def RunSnvplot(args):
cfg = Parse.generate_snvplot_cfg(args)
Parse.print_snvplot_options(cfg)
if not cfg['debug']:
logging.disable(logging.CRITICAL)
ro.r('suppressMessages(library(ggplot2))')
ro.r('suppressMessages(library(grid))')
handle=pysam.TabixFile(filename=cfg['file'],parser=pysam.asVCF())
header = [x for x in handle.header]
skip_rows = len(header)-1
cols = header[-1].split()
pcols = cfg['pcol'].split(',')
cols_extract = [cfg['chrcol'],cfg['bpcol']] + pcols
if cfg['qq_strat_freq']:
if cfg['freqcol'] not in cols:
print Process.Error("frequency column " + cfg['freqcol'] + " not found, unable to proceed with frequency stratified plots").out
return 1
else:
cols_extract = cols_extract + [cfg['freqcol']]
print "frequency column " + cfg['freqcol'] + " found"
if cfg['qq_strat_mac']:
if cfg['maccol'] not in cols:
print Process.Error("minor allele count column " + cfg['maccol'] + " not found, unable to proceed with minor allele count stratified plots").out
return 1
else:
cols_extract = cols_extract + [cfg['maccol']]
print "minor allele count column " + cfg['maccol'] + " found"
print "importing data"
r = pd.read_table(cfg['file'],sep='\t',skiprows=skip_rows,usecols=cols_extract,compression='gzip')
print str(r.shape[0]) + " total variants found"
for pcol in pcols:
print "plotting p-values for column " + pcol + " ..."
results = r[[cfg['chrcol'],cfg['bpcol'],cfg['freqcol'],pcol]] if cfg['freqcol'] in r else r[[cfg['chrcol'],cfg['bpcol'],pcol]]
results.dropna(inplace=True)
results = results[(results[pcol] > 0) & (results[pcol] <= 1)].reset_index(drop=True)
print " " + str(results.shape[0]) + " variants with plottable p-values"
results['logp'] = -1 * np.log10(results[pcol]) + 0.0
ro.globalenv['results'] = results
l = np.median(scipy.chi2.ppf([1-x for x in results[pcol].tolist()], df=1))/scipy.chi2.ppf(0.5,1)
# in R: median(qchisq(results$p, df=1, lower.tail=FALSE))/qchisq(0.5,1)
print " genomic inflation (all variants) = " + str(l)
if cfg['qq']:
print " generating standard qq plot"
print " minimum p-value: " + str(np.min(results[pcol]))
a = -1 * np.log10(ro.r('ppoints(' + str(len(results.index)) + ')'))
a.sort()
results.sort_values(by=['logp'], inplace=True)
print " maximum -1*log10(p-value): " + str(np.max(results['logp']))
ci_upper = -1 * np.log10(scipy.beta.ppf(0.95, range(1,len(results[pcol]) + 1), range(len(results[pcol]),0,-1)))
ci_upper.sort()
ci_lower = -1 * np.log10(scipy.beta.ppf(0.05, range(1,len(results[pcol]) + 1), range(len(results[pcol]),0,-1)))
ci_lower.sort()
ro.globalenv['df'] = ro.DataFrame({'a': ro.FloatVector(a), 'b': ro.FloatVector(results['logp']), 'ci_lower': ro.FloatVector(ci_lower), 'ci_upper': ro.FloatVector(ci_upper)})
dftext_label = 'lambda %~~% ' + str(l)
ro.globalenv['dftext'] = ro.DataFrame({'x': ro.r('Inf'), 'y': 0.5, 'lab': dftext_label})
if cfg['ext'] == 'tiff':
ggsave = 'ggsave(filename="%s",plot=pp,width=4,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq.tiff')
elif cfg['ext'] == 'eps':
ggsave = 'ggsave(filename="%s",plot=pp,width=4,height=4,bg="white",horizontal=True)' % (cfg['out'] + '.' + pcol + '.qq.eps')
else:
ggsave = 'ggsave(filename="%s",plot=pp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq.pdf')
ro.r("""
gp<-ggplot(df)
pp<-gp +
aes_string(x='a',y='b') +
geom_ribbon(aes_string(x='a',ymin='ci_lower',ymax='ci_upper'), data=df, alpha=0.25, fill='black') +
geom_point(size=2) +
geom_abline(intercept=0, slope=1, alpha=0.5) +
scale_x_discrete(expression(Expected~~-log[10](italic(p)))) +
scale_y_discrete(expression(Observed~~-log[10](italic(p)))) +
coord_fixed() +
theme_bw(base_size = 12) +
geom_text(aes_string(x='x', y='y', label='lab'), data = dftext, colour="black", vjust=0, hjust=1, size = 4, parse=TRUE) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14), legend.position = 'none',
panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.text = element_text(size=12))
%s
""" % (ggsave))
if np.max(results['logp']) > cfg['crop']:
print " generating cropped standard qq plot"
ro.r('df$b[df$b > ' + str(cfg['crop']) + ']<-' + str(cfg['crop']))
ro.r('df$shape<-0')
ro.r('df$shape[df$b == ' + str(cfg['crop']) + ']<-1')
if cfg['ext'] == 'tiff':
ggsave = 'ggsave(filename="%s",plot=pp,width=4,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq.cropped.tiff')
elif cfg['ext'] == 'eps':
ggsave = 'ggsave(filename="%s",plot=pp,width=4,height=4,bg="white",horizontal=True)' % (cfg['out'] + '.' + pcol + '.qq.cropped.eps')
else:
ggsave = 'ggsave(filename="%s",plot=pp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq.cropped.pdf')
ro.r("""
gp<-ggplot(df)
pp<-gp +
aes_string(x='a',y='b') +
geom_ribbon(aes_string(x='a',ymin='ci_lower',ymax='ci_upper'), data=df, alpha=0.25, fill='black') +
geom_point(aes(shape=factor(shape)),size=2) +
geom_abline(intercept=0, slope=1, alpha=0.5) +
scale_x_discrete(expression(Expected~~-log[10](italic(p)))) +
scale_y_discrete(expression(Observed~~-log[10](italic(p)))) +
coord_fixed() +
theme_bw(base_size = 12) +
geom_text(aes_string(x='x', y='y', label='lab'), data = dftext, colour="black", vjust=0, hjust=1, size = 4, parse=TRUE) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14), legend.position = 'none',
panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.text = element_text(size=12))
%s
""" % (ggsave))
if cfg['qq_strat_freq']:
print " generating frequency stratified qq plot"
strat_ticks = [0.005, 0.01, 0.03, 0.05]
results['UGA___QQ_BIN___'] = 'E'
results.loc[(results[cfg['freqcol']] >= 0.01) & (results[cfg['freqcol']] <= 0.99),'UGA___QQ_BIN___'] = 'D'
results.loc[(results[cfg['freqcol']] >= 0.03) & (results[cfg['freqcol']] <= 0.97),'UGA___QQ_BIN___'] = 'C'
results.loc[(results[cfg['freqcol']] >= 0.05) & (results[cfg['freqcol']] <= 0.95),'UGA___QQ_BIN___'] = 'B'
results.loc[(results[cfg['freqcol']] >= 0.1) & (results[cfg['freqcol']] <= 0.9),'UGA___QQ_BIN___'] = 'A'
lA='NA'
lB='NA'
lC='NA'
lD='NA'
lE='NA'
lE_n=len(results[pcol][(results[cfg['freqcol']] < 0.01) | (results[cfg['freqcol']] > 0.99)])
lD_n=len(results[pcol][((results[cfg['freqcol']] >= 0.01) & (results[cfg['freqcol']] < 0.03)) | ((results[cfg['freqcol']] <= 0.99) & (results[cfg['freqcol']] > 0.97))])
lC_n=len(results[pcol][((results[cfg['freqcol']] >= 0.03) & (results[cfg['freqcol']] < 0.05)) | ((results[cfg['freqcol']] <= 0.97) & (results[cfg['freqcol']] > 0.95))])
lB_n=len(results[pcol][((results[cfg['freqcol']] >= 0.05) & (results[cfg['freqcol']] < 0.1)) | ((results[cfg['freqcol']] <= 0.95) & (results[cfg['freqcol']] > 0.9))])
lA_n=len(results[pcol][(results[cfg['freqcol']] >= 0.1) & (results[cfg['freqcol']] <= 0.9)])
