def plot_dendrogram(target, source, env):
"""
"""
args = source[-1].read()
mat = npz_to_df(source[0].rstr())
modules["grDevices"].bitmap(target[0].rstr(), width=50 * mat.nrow, height=50 * mat.nrow, units="px", type="png256")
#modules["grDevices"].png(target[0].rstr(), width=50 * mat.nrow, height=50 * mat.nrow, units="px")
colors = []
if args.get("DIRECT", False):
dm = r["as.dist"](mat)
else:
dm = r["dist"](mat)
hc = r["hclust"](dm, method="average")
d = r["as.dendrogram"](hc)
r.par(mar=FloatVector([5, 5, 0, 25]))
r.par(**{"ps" : ((60 * mat.nrow) / mat.nrow) / 2.0, "lwd" : 3})
r["plot"](d, horiz=True, xlab="Cosine distance")
modules["grDevices"].dev_off()
return None
def plot_segments(cbs_fc, cbs_normfc, outdir='./'):
"""
:param cbs_fc: raw fold chnages
:param cbs_normfc: normalised fold changes
:param outdir:
:return:
"""
pdf_prm = {'file': "{}/09_Raw_vs_postCRISPRcleanR_segmentation_fold_changes.pdf".format(outdir),
'width': 7.5, 'height': 7.5}
grdevices.pdf(**pdf_prm)
r.par(mfrow=r.c(2, 1))
for chr_name, (_, _, cnseg_raw) in cbs_fc.items():
(_, _, cnseg_norm) = cbs_normfc[chr_name]
plot_prm = {'main': "raw_FCs_chr{}".format(chr_name), 'xlab': 'sgRNA_Index',
'ylab': 'FCs'}
dnacopy.plotSample(cnseg_raw, **plot_prm)
# plot normalised fold changes
plot_prm = {'main': "CRISPRcleanR_FCs_chr{}".format(chr_name), 'xlab': 'sgRNA_Index',
'ylab': 'FCs'}
dnacopy.plotSample(cnseg_norm, **plot_prm)
grdevices.dev_off()
def plot_qc_percents(qc_df):
"""
Plot percentage parts of pipeline QC file.
"""
# Record NA values as 0
qc_df = qc_df.fillna(0).set_index("sample")
r.par(mfrow=np.array([1,2]))
num_samples = len(qc_df.num_reads)
r_opts = r.options(scipen=10)
r.options(r_opts)
r.par(bty="n", lwd=1.7, lty=2)
r.dotchart(convert_to_r_matrix(qc_df[["percent_mapped",
"percent_unique",
"percent_ribo"]]),
xlab="Percent reads",
lcolor="black",
pch=19,
gcolor="darkblue",
cex=0.8)
r.par(bty="n")
r.dotchart(convert_to_r_matrix(qc_df[["percent_exons",
"percent_cds",
"percent_3p_utr",
"percent_5p_utr",
"percent_introns"]]),
xlab="Percent reads",
lcolor="black",
pch=19,
gcolor="darkblue",
cex=0.8)
def draw_survival_curves(feature,
surv,
assignment=None,
filename='tmp.png',
show=False,
title=True,
labels=None,
colors=['blue', 'red'],
ann=None,
show_legend=True,
q=.25,
std=None):
if assignment is None:
num_panels = 1
assignment = feature.map(lambda s: 1)
name = lambda v: str(feature.name) if feature.name != None else ''
else:
num_panels = len(assignment.unique())
name = lambda v: str(assignment.name) + ' = ' + str(v)
if (labels is None) and ((len(feature) / feature.nunique()) > 10):
labels = r.sort(r.c(*feature.unique())) # R sorts bad
colors = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black']
if feature.dtype == 'bool':
feature = feature.map({True: 'True', False: 'False'})
r.png(filename=filename, width=200 * (num_panels + 1), height=300, res=75)
fmla = robjects.Formula('Surv(days, event) ~ feature')
r.par(mfrow=r.c(1, num_panels))
r.par(mar=r.c(4, 5, 4, 1))
r.par(xpd=True)
if (get_vec_type(feature) == 'real') and (len(feature.unique()) > 10):
colors = ['blue', 'orange', 'red']
if q == .5:
labels = ['Bottom 50%', 'Top 50%']
else:
labels = [
'Bottom {}%'.format(int(q * 100)), 'Normal',
'Top {}%'.format(int(q * 100))
]
ls = r.c(*colors)
def plot_me(sub_f, label):
if (get_vec_type(sub_f) == 'real') and (len(sub_f.unique()) > 10):
sub_f = to_quants(sub_f, q=q, std=std)
m = get_cox_ph(surv, sub_f, formula=fmla)
r_data = m.rx2('call')[2]
p = log_rank(sub_f, surv)['p']
ls = r.c(*colors)
r.plot(survival.survfit(fmla, r_data),
lty=1,
col=ls,
lwd=4,
cex=1.25,
xlab='Years to Event',
ylab='Survival')
r.title(label, cex=3.)
