NORDic PMR module

NORDic.NORDic_PMR.functions module

NORDic.NORDic_PMR.functions.compute_similarities(f, x0, A, A_WT, gene_outputs, nb_sims, experiments, repeat=1, exp_name='', quiet=False)

Compute similarities between any attractor in WT and in mutants, weighted by their probabilities

…

Parameters

fBoolean Network (MPBN) object: the mutated network
x0MPBN object: initial state
AAttractor list: list of attractors in mutant
A_WTAttractor list: list of attractors in WT
gene_outputsPython character string list: list of node names to check
nb_simsPython integer: number of iterations to compute the probabilities
experimentsPython dictionary list: list of experiments (different rates/depths)
repeatPython integer: [default=1] : how many times should these experiments be repeated
exp_namePython character string: [default=””] : printed info about the experiment (if quiet=True)
quietPython bool: [default=False] : prints out verbose

Returns

simPython float: change in attractors induced by the mutation

NORDic.NORDic_PMR.functions.greedy(network_name, k, states, im_params, simu_params, save_folder=None, quiet=False)

Greedy Influence Maximization Algorithm [Kempe et al., 2003]. Finds iteratively the maximum spreader and adds it to the list until the list is of size k

…

Parameters

network_namePython character string: bnet network
kPython integer: maximum size of the spreader
im_paramsPython dictionary or None: [default=None] : parameters of the influence maximization
statesPandas DataFrame or None: [default=None] : list of initial states to consider
save_folderPython character string: [default=None] : where to save intermediary results (if None: do not save intermediary results)
quietPython bool: [default=False] : prints out verbose

Returns

S, spreadsPython character string list: nodes in the spreader set, Python dictionary: spread value associated with every tested subset of nodes

NORDic.NORDic_PMR.functions.run_experiments(network_name, spreader, gene_list, state, gene_outputs, simu_params, quiet=False)

NORDic.NORDic_PMR.functions.spread(network_name, spreader, gene_list, state, gene_outputs, simu_params, seednb=0, quiet=False)

Compute the spread of each gene in gene_inputs+spreader with initial state state on genes gene_outputs. Here, the (single state) spread is defined as the indicator of the emptyness of the intersection between WT and mutant attractors

…

Parameters

network_namePython character string: filename of the network in .bnet (needs to be pickable)
spreaderPython character string list: subset of node names
gene_listPython character string list: list of node names to perturb in addition to the spreader
statePandas DataFrame: binary initial state rows/[genes] x columns/[values in {-1,0,1}]
gene_outputsPython character string list: list of node names to check
simu_paramsPython dictionary: arguments to MPBN-SIM
seednbPython integer: [default=0] : random seed
quietPython bool: [default=False] : prints out verbose

Returns

spdsPython float dictionary: change in mutant attractor states for each gene in gene_list that is, the similarity between any attractor reachable from state in WT and any in mutant spreader+{g} where g in gene_list

NORDic.NORDic_PMR.functions.spread_multistate(network_name, spreader, gene_list, states, gene_outputs, im_params, simu_params, quiet=False)

Compute the spread of each gene in gene_inputs+spreader with initial states in states on genes gene_outputs. Here, the (single state) spread is defined as the indicator of the emptyness of the intersection between WT and mutant attractors

…

Parameters

network_namePython character string: filename of the network in .bnet (needs to be pickable)
spreaderPython character string list: subset of node names
gene_listPython character string list: list of node names to perturb in addition to the spreader
statesPandas DataFrame: binary initial state rows/[genes] x columns/[state ID]
gene_outputsPython character string list: list of node names to check
im_paramsPython dictionary: arguments to Influence Maximization
simu_paramsPython dictionary: arguments to MPBN-SIM
quietPython bool: [default=False] : prints out verbose

Returns

spdsPython float dictionary: change in mutant attractor states for each gene in gene_list that is, the geometric mean of similarities between any attractor reachable from state in states in WT and any in mutant spreader+{g} where g in gene_list