NORDic package
-Submodules
-NORDic.NORDic_NI.functions module
--
-
- -NORDic.NORDic_NI.functions.import_all_solutions(solution_fname, quiet=False) -
Import all solutions which have been generated
-…
-- -Parameters
--
-
- solution_fnamePython character string
header of solution files
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-- quietPython bool
[default=False] : prints out verbose
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-
- -Returns
--
-
- solutionsPandas DataFrame
rows/[genes] x columns/[solution IDs] containing regulatory functions for each gene in each solution
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-
-
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- -NORDic.NORDic_NI.functions.network_identification(file_folder, taxon_id, path_to_genes=None, disgenet_args=None, network_fname=None, string_args=None, experiments_fname=None, lincs_args=None, edge_args=None, sig_args=None, bonesis_args=None, weights=None, seed=0, njobs=1, force_experiments=True, accept_nonRNA=False, preserve_network_sign=True) -
Generates or retrieves the optimal network model
-…
-- -Parameters
--
-
- file_folderPython character string
path to which files should be saved
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-- taxon_idPython integer
NCBI Taxonomy ID for the considered species
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-- path_to_genesPython character string or None
[default=None] : path to the file containing gene names (one per line)
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-- disgenet_argsPython dictionary or None
[default=None] : arguments to the DisGeNET API (see test files)
-
-- network_fnamePython character string or None
[default=None] : path to the file containing the prior knowledge network (see test files)
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-- string_argsPython dictionary or None
[default=None] : arguments to the STRING API (see test files)
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-- experiments_fnamePython character string or None
[default=None] : path to the file containing the matrix of gene expression data (genes x samples) (see test files for format)
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-- lincs_argsPython dictionary or None
[default=None] : arguments to the LINCS L1000 API (see test files)
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-- edge_argsPython dictionary or None
[default=None] : arguments to the processing of edges (see test files)
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-- sig_argsPython dictionary or None
[default=None] : arguments to the processing of signatures (see test files)
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-- bonesis_argsPython dictionary or None
[default=None] : arguments to building constraints and generating solutions using BoneSIS (see test files)
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-- weightsPython dictionary or None
[default=None] : weights to the optimal model selection procedure (see test files)
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-- seedPython integer
[default=0] : random seed
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-- njobsPython integer
[default=1] : number of parallel jobs
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-- force_experimentsPython bool
[default=True] : if set to True, returns an error if no experimental profile associated with the genes is found
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-- accept_nonRNAPython bool
[default=False] : if set to False, ignores gene names which cannot be converted to EntrezIDs or which are not present in LINCS L1000
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-
- -Returns
--
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- solutionPython character string
optimal model selected by the procedure
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-
-
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- -NORDic.NORDic_NI.functions.select_optimal_model(sols, weights, file_folder) -
-
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- -NORDic.NORDic_NI.functions.solution2cytoscape(solution, fname) -
Convert a solution into a Cytoscape-readable file
-…
-- -Parameters
--
-
- solutionPandas Series
rows/[genes]
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-- fnamePython character string
path to Cytoscape-readable SIF and XML (style) files (no extension)
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-
- -Returns
--
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- None
writes a Cytoscape-readable file
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-
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- -NORDic.NORDic_NI.functions.solution_generation(file_folder, taxon_id, path_to_genes=None, disgenet_args=None, network_fname=None, string_args=None, experiments_fname=None, lincs_args=None, edge_args=None, sig_args=None, bonesis_args=None, weights=None, seed=0, njobs=1, force_experiments=True, accept_nonRNA=False, preserve_network_sign=True) -
Generates or retrieves the optimal network model
-…
-- -Parameters
--
-
- file_folderPython character string
path to which files should be saved
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-- taxon_idPython integer
NCBI Taxonomy ID for the considered species
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-- path_to_genesPython character string or None
path to the file containing gene names (one per line)
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-- disgenet_argsPython dictionary or None
arguments to the DisGeNET API (see test files)
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-- network_fnamePython character string or None
path to the file containing the prior knowledge network (see test files)
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-- string_argsPython dictionary or None
arguments to the STRING API (see test files)
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-- experiments_fnamePython character string or None
path to the file containing the matrix of gene expression data (genes x samples) (see test files for format)
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-- lincs_argsPython dictionary or None
arguments to the LINCS L1000 API (see test files)
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-- edge_argsPython dictionary or None
arguments to the processing of edges (see test files)
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-- sig_argsPython dictionary or None
arguments to the processing of signatures (see test files)
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-- bonesis_argsPython dictionary or None
arguments to building constraints and generating solutions using BoneSIS (see test files)
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-- seedPython integer
[default=0] : random seed
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-- njobsPython integer
[default=1] : number of parallel jobs
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-- force_experimentsPython bool
[default=True] : if set to True, returns an error if no experimental profile associated with the genes is found
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-- accept_nonRNAPython bool
[default=False] : if set to False, ignores gene names which cannot be converted to EntrezIDs or which are not present in LINCS L1000
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-
- -Returns
--
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- solutionPython character string
optimal model selected by the procedure
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-
-
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- -NORDic.NORDic_NI.functions.visualize_models(sols, file_folder) -
Selection of an optimal model in a set of solutions, based on a topology-based desirability function
-…
-- -Parameters
--
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- solsPandas DataFrame
rows/[genes] x columns/[solution IDs]
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-- weightsPython dictionary
weight for each graph characteristic
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-
- -Returns
--
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- solutionPandas DataFrame
rows/[genes] x column/[solution ID] selected solution
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-
NORDic.NORDic_PMR.functions module
--
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- -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
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-- x0MPBN object
initial state
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-- AAttractor list
list of attractors in mutant
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-- A_WTAttractor list
list of attractors in WT
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-- gene_outputsPython character string list
list of node names to check
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-- nb_simsPython integer
number of iterations to compute the probabilities
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-- experimentsPython dictionary list
list of experiments (different rates/depths)
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-- repeatPython integer
[default=1] : how many times should these experiments be repeated
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-- exp_namePython character string
[default=””] : printed info about the experiment (if quiet=True)
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-- quietPython bool
[default=False] : prints out verbose
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-
- -Returns
--
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- simPython float
change in attractors induced by the mutation
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-
-
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- -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
--
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- network_namePython character string
bnet network
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-- kPython integer
maximum size of the spreader
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-- im_paramsPython dictionary or None
[default=None] : parameters of the influence maximization
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-- statesPandas DataFrame or None
[default=None] : list of initial states to consider
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-- save_folderPython character string
[default=None] : where to save intermediary results (if None: do not save intermediary results)
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-- quietPython bool
[default=False] : prints out verbose
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-
- -Returns
--
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- S, spreadsPython character string list
nodes in the spreader set, Python dictionary: spread value associated with every tested subset of nodes
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-
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- -NORDic.NORDic_PMR.functions.run_experiments(network_name, spreader, gene_list, state, gene_outputs, simu_params, quiet=False) -
-
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- -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
--
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- network_namePython character string
filename of the network in .bnet (needs to be pickable)
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-- spreaderPython character string list
subset of node names
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-- gene_listPython character string list
list of node names to perturb in addition to the spreader
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-- statePandas DataFrame
binary initial state rows/[genes] x columns/[values in {-1,0,1}]
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-- gene_outputsPython character string list
list of node names to check
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-- simu_paramsPython dictionary
arguments to MPBN-SIM
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-- seednbPython integer
[default=0] : random seed
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-- quietPython bool
[default=False] : prints out verbose
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-
- -Returns
--
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- 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
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-
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- -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
--
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- network_namePython character string
filename of the network in .bnet (needs to be pickable)
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-- spreaderPython character string list
subset of node names
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-- gene_listPython character string list
list of node names to perturb in addition to the spreader
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-- statesPandas DataFrame
binary initial state rows/[genes] x columns/[state ID]
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-- gene_outputsPython character string list
list of node names to check
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-- im_paramsPython dictionary
arguments to Influence Maximization
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-- simu_paramsPython dictionary
arguments to MPBN-SIM
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-- quietPython bool
[default=False] : prints out verbose
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-
- -Returns
--
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- 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
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-
NORDic.NORDic_DS.functions module
--
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- -NORDic.NORDic_DS.functions.baseline(signatures, phenotype, is_binary=False) -
Compute the cosine scores between a set of signatures S and a differential phenotype P
-…
-- -Parameters
--
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- SPandas DataFrame
signatures rows/[genes] x columns/[drug names] [Treated || Control]
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-- PPandas DataFrame
differential phenotype rows/[genes] x column/[disease name] [Diseased || Healthy]
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-- is_binaryPython bool
[default=False] : if set to True, the signatures and phenotypes might be binary (that is, with values in {0,1})
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-
- -Returns
--
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- scoresPython float dictionary
dictionary (keys=drug names, values=scores), the higher the score, the higher the repurposing power
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-
-
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- -NORDic.NORDic_DS.functions.compute_frontier(df, samples, nbseed=0, quiet=False) -
Fit a model to classify control/treated phenotypes
-…
-- -Parameters
--
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- dfPandas DataFrame
rows/[genes] x columns/[samples] (either 1: active expression, -1: inactive expression, 0: undetermined expression)
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-- samplesPandas DataFrame
rows/[“annotation”] x columns/[samples ], values are 1 (healthy sample) or 2 (patient sample).