if lE_n > 0:
lE=np.median(scipy.chi2.ppf([1-x for x in results[pcol][(results[cfg['freqcol']] < 0.01) | (results[cfg['freqcol']] > 0.99)].tolist()], df=1))/scipy.chi2.ppf(0.5,1)
if lD_n > 0:
lD=np.median(scipy.chi2.ppf([1-x for x in results[pcol][((results[cfg['freqcol']] >= 0.01) & (results[cfg['freqcol']] < 0.03)) | ((results[cfg['freqcol']] <= 0.99) & (results[cfg['freqcol']] > 0.97))].tolist()], df=1))/scipy.chi2.ppf(0.5,1)
if lC_n > 0:
lC=np.median(scipy.chi2.ppf([1-x for x in results[pcol][((results[cfg['freqcol']] >= 0.03) & (results[cfg['freqcol']] < 0.05)) | ((results[cfg['freqcol']] <= 0.97) & (results[cfg['freqcol']] > 0.95))].tolist()], df=1))/scipy.chi2.ppf(0.5,1)
if lB_n > 0:
lB=np.median(scipy.chi2.ppf([1-x for x in results[pcol][((results[cfg['freqcol']] >= 0.05) & (results[cfg['freqcol']] < 0.1)) | ((results[cfg['freqcol']] <= 0.95) & (results[cfg['freqcol']] > 0.9))].tolist()], df=1))/scipy.chi2.ppf(0.5,1)
if lA_n > 0:
lA=np.median(scipy.chi2.ppf([1-x for x in results[pcol][(results[cfg['freqcol']] >= 0.1) & (results[cfg['freqcol']] <= 0.9)].tolist()], df=1))/scipy.chi2.ppf(0.5,1)
print " genomic inflation (MAF >= 10%, n=" + str(lA_n) + ") = " + str(lA)
print " genomic inflation (5% <= MAF < 10%, n=" + str(lB_n) + ") = " + str(lB)
print " genomic inflation (3% <= MAF < 5%, n=" + str(lC_n) + ") = " + str(lC)
print " genomic inflation (1% <= MAF < 3%, n=" + str(lD_n) + ") = " + str(lD)
print " genomic inflation (MAF < 1%, n=" + str(lE_n) + ") = " + str(lE)
a = np.array([])
b = np.array([])
c = np.array([])
results.sort_values(by=['logp'], inplace=True)
if len(results[results['UGA___QQ_BIN___'] == 'E'].index) > 0:
aa = -1 * np.log10(ro.r('ppoints(' + str(len(results[results['UGA___QQ_BIN___'] == 'E'].index)) + ')'))
aa.sort()
bb = results['logp'][results['UGA___QQ_BIN___'] == 'E']
#bb.sort()
cc = results['UGA___QQ_BIN___'][results['UGA___QQ_BIN___'] == 'E']
a = np.append(a,aa)
b = np.append(b,bb)
c = np.append(c,cc)
print " minimum p-value (MAF < 1%): " + str(np.min(results[pcol][results['UGA___QQ_BIN___'] == 'E']))
print " maximum -1*log10(p-value) (MAF < 1%): " + str(np.max(results['logp'][results['UGA___QQ_BIN___'] == 'E']))
if len(results[results['UGA___QQ_BIN___'] == 'D'].index) > 0:
aa = -1 * np.log10(ro.r('ppoints(' + str(len(results[results['UGA___QQ_BIN___'] == 'D'].index)) + ')'))
aa.sort()
bb = results['logp'][results['UGA___QQ_BIN___'] == 'D']
#bb.sort()
cc = results['UGA___QQ_BIN___'][results['UGA___QQ_BIN___'] == 'D']
a = np.append(a,aa)
b = np.append(b,bb)
c = np.append(c,cc)
print " minimum p-value (1% <= MAF < 3%): " + str(np.min(results[pcol][results['UGA___QQ_BIN___'] == 'D']))
print " maximum -1*log10(p-value) (1% <= MAF < 3%): " + str(np.max(results['logp'][results['UGA___QQ_BIN___'] == 'D']))
if len(results[results['UGA___QQ_BIN___'] == 'C'].index) > 0:
aa = -1 * np.log10(ro.r('ppoints(' + str(len(results[results['UGA___QQ_BIN___'] == 'C'].index)) + ')'))
aa.sort()
bb = results['logp'][results['UGA___QQ_BIN___'] == 'C']
#bb.sort()
cc = results['UGA___QQ_BIN___'][results['UGA___QQ_BIN___'] == 'C']
a = np.append(a,aa)
b = np.append(b,bb)
c = np.append(c,cc)
print " minimum p-value (3% <= MAF < 5%): " + str(np.min(results[pcol][results['UGA___QQ_BIN___'] == 'C']))
print " maximum -1*log10(p-value) (3% <= MAF < 5%): " + str(np.max(results['logp'][results['UGA___QQ_BIN___'] == 'C']))
if len(results[results['UGA___QQ_BIN___'] == 'B'].index) > 0:
aa = -1 * np.log10(ro.r('ppoints(' + str(len(results[results['UGA___QQ_BIN___'] == 'B'].index)) + ')'))
aa.sort()
bb = results['logp'][results['UGA___QQ_BIN___'] == 'B']
#bb.sort()
cc = results['UGA___QQ_BIN___'][results['UGA___QQ_BIN___'] == 'B']
a = np.append(a,aa)
b = np.append(b,bb)
c = np.append(c,cc)
print " minimum p-value (5% <= MAF < 10%): " + str(np.min(results[pcol][results['UGA___QQ_BIN___'] == 'B']))
print " maximum -1*log10(p-value) (5% <= MAF < 10%): " + str(np.max(results['logp'][results['UGA___QQ_BIN___'] == 'B']))
if len(results[results['UGA___QQ_BIN___'] == 'A'].index) > 0:
aa = -1 * np.log10(ro.r('ppoints(' + str(len(results[results['UGA___QQ_BIN___'] == 'A'].index)) + ')'))
aa.sort()
bb = results['logp'][results['UGA___QQ_BIN___'] == 'A']
#bb.sort()
cc = results['UGA___QQ_BIN___'][results['UGA___QQ_BIN___'] == 'A']
a = np.append(a,aa)
b = np.append(b,bb)
c = np.append(c,cc)
print " minimum p-value (MAF >= 10%): " + str(np.min(results[pcol][results['UGA___QQ_BIN___'] == 'A']))
print " maximum -1*log10(p-value) (MAF >= 10%): " + str(np.max(results['logp'][results['UGA___QQ_BIN___'] == 'A']))
ro.globalenv['df'] = ro.DataFrame({'a': ro.FloatVector(a), 'b': ro.FloatVector(b), 'UGA___QQ_BIN___': ro.StrVector(c)})
if cfg['ext'] == 'tiff':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq_strat_freq.tiff')
elif cfg['ext'] == 'eps':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white",horizontal=True)' % (cfg['out'] + '.' + pcol + '.qq_strat_freq.eps')
else:
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq_strat_freq.pdf')
ro.r("""
gp<-ggplot(df, aes_string(x='a',y='b')) +
geom_point(aes_string(color='UGA___QQ_BIN___'), size=2) +
scale_colour_manual(values=c("E"="#a8ddb5", "D"="#7bccc4", "C"="#4eb3d3", "B"="#2b8cbe", "A"="#08589e"), labels=c("E"="MAF < 1%%","D"="1%% <= MAF < 3%%","C"="3%% <= MAF < 5%%","B"="5%% <= MAF < 10%%","A"="MAF >= 10%%")) +
geom_abline(intercept=0, slope=1, alpha=0.5) +
scale_x_discrete(expression(Expected~~-log[10](italic(p)))) +
scale_y_discrete(expression(Observed~~-log[10](italic(p)))) +
coord_fixed() +
theme_bw(base_size = 12) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14), legend.title = element_blank(),
legend.key.height = unit(0.1,"in"), legend.text = element_text(size=5), legend.key = element_blank(), legend.justification = c(0,1),
legend.position = c(0,1), panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.text = element_text(size=12))
%s
""" % (ggsave))
if np.max(results['logp']) > cfg['crop']:
print " generating cropped frequency stratified qq plot"
ro.r('df$b[df$b > ' + str(cfg['crop']) + ']<-' + str(cfg['crop']))
ro.r('df$shape<-0')
ro.r('df$shape[df$b == ' + str(cfg['crop']) + ']<-1')
if cfg['ext'] == 'tiff':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq_strat_freq.cropped.tiff')
elif cfg['ext'] == 'eps':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white",horizontal=True)' % (cfg['out'] + '.' + pcol + '.qq_strat_freq.cropped.eps')
else:
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq_strat_freq.cropped.pdf')
ro.r("""
