term_var)
+
+
+def and_constraints(var, arguments):
+    """Create constraints for boolean AND.
+
+    Creates constraints for: var = And(arguments) where var and each argument
+    is a binary variable.
+    """
+    var_sum = 0
+    for arg in arguments:
+        yield var <= arg
+        var_sum += arg
+    yield var >= var_sum - (len(arguments) - 1)
+
+
+def or_constraints(var, arguments):
+    """Create constraints for boolean OR.
+
+    Creates constraints for: var = Or(arguments) where var and each argument
+    is a binary variable.
+    """
+    var_sum = 0
+    for arg in arguments:
+        yield var >= arg
+        var_sum += arg
+    yield var <= var_sum
+
+
+def open_file(path):
+    """Returns the contents of model file in a tuple of dictionaries.
+    File Form: tsv format, FOUR Columns: (1) Gene name, (2) Condition 1 Data,
+    (3) Condition 2 Data, (4) P-value of the fold change for transition 1->2.
+    """
+    file1 = open(path)
+    con1_dict = {}
+    con2_dict = {}
+    pval_dict = {}
+
+    file1.readline()
+    for row in csv.reader(file1, delimiter=str('\t')):
+        con1_dict[row[0]] = float(row[1])
+        con2_dict[row[0]] = float(row[2])
+        if float(row[3]) == float(0.0):
+            pval_dict[row[0]] = 1e-400
+        else:
+            pval_dict[row[0]] = float(row[3])
+
+    return con1_dict, con2_dict, pval_dict
+
+
+def idc(dicts):
+    """Used for accessing the list of dictionaries created in open_file()
+    Creates a dictionary for the gene ID and a value = [-1, 0, +1]
+    corresponding to decreasing, constant, and inreasing expression between the
+    conditions.
+    """
+    con1 = dicts[0]
+    con2 = dicts[1]
+    pval = dicts[2]
+    diff = {}
+
+    for key in con1:
+        if con2[key] == con1[key]:
+            diff[key] = 0
+        elif con2[key] > con1[key]:
+            diff[key] = 1
+        else:
+            diff[key] = -1
+
+    return con1, con2, pval, diff
+
+
+def add_final_constraints(mm, prob, v, z):
+    """Takes the metabolic model, the LP Problem, and the binary
+    dictionaries of each condition.  Adds constraints connecting flux
+    variables, reactions, and their flux bounds.
+    """
+    for rxn in mm.reactions:
+        vmin = mm.limits[rxn].lower
+        vmax = mm.limits[rxn].upper
+        flux_var = v(rxn)
+        active = z(rxn)
+
+        prob.add_linear_constraints(
+            active*vmax >= flux_var, flux_var >= active*vmin)

setup.py

@@ -63,6 +63,7 @@
             'gapcheck = psamm.commands.gapcheck:GapCheckCommand',
             'gapfill = psamm.commands.gapfill:GapFillCommand',
             'genedelete = psamm.commands.genedelete:GeneDeletionCommand',
+            'made = psamm.commands.made:MadeFluxBalance',
             'masscheck = psamm.commands.masscheck:MassConsistencyCommand',
             'randomsparse = psamm.commands.randomsparse:RandomSparseNetworkCommand',
             'robustness = psamm.commands.robustness:RobustnessCommand',