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-- nbseedPython integer
[default=0] : random seed
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-- quietPython bool
[default=False] : prints out verbose
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-
- -Returns
--
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- modelPython object
object with a function “predict” that returns predictions (1: control, or 2: treated) on phenotypes
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- -NORDic.NORDic_DS.functions.compute_metrics(rewards, ground_truth, K=[2, 5, 10], use_negative_class=False, nperms=10000, thres=0.0, beta=1.0) -
Compute AUC, Hit Ratio @ k of method for positive/negative class with p-value
-…
-- -Parameters
--
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- rewardsPython float list
predicted scores
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-- ground_truthPython float list
ground truth scores
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-- KPython integer list
[default=[2,5,10]] : ranks at which the hit ratio should be computed
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-- use_negative_classPython bool
[default=False] : if set to True, compute the performance with respect to the negative class instead of the positive class
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-- npermsPython integer
[default=10000] : number of permutations to perform
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-- thresPython float
[default=0.] : decision threshold to determine the positive (resp. negative) class
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-- betaPython float
[default=1.] : value of the coefficient in the F-measure
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-
- -Returns
--
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- res_diPython dictionary
(keys=metrics, values=values of the metrics)
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-
-
-
- -NORDic.NORDic_DS.functions.compute_score(f, x0, A, score, genes, 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
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-- x0MPBN object
initial state
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-- AAttractor list
list of attractors in mutant network
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-- scorePython object
scoring of attractors
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-- genesPython character string list
list of genes in the model frontier
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-- nb_simsPython integer
number of iterations to compute the probabilities
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-- experimentsPython dictionary list
list of experiments (different rates/depths)
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-- repeatPython integer
[default=1] : how many times should these experiments be repeated
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-- exp_namePython character string
[default=””] : printable for an experiment
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-- quietPython bool
[default=False] : prints out verbose
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-
- -Returns
--
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- scorePython float
change in attractors induced by the mutation
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-
-
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- -NORDic.NORDic_DS.functions.empirical_pvalue(sorted_rewards, sorted_ground_truth, nperms, method='ks_2samp') -
Compute an empirical p-value corresponding to testing whether the distributions in the predicted scores and the ground truth are similar by randomly permuting the values of the predictions and averaging the number of significant statistics across all permutations
-…
-- -Parameters
--
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- sorted_rewardsPython float list
predicted scores sorted by genes of increasing predicted value
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-- sorted_ground_truthPython float list
ground truth scores sorted by genes of increasing predicted value
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-- npermsPython integer
number of permutations to perform
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-- methodPython character string
[default=”ks_2samp”] : statistical test to perform at each permutation (should belong to scipy.stats)
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-
- -Returns
--
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- pvaluePython float
empirical p-value corresponding to the test across all @nperms permutations
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-
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- -NORDic.NORDic_DS.functions.simulate(network_fname, targets, patients, score, simu_params={}, nbseed=0, quiet=False) -
Simulate and score the individual effects of drugs on patient phenotypes, compared to controls
-…
-- -Parameters
--
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- network_fnamePython character string
(relative) path to a network .BNET file
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-- targetsPandas DataFrame
rows/[genes] x columns/[drugs to test] (either 1: active expression, -1: inactive expression, 0: undetermined expression)
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-- patientsPandas DataFrame
rows/[genes] x columns/[samples] (either 1: activatory, -1: inhibitory, 0: no regulation).
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-- scorePython object
scoring of attractors
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-- simu_paramsPython dictionary
[default={}] : arguments to MPBN-SIM
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-- nbseedPython integer
[default=0] : random seed
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-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
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- scoresPandas DataFrame
rows/[patient phenotypes] x columns/[drug names], values are the associated scores
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-
-
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- -NORDic.NORDic_DS.functions.simulate_treatment(network_name, targets, score, state, simu_params={}, quiet=False) -
Compute the score assigned to a drug with targets in @targets[[drug]] in network
-…
-- -Parameters
--
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- network_namePython character string
filename of the network in .bnet (needs to be pickable)
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-- targetsPandas DataFrame
rows/[genes] x columns/[columns]
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-- scorePython object
scoring of attractors
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-- statePandas DataFrame
binary patient initial state rows/[genes] x columns/[values in {-1,0,1}]
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-- simu_paramsPython dictionary
[default={}] : arguments to MPBN-SIM
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-- seednbPython integer
[default=0] : random seed
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-- quietPython bool
[default=False] : prints out verbose
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-
- -Returns
--
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- effectsPython float dictionary
distance from attractors from treated networks to control profiles
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NORDic.NORDic_DS.get_drug_signatures module
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- -NORDic.NORDic_DS.get_drug_signatures.compute_drug_signatures_L1000(pubchem_cids, lincs_args, binarize=True, gene_list=None, chunksize=10) -
Get drug signatures from LINCS L1000
-…
-- -Parameters
--
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- pubchem_cidsPython integer list
list of drug PubChem CIDs
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-- lincs_argsPython dictionary
additional arguments for LINCS L1000 requests
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-- binarizePython bool
[default=True] : should the resulting signatures be binarized?
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-
- -Returns
--
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- sigsPandas DataFrame
rows/[genes] x columns/[drug names]
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-
-
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- -NORDic.NORDic_DS.get_drug_signatures.drugname2pubchem(drug_names, lincs_args) -
Convert drug names into PubChem CIDs
-…
-- -Parameters
--
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- drug_namesPython character string list
list of drug names
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-- lincs_argsPython dictionary
additional arguments for LINCS L1000 requests
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-
- -Returns
--
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- pubchem_cidsPython dictionary
(keys=drug names, values=PubChem CIDs)
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-
-
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- -NORDic.NORDic_DS.get_drug_signatures.get_ranking(CD) -
Retrieve ranking (50 first drugs) from L1000 CDS^2 search engine
-…
-- -Parameters
--
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- CDPandas DataFrame
rows/[genes] x column/[value] differential phenotype
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-
- -Returns
--
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- resulsPandas DataFrame
rows/[drug names] x column/[“L1000 CDS2”] ranking of the drugs according to their ability to reverse the phenotype
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-
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- -NORDic.NORDic_DS.get_drug_signatures.pubchem2drugname(pubchem_cids, lincs_args) -
Convert drug names into PubChem CIDs
-…
-- -Parameters
--
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- pubchem_cidsPython integer list
list of drug PubChem CIDs
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-- lincs_argsPython dictionary
additional arguments for LINCS L1000 requests
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-
- -Returns
--
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- pert_inamesPython dictionary
(keys=PubChem CIDs, values=drug names)
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-
-
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- -NORDic.NORDic_DS.get_drug_signatures.retrieve_drug_signature(pubchem_cid, cell_ids, gene_list, lincs_args, binarize, quiet=False) -
Retrieve control & treated samples from LINCS L1000 and compute the corresponding drug signature
-…
-- -Parameters
--
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- pubchem_cidPython integer
drug PubChem CID
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-- cell_idsPython character string list
list of candidate cell lines in LINCS L1000
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-- gene_listPython integer list
list of EntrezID genes
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-- lincs_argsPython dictionary
additional arguments for LINCS L1000 requests
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-- binarizePython bool
should the resulting signatures be binarized?
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-- quietPython bool
[default=False] : prints out verbose
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-
- -Returns
--
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- sigPandas DataFrame
rows/[genes] x column/[drug PubChem]
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NORDic.NORDic_DS.get_drug_targets module
--
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- -NORDic.NORDic_DS.get_drug_targets.get_targets_DrugBank(drug_names, path_to_drugbank, drug_fname, target_fname, quiet=False) -
Utility function which gets drug targets from a local DrugBank database
-…
-- -Parameters
--
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- drug_namesPython character string list
list of common drug names
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-- path_to_drugbankPython character string
(relative) path to DrugBank files
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-- drug_fnamePython character string
file name of .XML file of the complete database (full_database.xml) in DrugBank
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-- target_fnamePython character string
file name of .CSV file of the protein targets (all.csv) in DrugBank
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-- quietPython bool
prints out verbose (default=False)
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-
- -Returns
--
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- target_dfPandas DataFrame
rows/[lists of HGNC symbols of targets] x columns/[drug names], values are “*” (known regulator) or “” (no known regulation)
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-
-
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- -NORDic.NORDic_DS.get_drug_targets.get_targets_DrugCentral(drug_names, quiet=False) -
Utility function to retrieve drug targets from the DrugCentral 2021
-…
-- -Parameters
--
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- drug_namePython character string list
list of common drug names
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-- quietPython bool
prints out verbose (default=False)
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-
- -Returns
--
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- target_dfPandas DataFrame
rows/[lists of HGNC symbols of targets] x columns/[drug names], values are “*” (known regulator) or “” (no known regulation)
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-
-
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- -NORDic.NORDic_DS.get_drug_targets.get_targets_LINCS(drug_names, path_to_lincs, credentials, selection=None, nsigs=None, quiet=False) -
Utility function to retrieve drug targets from the LINCS L1000 database
-…
-- -Parameters
--
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- drug_namesPython character string list
list of common drug names
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-- path_to_lincsPython character string
(relative) path to LINCS files
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-- credentialsPython character string
(relative) path to LINCS credentials file
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-- quietPython bool
prints out verbose (default=False)
-
-
- -Returns
--
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- target_dfPandas DataFrame
rows/[lists of HGNC symbols of targets] x columns/[drug names], values are “*” (known regulator) or “” (no known regulation)
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-
-
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- -NORDic.NORDic_DS.get_drug_targets.get_targets_MINERVA(drug_names, quiet=False) -
Utility function using httr:GET to send queries to a given MINERVA Platform instance
-…
-- -Parameters
--
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- drug_namesPython character string list
list of common drug names
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-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
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- target_dfPandas DataFrame
rows/[lists of HGNC symbols of targets] x columns/[drug names], values are “*” (known regulator) or “” (no known regulation)
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-
-
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- -NORDic.NORDic_DS.get_drug_targets.get_targets_TTD(drug_names, quiet=False) -
Utility function to retrieve drug targets from the Therapeutic Target Database (TTD)
-…
-- -Parameters
--
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- drug_namePython character string list
list of common drug names
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-- quietPython bool
prints out verbose (default=False)
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-
- -Returns
--
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- target_dfPandas DataFrame
rows/[lists of HGNC symbols of targets] x columns/[drug names], values are “-” (inhibitor) or “+” (activator) or “” (no known regulation)
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-
-
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- -NORDic.NORDic_DS.get_drug_targets.retrieve_drug_targets(file_folder, drug_names, TARGET_args={}, gene_list=[], sources=['DrugBank', 'MINERVA', 'LINCS', 'TTD', 'DrugCentral'], quiet=False) -
Retrieve drug targets from several online sources
-…
-- -Parameters
--
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- drug_namesPython character string list
list of common drug names
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-- TARGET_argsPython dictionary
[default={}] : see for each source
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-- gene_listPython character string list
[default=[]] : list of HGNC symbols
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-- sourcesPython character string list
[default=[“DrugBank”,”MINERVA”,”LINCS”,”TTD”,”DrugCentral”]] : list of source names (in [“DrugBank”,”MINERVA”,”LINCS”,”TTD”,”DrugCentral”])
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-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
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- targets_dfPandas DataFrame
rows/[genes] x columns/[drug names], values are the number of times a target is connected to a drug across all queried databases
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NORDic.NORDic_DR.bandits module
- - - - --
-
- -class NORDic.NORDic_DR.bandits.Learner(K, name='UnimplementedLearner') -
Bases:
-object-
-
- -act() -
-
-
- -incur(w) -
-
-
- -class NORDic.NORDic_DR.bandits.LinGapE(method_args) -
Bases:
-Misspecified-
-
- -apply(problem, greedy_sampling=True, lambda_val=1.0) -
-
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- -greedy(problem, b_t, c_t, Vinv) -
-
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- -optimized(problem, b_t, c_t, Vinv) -
-
-
- -stopping_rule() -
-
-
- -update(problem, candidates, Vinv, b) -
-
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- -class NORDic.NORDic_DR.bandits.MisLid(method_args) -
Bases:
-Misspecified-
-
- -apply(problem, precision=1e-07) -
-
-
- -best_answer(means) -
-
-
- -stopping_rule(quiet=True) -
-
-
- -update(problem, candidates, Vinv, b, Vinv_val=None) -
-
-
- -class NORDic.NORDic_DR.bandits.Misspecified(method_args) -
Bases:
-object-
-
- -clear() -
-
-
- -run(problem, nsimu, run_id=None, quiet=False) -
-
-
- -sample(problem, candidates) -
-
-
- -NORDic.NORDic_DR.bandits.gaussian(X, delta, sigma, c) -
-
-
- -NORDic.NORDic_DR.bandits.heuristic(X, delta, sigma, c) -
-
-
- -NORDic.NORDic_DR.bandits.misspecified(X, delta, sigma, c) -
-
-
- -NORDic.NORDic_DR.bandits.subheuristic(X, delta, sigma, c) -
NORDic.NORDic_DR.functions module
--
-
- -NORDic.NORDic_DR.functions.adaptive_testing(network_name, signatures, targets, score, states, simu_params={}, bandit_args={}, reward_fname=None, quiet=False) -
-
-Perform adaptive testing and recommends most promising treatments (=maximizing score)
-…
-- -Parameters
--
-
- network_namePython character string
(relative) path to a network .BNET file
-
-- signaturesPandas DataFrame
rows/[features] x columns/[drugs to test]
-
-- targetsPandas DataFrame
rows/[genes] x columns/[drugs to test] (either 1: active expression, -1: inactive expression, 0: undetermined expression)
-
-- scorePython object
scoring of attractors
-
-- statesPandas DataFrame
rows/[gene] x columns/[patient samples] (either 1: activatory, -1: inhibitory, 0: no regulation).