gp<-ggplot(df, aes_string(x='a',y='b')) +
geom_point(aes(shape=factor(shape), color=UGA_MAF), size=2) +
scale_colour_manual(values=c("E"="#a8ddb5", "D"="#7bccc4", "C"="#4eb3d3", "B"="#2b8cbe", "A"="#08589e"), labels=c("E"="MAF < 1%%","D"="1%% <= MAF < 3%%","C"="3%% <= MAF < 5%%","B"="5%% <= MAF < 10%%","A"="MAF >= 10%%")) +
geom_abline(intercept=0, slope=1, alpha=0.5) +
scale_x_discrete(expression(Expected~~-log[10](italic(p)))) +
scale_y_discrete(expression(Observed~~-log[10](italic(p)))) +
coord_fixed() +
theme_bw(base_size = 12) +
guides(shape=FALSE) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14), legend.title = element_blank(),
legend.key.height = unit(0.1,"in"), legend.text = element_text(size=5), legend.key = element_blank(), legend.justification = c(0,1),
legend.position = c(0,1), panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.text = element_text(size=12))
%s
""" % (ggsave))
#if cfg['qq_strat_mac']:
# print " generating minor allele count stratified qq plot"
#
# results['UGA_MAC'] = 'E'
# results.loc[results[cfg['maccol']] < 5),'UGA_MAC'] = 'D'
# results.loc[(results[cfg['maccol']] >= 0.03) & (results[cfg['maccol']] <= 0.97),'UGA_MAC'] = 'C'
# results.loc[(results[cfg['maccol']] >= 0.05) & (results[cfg['maccol']] <= 0.95),'UGA_MAC'] = 'B'
# results.loc[(results[cfg['maccol']] >= 0.1) & (results[cfg['maccol']] <= 0.9),'UGA_MAC'] = 'A'
# lA='NA'
# lB='NA'
# lC='NA'
# lD='NA'
# lE='NA'
# lE_n=len(results[pcol][(results[cfg['maccol']] < 0.01) | (results[cfg['maccol']] > 0.99)])
# lD_n=len(results[pcol][((results[cfg['maccol']] >= 0.01) & (results[cfg['maccol']] < 0.03)) | ((results[cfg['maccol']] <= 0.99) & (results[cfg['maccol']] > 0.97))])
# lC_n=len(results[pcol][((results[cfg['maccol']] >= 0.03) & (results[cfg['maccol']] < 0.05)) | ((results[cfg['maccol']] <= 0.97) & (results[cfg['maccol']] > 0.95))])
# lB_n=len(results[pcol][((results[cfg['maccol']] >= 0.05) & (results[cfg['maccol']] < 0.1)) | ((results[cfg['maccol']] <= 0.95) & (results[cfg['maccol']] > 0.9))])
# lA_n=len(results[pcol][(results[cfg['maccol']] >= 0.1) & (results[cfg['maccol']] <= 0.9)])
# if lE_n > 0:
# lE=np.median(scipy.chi2.ppf([1-x for x in results[pcol][(results[cfg['maccol']] < 0.01) | (results[cfg['maccol']] > 0.99)].tolist()], df=1))/scipy.chi2.ppf(0.5,1)
# if lD_n > 0:
# lD=np.median(scipy.chi2.ppf([1-x for x in results[pcol][((results[cfg['maccol']] >= 0.01) & (results[cfg['maccol']] < 0.03)) | ((results[cfg['maccol']] <= 0.99) & (results[cfg['maccol']] > 0.97))].tolist()], df=1))/scipy.chi2.ppf(0.5,1)
# if lC_n > 0:
# lC=np.median(scipy.chi2.ppf([1-x for x in results[pcol][((results[cfg['maccol']] >= 0.03) & (results[cfg['maccol']] < 0.05)) | ((results[cfg['maccol']] <= 0.97) & (results[cfg['maccol']] > 0.95))].tolist()], df=1))/scipy.chi2.ppf(0.5,1)
# if lB_n > 0:
# lB=np.median(scipy.chi2.ppf([1-x for x in results[pcol][((results[cfg['maccol']] >= 0.05) & (results[cfg['maccol']] < 0.1)) | ((results[cfg['maccol']] <= 0.95) & (results[cfg['maccol']] > 0.9))].tolist()], df=1))/scipy.chi2.ppf(0.5,1)
# if lA_n > 0:
# lA=np.median(scipy.chi2.ppf([1-x for x in results[pcol][(results[cfg['maccol']] >= 0.1) & (results[cfg['maccol']] <= 0.9)].tolist()], df=1))/scipy.chi2.ppf(0.5,1)
# print " genomic inflation (MAF >= 10%, n=" + str(lA_n) + ") = " + str(lA)
# print " genomic inflation (5% <= MAF < 10%, n=" + str(lB_n) + ") = " + str(lB)
# print " genomic inflation (3% <= MAF < 5%, n=" + str(lC_n) + ") = " + str(lC)
# print " genomic inflation (1% <= MAF < 3%, n=" + str(lD_n) + ") = " + str(lD)
# print " genomic inflation (MAF < 1%, n=" + str(lE_n) + ") = " + str(lE)
#
# a = np.array([])
# b = np.array([])
# c = np.array([])
# results.sort_values(by=['logp'], inplace=True)
# if len(results[results['UGA_MAC'] == 'E'].index) > 0:
# aa = -1 * np.log10(ro.r('ppoints(' + str(len(results[results['UGA_MAC'] == 'E'].index)) + ')'))
# aa.sort()
# bb = results['logp'][results['UGA_MAC'] == 'E']
# #bb.sort()
# cc = results['UGA_MAC'][results['UGA_MAC'] == 'E']
# a = np.append(a,aa)
# b = np.append(b,bb)
# c = np.append(c,cc)
# print " minimum p-value (MAF < 1%): " + str(np.min(results[pcol][results['UGA_MAC'] == 'E']))
# print " maximum -1*log10(p-value) (MAF < 1%): " + str(np.max(results['logp'][results['UGA_MAC'] == 'E']))
# if len(results[results['UGA_MAC'] == 'D'].index) > 0:
# aa = -1 * np.log10(ro.r('ppoints(' + str(len(results[results['UGA_MAC'] == 'D'].index)) + ')'))
# aa.sort()
# bb = results['logp'][results['UGA_MAC'] == 'D']
# #bb.sort()
# cc = results['UGA_MAC'][results['UGA_MAC'] == 'D']
# a = np.append(a,aa)
# b = np.append(b,bb)
# c = np.append(c,cc)
# print " minimum p-value (1% <= MAF < 3%): " + str(np.min(results[pcol][results['UGA_MAC'] == 'D']))
# print " maximum -1*log10(p-value) (1% <= MAF < 3%): " + str(np.max(results['logp'][results['UGA_MAC'] == 'D']))
# if len(results[results['UGA_MAC'] == 'C'].index) > 0:
# aa = -1 * np.log10(ro.r('ppoints(' + str(len(results[results['UGA_MAC'] == 'C'].index)) + ')'))
# aa.sort()
# bb = results['logp'][results['UGA_MAC'] == 'C']
# #bb.sort()
# cc = results['UGA_MAC'][results['UGA_MAC'] == 'C']
# a = np.append(a,aa)
# b = np.append(b,bb)
# c = np.append(c,cc)
# print " minimum p-value (3% <= MAF < 5%): " + str(np.min(results[pcol][results['UGA_MAC'] == 'C']))
# print " maximum -1*log10(p-value) (3% <= MAF < 5%): " + str(np.max(results['logp'][results['UGA_MAC'] == 'C']))
# if len(results[results['UGA_MAC'] == 'B'].index) > 0:
# aa = -1 * np.log10(ro.r('ppoints(' + str(len(results[results['UGA_MAC'] == 'B'].index)) + ')'))
# aa.sort()
# bb = results['logp'][results['UGA_MAC'] == 'B']
# #bb.sort()
# cc = results['UGA_MAC'][results['UGA_MAC'] == 'B']
# a = np.append(a,aa)
# b = np.append(b,bb)
# c = np.append(c,cc)
# print " minimum p-value (5% <= MAF < 10%): " + str(np.min(results[pcol][results['UGA_MAC'] == 'B']))
# print " maximum -1*log10(p-value) (5% <= MAF < 10%): " + str(np.max(results['logp'][results['UGA_MAC'] == 'B']))
# if len(results[results['UGA_MAC'] == 'A'].index) > 0:
# aa = -1 * np.log10(ro.r('ppoints(' + str(len(results[results['UGA_MAC'] == 'A'].index)) + ')'))
# aa.sort()
# bb = results['logp'][results['UGA_MAC'] == 'A']
# #bb.sort()
# cc = results['UGA_MAC'][results['UGA_MAC'] == 'A']
# a = np.append(a,aa)
# b = np.append(b,bb)
# c = np.append(c,cc)
# print " minimum p-value (MAF >= 10%): " + str(np.min(results[pcol][results['UGA_MAC'] == 'A']))
# print " maximum -1*log10(p-value) (MAF >= 10%): " + str(np.max(results['logp'][results['UGA_MAC'] == 'A']))
#