if ann == 'p':
r.text(.2, 0, labels='logrank p = {0:.1e}'.format(p), pos=4)
elif ann != None:
r.text(0, labels=ann, pos=4)
if show_legend == 'out':
r.par(xpd=True, mar=r.c(4, 5, 5, 8))
for value in sorted(assignment.ix[feature.index].dropna().unique()):
f = feature.ix[assignment[assignment == value].index]
if len(f.unique()) > 1:
plot_me(f, name(value))
if show_legend == True:
mean_s = surv.ix[:, 'event'].ix[assignment[assignment ==
value].index].mean()
if mean_s < .5:
r.legend(surv.ix[:, 'days'].max() * .05 / 365.,
.45,
labels,
lty=1,
col=ls,
lwd=3,
bty='o')
else:
r.legend(surv.ix[:, 'days'].max() * .4 / 365,
.9,
labels,
lty=1,
col=ls,
lwd=3,
bty='o')
elif show_legend == 'out':
r.legend(surv.ix[:, 'days'].max() * 1.1 / 365,
.9,
labels,
lty=1,
col=ls,
lwd=3,
bty='o')
r('dev.off()')
if show:
return Show(filename)
def draw_survival_curves(feature, surv, assignment=None, filename='tmp.png', show=False,
title=True, labels=None, colors=['blue', 'red'], ann=None,
show_legend=True, q=.25, std=None):
if assignment is None:
num_panels = 1
assignment = feature.map(lambda s: 1)
name = lambda v: str(feature.name) if feature.name != None else ''
else:
num_panels = len(assignment.unique())
name = lambda v: str(assignment.name) + ' = ' + str(v)
if (labels is None) and ((len(feature) / feature.nunique()) > 10):
labels = r.sort(r.c(*feature.unique())) # R sorts bad
colors = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black']
if feature.dtype == 'bool':
feature = feature.map({True: 'True', False: 'False'})
r.png(filename=filename, width=200 * (num_panels + 1), height=300, res=75)
fmla = robjects.Formula('Surv(days, event) ~ feature')
r.par(mfrow=r.c(1, num_panels))
r.par(mar=r.c(4, 5, 4, 1))
r.par(xpd=True)
if (get_vec_type(feature) == 'real') and (len(feature.unique()) > 10):
colors = ['blue', 'orange', 'red']
if q == .5:
labels = ['Bottom 50%', 'Top 50%']
else:
labels = ['Bottom {}%'.format(int(q * 100)), 'Normal', 'Top {}%'.format(int(q * 100))]
ls = r.c(*colors)
def plot_me(sub_f, label):
if (get_vec_type(sub_f) == 'real') and (len(sub_f.unique()) > 10):
sub_f = to_quants(sub_f, q=q, std=std)
m = get_cox_ph(surv, sub_f, formula=fmla)
r_data = m.rx2('call')[2]
p = log_rank(sub_f, surv)['p']
ls = r.c(*colors)
r.plot(survival.survfit(fmla, r_data), lty=1, col=ls, lwd=4, cex=1.25,
xlab='Years to Event', ylab='Survival');
r.title(label, cex=3.)
if ann == 'p':
r.text(.2, 0, labels='logrank p = {0:.1e}'.format(p), pos=4)
elif ann != None:
r.text(0, labels=ann, pos=4)
if show_legend == 'out':
r.par(xpd=True, mar=r.c(4, 5, 5, 8))
for value in sorted(assignment.ix[feature.index].dropna().unique()):
f = feature.ix[assignment[assignment == value].index]
if len(f.unique()) > 1:
plot_me(f, name(value))
if show_legend == True:
mean_s = surv.ix[:, 'event'].ix[assignment[assignment == value].index].mean()
if mean_s < .5:
r.legend(surv.ix[:, 'days'].max() * .05 / 365., .45, labels,
lty=1, col=ls, lwd=3, bty='o')
else:
r.legend(surv.ix[:, 'days'].max() * .4 / 365, .9, labels,
lty=1, col=ls, lwd=3, bty='o')
elif show_legend == 'out':
r.legend(surv.ix[:, 'days'].max() * 1.1 / 365, .9, labels,
lty=1, col=ls, lwd=3, bty='o')
r('dev.off()')
if show:
return Show(filename)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = E.OptionParser(
version="%prog version: $Id: r_table2scatter.py 2782 2009-09-10 11:40:29Z andreas $")
parser.add_option("-c", "--columns", dest="columns", type="string",
help="columns to take from table. Choices are 'all', 'all-but-first' or a ','-separated list of columns.")
