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View Code? Open in Web Editor NEWK-means clustering of genes under common regulatory networks from Gene Ontology Microarray Data
K-means clustering of genes under common regulatory networks from Gene Ontology Microarray Data
Ruhani Mumick & Alex Muralles KCUTSOLVER.PY AND SUPPORTING SNIPPETS OF CODE ———————— INPUT FORMAT ——————— All files converted to a tabulated format from the NCBI GEO gene ontology database using the bioconductor package in R, sample R-script below: library(Biobase) library(GEOquery) gds6010 <- getGEO("GDS6010") write.table(Table(gds6010), file = "gds6010.txt") tabulated textfile in the following format: ID_REF IDENTIFIER GSM1626004 GSM1626005 GSM1626006 GSM1625995 GSM1625996 GSM1625997 GSM1626007 GSM1626008 GSM1626009 GSM1625998 GSM1625999 GSM1626000 GSM1626010 GSM1626011 GSM1626012 GSM1626001 GSM1626002 GSM1626003 Currently script only suitable for 18-sample databases, but can (and will) easily be fitted for more ———————— FILE OUTPUT TYPE ———————— randomizeSelection.py - Python code to select randomly from the tabulated file input, used for runtime analysis and data selection (no inputs, used in shell) kCutSolver.py- Main Python Script, used to generate graph and run k-cut solver ———————— USAGE ———————— CALLING: python kCutSolver.py [Processed Microarray Filename (see above for format)] [k] [function Option (1,2)] [Edge Threshold for graph creation] [Cost Threshold (for brute force only)] [Processed Microarray Filename (see above for format)] - tabulated text file [k]- # of clusters, int [function Option (1,2)] - 1 = greedy algorithm, 2 = brute force algorithm [Edge Threshold for graph creation] - minimum weight required to form an edge on the graph, will be done in graph creation [Cost Threshold (for brute force only)] - cost threshold for brute force algorithm, optional OUTPUT: kCutSolverOutput.txt
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