-
-- simu_params: Python dictionary
[default={}] : arguments to MPBN-SIM
-
-- bandit_paramsPython dictionary
[default={}] : arguments to the bandit algorithms
-
-- reward_fnamePython character string
[default=None] : path to a reward matrix rows/[patients] x columns/[drugs]
-
-- quietPython bool
[default=False] : prints verbose
-
-
- -Returns
--
-
- empirical_recPandas DataFrame
rows/[drugs to test] x column/[“Frequency”], the percentage of times across all simulations at the end of which the considered drug is recommended
-
-
NORDic.NORDic_DR.utils module
--
-
- -NORDic.NORDic_DR.utils.argmax_m(ls, m) -
-
-
- -NORDic.NORDic_DR.utils.barycentric_spanner(X, C=1, quiet=True, precision=1e-06) -
-
-
- -NORDic.NORDic_DR.utils.c_kt(direction, problem, na, t, Vinv, M, c, x=None, confidence_width=None, cnorm=None) -
-
-
- -NORDic.NORDic_DR.utils.closest_alternative(problem, b, means, theta, eta, w, c, S_t, constraint='L_inf', subsample=False, alternative_arms=[]) -
-
-
- -NORDic.NORDic_DR.utils.cnorm(x, norm='L_inf') -
-
-
- -NORDic.NORDic_DR.utils.lambert(y, approx=False) -
-
-
- -NORDic.NORDic_DR.utils.mahalanobis(x, M, power=1) -
-
-
- -NORDic.NORDic_DR.utils.optimistic_gradient(problem, Vinv, mu, lambda_, na, t, M, c, gain_type, x=None) -
-
-
- -NORDic.NORDic_DR.utils.projection(Vinv, b, x_hat, X, nb_pulls, c) -
-
-
- -NORDic.NORDic_DR.utils.quadprog_solve_qp(P, q, G=None, h=None, A=None, b=None) -
-
-
- -NORDic.NORDic_DR.utils.randf(ls, m, f) -
-
-
- -NORDic.NORDic_DR.utils.sherman_morrison(M, x) -
-
-
- -NORDic.NORDic_DR.utils.solve_alternative_quadprog(problem, b, theta_emp, eta_emp, a, i_t, w, epsilon, constraint=None) -
-
-
- -NORDic.NORDic_DR.utils.tracking_rule(w, sum_w, na, t, tracking_type, forced_exploration=False) -
-
-
- -NORDic.NORDic_DR.utils.update_misspecified(problem, candidates, Vinv, b, c, na, rewards, Vinv_val=None) -
NORDic.UTILS.DISGENET_utils module
--
-
- -NORDic.UTILS.DISGENET_utils.get_genes_evidences_from_DISGENET(gene_list, disease, limit=3000, source='CURATED', min_score=0, chunksize=100, user_key=None, quiet=False) -
Retrieves the references for the association between each gene in the list and the disease
-…
-- -Parameters
--
-
- gene_listPython character string list
list of associated genes
-
-- diseasePython character string
Concept ID (CID) from MedGen
-
-- limitPython integer
[default=3000] : limit of the number of references
-
-- sourcePython character string
[default=”CURATED”] : DisGeNET data sources [“CURATED”,”ANIMAL MODELS”,”INFERRED”,”ALL”] (see https://www.disgenet.org/dbinfo)
-
-- min_scorePython float
[default=0] : minimum evidence score
-
-- chunksizePython integer
[default=100] : size of chunks (1 chunk per request)
-
-- user_keyPython character string or None
[default=None] : API key from DisGeNET
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- res_dfPandas DataFrame
rows/[row number] x columns/[“gene_symbol”, “sentence”, “associationtype”, “pmid”, “year”, “score”]
-
-
-
-
- -NORDic.UTILS.DISGENET_utils.get_genes_proteins_from_DISGENET(disease_list, limit=3000, source='CURATED', min_score=0, min_ei=0, min_dsi=0.25, min_dpi=0, chunksize=100, user_key=None, quiet=False) -
Retrieves a list of protein names (and associated gene names) related to the input disease CIDs
-…
-- -Parameters
--
-
- disease_listPython character string list
list of Concept IDs (CID) from Medgen for each disease
-
-- limitPython integer
[default=3000] : max. number of proteins
-
-- sourcePython character string
[default=”CURATED”] : DisGeNET data sources [“CURATED”,”ANIMAL MODELS”,”INFERRED”,”ALL”] (see https://www.disgenet.org/dbinfo)
-
-- min_scorePython float
[default=0] : minimum global score
-
-- min_eiPython float
[default=0] : minimimum Evidence Index
-
-- min_dsiPython float
[default=0.25] : minimum Disease Specificity Index
-
-- min_dpiPython float
[default=0] : minimum Disease Pleiotropy Index
-
-- chunksizePython integer
[default=100] : size of chunks (1 chunk per request)
-
-- user_keyPython character string or None
[default=None] : API key from DisGeNET
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- res_dfPandas DataFrame
rows/[Disease CID] x columns/[“Protein”, “Gene Name”] or None if Not found.