# ro.globalenv['df'] = ro.DataFrame({'a': ro.FloatVector(a), 'b': ro.FloatVector(b), 'UGA_MAC': ro.StrVector(c)})
#
# if cfg['ext'] == 'tiff':
# ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq_strat.tiff')
# elif cfg['ext'] == 'eps':
# ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white",horizontal=True)' % (cfg['out'] + '.' + pcol + '.qq_strat.eps')
# else:
# ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq_strat.pdf')
# ro.r("""
# gp<-ggplot(df, aes_string(x='a',y='b')) +
# geom_point(aes_string(color='UGA_MAC'), size=2) +
# scale_colour_manual(values=c("E"="#a8ddb5", "D"="#7bccc4", "C"="#4eb3d3", "B"="#2b8cbe", "A"="#08589e"), labels=c("E"="MAF < 1%%","D"="1%% <= MAF < 3%%","C"="3%% <= MAF < 5%%","B"="5%% <= MAF < 10%%","A"="MAF >= 10%%")) +
# geom_abline(intercept=0, slope=1, alpha=0.5) +
# scale_x_continuous(expression(Expected~~-log[10](italic(p)))) +
# scale_y_continuous(expression(Observed~~-log[10](italic(p)))) +
# theme_bw(base_size = 12) +
# theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14), legend.title = element_blank(),
# legend.key.height = unit(0.1,"in"), legend.text = element_text(size=5), legend.key = element_blank(), legend.justification = c(0,1),
# legend.position = c(0,1), panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
# panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.text = element_text(size=12))
# %s
# """ % (ggsave))
#
# if np.max(results['logp']) > cfg['crop']:
# print " generating cropped frequency stratified qq plot"
# ro.r('df$b[df$b > ' + str(cfg['crop']) + ']<-' + str(cfg['crop']))
# ro.r('df$shape<-0')
# ro.r('df$shape[df$b == ' + str(cfg['crop']) + ']<-1')
# if cfg['ext'] == 'tiff':
# ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq_strat.cropped.tiff')
# elif cfg['ext'] == 'eps':
# ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white",horizontal=True)' % (cfg['out'] + '.' + pcol + '.qq_strat.cropped.eps')
# else:
# ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq_strat.cropped.pdf')
# ro.r("""
# gp<-ggplot(df, aes_string(x='a',y='b')) +
# geom_point(aes(shape=factor(shape), color=UGA_MAC), size=2) +
# scale_colour_manual(values=c("E"="#a8ddb5", "D"="#7bccc4", "C"="#4eb3d3", "B"="#2b8cbe", "A"="#08589e"), labels=c("E"="MAF < 1%%","D"="1%% <= MAF < 3%%","C"="3%% <= MAF < 5%%","B"="5%% <= MAF < 10%%","A"="MAF >= 10%%")) +
# geom_abline(intercept=0, slope=1, alpha=0.5) +
# scale_x_continuous(expression(Expected~~-log[10](italic(p)))) +
# scale_y_continuous(expression(Observed~~-log[10](italic(p)))) +
# theme_bw(base_size = 12) +
# guides(shape=FALSE) +
# theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14), legend.title = element_blank(),
# legend.key.height = unit(0.1,"in"), legend.text = element_text(size=5), legend.key = element_blank(), legend.justification = c(0,1),
# legend.position = c(0,1), panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
# panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.text = element_text(size=12))
# %s
# """ % (ggsave))
if cfg['mht']:
print " generating standard manhattan plot"
print " minimum p-value: " + str(np.min(results[pcol]))
print " maximum -1*log10(p-value): " + str(np.max(results['logp']))
if cfg['gc'] and l > 1:
print " adjusting p-values for genomic inflation for p-value column " + pcol
results[pcol]=2 * scipy.norm.cdf(-1 * np.abs(scipy.norm.ppf(0.5*results[pcol]) / math.sqrt(l)))
print " minimum post-gc adjustment p-value: " + str(np.min(results[pcol]))
print " maximum post-gc adjustment -1*log10(p-value): " + str(np.max(results['logp']))
else:
print " skipping genomic inflation correction"
print " calculating genomic positions"
results.sort_values(by=[cfg['chrcol'],cfg['bpcol']], inplace=True)
ticks = []
lastbase = 0
results['gpos'] = 0
nchr = len(list(np.unique(results[cfg['chrcol']].values)))
chrs = np.unique(results[cfg['chrcol']].values)
if cfg['color']:
colours = ["#08306B","#41AB5D","#000000","#F16913","#3F007D","#EF3B2C","#08519C","#238B45","#252525","#D94801","#54278F","#CB181D","#2171B5","#006D2C","#525252","#A63603","#6A51A3","#A50F15","#4292C6","#00441B","#737373","#7F2704","#807DBA","#67000D"]
else:
colours = ["#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3"]
if nchr == 1:
results['gpos'] = results[cfg['bpcol']]
results['colours'] = "#08589e"
if results['gpos'].max() - results['gpos'].min() <= 1000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 100 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 10000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 1000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 100000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 10000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 200000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 20000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 300000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 30000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 400000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 40000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 500000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 50000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 600000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 60000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 700000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 70000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 800000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 80000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 900000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 90000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 1000000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 100000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 10000000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 1000000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 100000000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 10000000 == 0]
elif results['gpos'].max() - results['gpos'].min() > 100000000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 25000000 == 0]
else:
results['colours'] = "#000000"
for i in range(len(chrs)):
print " processed chromosome " + str(int(chrs[i]))
if i == 0:
results.loc[results[cfg['chrcol']] == chrs[i],'gpos'] = results.loc[results[cfg['chrcol']] == chrs[i],cfg['bpcol']]
else:
lastbase = lastbase + results.loc[results[cfg['chrcol']] == chrs[i-1],cfg['bpcol']].iloc[-1]
results.loc[results[cfg['chrcol']] == chrs[i],'gpos'] = (results.loc[results[cfg['chrcol']] == chrs[i],cfg['bpcol']]) + lastbase
