Mercurial > repos > xuebing > sharplabtool
view tools/multivariate_stats/pca.py @ 1:cdcb0ce84a1b
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author | xuebing |
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date | Fri, 09 Mar 2012 19:45:15 -0500 |
parents | 9071e359b9a3 |
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#!/usr/bin/env python from galaxy import eggs import sys, string from rpy import * import numpy def stop_err(msg): sys.stderr.write(msg) sys.exit() infile = sys.argv[1] x_cols = sys.argv[2].split(',') method = sys.argv[3] outfile = sys.argv[4] outfile2 = sys.argv[5] if method == 'svd': scale = center = "FALSE" if sys.argv[6] == 'both': scale = center = "TRUE" elif sys.argv[6] == 'center': center = "TRUE" elif sys.argv[6] == 'scale': scale = "TRUE" fout = open(outfile,'w') elems = [] for i, line in enumerate( file ( infile )): line = line.rstrip('\r\n') if len( line )>0 and not line.startswith( '#' ): elems = line.split( '\t' ) break if i == 30: break # Hopefully we'll never get here... if len( elems )<1: stop_err( "The data in your input dataset is either missing or not formatted properly." ) x_vals = [] for k,col in enumerate(x_cols): x_cols[k] = int(col)-1 x_vals.append([]) NA = 'NA' skipped = 0 for ind,line in enumerate( file( infile )): if line and not line.startswith( '#' ): try: fields = line.strip().split("\t") valid_line = True for k,col in enumerate(x_cols): try: xval = float(fields[col]) except: skipped += 1 valid_line = False break if valid_line: for k,col in enumerate(x_cols): xval = float(fields[col]) x_vals[k].append(xval) except: skipped += 1 x_vals1 = numpy.asarray(x_vals).transpose() dat= r.list(array(x_vals1)) set_default_mode(NO_CONVERSION) try: if method == "cor": pc = r.princomp(r.na_exclude(dat), cor = r("TRUE")) elif method == "cov": pc = r.princomp(r.na_exclude(dat), cor = r("FALSE")) elif method=="svd": pc = r.prcomp(r.na_exclude(dat), center = r(center), scale = r(scale)) except RException, rex: stop_err("Encountered error while performing PCA on the input data: %s" %(rex)) set_default_mode(BASIC_CONVERSION) summary = r.summary(pc, loadings="TRUE") ncomps = len(summary['sdev']) if type(summary['sdev']) == type({}): comps_unsorted = summary['sdev'].keys() comps=[] sd = summary['sdev'].values() for i in range(ncomps): sd[i] = summary['sdev'].values()[comps_unsorted.index('Comp.%s' %(i+1))] comps.append('Comp.%s' %(i+1)) elif type(summary['sdev']) == type([]): comps=[] for i in range(ncomps): comps.append('Comp.%s' %(i+1)) sd = summary['sdev'] print >>fout, "#Component\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)])) print >>fout, "#Std. deviation\t%s" %("\t".join(["%.4g" % el for el in sd])) total_var = 0 vars = [] for s in sd: var = s*s total_var += var vars.append(var) for i,var in enumerate(vars): vars[i] = vars[i]/total_var print >>fout, "#Proportion of variance explained\t%s" %("\t".join(["%.4g" % el for el in vars])) print >>fout, "#Loadings\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)])) xcolnames = ["c%d" %(el+1) for el in x_cols] if 'loadings' in summary: #in case of princomp loadings = 'loadings' elif 'rotation' in summary: #in case of prcomp loadings = 'rotation' for i,val in enumerate(summary[loadings]): print >>fout, "%s\t%s" %(xcolnames[i], "\t".join(["%.4g" % el for el in val])) print >>fout, "#Scores\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)])) if 'scores' in summary: #in case of princomp scores = 'scores' elif 'x' in summary: #in case of prcomp scores = 'x' for obs,sc in enumerate(summary[scores]): print >>fout, "%s\t%s" %(obs+1, "\t".join(["%.4g" % el for el in sc])) r.pdf( outfile2, 8, 8 ) r.biplot(pc) r.dev_off()