-
-
-
-
- -NORDic.UTILS.DISGENET_utils.get_user_key_DISGENET(fname) -
Retrieves the user key from DisGeNET to call the API
-…
-- -Parameters
--
-
- fnamePython character string
path of text file containing on the first line the email, the second the password
-
-
- -Returns
--
-
- user_keyPython character string
from DisGeNET
-
-
NORDic.UTILS.LINCS_utils module
--
-
- -NORDic.UTILS.LINCS_utils.binarize_via_CD(df, samples, binarize=1, nperm=10000, quiet=False) -
Run a differential expression analysis on a dataframe using Characteristic Direction (CD) [1] (implementation: www.maayanlab.net/CD/) -[1] doi.org/10.1186/1471-2105-15-79
-…
-- -Parameters
--
-
- dfPandas DataFrame
one transcriptional profile per column (/!if #genes>25,000, then the 25,000 genes with highest variance will be considered)
-
-- samplesPython integer list
indicates which columns correspond to control (=1) / treated (=2) samples
-
-- binarizePython integer
[default=1] : whether to return a binary signature or a real-valued column ~magnitude of change in expression
-
-- npermPython integer
[default=10000] : number of iterations to build the null distribution on which p-values will be computed
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- signaturePandas DataFrame
rows/[gene index] x columns/[“aggregated”]: 0=down-regulated (DR), 1=up-regulated (UR) (if binarize=1) else <0=DR, >0=UR
-
-
-
-
- -NORDic.UTILS.LINCS_utils.build_url(endpoint, method, params, user_key=None) -
Builds the request to CLUE API
-…
-- -Parameters
--
-
- endpointPython character string
in [“sigs”, “cells”, “genes”, “perts”, “plates”, “profiles”, “rep_drugs”, “rep_drug_indications”, “pcls”]
-
-- methodPython character string
in [“count”, “filter”, “distinct”]
-
-- paramsPython dictionary
additional arguments for the request
-
-- user_keyPython character string
[default=None] : API key for LINCS CLUE.io
-
-
- -Returns
--
-
- urlPython character string
URL of request
-
-
-
-
- -NORDic.UTILS.LINCS_utils.compute_interference_scale(sigs, samples, entrez_id, is_oe, taxon_id, lincs_specific_ctl_genes, quiet=True, eps=2e-07) -
Computes the interference scale [1] which determines whether a genetic perturbation was successful -[1] doi.org/10.1002/psp4.12107
-…
-- -Parameters
--
-
- sigsPandas DataFrame
rows/[genes] x columns/[control and treated samples]
-
-- samplesPython integer list
contains 1 for control samples, 2 for treated ones for each column of @sigs
-
-- entrez_idPython integer
EntrezID of the perturbed gene
-
-- is_oePython bool
is the experiment an overexpression of the perturbed gene (is_oe=True) or a knockdown
-
-- taxon_idPython integer
NCBI taxonomy ID
-
-- lincs_specific_ctl_genesPython string list
list of HNCG gene symbols for housekeeping genes
-
-- quietPython bool
[default=True] : prints out verbose
-
-- epsPython float
[default=2e-7] : avoids numerical errors for low-expression housekeeping genes
-
-
- -Returns
--
-
- iscalePython float
interference scale for the input experiment
-
-
-
-
- -NORDic.UTILS.LINCS_utils.convert_ctrlgenes_EntrezGene(taxon_id) -
Retrieves EntrezID from control genes in LINCS L1000 [1] -[1] doi.org/10.1002/psp4.12107
-…
-- -Parameters
--
-
- taxon_idPython integer
NCBI taxonomy ID
-
-
- -Returns
--
-
- lincs_specific_ctl_genesPython character string list
list of EntrezGene IDs for all genes in input list
-
-
-
-
- -NORDic.UTILS.LINCS_utils.create_restricted_drug_signatures(sig_ids, entrezid_list, path_to_lincs, which_lvl=[3], strict=True, quiet=False) -
Create dataframe of drug signatures from LINCS L1000 from a subset of signature and gene IDs
-…
-- -Parameters
--
-
- sig_idsPython character string list
list of signature IDs from LINCS L1000 (Level 3: “distil_id”, Level 5: “sig_id”)
-
-- entrezid_listPython character string list
list of EntrezIDs
-
-- path_to_lincsPython character string
folder in which LINCS L1000-related files are stored
-
-- which_lvlPython integer list
[3] for Level 3, [5] for Level 5
-
-- strictPython bool
[default=True] : if set to True, if not all signatures are retrieved, then return None. If set to False, return the (sub)set of retrievable signatures
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- sigs Pandas DataFrame
rows/[genes] x columns/[drugs]
-
-
-
-
- -NORDic.UTILS.LINCS_utils.download_file(path_to_lincs, file_name, base_url, file_sha, check_SHA=True, quiet=False) -
Downloads automatically LINCS L1000-related files from Gene Expression Omnibus (GEO) (/!can be time-consuming: expect waiting times up to 20 min with a good Internet connection)
-…
-- -Parameters
--
-
- path_to_lincsPython character string
path to local LINCS L1000 folder in which the files will be downloaded
-
-- file_namePython character string
file name to download on GEO
-
-- base_urlPython character string
path to GEO repository
-
-- file_shaPython character string
file name of corresponding SHA hash to check file integrity
-
-- check_SHAPython bool
[default=True] : whether to check the file integrity
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- 0Python integer
0 means that the download was successful
-
-
-
-
- -NORDic.UTILS.LINCS_utils.download_lincs_files(path_to_lincs, which_lvl) -
Returns and downloads the proper LINCS L1000 files from Gene Expression Omnibus (GEO)
-…
-- -Parameters
--
-
- path_to_lincsPython character string
path to folder in which LINCS L1000-related files will be locally stored
-
-- which_lvlPython integer list
LINCS L1000 Level to download (either [3] -normalized gene expression-, [5] -binary experimental signatures-, [3,5])
-
-
- -Returns
--
-
- file_listPython list of 4 Python character string lists
gene_files, sig_files, lvl3_files, lvl5_files Python lists of character strings
-
-
-
-
- -NORDic.UTILS.LINCS_utils.get_treated_control_dataset(treatment, pert_type, cell, filters, entrez_ids, taxon_id, user_key, path_to_lincs, entrez_id=None, selection='distil_ss', dose=None, iunit=None, itime=None, which_lvl=[5], nsigs=2, same_plate=True, quiet=False, trim_w_interference_scale=True, return_metrics=[]) -
Retrieve set of experimental profiles, with at least nsigs treated and control sample
-…
-- -Parameters
--
-
- treatmentPython character string
HUGO gene symbol
-
-- pert_typePython character string
type of perturbation as accepted by LINCS L1000
-
-- cellPython character string
cell line existing in LINCS L1000
-
-- filtersPython dictionary
additional parameters for the LINCS L1000 requests
-
-- entrez_idsPython integer list
EntrezID genes
-
-- taxon_idPython integer
NCBI taxonomy ID
-
-- user_keyPython character string
LINCS L1000 user API key
-
-- path_to_lincsPython character string
path where LINCS L1000 files are locally stored
-
-- entrez_idPython integer
EntrezID identifier for HUGO gene symbol treatment
-
-- selectionPython character string
[default=”distil_ss”] : LINCS L1000 metric which is maximized by a given experiment
-
-- dosePython character string or None
[default=None] : filter by dose (if not None)
-
-- iunitPython character string or None
[default=None] : unit of dose
-
-- itimePython character string or None
[default=None] : filter by exposure time (if not None)
-
-- which_lvlPython integer list
[default=[3]] : LINCS L1000 data level to consider (either 3 or 5)
-
-- nsigsPython integer
[default=2] : minimal number of samples of each condition in each experiment
-
-- same_platePython bool
[default=True] : select samples from the same plate for each experiment and condition
-
-- quietPython bool
[default=True] : prints out verbose
-
-- trim_w_interference_scalePython bool
[default=True] : computes the interference scale criteria for further trimming
-
-- return_metricsPython character string list
[default=[]] : list of LINCS L1000 metrics to return as the same time as the profiles
-
-
- -Returns
--
-
- sigsPandas DataFrame
rows/[genes+”annotation”+”signame”+”sigid”] x columns/[profiles] or None
-
-
-
-
- -NORDic.UTILS.LINCS_utils.get_user_key(fname) -
Retrieves user key for interacting with LINCS L1000 CLUE API
-…
-- -Parameters
--
-
- fnamePython character string
path to file containing credentials for LINCS L1000 (first line: username, second line: password, third line: user key)
-
-
- -Returns
--
-
- user_keyPython character string
identifier for the LINCS L1000 CLUE API
-
-
-
-
- -NORDic.UTILS.LINCS_utils.post_request(url, quiet=True, pause_time=1) -
Post request to API
-…
-- -Parameters
--
-
- urlPython character string
URL formatted as in build_url
-
-- quietPython bool
[default=True] : prints out verbose
-
-- pause_timePython integer
[default=1] : minimum time in seconds between each request
-
-
- -Returns
--
-
- dataPython dictionary
(JSON) or Python character string list (if request was method=”distinct”)
-
-
-
-
- -NORDic.UTILS.LINCS_utils.select_best_sig(params, filters, user_key, selection='distil_ss', nsigs=2, same_plate=True, iunit=None, quiet=False) -
Select “best” set of profiles (“experiment”) (in terms of quality, or criterion “selection”) according to filters
-…
-- -Parameters
--
-
- paramsPython dictionary
additional arguments for the request
-
-- filtersPython dictionary
additional arguments for filtering the results of the request (defined with params)
-
-- selectionPython character string
[default=”distil_ss”] : name of the metric in LINCS L1000 to define the best signature
-
-- nsigsPython integer
[default=2] : minimum number of signatures to retrieve
-
-- same_platePython bool
[default=True] : whether to retrieve signatures from the same plate or not
-
-- iunitPython character string or None
[default=None] : unit of dose (if None, any)
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- dataPython dictionary list
the list of profile IDs to retrieve from LINCS L1000
-
-
NORDic.UTILS.STRING_utils module
--
-
- -NORDic.UTILS.STRING_utils.get_app_name_STRING(fname) -
Retrieves app name from STRING to interact with the API
-…
-- -Parameters
--
-
- fnamePython character string
path to file with a unique line = email adress
-
-
- -Returns
--
-
- app_namePython character string
identifier for the STRING API
-
-
-
-
- -NORDic.UTILS.STRING_utils.get_image_from_STRING(my_genes, taxon_id, file_name='network.png', min_score=0, network_flavor='evidence', network_type='functional', app_name=None, version='11.5', quiet=False) -
Retrieves protein IDs in STRING associated with input genes in the correct species
-…
-- -Parameters
--
-
- genes_listPython character list
list of gene symbols
-
-- taxon_idPython integer
taxon ID from NCBI
-
-- file_namePython character string
[default=”network.png”] : image file name
-
-- min_scorePython float
[default=0] : confidence lower threshold (in [0,1])
-
-- network_flavorPython character string
[default=”evidence”] : show links related to [“confidence”, “action”, “evidence”]
-
-- network_typePython character string
[default=”functional”] : show “functional” or “physical” network
-
-- app_namePython character string
[default=None] : identifier for STRING
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- None
writes the network image to a file file_name
-
-
-
-
- -NORDic.UTILS.STRING_utils.get_interactions_from_STRING(gene_list, taxon_id, min_score=0, app_name=None, file_folder=None, version='11.0', strict=False, quiet=False) -
Retrieves (un)directed and (un)signed physical interactions from the STRING database
-…
-- -Parameters
--
-
- gene_listPython character string list
list of genes
-
-- taxon_idPython integer
NCBI taxonomy ID
-
-- min_scorePython integer
[default=0] : in [0,1] STRING combined score
-
-- app_namePython character string
[default=None] : identifier for STRING
-
-- file_folderPython character string
[default=None]: where to save the file from STRING (if None, the file is not saved)
-
-- versionPython character string
[default=”v11.0”] : STRING database version
-
-- strictPython bool