if results.loc[results[cfg['chrcol']] == chrs[i]].shape[0] > 1:
ticks.append(results.loc[results[cfg['chrcol']] == chrs[i],'gpos'].iloc[0] + (results.loc[results[cfg['chrcol']] == chrs[i],'gpos'].iloc[-1] - results.loc[results[cfg['chrcol']] == chrs[i],'gpos'].iloc[0])/2)
else:
ticks.append(results.loc[results[cfg['chrcol']] == chrs[i],'gpos'].iloc[0])
results.loc[results[cfg['chrcol']] == chrs[i],'colours'] = colours[int(chrs[i])]
results['logp'] = -1 * np.log10(results[pcol])
if results.shape[0] >= 1000000:
sig = 5.4e-8
else:
sig = 0.05 / results.shape[0]
print " significance level set to p-value = " + str(sig) + " (-1*log10(p-value) = " + str(-1 * np.log10(sig)) + ")"
print " " + str(len(results[pcol][results[pcol] <= sig])) + " genome wide significant variants"
chr = results[cfg['chrcol']][0]
maxy=int(max(np.ceil(-1 * np.log10(sig)),np.ceil(results['logp'].max())))
if maxy > 20:
y_breaks = range(0,maxy,5)
y_labels = range(0,maxy,5)
else:
y_breaks = range(0,maxy)
y_labels = range(0,maxy)
ro.globalenv['df'] = ro.DataFrame({'gpos': ro.FloatVector(results['gpos']), 'logp': ro.FloatVector(results['logp']), 'colours': ro.FactorVector(results['colours'])})
ro.globalenv['ticks'] = ro.FloatVector(ticks)
ro.globalenv['labels'] = ro.Vector(["{:,}".format(x/1000) for x in ticks])
ro.globalenv['colours'] = ro.StrVector(colours)
ro.globalenv['chrs'] = ro.FloatVector(chrs)
print " generating manhattan plot"
if cfg['ext'] == 'tiff':
ggsave = 'ggsave(filename="%s",plot=gp,width=16,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.mht.tiff')
elif cfg['ext'] == 'eps':
ggsave = 'ggsave(filename="%s",plot=gp,width=16,height=4,bg="white",horizontal=True)' % (cfg['out'] + '.' + pcol + '.mht.eps')
else:
ggsave = 'ggsave(filename="%s",plot=gp,width=16,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.mht.pdf')
if nchr == 1:
ro.r("""
gp<-ggplot(df, aes_string(x='gpos',y='logp')) +
geom_hline(yintercept = -1 * log10(%g),colour="#B8860B", linetype=5, size = 0.25) +
geom_point(size=1.5) +
scale_x_continuous(expression(Chromosome~~%d~~(kb))'),breaks=ticks,labels=labels) + \
scale_y_continuous(expression(-log[10](italic(p))),breaks=seq(0,%d,1),limits=c(0,%d)) + \
theme_bw(base_size = 8) + \
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14),
panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.title = element_text(size=10),
axis.text = element_text(size=12), legend.position = 'none')
%s
""" % (sig, chr, maxy, maxy, ggsave))
else:
ro.r("""
gp = ggplot(df, aes_string(x='gpos',y='logp',colour='colours')) +
geom_hline(yintercept = -1 * log10(%g),colour="#B8860B", linetype=5, size = 0.25) +
geom_point(size=1.5) +
scale_colour_manual(values=colours) +
scale_x_continuous(expression(Chromosome),breaks=ticks,labels=chrs) +
scale_y_continuous(expression(-log[10](italic(p))),breaks=seq(0,%d,1),limits=c(0,%d)) +
theme_bw(base_size = 8) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14),
panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.title = element_text(size=10),
axis.text = element_text(size=12), legend.position = 'none')
%s
""" % (sig, maxy, maxy, ggsave))
if maxy > cfg['crop']:
maxy = cfg['crop']
ro.r('df$logp[df$logp > ' + str(cfg['crop']) + ']<-' + str(cfg['crop']))
ro.r('df$shape<-0')
ro.r('df$shape[df$logp == ' + str(cfg['crop']) + ']<-1')
print " generating cropped manhattan plot"
if cfg['ext'] == 'tiff':
ggsave = 'ggsave(filename="%s",plot=gp,width=16,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.mht.cropped.tiff')
elif cfg['ext'] == 'eps':
ggsave = 'ggsave(filename="%s",plot=gp,width=16,height=4,bg="white",horizontal=True)' % (cfg['out'] + '.' + pcol + '.mht.cropped.eps')
else:
ggsave = 'ggsave(filename="%s",plot=gp,width=16,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.mht.cropped.pdf')
if nchr == 1:
ro.r("""
gp<-ggplot(df, aes_string(x='gpos',y='logp')) +
geom_hline(yintercept = -1 * log10(%g),colour="#B8860B", linetype=5, size = 0.25) +
geom_point(aes(shape=factor(shape)),size=1.5) +
scale_x_continuous(expression(Chromosome~~%d~~(kb))'),breaks=ticks,labels=labels) +
scale_y_continuous(expression(-log[10](italic(p))),breaks=seq(0,%d,1),limits=c(0,%d)) +
theme_bw(base_size = 8) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14),
panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.title = element_text(size=10),
axis.text = element_text(size=12), legend.position = 'none')
%s
""" % (sig, chr, maxy, maxy, ggsave))
else:
ro.r("""
gp = ggplot(df, aes_string(x='gpos',y='logp',colour='colours')) +
geom_hline(yintercept = -1 * log10(%g),colour="#B8860B", linetype=5, size = 0.25) +
geom_point(aes(shape=factor(shape)),size=1.5) +
scale_colour_manual(values=colours) +
scale_x_continuous(expression(Chromosome),breaks=ticks,labels=chrs) +
scale_y_continuous(expression(-log[10](italic(p))),breaks=seq(0,%d,1),limits=c(0,%d)) +
theme_bw(base_size = 8) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14),
panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.title = element_text(size=8),
axis.text = element_text(size=12), legend.position = 'none')
%s
""" % (sig, maxy, maxy, ggsave))
print "process complete"
return 0
def RunSnvplot(args):
cfg = Parse.generate_snvplot_cfg(args)
Parse.print_snvplot_options(cfg)
if not cfg['debug']:
logging.disable(logging.CRITICAL)
ro.r('suppressMessages(library(ggplot2))')
ro.r('suppressMessages(library(grid))')
ro.r('suppressMessages(library(RColorBrewer))')
handle=pysam.TabixFile(filename=cfg['file'],parser=pysam.asVCF())
header = [x for x in handle.header]
skip_rows = len(header)-1
cols = header[-1].split()
pcols = cfg['pcol'].split(',')
cols_extract = [cfg['chrcol'],cfg['bpcol']] + pcols
if cfg['qq_strat_freq']:
if cfg['freqcol'] not in cols:
print Process.Error("frequency column " + cfg['freqcol'] + " not found, unable to proceed with frequency stratified plots").out
return 1
else:
cols_extract = cols_extract + [cfg['freqcol']]
print "frequency column " + cfg['freqcol'] + " found"
if cfg['qq_strat_mac']:
if cfg['maccol'] not in cols:
print Process.Error("minor allele count column " + cfg['maccol'] + " not found, unable to proceed with minor allele count stratified plots").out
return 1
else:
cols_extract = cols_extract + [cfg['maccol']]
print "minor allele count column " + cfg['maccol'] + " found"
print "importing data"
r = pd.read_table(cfg['file'],sep='\t',skiprows=skip_rows,usecols=cols_extract,compression='gzip')
print str(r.shape[0]) + " total variants found"
for pcol in pcols:
print "plotting p-values for column " + pcol + " ..."
