Functions for weighting graphNode objects based on stoichiometry data. More...

Detailed Description

Functions for weighting graphNode objects based on stoichiometry data.

Functions
def	calc_likelihood
	Function that adds a score for the compositional likelihood for all states represented as nodes in the graph. More...

def	calc_likelihood_state
	Function that adds a score for the compositional likelihood for all states, similar to how composition_likelihood_function calculates the composition likelihood of a node. More...

def	composition_likelihood_function
	Function that calculates the likelihood of an individual node, used by calc_likelihood(). More...

def	get_state
	function to calculate how many times a protein appears in a list of proteins, which can be accessed from a graphNode object using node.get_subcomplex_components() More...

Function Documentation

def IMP.spatiotemporal.composition_scoring.calc_likelihood	(	exp_comp_map,
		nodes
	)

Function that adds a score for the compositional likelihood for all states represented as nodes in the graph.

The composition likelihood assumes a Gaussian distribution for copy number of each protein or subcomplex with means and standard deviatiations derived from experiment. Returns the nodes, with the new weights added.

Parameters

exp_comp_map dictionary, which describes protein stoicheometery. The key describes the protein, which should correspond to names within the expected_subcomplexes. Only copy numbers for proteins or subcomplexes included in this dictionary will be scored. For each of these proteins, a csv file should be provided with protein copy number data. The csv file should have 3 columns, 1) "Time", which matches up to the possible times in the graph, 2) "mean", the average protein copy number at that time point from experiment, and 3) "std", the standard deviation of that protein copy number from experiment.

nodes list of graphNode objects, which have been already been initiated with static scores

Returns: nodes: edited list of graphNode objects, which now have static and composition scores

Note: This function is only available in Python.

Definition at line 64 of file composition_scoring.py.

def IMP.spatiotemporal.composition_scoring.calc_likelihood_state	(	exp_comp_map,
		t,
		state
	)

Function that adds a score for the compositional likelihood for all states, similar to how composition_likelihood_function calculates the composition likelihood of a node.

Used by prepare_protein_library. The composition likelihood assumes a Gaussian distribution for copy number of each protein or subcomplex with means and standard deviatiations derived from experiment. Returns the nodes, with the new weights added.

Parameters

exp_comp_map	dictionary, which describes protein stoicheometery. The key describes the protein, which should correspond to names within the expected_subcomplexes. Only copy numbers for proteins or subcomplexes included in this dictionary will be scored. For each of these proteins, a csv file should be provided with protein copy number data. The csv file should have 3 columns, 1) "Time", which matches up to the possible times in the graph, 2) "mean", the average protein copy number at that time point from experiment, and 3) "std", the standard deviation of that protein copy number from experiment.
t	string, time at which the composition likelihood should be calculated. Should match one a possible value in the first column of the exp_comp_map.
state	list of integers, an array of the number of protein copy numbers for which the likelihood will be calculated. This array should list the proteins in the same order as the exp_comp_map.

Returns: weight: float, the weight of the graphNode according to the composition likelihood function.

Note: This function is only available in Python.

Definition at line 120 of file composition_scoring.py.

def IMP.spatiotemporal.composition_scoring.composition_likelihood_function	(	mean,
		std,
		prots,
		node
	)

Function that calculates the likelihood of an individual node, used by calc_likelihood().

Parameters

mean	dictionary of dictionaries where the first key is the protein, the second key is the time, and the expected mean copy number from experiment is returned.
std	dictionary of dictionaries where the first key is the protein, the second key is the time, and the expected standard deviation of protein copy number from experiment is returned.
prots	list of proteins or subcomplexes which will be scored according to this likelihood function
node	the graphNode object for which the likelihood will be calculated.

Returns: w: float, the weight of the graphNode according to the composition likelihood function.

Note: This function is only available in Python.

Definition at line 34 of file composition_scoring.py.

def IMP.spatiotemporal.composition_scoring.get_state	(	subcomplex_components,
		prot
	)

function to calculate how many times a protein appears in a list of proteins, which can be accessed from a graphNode object using node.get_subcomplex_components()

Parameters

subcomplex_components	subcomplexes or components in a given node, which can be accessed by graphNode.get_subcomplex_components()
prot	string, protein or subcomplex we are interested in finding

Returns: state, int, number of times the protein or subcomplex appears in subcomplex_components

Note: This function is only available in Python.

Definition at line 15 of file composition_scoring.py.