[default=False] : if set to True, only keep interactions involving genes BOTH in @gene_list
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- res_dfPandas Dataframe
rows/[interation number] x columns/[“preferredName_A”, “preferredName_B”, “sign”, “directed”, “score”]
-
-
-
-
- -NORDic.UTILS.STRING_utils.get_interactions_partners_from_STRING(gene_list, taxon_id, min_score=0, network_type='functional', add_nodes=0, limit=5, app_name=None, version='11.5', quiet=False) -
Retrieves undirected and unsigned interactions from the STRING database
-…
-- -Parameters
--
-
- gene_listPython character string list
list of gene symbols
-
-- taxon_idPython integer
NCBI taxonomy ID
-
-- min_scorePython integer
[default=0] : minimum STRING combined edge score in [0,1]
-
-- network_typePython character string
[default=”functional”] : returns “functional” or “physical” network
-
-- limitPython integer
[default=5] : limits the number of interaction partners retrieved per protein (most confident interactions come first)
-
-- app_namePython character string
[default=None] : identifier for STRING
-
-- versionPython character string
[default=”11.5”] : STRING version
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- networkPandas DataFrame
rows/[row number] x columns/[“preferredName_A”,”preferredName_B”,”score”,”directed”]
-
-
-
-
- -NORDic.UTILS.STRING_utils.get_network_from_STRING(gene_list, taxon_id, min_score=0, network_type='functional', add_nodes=0, app_name=None, version='11.5', quiet=False) -
Retrieves undirected and unsigned interactions from the STRING database
-…
-- -Parameters
--
-
- gene_listPython character string list
list of gene symbols
-
-- taxon_idPython integer
NCBI taxonomy ID
-
-- min_scorePython integer
[default=0] : minimum STRING combined edge score in [0,1]
-
-- network_typePython character string
[default=”functional”] : returns “functional” or “physical” network
-
-- add_nodesPython integer
[default=0] : add nodes in the closest interaction neighborhood involved with the genes in @gene_list if set to 1
-
-- app_namePython character string
[default=None] : identifier for STRING
-
-- versionPython character string
[default=”11.5”] : STRING version
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- networkPandas DataFrame
rows/[row number] x columns/[“preferredName_A”,”preferredName_B”,”score”,”directed”]
-
-
-
-
- -NORDic.UTILS.STRING_utils.get_protein_names_from_STRING(gene_list, taxon_id, app_name=None, version='11.5', quiet=False) -
Retrieves protein IDs in STRING associated with input genes in the correct species
-…
-- -Parameters
--
-
- genes_listPython character list
list of gene symbols
-
-- taxon_idPython integer
taxon ID from NCBI
-
-- versionPython character string
[default=”11.5”] : STRING version
-
-- app_namePython character string
[default=None] : identifier to access STRING
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- res_dfPandas DataFrame
rows/[row number] x columns/[“queryItem”, “stringId”, “preferredName”, “annotation”]
-
-
-
-
- -NORDic.UTILS.STRING_utils.string_api_url(v) -
NORDic.UTILS.utils_data module
--
-
- -NORDic.UTILS.utils_data.convert_EntrezGene_LINCSL1000(file_folder, EntrezGenes, user_key, quiet=False) -
Converts EntrezIDs to Gene Symbols present in LINCS L1000
-…
-- -Parameters
--
-
- file_folderPython character string
path to folder of intermediate results
-
-- EntrezGenesPython character string list
list of EntrezGene IDs
-
-- user_keyPython character string
LINCS L1000 user key
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- PandasPandas DataFrame
rows/[EntrezID] x columns/[“Gene Symbol”,”Entrez ID”] (“-” if they do not exist)
-
-
-
-
- -NORDic.UTILS.utils_data.convert_genes_EntrezGene(gene_list, taxon_id, app_name, chunksize=100, missing_genes={'C11ORF74': 'IFTAP', 'ENSP00000451560': 'TPPP2', 'RP11-566K11.2': 'TUBB4'}, quiet=False) -
Convert gene symbols into EntrezGene CID
-…
-- -Parameters
--
-
- gene_listPython character string list
list of genes
-
-- taxon_idPython character string
NCBI taxonomy ID
-
-- app_namePython character string
STRING identifier
-
-- missing_genesPython dictionary of character string x character string
known conversions
-
-- chunksizePython integer
[default=100] : 1 chunk per request
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- res_dfPandas DataFrame
rows/[“InputValue”] x columns/[“Gene ID”/might be separated by “; “] (“-” if they do not exist) or None if no identifier has been found
-
-
-
-
- -NORDic.UTILS.utils_data.get_all_celllines(pert_inames, user_key, quiet=False) -
Get all cell lines in which one gene in the input list has been specifically perturbed (genetic perturbation)
-…
-- -Parameters
--
-
- pert_inamesPython character string
List of genes (symbols from LINCS L1000)
-
-- user_keyPython character string
user key from LINCS L1000 CLUE API
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- cell_linesPython character string list
list of cell lines in which at least one gene from pert_inames has been perturbed
-
-
-
-
- -NORDic.UTILS.utils_data.request_biodbnet(probe_list, from_, to_, taxon_id, chunksize=500, quiet=False) -
Converts gene identifier from from to to in a given species
-…
-- -Parameters
--
-
- probe_listPython character string list
list of probes to convert (of type from_)
-
-- from_Python character string
an identifier type as recognized by BioDBnet
-
-- to_Python character string
an identifier type as recognized by BioDBnet
-
-- taxonIdPython integer
NCBI taxonomy ID
-
-- chunksizePython integer
[default=500] : 1 chunk per request
-
-
- -Returns
- -
NORDic.UTILS.utils_exp module
--
-
- -NORDic.UTILS.utils_exp.get_experimental_constraints(file_folder, cell_lines, pert_types, pert_di, taxon_id, selection, user_key, path_to_lincs, thres_iscale=None, nsigs=2, quiet=False) -
Retrieve experimental profiles from the provided cell lines, perturbation types, list of genes, in the given species (taxon ID)
-…
-- -Parameters
--
-
- file_folderPython character string
folder where to store intermediary results
-
-- cell_linesPython character string list
cell lines present in LINCS L1000
-
-- pert_typesPython character string list
types of perturbations as supported by LINCS L1000
-
-- pert_diPython dictionary
(keys=Python character string, values=Python integer) associates HUGO gene symbols to their EntrezGene IDs
-
-- taxon_idPython integer
NCBI taxonomy ID
-
-- selectionPython character string
LINCS L1000 metric to maximize
-
-- user_keyPython character string
LINCS L1000 user API key
-
-- path_to_lincsPython character string
path to local LINCS L1000 files
-
-- thres_iscalePython float or None
[default=None] : lower threshold on the interference scale which quantifies the success of a genetic experiment
-
-- nsigsPython integer
[default=2] : minimal number of profiles per experiment and condition
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- signaturesPandas DataFrame
rows/[genes+annotations] x columns/[profile/signature IDs]
-
-
-
-
- -NORDic.UTILS.utils_exp.profiles2signatures(profiles_df, user_key, path_to_lincs, save_fname, backgroundfile=False, selection='distil_ss', thres=0.5, bin_method='binary', nbackground_limits=(4, 30), quiet=False) -
Convert experimental profiles into signatures (1 for control samples, 1 for treated ones)
-…
-- -Parameters
--
-
- profiles_dfPandas DataFrame
rows/[genes+annotations] x columns/[samples]
-
-- user_keyPython character string
LINCS L1000 user API key
-
-- path_to_lincsPython character string
path to local LINCS L1000 files
-
-- save_fnamePython character string
path to save normalized expression profiles per cell line
-
-- background_filePython bool
[default=False] : retrieves from LINCS L1000 supplementary expression values if set to True to compute more precise basal gene expression levels
-
-- selectionPython character string
[default=”distil_ss”] : LINCS L1000 metric to maximize for the “background” data
-
-- thresPython float
[default=0.5] : threshold for cutoff normalized gene expression values (in [0,0.5])
-
-- bin_methodPython character string
[default=”binary”] : binarization approach
-
-- nbackground_limitsPython integer tuple
[default=(4,30)] : lower and upper bounds on the number of profiles for the background expression data
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- signatures_df Pandas DataFrame
rows/[genes] x columns/[signature ID]
-
-
NORDic.UTILS.utils_grn module
--
-
- -NORDic.UTILS.utils_grn.CL(influences) -
Computes the average of node-wise clustering coefficients. The clustering coefficient of a node is the ratio of the degree of the considered node and the maximum possible number of connections such that this node and its current neighbors form a clique
-…
-- -Parameters
--
-
- influencesPandas DataFrame
rows/[genes] x columns/[genes]
-
-
- -Returns
--
-
- CLPython float
network clustering coefficient
-
-
-
-
- -NORDic.UTILS.utils_grn.Centr(influences) -
Computes the network centralization, which is correlated with the similarity of the network to a graph with a star topology
-…
-- -Parameters
--
-
- influencesPandas DataFrame
rows/[genes] x columns/[genes]
-
-
- -Returns
--
-
- CentrPython float
network centralization
-
-
-
-
- -NORDic.UTILS.utils_grn.DS(influences) -
Computes the number of edges over the maximum number of possible connections between the nodes in the network
-…
-- -Parameters
--
-
- influencesPandas DataFrame
rows/[genes] x columns/[genes]
-
-
- -Returns
--
-
- DSPython float
network density
-
-
-
-
- -NORDic.UTILS.utils_grn.GT(influences) -
Computes the network heterogeneity, which quantifies the non-uniformity of the node degrees across the network
-…
-- -Parameters
--
-
- influencesPandas DataFrame
rows/[genes] x columns/[genes]
-
-
- -Returns
--
-
- GTPython float
network heterogeneity
-
-
-
-
- -NORDic.UTILS.utils_grn.build_influences(network_df, tau, beta=1, cor_method='pearson', expr_df=None, accept_nonRNA=False, quiet=False) -
Filters out (and signs of unsigned) edges based on gene expression
-…
-- -Parameters
--
-
- network_dfPandas DataFrame
rows/[index] x columns/[[“Input”, “Output”, “SSign”]] interactions
-
-- tauPython float
threshold on genepairwise expression correlation
-
-- betaPython integer
[default=1] : power applied to the adjacency matrix
-
-- cor_methodPython character string
[default=”pearson”] : type of correlation
-
-- expr_dfPandas DataFrame
[default=None] : rows/[genes] x columns/[samples] gene expression data
-
-- accept_nonRNAPython bool
[default=False] : if set to False, ignores gene names which are not present in expr_df
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- influencesPandas DataFrame
rows/[genes] x columns/[genes] signed adjacency matrix with only interactions s.t. corr^beta>=tau
-
-
-
-
- -NORDic.UTILS.utils_grn.build_observations(grn, signatures, quiet=False) -
Implement experimental constraints from perturbation experiments in signatures. Experimental constraints are of the form Initial state masked by single-gene perturbation can lead to a steady attractor state Final
-…
-- -Parameters
--
-
- grnInfluenceGraph (from BoneSiS)
contains topological constraints
-
-- signaturesPandas DataFrame
rows/[genes] x columns/[experiment IDs]. Experiment IDs is of the form “<pert. gene>_<pert. type>_<…>_<cell line>” (treated) or “initial_<cell line>” (control)
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- BOBoNesis object (from BoneSiS)
BoNesis object which can be evaluated
-
-
-
-
- -NORDic.UTILS.utils_grn.create_grn(influences, exact=False, max_maxclause=3, quiet=False) -
Create a BoneSiS InfluenceGraph
-…
-- -Parameters
--
-
- influencesPandas DataFrame
rows/[genes] x columns/[genes] of interactions, values in {-1,1,0} -1:negative,1:positive,0:absent
-
-- exactPython bool
[default=False] : should all interactions be preserved?