extract_cols = [cfg['chrcol'],cfg['bpcol'],pcol]
if cfg['freqcol'] in r:
extract_cols = extract_cols + [cfg['freqcol']]
if cfg['maccol'] in r:
extract_cols = extract_cols + [cfg['maccol']]
results = r[extract_cols]
results.dropna(inplace=True)
results = results[(results[pcol] > 0) & (results[pcol] <= 1)].reset_index(drop=True)
print " " + str(results.shape[0]) + " variants with plottable p-values"
results['logp'] = -1 * np.log10(results[pcol]) + 0.0
ro.globalenv['results'] = results
l = np.median(scipy.chi2.ppf([1-x for x in results[pcol].tolist()], df=1))/scipy.chi2.ppf(0.5,1)
# in R: median(qchisq(results$p, df=1, lower.tail=FALSE))/qchisq(0.5,1)
print " genomic inflation (all variants) = " + str(l)
if cfg['qq']:
print " generating standard qq plot"
print " minimum p-value: " + str(np.min(results[pcol]))
a = -1 * np.log10(ro.r('ppoints(' + str(len(results.index)) + ')'))
a.sort()
results.sort_values(by=['logp'], inplace=True)
print " maximum -1*log10(p-value): " + str(np.max(results['logp']))
ci_upper = -1 * np.log10(scipy.beta.ppf(0.95, range(1,len(results[pcol]) + 1), range(len(results[pcol]),0,-1)))
ci_upper.sort()
ci_lower = -1 * np.log10(scipy.beta.ppf(0.05, range(1,len(results[pcol]) + 1), range(len(results[pcol]),0,-1)))
ci_lower.sort()
ro.globalenv['df'] = ro.DataFrame({'a': ro.FloatVector(a), 'b': ro.FloatVector(results['logp']), 'ci_lower': ro.FloatVector(ci_lower), 'ci_upper': ro.FloatVector(ci_upper)})
dftext_label = 'lambda %~~% ' + str(round(l,3))
ro.globalenv['dftext'] = ro.DataFrame({'x': ro.r('Inf'), 'y': 0.5, 'lab': dftext_label})
if cfg['ext'] == 'tiff':
ggsave = 'ggsave(filename="%s",plot=pp,width=4,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq.tiff')
elif cfg['ext'] == 'png':
ggsave = 'ggsave(filename="%s",plot=pp,width=4,height=4,units="in",bg="white",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq.png')
elif cfg['ext'] == 'eps':
ggsave = 'ggsave(filename="%s",plot=pp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq.eps')
else:
ggsave = 'ggsave(filename="%s",plot=pp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq.pdf')
ro.r("""
gp<-ggplot(df)
pp<-gp +
aes_string(x='a',y='b') +
geom_ribbon(aes_string(x='a',ymin='ci_lower',ymax='ci_upper'), data=df, alpha=0.25, fill='black') +
geom_point(size=2) +
geom_abline(intercept=0, slope=1, alpha=0.5) +
scale_x_discrete(expression(Expected~~-log[10](italic(p)))) +
scale_y_discrete(expression(Observed~~-log[10](italic(p)))) +
coord_fixed() +
theme_bw(base_size = 12) +
geom_text(aes_string(x='x', y='y', label='lab'), data = dftext, colour="black", vjust=0, hjust=1, size = 4, parse=TRUE) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14), legend.position = 'none',
panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.text = element_text(size=12))
%s
""" % (ggsave))
if np.max(results['logp']) > cfg['crop']:
print " generating cropped standard qq plot"
ro.r('df$b[df$b > ' + str(cfg['crop']) + ']<-' + str(cfg['crop']))
ro.r('df$shape<-0')
ro.r('df$shape[df$b == ' + str(cfg['crop']) + ']<-1')
if cfg['ext'] == 'tiff':
ggsave = 'ggsave(filename="%s",plot=pp,width=4,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq.cropped.tiff')
elif cfg['ext'] == 'png':
ggsave = 'ggsave(filename="%s",plot=pp,width=4,height=4,units="in",bg="white",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq.cropped.png')
elif cfg['ext'] == 'eps':
ggsave = 'ggsave(filename="%s",plot=pp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq.cropped.eps')
else:
ggsave = 'ggsave(filename="%s",plot=pp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq.cropped.pdf')
ro.r("""
gp<-ggplot(df)
pp<-gp +
aes_string(x='a',y='b') +
geom_ribbon(aes_string(x='a',ymin='ci_lower',ymax='ci_upper'), data=df, alpha=0.25, fill='black') +
geom_point(aes(shape=factor(shape)),size=2) +
geom_abline(intercept=0, slope=1, alpha=0.5) +
scale_x_discrete(expression(Expected~~-log[10](italic(p)))) +
scale_y_discrete(expression(Observed~~-log[10](italic(p)))) +
coord_fixed() +
theme_bw(base_size = 12) +
geom_text(aes_string(x='x', y='y', label='lab'), data = dftext, colour="black", vjust=0, hjust=1, size = 4, parse=TRUE) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14), legend.position = 'none',
panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.text = element_text(size=12))
%s
""" % (ggsave))
def ppoints(n, a):
try:
n = np.float(len(n))
except TypeError:
n = np.float(n)
return (np.arange(n) + 1 - a)/(n + 1 - 2*a)
if cfg['qq_strat_freq']:
print " generating frequency stratified qq plot"
strat_ticks = np.sort([np.float(x) for x in cfg['freq_ticks'].split(',')])
results['UGA___QQ_BIN___'] = 0
for i in xrange(len(strat_ticks)):
results.loc[(results[cfg['freqcol']] >= strat_ticks[i]) & (results[cfg['freqcol']] <= 1-strat_ticks[i]),'UGA___QQ_BIN___'] = i+1
bin_values = results['UGA___QQ_BIN___'].value_counts()
for i in xrange(len(strat_ticks)+1):
if i not in bin_values.index:
bin_values[i] = 0
counts = pd.DataFrame(bin_values)
counts['lambda'] = np.nan
results['description'] = 'NA'
for i in xrange(len(strat_ticks)+1):
if counts.loc[i,'UGA___QQ_BIN___'] > 0:
counts.loc[i,'lambda'] = np.median(scipy.chi2.ppf([1-x for x in results[pcol][results['UGA___QQ_BIN___'] == i].tolist()], df=1))/scipy.chi2.ppf(0.5,1)
else:
counts.loc[i,'lambda'] = np.nan
if i == 0:
results.loc[results['UGA___QQ_BIN___'] == i,'description'] = "(0," + str(strat_ticks[i]) + ") ~" + str(round(counts.loc[i,'lambda'],3))
print " MAF (0," + str(strat_ticks[i]) + "): n=" + str(np.int(counts.loc[i,'UGA___QQ_BIN___'])) + ", lambda=" + str(counts.loc[i,'lambda'])
elif i < len(strat_ticks):
results.loc[results['UGA___QQ_BIN___'] == i,'description'] = "[" + str(strat_ticks[i-1]) + "," + str(strat_ticks[i]) + ") ~" + str(round(counts.loc[i,'lambda'],3))
print " MAF [" + str(strat_ticks[i-1]) + "," + str(strat_ticks[i]) + "): n=" + str(np.int(counts.loc[i,'UGA___QQ_BIN___'])) + ", lambda=" + str(counts.loc[i,'lambda'])
else:
results.loc[results['UGA___QQ_BIN___'] == i,'description'] = "[" + str(strat_ticks[i-1]) + ",0.5] ~" + str(round(counts.loc[i,'lambda'],3))
print " MAF [" + str(strat_ticks[i-1]) + ",0.5]: n=" + str(np.int(counts.loc[i,'UGA___QQ_BIN___'])) + ", lambda=" + str(counts.loc[i,'lambda'])
results.sort_values(['UGA___QQ_BIN___','logp'],inplace=True)
results['expected'] = 0
for i in counts.index:
if counts.loc[i,'UGA___QQ_BIN___'] > 0:
results.loc[results['UGA___QQ_BIN___'] == i,'expected'] = np.sort(-1 * np.log10(ppoints(len(results.loc[results['UGA___QQ_BIN___'] == i,'expected']),0)))
ro.globalenv['df'] = ro.DataFrame({'expected': ro.FloatVector(results['expected']), 'logp': ro.FloatVector(results['logp']), 'UGA___QQ_BIN___': ro.IntVector(results['UGA___QQ_BIN___']), 'description': ro.StrVector(results['description'])})
ro.r("df<-df[order(df$UGA___QQ_BIN___),]")
ro.r("df$description<-ordered(df$description,levels=unique(df$description))")
if cfg['ext'] == 'tiff':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq_strat_freq.tiff')
elif cfg['ext'] == 'png':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,units="in",bg="white",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq_strat_freq.png')
elif cfg['ext'] == 'eps':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq_strat_freq.eps')
else:
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq_strat_freq.pdf')
ro.r("""
gp<-ggplot(df, aes_string(x='expected',y='logp')) +
geom_point(aes_string(color='description'), size=2) +
scale_colour_manual(values=colorRampPalette(brewer.pal(9,"Blues"))(length(unique(df$description))+2)[3:(length(unique(df$description))+2)]) +
geom_abline(intercept=0, slope=1, alpha=0.5) +
scale_x_discrete(expression(Expected~~-log[10](italic(p)))) +
scale_y_discrete(expression(Observed~~-log[10](italic(p)))) +
coord_fixed() +
theme_bw(base_size = 12) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14), legend.title = element_blank(),
legend.key.height = unit(0.1,"in"), legend.text = element_text(size=6), legend.key = element_blank(), legend.justification = c(0,1),
legend.position = c(0,1), panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.text = element_text(size=12))
