MCI_analysis Class Reference

Public Member Functions
	get_the_list_with_signed_scores (self, ref_contacts, sorted_contacts_counts_map)
	Returns a list of contact signed scores in the order of decreasing contact score (by magnitude) For instance, L = [100, -75, 50] ;sign is ignored while rearranging.
	plot_contacts_counts (self, fstring_prefix, sorted_contacts_counts_map, list_contacts_signed_scores, verbose)
	Plots Contact scores (barchart) with contacts placed on x-axis If reference contacts are known, Score is replaced by a signed score when positive score signifies a true contact and negative score signifies a false contact (w.r.t to the reference contacts)
	get_coverage_score_tuple (self, protein_neighbor_detection_triple_map)
	Takes a map and return number of false, true and missed contacts and the normalized coverage score.
	compute_and_get_contacts_stats (self, set_of_solutions, ofname, verbose)
	This function computes the stats for a set of solutions - either union of solutions or consensus solutions A function that returns sensitivity, specificity and coverage of solutions w.r.t to the reference contacts ref contacts positives, P = ref \cap possible_contacts missed, M = ref \ P negatives, N = possible_contacts \ P for each solution:
	plot_and_dump_contact_stats (self, mci_solution_object, fout_prefix, verbose)
	separate solution is given to be able to plot contact counts separately for all solutions, consensus solutions and post-bootstrap solutions

Detailed Description

A class to analyze the solutions of a connectivity inference problem.

This class performs analysis and plots statistics. Gnuplot is used for plotting.

To use Gnuplot with SAGE , download gnuplotpy-<version>.spkg from http://sagemath.org/packages/optional/ and install.

Here is an example to set the preamble of any gnuplot plot: setting the labels (x-axis, y-axis), the tics (x and y), and name of the output file
opts='[gnuplot_preamble, "set xtics 1"],[gnuplot_term, ps],[gnuplot_out_file, "%s"],[style,points],[xlabel, "Contacts"],[ylabel,"Counts"]'%(of_name)

Plot using the previous preamble:
maxima.plot_list(xlist, ylist,opts)

Member Function Documentation

compute_and_get_contacts_stats()

compute_and_get_contacts_stats	(		self,
			set_of_solutions,
			ofname,
			verbose )

This function computes the stats for a set of solutions - either union of solutions or consensus solutions A function that returns sensitivity, specificity and coverage of solutions w.r.t to the reference contacts ref contacts positives, P = ref \cap possible_contacts missed, M = ref \ P negatives, N = possible_contacts \ P for each solution:

tp = ref \cap solution_contacts fp = solution_contacts \ tp for loop ends

TP = union of all tps; FN = P - TP FP = union of all fps; TN = N - FP

sensitivity = TP/P specificity = TN/N = (N-FP)/N coverage = (TP - (FP+FN))/((TP+FN)*1.0)

get_coverage_score_tuple()

get_coverage_score_tuple	(		self,
			protein_neighbor_detection_triple_map )

Takes a map and return number of false, true and missed contacts and the normalized coverage score.

get_the_list_with_signed_scores()

get_the_list_with_signed_scores	(		self,
			ref_contacts,
			sorted_contacts_counts_map )

Returns a list of contact signed scores in the order of decreasing contact score (by magnitude) For instance, L = [100, -75, 50] ;sign is ignored while rearranging.

plot_and_dump_contact_stats()

plot_and_dump_contact_stats	(		self,
			mci_solution_object,
			fout_prefix,
			verbose )

separate solution is given to be able to plot contact counts separately for all solutions, consensus solutions and post-bootstrap solutions

plot_contacts_counts()

plot_contacts_counts	(		self,
			fstring_prefix,
			sorted_contacts_counts_map,
			list_contacts_signed_scores,
			verbose )

Plots Contact scores (barchart) with contacts placed on x-axis If reference contacts are known, Score is replaced by a signed score when positive score signifies a true contact and negative score signifies a false contact (w.r.t to the reference contacts)