-
-- max_maxclausePython integer
[default=3] : upper bound on the number of clauses in DNF form
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- grnBoneSiS InfluenceGraph class object
BoneSiS GRN object
-
-
-
-
- -NORDic.UTILS.utils_grn.desirability(x, f_weight_di, A=0, B=1) -
Harrington’s desirability function, used by [1] -Converts a list of functions to maximize into a single scalar function to maximize with values in [@A,@B]
--
-
- [1] http://ceur-ws.org/Vol-2488/paper17.pdf
https://cran.r-project.org/web/packages/desirability/vignettes/desirability.pdf
-
-
…
-- -Parameters
--
-
- xdatapoint
any input to functions in f_weight_di
-
-- f_weight_diPython dictionary
function with arguments as the same type as x, and associated weight
-
-- APython float
[default=0] : lower bound of the function interval
-
-- BPython float
[default=1] : upper bound of the function interval
-
-
- -Returns
--
-
- des(x)Python float
value of the desirability function at point x
-
-
-
-
- -NORDic.UTILS.utils_grn.general_topological_parameter(influences, weights) -
Computes the general topological parameter (GTP) associated with the input network
-…
-- -Parameters
--
-
- influencesPandas DataFrame
rows/[genes] x columns/[genes]
-
-- weightsPython dictionary of (Python character string x Python float)
all keys must be in [“DS”,”CL”,”Centr”,”GT”]
-
-
- -Returns
--
-
- scorePython float
score using the Harrington’s desirability function
-
-
-
-
- -NORDic.UTILS.utils_grn.get_genes_downstream(network_fname, gene, n=-1) -
Get the list of genes downstream of a gene in a network
-…
-- -Parameters
--
-
- network_fnamePython character string
path to the .BNET file associated with the network
-
-- genePython character string
gene name in the network
-
-- nPython integer
[default=-1] : number of recursions (if<0, recursively get all downstream genes)
-
-
- -Returns
--
-
- lst_downstreamPython character string list
list of nodes downstream of @gene
-
-
-
-
- -NORDic.UTILS.utils_grn.get_genes_interactions_from_PPI(ppi, connected=False, score=0, filtering=True, quiet=False) -
Filtering edges to decrease computational cost while preserving network connectivity (if needed)
-…
-- -Parameters
--
-
- ppiPandas DataFrame
rows/[index] x columns[{“preferredName_A”, “preferredName_B”, “sign”, “directed”, “score”]]; sign in {-1,1,2}, directed in {0,1}, score in [0,1]
-
-- connectedPython bool
[default=True] : if set to True, preserve/enforce connectivity on the final network
-
-- scorePython float
[default=0] : Lower bound on the edge-associated score
-
-- filteringPython bool
[default=True] : Whether to filter out edges by a correlation threshold
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- ppi_acceptedPandas DataFrame
rows/[index] x columns/[[“Input”, “Output”]]
-
-
-
-
- -NORDic.UTILS.utils_grn.get_genes_most_variable(control_profiles, treated_profiles, p=0.8) -
Get the list of genes which contribute most to the variation between two conditions (in the @pth percentile of change)
-…
-- -Parameters
--
-
- control_profilesPandas DataFrame
rows/[genes] x columns/[samples] profiles from condition 1
-
-- treated_profilesPandas DataFrame
rows/[genes] x columns/[samples] profiles from condition 1
-
-- pPython float
100*p th percentile to consider
-
-
- -Returns
--
-
- lst_genesPython character string list
list of nodes which contribute most to the variation between conditions
-
-
-
-
- -NORDic.UTILS.utils_grn.get_grfs_from_solution(solution) -
Retrieve all gene regulatory functions (GRFs) from a given solution
-…
-- -Parameters
--
-
- solutionPandas Series
rows/[genes]
-
-
- -Returns
--
-
- grfsPython dictionary
{gene: {regulator: sign, …}, …} where sign in {-1,1} -1: inhibitor, 1: activator
-
-
-
-
- -NORDic.UTILS.utils_grn.get_maxdegree(influences, activatory=True, quiet=False) -
Computes the maximum ingoing degree (or the maximum number of potential activatory regulators) in a graph
-…
-- -Parameters
--
-
- influencesPandas DataFrame
rows/[genes] x columns/[genes] of interactions: -1:negative,1:positive,0:absent
-
-- activatoryPython bool
[default=True] : computes the maximum number of potential activatory regulators instead
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- maxindegreePython integer
maximum ingoing degree (or the maximum number of potential activatory regulators)
-
-
-
-
- -NORDic.UTILS.utils_grn.get_minimal_edges(R, maximal=False) -
Return one of the solutions with the smallest (or greatest) number of edges
-…
-- -Parameters
--
-
- RPandas DataFrame
rows/[genes] x columns/[solution IDs]
-
-- connectedPython bool
[default=False] : if set to True, return the CONNECTED solution which satisfies those constraints
-
-- maximalPython bool
[default=False] : if set to True, return the solution with the greatest number of edges
-
-
- -Returns
--
-
- solution, nedgesPython integer x Python integer
solution and corresponding number of edges
-
-
-
-
- -NORDic.UTILS.utils_grn.get_weakly_connected(network_df, gene_list, index_col='preferredName_A', column_col='preferredName_B', score_col='sscore') -
Depth-first search (DFS) on undirected network
-…
-- -Parameters
--
-
- network_dfPandas DataFrame
rows/[index] x columns/[[“Input”,”Output”]]
-
-- gene_listPython character string list
list of genes (needed to take into account isolated genes in the network)
-
-- index_colPython character string
[default=”preferredName_A”] : column in network_df (input gene)
-
-- column_colPython character string
[default=”preferredName_B”] : column in network_df (output gene)
-
-- score_colPython character string
[default=”sscore”] : column in network_df (edge weight)
-
-
- -Returns
--
-
- componentsType of @network_df.loc[network_df.index[0]][“Input”] Python list of Python list
list of weakly connected components in the network, ordered by decreasing size
-
-
-
-
- -NORDic.UTILS.utils_grn.infer_network(BO, njobs=1, fname='solutions', use_diverse=True, limit=50, niterations=1) -
Infer solutions matching topological & experimental constraints
-…
-- -Parameters
--
-
- BOBonesis object (from BoneSiS)
contains topological & experimental constraints
-
-- fnamePython character string
[default=”solutions”] : path to solution files
-
-- use_diversePython bool
[default=True] : use the “diverse” procedure in BoneSiS
-
-- limitPython integer
[default=50] : maximum number of solutions to generate per interation
-
-- niterationsPython integer
[default=1] : maximum number of iterations
-
-
- -Returns
--
-
- nsolutionsPython integer
list of # solutions per iteration
-
-
-
-
- -NORDic.UTILS.utils_grn.load_grn(fname) -
Loads GRN as MPBN class element
-…
-- -Parameters
--
-
- fnamePython character string
BNET file
-
-
- -Returns
--
-
- BNmpbn.MPBooleanNetwork object
Boolean network with Most Permissive semantics
-
-
-
-
- -NORDic.UTILS.utils_grn.reconnect_network(network_fname) -
Write the network with all isolated nodes (no ingoing/outgoing edges) filtered out
-…
-- -Parameters
--
-
- network_fnamePython character string
path to the .BNET associated with the network
-
-
- -Returns
--
-
- fnamePython character string
path to the .BNET associated with the reconnected network
-
-
-
-
- -NORDic.UTILS.utils_grn.save_grn(solution, fname, sep=', ', quiet=False, max_show=5, write=True) -
Write and/or print .bnet file
-…
-- -Parameters
--
-
- solutionPandas Series
rows/[genes] contains gene regulatory functions (GRF)
-
-- fnamePython character string
where to write the file (w/o .bnet extension)
-
-- sepPython character string
what separates regulators from regulated genes
-
-- quietPython bool
[default=False] : prints out verbose
-
-- max_showPython integer
[default=5] : maximum number of printed GRFs
-
-- writePython bool
[default=True] : if set to True, write to a .bnet file
-
-
- -Returns
--
-
- None
writes the GRN to a file fname
-
-
-
-
- -NORDic.UTILS.utils_grn.save_solutions(bnetworks, fname, limit) -
Enumerate and save solutions
-…
-- -Parameters
--
-
- bnetworksBonesis object
Output of the inference
-
-- fnamePython character string
ZIP filename to store the solutions
-
-- limitPython integer
maximum number of solutions to enumerate
-
-
- -Parameters
--
-
- nPython integer
number of enumerated solutions
-
-
-
-
- -NORDic.UTILS.utils_grn.solution2influences(solution) -
Converts a solution object into a influences object
-…
-- -Parameters
--
-
- solutionPandas Series
rows/[genes]
-
-
- -Returns
--
-
- influencesPandas DataFrame
rows/[genes] x columns/[genes] contains values {-1,1,0,2} -1: negative, 1: positive, 0: absent, 2: non monotonic
-
-
-
-
- -NORDic.UTILS.utils_grn.zip2df(fname) -
Extract solutions in ZIP file as DataFrames
-…
-- -Parameters
--
-
- fnamePython character string
zip file which contains BNET solutions
-
-
- -Returns
--
-
- solutionsPandas DataFrame
rows/[genes] x columns/[solutions] the GRFs for each gene in each solution
-
-
NORDic.UTILS.utils_network module
--
-
- -NORDic.UTILS.utils_network.aggregate_networks(file_folder, gene_list, taxon_id, min_score, network_type, app_name, version_net='11.5', version_act='11.0', quiet=0) -