%s
""" % (ggsave))
if np.max(results['logp']) > cfg['crop']:
print " generating cropped frequency stratified qq plot"
ro.r('df$logp[df$logp > ' + str(cfg['crop']) + ']<-' + str(cfg['crop']))
ro.r('df$shape<-0')
ro.r('df$shape[df$logp == ' + str(cfg['crop']) + ']<-1')
if cfg['ext'] == 'tiff':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq_strat_freq.cropped.tiff')
elif cfg['ext'] == 'png':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,units="in",bg="white",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq_strat_freq.cropped.png')
elif cfg['ext'] == 'eps':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq_strat_freq.cropped.eps')
else:
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq_strat_freq.cropped.pdf')
ro.r("""
gp<-ggplot(df, aes_string(x='expected',y='logp')) +
geom_point(aes(shape=factor(shape), color=description), size=2) +
scale_colour_manual(values=colorRampPalette(brewer.pal(9,"Blues"))(length(unique(df$description))+2)[3:(length(unique(df$description))+2)]) +
geom_abline(intercept=0, slope=1, alpha=0.5) +
scale_x_discrete(expression(Expected~~-log[10](italic(p)))) +
scale_y_discrete(expression(Observed~~-log[10](italic(p)))) +
coord_fixed() +
theme_bw(base_size = 12) +
guides(shape=FALSE) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14), legend.title = element_blank(),
legend.key.height = unit(0.1,"in"), legend.text = element_text(size=6), legend.key = element_blank(), legend.justification = c(0,1),
legend.position = c(0,1), panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.text = element_text(size=12))
%s
""" % (ggsave))
if cfg['qq_strat_mac']:
print " generating minor allele count stratified qq plot"
strat_ticks = np.sort([np.float(x) for x in cfg['mac_ticks'].split(',')])
results['UGA___QQ_BIN___'] = 0
for i in xrange(len(strat_ticks)):
results.loc[results[cfg['maccol']] >= strat_ticks[i],'UGA___QQ_BIN___'] = i+1
bin_values = results['UGA___QQ_BIN___'].value_counts()
for i in xrange(len(strat_ticks)+1):
if i not in bin_values.index:
bin_values[i] = 0
counts = pd.DataFrame(bin_values)
counts['lambda'] = 0
results['description'] = 'NA'
for i in np.sort(counts.index):
if counts.loc[i,'UGA___QQ_BIN___'] > 0:
counts.loc[i,'lambda'] = np.median(scipy.chi2.ppf([1-x for x in results[pcol][results['UGA___QQ_BIN___'] == i].tolist()], df=1))/scipy.chi2.ppf(0.5,1)
else:
counts.loc[i,'lambda'] = np.nan
if i == 0:
results.loc[results['UGA___QQ_BIN___'] == i,'description'] = "(0," + str(int(strat_ticks[i])) + ") ~" + str(round(counts.loc[i,'lambda'],3))
print " MAC (0," + str(int(strat_ticks[i])) + "): n=" + str(np.int(counts.loc[i,'UGA___QQ_BIN___'])) + ", lambda=" + str(counts.loc[i,'lambda'])
elif i < len(strat_ticks):
results.loc[results['UGA___QQ_BIN___'] == i,'description'] = "[" + str(int(strat_ticks[i-1])) + "," + str(int(strat_ticks[i])) + ") ~" + str(round(counts.loc[i,'lambda'],3))
print " MAC [" + str(int(strat_ticks[i-1])) + "," + str(int(strat_ticks[i])) + "): n=" + str(np.int(counts.loc[i,'UGA___QQ_BIN___'])) + ", lambda=" + str(counts.loc[i,'lambda'])
else:
results.loc[results['UGA___QQ_BIN___'] == i,'description'] = "[" + str(int(strat_ticks[i-1])) + ",...] ~" + str(round(counts.loc[i,'lambda'],3))
print " MAC [" + str(int(strat_ticks[i-1])) + ",...]: n=" + str(np.int(counts.loc[i,'UGA___QQ_BIN___'])) + ", lambda=" + str(counts.loc[i,'lambda'])
results.sort_values(['UGA___QQ_BIN___','logp'],inplace=True)
results['expected'] = 0
for i in counts.index:
results.loc[results['UGA___QQ_BIN___'] == i,'expected'] = np.sort(-1 * np.log10(ppoints(len(results.loc[results['UGA___QQ_BIN___'] == i,'expected']),0)))
ro.globalenv['df'] = ro.DataFrame({'expected': ro.FloatVector(results['expected']), 'logp': ro.FloatVector(results['logp']), 'UGA___QQ_BIN___': ro.IntVector(results['UGA___QQ_BIN___']), 'description': ro.StrVector(results['description'])})
ro.r("df<-df[order(df$UGA___QQ_BIN___),]")
ro.r("df$description<-ordered(df$description,levels=unique(df$description))")
if cfg['ext'] == 'tiff':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq_strat_mac.tiff')
elif cfg['ext'] == 'png':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,units="in",bg="white",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq_strat_mac.png')
elif cfg['ext'] == 'eps':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq_strat_mac.eps')
else:
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq_strat_mac.pdf')
ro.r("""
gp<-ggplot(df, aes_string(x='expected',y='logp')) +
geom_point(aes_string(color='description'), size=2) +
scale_colour_manual(values=colorRampPalette(brewer.pal(9,"Blues"))(length(unique(df$description))+2)[3:(length(unique(df$description))+2)]) +
geom_abline(intercept=0, slope=1, alpha=0.5) +
scale_x_discrete(expression(Expected~~-log[10](italic(p)))) +
scale_y_discrete(expression(Observed~~-log[10](italic(p)))) +
coord_fixed() +
theme_bw(base_size = 12) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14), legend.title = element_blank(),
legend.key.height = unit(0.1,"in"), legend.text = element_text(size=6), legend.key = element_blank(), legend.justification = c(0,1),
legend.position = c(0,1), panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.text = element_text(size=12))
%s
""" % (ggsave))
if np.max(results['logp']) > cfg['crop']:
print " generating cropped frequency stratified qq plot"
ro.r('df$logp[df$logp > ' + str(cfg['crop']) + ']<-' + str(cfg['crop']))
ro.r('df$shape<-0')
ro.r('df$shape[df$logp == ' + str(cfg['crop']) + ']<-1')
if cfg['ext'] == 'tiff':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq_strat_mac.cropped.tiff')
elif cfg['ext'] == 'png':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,units="in",bg="white",dpi=300)' % (cfg['out'] + '.' + pcol + '.qq_strat_mac.cropped.png')
elif cfg['ext'] == 'eps':
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq_strat_mac.cropped.eps')
else:
ggsave = 'ggsave(filename="%s",plot=gp,width=4,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.qq_strat_mac.cropped.pdf')
ro.r("""
gp<-ggplot(df, aes_string(x='expected',y='logp')) +
geom_point(aes(shape=factor(shape), color=description), size=2) +
scale_colour_manual(values=colorRampPalette(brewer.pal(9,"Blues"))(length(unique(df$description))+2)[3:(length(unique(df$description))+2)]) +
geom_abline(intercept=0, slope=1, alpha=0.5) +
scale_x_discrete(expression(Expected~~-log[10](italic(p)))) +
scale_y_discrete(expression(Observed~~-log[10](italic(p)))) +
coord_fixed() +
theme_bw(base_size = 12) +
guides(shape=FALSE) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14), legend.title = element_blank(),
legend.key.height = unit(0.1,"in"), legend.text = element_text(size=6), legend.key = element_blank(), legend.justification = c(0,1),
legend.position = c(0,1), panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.text = element_text(size=12))
%s
""" % (ggsave))
if cfg['mht']:
print " generating standard manhattan plot"
print " minimum p-value: " + str(np.min(results[pcol]))
print " maximum -1*log10(p-value): " + str(np.max(results['logp']))
if cfg['gc'] and l > 1:
print " adjusting p-values for genomic inflation for p-value column " + pcol
results[pcol]=2 * scipy.norm.cdf(-1 * np.abs(scipy.norm.ppf(0.5*results[pcol]) / math.sqrt(l)))
print " minimum post-gc adjustment p-value: " + str(np.min(results[pcol]))
print " maximum post-gc adjustment -1*log10(p-value): " + str(np.max(results['logp']))
else:
print " skipping genomic inflation correction"
print " calculating genomic positions"
results.sort_values(by=[cfg['chrcol'],cfg['bpcol']], inplace=True)
ticks = []
lastbase = 0
results['gpos'] = 0
nchr = len(list(np.unique(results[cfg['chrcol']].values)))
chrs = np.unique(results[cfg['chrcol']].values)
if cfg['color']:
colours = ["#08306B","#41AB5D","#000000","#F16913","#3F007D","#EF3B2C","#08519C","#238B45","#252525","#D94801","#54278F","#CB181D","#2171B5","#006D2C","#525252","#A63603","#6A51A3","#A50F15","#4292C6","#00441B","#737373","#7F2704","#807DBA","#67000D"]
else:
colours = ["#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3","#08589e","#4eb3d3"]
if nchr == 1:
results['gpos'] = results[cfg['bpcol']]
results['colours'] = "#08589e"
if results['gpos'].max() - results['gpos'].min() <= 1000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 100 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 10000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 1000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 100000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 10000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 200000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 20000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 300000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 30000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 400000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 40000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 500000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 50000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 600000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 60000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 700000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 70000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 800000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 80000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 900000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 90000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 1000000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 100000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 10000000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 1000000 == 0]
elif results['gpos'].max() - results['gpos'].min() <= 100000000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 10000000 == 0]
elif results['gpos'].max() - results['gpos'].min() > 100000000:
ticks = [x for x in range(results['gpos'].min(),results['gpos'].max()) if x % 25000000 == 0]
else:
results['colours'] = "#000000"
for i in range(len(chrs)):
print " processed chromosome " + str(int(chrs[i]))
if i == 0:
results.loc[results[cfg['chrcol']] == chrs[i],'gpos'] = results.loc[results[cfg['chrcol']] == chrs[i],cfg['bpcol']]
else:
lastbase = lastbase + results.loc[results[cfg['chrcol']] == chrs[i-1],cfg['bpcol']].iloc[-1]
results.loc[results[cfg['chrcol']] == chrs[i],'gpos'] = (results.loc[results[cfg['chrcol']] == chrs[i],cfg['bpcol']]) + lastbase
if results.loc[results[cfg['chrcol']] == chrs[i]].shape[0] > 1:
ticks.append(results.loc[results[cfg['chrcol']] == chrs[i],'gpos'].iloc[0] + (results.loc[results[cfg['chrcol']] == chrs[i],'gpos'].iloc[-1] - results.loc[results[cfg['chrcol']] == chrs[i],'gpos'].iloc[0])/2)
else:
ticks.append(results.loc[results[cfg['chrcol']] == chrs[i],'gpos'].iloc[0])
results.loc[results[cfg['chrcol']] == chrs[i],'colours'] = colours[int(chrs[i])]
results['logp'] = -1 * np.log10(results[pcol])
if results.shape[0] >= 1000000:
sig = 5.4e-8
else:
sig = 0.05 / results.shape[0]
print " significance level set to p-value = " + str(sig) + " (-1*log10(p-value) = " + str(-1 * np.log10(sig)) + ")"
print " " + str(len(results[pcol][results[pcol] <= sig])) + " genome wide significant variants"
chr = results[cfg['chrcol']][0]
maxy=int(max(np.ceil(-1 * np.log10(sig)),np.ceil(results['logp'].max())))
if maxy > 20:
y_breaks = range(0,maxy,5)
y_labels = range(0,maxy,5)
else:
y_breaks = range(0,maxy)
y_labels = range(0,maxy)
ro.globalenv['df'] = ro.DataFrame({'gpos': ro.FloatVector(results['gpos']), 'logp': ro.FloatVector(results['logp']), 'colours': ro.FactorVector(results['colours'])})
ro.globalenv['ticks'] = ro.FloatVector(ticks)
ro.globalenv['labels'] = ro.Vector(["{:,}".format(x/1000) for x in ticks])
ro.globalenv['colours'] = ro.StrVector(colours)
ro.globalenv['chrs'] = ro.FloatVector(chrs)
print " generating manhattan plot"
if cfg['ext'] == 'tiff':
ggsave = 'ggsave(filename="%s",plot=gp,width=16,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.mht.tiff')
elif cfg['ext'] == 'png':
ggsave = 'ggsave(filename="%s",plot=gp,width=16,height=4,units="in",bg="white",dpi=300)' % (cfg['out'] + '.' + pcol + '.mht.png')
elif cfg['ext'] == 'eps':
ggsave = 'ggsave(filename="%s",plot=gp,width=16,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.mht.eps')
else:
ggsave = 'ggsave(filename="%s",plot=gp,width=16,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.mht.pdf')
if nchr == 1:
ro.r("""
gp<-ggplot(df, aes_string(x='gpos',y='logp')) +
geom_hline(yintercept = -1 * log10(%g),colour="#B8860B", linetype=5, size = 0.25) +
geom_point(size=1.5) +
scale_x_continuous(expression(Chromosome~~%d~~(kb))'),breaks=ticks,labels=labels) + \
scale_y_continuous(expression(-log[10](italic(p))),breaks=seq(0,%d,1),limits=c(0,%d)) + \
theme_bw(base_size = 8) + \
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14),
panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.title = element_text(size=10),
axis.text = element_text(size=12), legend.position = 'none')
%s
""" % (sig, chr, maxy, maxy, ggsave))
else:
ro.r("""
gp = ggplot(df, aes_string(x='gpos',y='logp',colour='colours')) +
geom_hline(yintercept = -1 * log10(%g),colour="#B8860B", linetype=5, size = 0.25) +
geom_point(size=1.5) +
scale_colour_manual(values=colours) +
scale_x_continuous(expression(Chromosome),breaks=ticks,labels=chrs) +
scale_y_continuous(expression(-log[10](italic(p))),breaks=seq(0,%d,1),limits=c(0,%d)) +
theme_bw(base_size = 8) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14),
panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.title = element_text(size=10),
axis.text = element_text(size=12), legend.position = 'none')
%s
""" % (sig, maxy, maxy, ggsave))
if maxy > cfg['crop']:
maxy = cfg['crop']
ro.r('df$logp[df$logp > ' + str(cfg['crop']) + ']<-' + str(cfg['crop']))
ro.r('df$shape<-0')
ro.r('df$shape[df$logp == ' + str(cfg['crop']) + ']<-1')
print " generating cropped manhattan plot"
if cfg['ext'] == 'tiff':
ggsave = 'ggsave(filename="%s",plot=gp,width=16,height=4,units="in",bg="white",compression="lzw",dpi=300)' % (cfg['out'] + '.' + pcol + '.mht.cropped.tiff')
elif cfg['ext'] == 'png':
ggsave = 'ggsave(filename="%s",plot=gp,width=16,height=4,units="in",bg="white",dpi=300)' % (cfg['out'] + '.' + pcol + '.mht.cropped.png')
elif cfg['ext'] == 'eps':
ggsave = 'ggsave(filename="%s",plot=gp,width=16,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.mht.cropped.eps')
else:
ggsave = 'ggsave(filename="%s",plot=gp,width=16,height=4,bg="white")' % (cfg['out'] + '.' + pcol + '.mht.cropped.pdf')
if nchr == 1:
ro.r("""
gp<-ggplot(df, aes_string(x='gpos',y='logp')) +
geom_hline(yintercept = -1 * log10(%g),colour="#B8860B", linetype=5, size = 0.25) +
geom_point(aes(shape=factor(shape)),size=1.5) +
scale_x_continuous(expression(Chromosome~~%d~~(kb))'),breaks=ticks,labels=labels) +
scale_y_continuous(expression(-log[10](italic(p))),breaks=seq(0,%d,1),limits=c(0,%d)) +
theme_bw(base_size = 8) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14),
panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.title = element_text(size=10),
axis.text = element_text(size=12), legend.position = 'none')
%s
""" % (sig, chr, maxy, maxy, ggsave))
else:
ro.r("""
gp = ggplot(df, aes_string(x='gpos',y='logp',colour='colours')) +
geom_hline(yintercept = -1 * log10(%g),colour="#B8860B", linetype=5, size = 0.25) +
geom_point(aes(shape=factor(shape)),size=1.5) +
scale_colour_manual(values=colours) +
scale_x_continuous(expression(Chromosome),breaks=ticks,labels=chrs) +
scale_y_continuous(expression(-log[10](italic(p))),breaks=seq(0,%d,1),limits=c(0,%d)) +
theme_bw(base_size = 8) +
theme(axis.title.x = element_text(vjust=-0.5,size=14), axis.title.y = element_text(vjust=1,angle=90,size=14),
panel.background = element_blank(), panel.border = element_blank(), panel.grid.minor = element_blank(),
panel.grid.major = element_blank(), axis.line = element_line(colour="black"), axis.title = element_text(size=8),
axis.text = element_text(size=12), legend.position = 'none')
%s
""" % (sig, maxy, maxy, ggsave))
print "process complete"
return 0