This function performs the following pipeline to build a prior knowledge network based on a subset of genes -- Retrieve protein actions and predicted PPIs from STRING -- Merge the two networks while solving all inconsistencies (duplicates, paradoxes, etc.) in signs, directions, scores -- Determine the greatest threshold on the edge score which allows all of the core gene set to be connected (binary search) -- Trim out edges which scores are below the threshold, and remove all isolated nodes
-…
-- -Parameters
--
-
- file_folderPython character string
relative path where to store files
-
-- gene_listPython character string list
list of core gene symbols to preserve in the network
-
-- taxon_idPython integer
NCBI taxonomy ID
-
-- min_scorePython integer
minimum score on edges retrieved from the STRING database
-
-- app_namePython character string
Identifier for STRING requests
-
-- version_netPython character string
[default=”11.5”] : Number of version for interaction data in the STRING database. To avoid compatibility issues, it is strongly advised not to change this parameter
-
-- version_actPython character string
[default=”11.0”] : Number of version for protein action data in the STRING database. To avoid compatibility issues, it is strongly advised not to change this parameter
-
-- quietPython bool
[default=None] : prints out verbose
-
-
- -Returns
--
-
- final_networkPandas DataFrame
rows/[interactions] x columns/[[“preferredName_A”, “preferredName_B”, “sign”, “directed”, “score”]]
-
-
-
-
- -NORDic.UTILS.utils_network.capture() -
-
-
- -NORDic.UTILS.utils_network.determine_edge_threshold(network, core_gene_set, quiet=True) -
Determine the greatest threshold on the edge score which allows all of the core gene set to be connected (binary search)
-…
-- -Parameters
--
-
- networkPandas DataFrame
rows/[interactions] x at least three columns “preferredName_A” (input node), “preferredName_B” (output node), “score” (edge score)
-
-- core_gene_setPython character string list
list of genes that should remain connected
-
-- quietPython bool
[default=None]: prints out verbose
-
-
- -Returns
--
-
- tPython float
maximum threshold which allows the connection of all genes in the core set
-
-
-
-
- -NORDic.UTILS.utils_network.get_network_from_OmniPath(gene_list=None, disease_name=None, species='human', sources_int='omnipath', domains_int=None, types_int=None, min_curation_effort=-1, domains_annot='HPA_tissue', quiet=False) -
Retrieve a network from OmniPath
-…
-- -Parameters
--
-
- gene_listPython character string
[default=None] : List of genes to consider (or do not filter the interactions from Omnipath if =None)
-
-- disease_namePython character string
[default=None] : Disease name (in letters) to consider
-
-- speciesPython character string
[default=None] : Species to consider (either “human”, “mouse”, or “rat”)
-
-- sources_intPython character string
[default=None] : Which databases for interactions to consider (if =None, consider them all)
-
-- domains_intPython character string
[default=None] : source of interactions in OmniPath
-
-- types_intPython character string
[default=None] : Types of interactions, e.g., “post_translational”, “transcriptional”, “post_transcriptional”, “mirna_transcriptional”
-
-- min_curation_effortPython integer
[default=-1] : if positive, select edges based on that criteria (the higher, the better). Counts the unique database-citation pairs, i.e. how many times was an interaction described in a paper and mentioned in a database
-
-- domain_annotPython character string
[default=’HPA_tissue’] : source of annotations in OmniPath
-
-- quietPython bool
[default=False] : prints out verbose
-
-
- -Returns
--
-
- final_networkPandas DataFrame
rows/[interactions] x columns/[[“preferredName_A”, “preferredName_B”, “sign”, “directed”, “score”]]
-
-- annot_widePandas DataFrame
rows/[gene symbols] x columns/[annotations from the database @domains_annot]
-
-
-
-
- -NORDic.UTILS.utils_network.merge_network_PPI(network, PPI, quiet=True) -
Merge two network while solving all inconsistencies (duplicates, paradoxes, etc.) in signs, directions, scores
-…
-- -Parameters
--
-
- networkPandas DataFrame
rows/[interactions] x at least three columns “preferredName_A” (input node), “preferredName_B” (output node), “score” (edge score)
-
-- PPIPandas DataFrame
rows/[interactions] x at least three columns “preferredName_A” (input node), “preferredName_B” (output node), “score” (edge score)
-
-- quietPython bool
[default=None] : prints out verbose
-
-
- -Returns
--
-
- final_networkPandas DataFrame
rows/[interactions] x columns/[[“preferredName_A”, “preferredName_B”, “sign”, “directed”, “score”]]
-
-
-
-
- -NORDic.UTILS.utils_network.remove_isolated(network, quiet=False) -
Remove all nodes which do not belong to the largest connected component from the network
-…
-- -Parameters
--
-
- networkPandas DataFrame
rows/[interactions] x columns/[[“preferredName_A”, “preferredName_B”, “sign”, “directed”, “score”]]
-
-- quietPython bool
[default=None] : prints out verbose
-
-
- -Returns
--
-
- trimmed_networkPandas DataFrame
rows/[interactions] x columns/[[“preferredName_A”, “preferredName_B”, “sign”, “directed”, “score”]]
-
-
NORDic.UTILS.utils_plot module
--
-
- -NORDic.UTILS.utils_plot.influences2graph(influences, fname, optional=False, compile2png=True, engine='sfdp') -
Plots a network by conversion to a DOT file and then to PNG
-…
-- -Parameters
--
-
- influencesPandas DataFrame
rows/[genes] x columns/[genes], contains {-1,1,2}
-
-- fnamePython character string
filename of png file
-
-- optionalPython bool
[default=False] : should interactions be drawn as optional (dashed lines)?
-
-
- -Returns
--
-
- None
writes a DOT file which can be converted to PNG image (if compile2png=True)
-
-
-
-
- -NORDic.UTILS.utils_plot.plot_boxplots(scores, patient_scores, ground_truth=None, fsize=12, msize=5, fname='boxplots.pdf') -
Plots one boxplot per treatment (all values obtained on patient profiles)
-…
-- -Parameters
--
-
- scoresPandas DataFrame
rows/[drug names] x column/[value]
-
-- patient_scoresPandas DataFrame
rows/[drug names] x columns/[patient samples]
-
-- ground_truthPandas DataFrame
[default=None] : rows/[drug names] x column/[class] Values in 1: treatment, 0: unknown, -1: aggravating. If not provided: does not color boxplots according to the class
-
-- fsizePython integer
[default=18] : font size
-
-- msizePython integer
[default=5] : marker size
-
-- fnamePython character string
[default=”boxplots”] : file name for the plot
-
-
- -Returns
--
-
- None
create boxplots of reward scores across patients for each drug
-
-
-
-
- -NORDic.UTILS.utils_plot.plot_discrete_distributions(signatures, fname='signature_expression_distribution.png') -
Plots the distributions (histograms) of genes with determined status across signatures
-…
-- -Parameters
--
-
- signaturesPandas DataFrame
rows/[genes] x columns/[samples] with values in {0,NaN,1}. Determined status is either 0 or 1.
-
-- fnamePython character string
[default=”signature_expression_distribution.png”] : file name
-
-
- -Returns
--
-
- None
plots the number of genes with expression values 0, 1 or NaN in each signature
-
-
-
-
- -NORDic.UTILS.utils_plot.plot_distributions(profiles, fname='gene_expression_distribution.png', thres=None) -
Plots the distributions (boxplots) of gene expression across samples for each gene, and the selected threshold for binarization
-…
-- -Parameters
--
-
- profilesPandas DataFrame
rows/[genes+annotations] x columns/[samples]
-
-- fnamePython character string
[default=”gene_expression_distribution.png”] : file name
-
-- thresPython float or None
[default=None] : binarization threshold (if there is any)
-
-
- -Returns
--
-
- None
plots boxplots of expression for each gene in profiles
-
-
-
-
- -NORDic.UTILS.utils_plot.plot_heatmap(X, ground_truth=None, fname='heatmap.pdf', w=20, h=20, bfsize=20, fsize=20, rot=75) -
Plots an heatmap of the signatures, with the potential ground truth
-…
-- -Parameters
--
-
- XPandas DataFrame
rows/[features] x columns/[drug names]
-
-- ground_truthPandas DataFrame
[default=None] : rows/[drug names] x column/[class] Values in 1: treatment, 0: unknown, -1: aggravating. If not provided: does not color boxplots according to the class
-
-- fnamePython character string
[default=”heatmap.pdf”] : file name for the plot
-
-- wPython integer
[default=20] : figure width
-
-- hPython integer
[default=20] : figure height
-
-- bfsizePython integer
[default=20] : font size in the color bar
-
-- rotPython integer
[default=75] : rotation angle of labels
-
-
- -Returns
--
-
- None
plots an heatmap of similarity across drugs based on the Pearson correlation
-
-
-
-
- -NORDic.UTILS.utils_plot.plot_influence_graph(network_df, input_col, output_col, sign_col, direction_col=None, fname='graph.png', optional=True) -
Converts a network into a PNG picture
-…
-- -Parameters
--
-
- network_dfPandas DataFrame
rows/[index] x columns/[input_col,output_col,sign_col]
-
-- input_col,output_col,sign_col,direction_colPython character string
columns of network_df
-
-- fnamePython character string
[default=”graph.png”] : file name for PNG picture
-
-- optionalPython bool
[default=True] : should edges be plotted as dashed lines?
-
-
- -Returns
--
-
- None
Creates a image of the graph in file fname
-
-
-
-
- -NORDic.UTILS.utils_plot.plot_precision_recall(pr, prs, tr, beta=1, thres=0.5, fname='PRC.pdf', method_name='predictor', fsize=18) -
Plots a Precision-Recall curve (with variations across samples)
-…
-- -Parameters
--
-
- prPandas DataFrame
rows/[drug names] x column/[value]
-
-- prsPandas DataFrame
rows/[drug names] x columns/[patient samples]
-
-- trPandas DataFrame
[default=None] : rows/[drug names] x column/[class]
-
-- betaPython float
[default=1] : value of coefficient beta for the F-measure
-
-- thresPython float
[default=0.5] : decision threshold
-
-- fnamePython character string
[default=”PRC.pdf”] : file name for the plot
-
-- method_namePython character string
[default=”predictor”] : name of the predictor
-
-- fsizePython integer
[default=18] : font size
-
-
- -Returns
--
-
- None
Plots a Precision-Recall curve based on the drug repurposing predictions
-
-
-
-
- -NORDic.UTILS.utils_plot.plot_roc_curve(pr, prs, tr, fname='ROC.pdf', method_name='predictor', fsize=18) -
Plots a ROC curve (with variations across samples)
-…
-- -Parameters
--
-
- prPandas DataFrame
rows/[drug names] x column/[value]
-
-- prsPandas DataFrame
rows/[drug names] x columns/[patient samples]
-
-- trPandas DataFrame
[default=None] : rows/[drug names] x column/[class]
-
-- fnamePython character string
[default=”ROC.pdf”] : file name for the plot
-
-- method_namePython character string
[default=”predictor”] : name of the predictor
-
-- fsizePython integer
[default=18] : font size
-
-
- -Returns
--
-
- None
Plots a ROC curve based on the drug repurposing predictions
-
-
-
-
- -NORDic.UTILS.utils_plot.plot_signatures(signatures, perturbed_genes=None, width=10, height=10, max_show=50, fname='signatures') -
Print signatures
-…
-- -Parameters
--
-
- signaturesPandas DataFrame
rows/[genes] x columns/[signature IDs]
-
-- perturbed_genesPython character string list
[default=None] : list of gene names perturbed in the signatures
-
-- width, heightPython integer
[default=10] : dimensions of image
-
-- max_showPython integer
[default=50] : maximum number of genes shown (as only the @max_show genes with highest variance across signatures are plotted)
-
-- fnamePython character string
[default=”signatures”] : path of resulting PNG image
-
-
- -Returns
--
-
- None
plots the signatures as heatmaps in file fname
-
-
NORDic.UTILS.utils_sim module
--
-
- -class NORDic.UTILS.utils_sim.BN_SIM(seednb=0, njobs=None) -
Bases:
-object-
-
- -add_initial_states(initial, final=None) -
-
-
- -add_permanent_mutation(mutation) -
-
-
- -add_transient_mutation(mutation) -
-
-
- -attrs_similarity(attrs1, attrs2, gene_outputs=None) -
-
-
- -boxplot() -
-
-
- -enumerate_attractors(verbose=False) -
-
-
- -generate_trajectories(params={}, outputs=[]) -
-
-
- -initialize_network(network_fname) -
-
-
- -up_to_attractors(network_fname, initial, final, mutation_permanent={}, mutation_transient={}, verbose=True) -
-
-
- -update_network(network_fname, initial, final=None, mutation_permanent={}, mutation_transient={}, verbose=True) -
-
-
- -class NORDic.UTILS.utils_sim.BONESIS_SIM(seednb=0, njobs=None) -
Bases:
-BN_SIM-
-
- -add_initial_states(initial, final) -
-
-
- -add_permanent_mutation(mutation) -
-
-
- -add_transient_mutation(mutation) -
-
-
- -enumerate_attractors(verbose=True) -
-
-
- -generate_trajectories(params={}, outputs=[]) -
-
-
- -initialize_network(network_fname) -
-
-
- -class NORDic.UTILS.utils_sim.MABOSS_SIM(seednb=0, njobs=None) -
Bases:
-BN_SIM-
-
- -add_initial_states(initial, final=None) -
-
-
- -add_permanent_mutation(mutation) -
-
-
- -add_transient_mutation(mutation) -
-
-
- -enumerate_attractors(verbose=True) -
-
-
- -generate_trajectories(params={}, outputs=[]) -
-
-
- -initialize_network(network_fname) -
-
-
- -class NORDic.UTILS.utils_sim.MPBN_SIM(seednb=0, njobs=None) -
Bases:
-BN_SIM-
-
- -add_initial_states(initial, final=None) -
-
-
- -add_permanent_mutation(mutation) -
-
-
- -add_transient_mutation(mutation) -
-
-
- -enumerate_attractors(max_attrs=-1, verbose=True) -
-
-
- -generate_trajectories(params={}, outputs=[], show_plot=True) -
-
-
- -initialize_network(network_fname) -
-
-
- -NORDic.UTILS.utils_sim.capture() -
-
-
- -NORDic.UTILS.utils_sim.choice(a, size=None, replace=True, p=None) -
Generates a random sample from a given 1-D array
---New in version 1.7.0.
---Note
-New code should use the ~numpy.random.Generator.choice -method of a ~numpy.random.Generator instance instead; -please see the random-quick-start.
-- -Parameters
--
-
- a1-D array-like or int
If an ndarray, a random sample is generated from its elements. -If an int, the random sample is generated as if it were
-np.arange(a)
-- sizeint or tuple of ints, optional
Output shape. If the given shape is, e.g.,
-(m, n, k), then -m * n * ksamples are drawn. Default is None, in which case a -single value is returned.
-- replaceboolean, optional
Whether the sample is with or without replacement. Default is True, -meaning that a value of
-acan be selected multiple times.
-- p1-D array-like, optional
The probabilities associated with each entry in a. -If not given, the sample assumes a uniform distribution over all -entries in
-a.
-
- -Returns
--
-
- samplessingle item or ndarray
The generated random samples
-
-
- -Raises
--
-
- ValueError
If a is an int and less than zero, if a or p are not 1-dimensional, -if a is an array-like of size 0, if p is not a vector of -probabilities, if a and p have different lengths, or if -replace=False and the sample size is greater than the population -size
-
-
- -See Also
-randint, shuffle, permutation -random.Generator.choice: which should be used in new code
-- -Notes
-Setting user-specified probabilities through
-puses a more general but less -efficient sampler than the default. The general sampler produces a different sample -than the optimized sampler even if each element ofpis 1 / len(a).Sampling random rows from a 2-D array is not possible with this function, -but is possible with Generator.choice through its
-axiskeyword.- -Examples
-Generate a uniform random sample from np.arange(5) of size 3:
--->>> np.random.choice(5, 3) -array([0, 3, 4]) # random ->>> #This is equivalent to np.random.randint(0,5,3) -
Generate a non-uniform random sample from np.arange(5) of size 3:
--->>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0]) -array([3, 3, 0]) # random -
Generate a uniform random sample from np.arange(5) of size 3 without -replacement:
--->>> np.random.choice(5, 3, replace=False) -array([3,1,0]) # random ->>> #This is equivalent to np.random.permutation(np.arange(5))[:3] -
Generate a non-uniform random sample from np.arange(5) of size -3 without replacement:
--->>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0]) -array([2, 3, 0]) # random -
Any of the above can be repeated with an arbitrary array-like -instead of just integers. For instance:
--->>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher'] ->>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3]) -array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], # random - dtype='<U11') -
-
-
- -NORDic.UTILS.utils_sim.test(enumerator, seednb, njobs, network_fname, control_profile, treated_profiles, compare_to, mutation_permanent={}, mutation_transient={}, gene_outputs=None, print_boxplot=False, verbose=True) -
NORDic.UTILS.utils_state module
--
-
- -NORDic.UTILS.utils_state.binarize_experiments(data, thres=0.5, method='binary', strict=True, njobs=1) -
Binarize experimental profiles
-…
-- -Parameters
--
-
- dataPandas DataFrame
rows/[genes] x columns/[samples]
-
-- thresPython float
[default=0.5] : threshold for @method=”binary” (in [0,0.5])
-
-- methodPython character string
[default=”binary”] : binarization method in [“binary”,”probin”]
-
-- strictPython bool
[default=True] : takes into account equalities (if set to True, value=thres will lead to undefined for the corresponding gene)
-
-- njobsPython integer
[default=1] : parallelism if needed
-
-
- -Returns
-signatures : Pandas DataFrame: rows/[genes] x columns[samples] with values in [0,1,NaN]
-
-
-
- -NORDic.UTILS.utils_state.compare_states(x, y, genes=None) -
Computes the similarity between two sets of Boolean states
-…
-- -Parameters
--
-
- xPandas DataFrame
rows/[genes] x columns/[state IDs] contains (0, 1, NaN)
-
-- yPandas DataFrame
rows/[genes] x columns/[state IDs] contains (0, 1, NaN)
-
-- genesPython character string list
list of gene symbols
-
-
- -Returns
--
-
- simsNumPy array
similarities between each column of x and each columns of y, on the list of N present genes in genes (if provided) otherwise on the union of N genes in x and y
-
-- NPython integer
number of genes on which the similarity is computed
-
-
-
-
- -NORDic.UTILS.utils_state.finetune_binthres(df, samples, network_fname, mutation, step=0.005, maxt=0.5, mint=0, score_binthres=<function <lambda>>, njobs=1, verbose=True) -
Select the binarization threshold (in function @binarize_experiments) which maximize the dissimilarity interconditions and the similarity intracondition -…
-- -Parameters
--
-
- dfPandas DataFrame
rows/[genes] x columns/[samples]: profiles
-
-- samplesPython character string list
annotations of conditions for each sample in df
-
-- network_fnamePython character string
file name containing the network
-
-- mutationPython dictionary
dictionary (key=gene, value=perturbation type) gene perturbations which are considered
-
-- stepPython float
[default=0.005] step in the interval to look for the threshold value
-
-- maxtPython float
[default=0.5] maximum threshold value
-
-- mintPython float
[default=0.] minimum threshold value
-
-- score_binthresPython lambda function
[default=lambda itc,ita_c,ita_t:(1-itc)*ita_c*ita_t] fitness function for the threshold value
-
-- njobsPython integer
[default=1] number of parallel jobs
-
-- verbosePython bool
[default=True] prints out verbose
-
-
- -Returns
--
-
- max_thresPython float
threshold value maximizing the fitness function
-
-
-
-
- -NORDic.UTILS.utils_state.quantile_normalize(df, njobs=1) -