parser.add_option("--logscale", dest="logscale", type="string",
help="log-transform one or both axes [default=%Default].")
parser.add_option("-a", "--hardcopy", dest="hardcopy", type="string",
help="write hardcopy to file [default=%default].",
metavar="FILE")
parser.add_option("-f", "--file", dest="input_filename", type="string",
help="filename with table data [default=%default].",
metavar="FILE")
parser.add_option("-2", "--file2", dest="input_filename2", type="string",
help="additional data file [default=%default].",
metavar="FILE")
parser.add_option("-s", "--stats", dest="statistics", type="choice",
choices=("correlation", "spearman", "pearson", "count"),
help="statistical quantities to compute [default=%default]",
action="append")
parser.add_option("-p", "--plot", dest="plot", type="choice",
choices=("scatter", "pairs", "panel", "bar", "bar-stacked",
"bar-besides", "1_vs_x", "matched", "boxplot", "scatter+marginal",
"scatter-regression"),
help="plots to plot [default=%default]",
action="append")
parser.add_option("-t", "--threshold", dest="threshold", type="float",
help="min threshold to use for counting method [default=%default].")
parser.add_option("-o", "--colours", dest="colours", type="int",
help="column with colour information [default=%default].")
parser.add_option("-l", "--plot-labels", dest="labels", type="string",
help="column labels for x and y in matched plots [default=%default].")
parser.add_option("-d", "--add-diagonal", dest="add_diagonal", action="store_true",
help="add diagonal to plot [default=%default].")
parser.add_option("-e", "--plot-legend", dest="legend", type="int",
help="column with legend [default=%default].")
parser.add_option("-r", "--options", dest="r_options", type="string",
help="R plotting options [default=%default].")
parser.add_option("--format", dest="format", type="choice",
choices=("full", "sparse"),
help="output format [default=%default].")
parser.add_option("--title", dest="title", type="string",
help="""plot title [default=%default].""")
parser.add_option("", "--xrange", dest="xrange", type="string",
help="x viewing range of plot [default=%default].")
parser.add_option("", "--yrange", dest="yrange", type="string",
help="y viewing range of plot[default=%default].")
parser.add_option("--allow-empty-file", dest="fail_on_empty", action="store_false",
help="do not fail on empty input [default=%default].")
parser.add_option("--fail-on-empty", dest="fail_on_empty", action="store_true",
help="fail on empty input [default=%default].")
parser.set_defaults(
hardcopy=None,
input_filename="",
input_filename2=None,
columns="all",
logscale=None,
statistics=[],
plot=[],
threshold=0.0,
labels="x,y",
colours=None,
diagonal=False,
legend=None,
title=None,
xrange=None,
yrange=None,
r_options="",
fail_on_empty=True,
format="full")
(options, args) = E.Start(parser)
if len(args) == 1 and not options.input_filename:
options.input_filename = args[0]
if options.columns not in ("all", "all-but-first"):
options.columns = [int(x) - 1 for x in options.columns.split(",")]
if options.colours:
options.colours -= 1
if options.legend:
options.legend -= 1
table = {}
headers = []
# read data matrix
if options.input_filename:
lines = IOTools.openFile(options.input_filename, "r").readlines()
else:
# note: this will not work for interactive viewing, but
# creating hardcopy plots works.
lines = sys.stdin.readlines()
lines = [x for x in lines if x[0] != "#"]
if len(lines) == 0:
if options.fail_on_empty:
raise IOError("no input")
E.warn("empty input")
E.Stop()
return
matrix, headers, colours, legend = readTable(lines,
"matrix",
take_columns=options.columns,
headers=True,
colours=options.colours,
row_names=options.legend)
if options.input_filename2:
# read another matrix (should be of the same format.
matrix2, headers2, colours2, legend2 = readTable(
lines,
"matrix2",
take_columns=options.columns,
headers=True,
colours=options.colours,
row_names=options.legend)
R.assign("headers", headers)
ndata = R("""length( matrix[,1] )""")[0]
if options.loglevel >= 1:
options.stdlog.write("# read matrix: %ix%i\n" % (len(headers), ndata))
if colours:
R.assign("colours", colours)
for method in options.statistics:
if method == "correlation":
cor = R.cor(matrix, use="pairwise.complete.obs")
writeMatrix(sys.stdout, cor, headers=headers, format="%5.2f")
elif method == "pearson":
options.stdout.write("\t".join(("var1",
"var2",
"coeff",
"passed",
"pvalue",
"n",
"method",
"alternative")) + "\n")
for x in range(len(headers) - 1):
for y in range(x + 1, len(headers)):
try:
result = R(
"""cor.test( matrix[,%i], matrix[,%i] )""" % (x + 1, y + 1))
except rpy.RPyException as msg:
E.warn("correlation not computed for columns %i(%s) and %i(%s): %s" % (
x, headers[x], y, headers[y], msg))
options.stdout.write("%s\t%s\t%s\t%s\t%s\t%i\t%s\t%s\n" %
(headers[x], headers[y],
"na",
"na",
"na",
0,
"na",
"na"))
else:
options.stdout.write(
"%s\t%s\t%6.4f\t%s\t%e\t%i\t%s\t%s\n" %
(headers[x], headers[y],
result.rx2('estimate').rx2(
'cor')[0],
Stats.getSignificance(
float(result.rx2('p.value')[0])),
result.rx2('p.value')[0],
result.rx2('parameter').rx2(
'df')[0],
result.rx2('method')[0],
result.rx2('alternative')[0]))
elif method == "spearman":
options.stdout.write("\t".join(("var1", "var2",
"coeff",
"passed",
"pvalue",
"method",
"alternative")) + "\n")
for x in range(len(headers) - 1):
for y in range(x + 1, len(headers)):
result = R(
"""cor.test( matrix[,%i], matrix[,%i], method='spearman')""" % (x + 1, y + 1))
options.stdout.write(
"%s\t%s\t%6.4f\t%s\t%e\t%i\t%s\t%s\n" %
(headers[x], headers[y],
result['estimate']['rho'],
Stats.getSignificance(float(result['p.value'])),
result['p.value'],
result['parameter']['df'],
result['method'],
result['alternative']))
elif method == "count":
# number of shared elements > threshold
m, r, c = MatlabTools.ReadMatrix(open(options.input_filename, "r"),
take=options.columns,
headers=True)
mask = numpy.greater(m, options.threshold)
counts = numpy.dot(numpy.transpose(mask), mask)
writeMatrix(options.stdout, counts, headers=c, format="%i")
if options.plot:
# remove columns that are completely empty
if "pairs" in options.plot:
colsums = R('''colSums( is.na(matrix ))''')
take = [x for x in range(len(colsums)) if colsums[x] != ndata]
if take:
E.warn("removing empty columns %s before plotting" % str(take))
matrix = R.subset(matrix, select=[x + 1 for x in take])
R.assign("""matrix""", matrix)
headers = [headers[x] for x in take]
if legend:
legend = [headers[x] for x in take]
if options.r_options:
extra_options = ", %s" % options.r_options
else:
extra_options = ""
if options.legend is not None and len(legend):
extra_options += ", legend=c('%s')" % "','".join(legend)
if options.labels:
xlabel, ylabel = options.labels.split(",")
extra_options += ", xlab='%s', ylab='%s'" % (xlabel, ylabel)
else:
xlabel, ylabel = "", ""
if options.colours:
extra_options += ", col=colours"
if options.logscale:
extra_options += ", log='%s'" % options.logscale
if options.xrange:
extra_options += ", xlim=c(%f,%f)" % tuple(
map(float, options.xrange.split(",")))
if options.yrange:
extra_options += ", ylim=c(%f,%f)" % tuple(
map(float, options.yrange.split(",")))
if options.hardcopy:
if options.hardcopy.endswith(".eps"):
R.postscript(options.hardcopy)
elif options.hardcopy.endswith(".png"):
R.png(options.hardcopy, width=1024, height=768, type="cairo")
elif options.hardcopy.endswith(".jpg"):
R.jpg(options.hardcopy, width=1024, height=768, type="cairo")
for method in options.plot:
if ndata < 100:
point_size = "1"
pch = "o"
elif ndata < 1000:
point_size = "1"
pch = "o"
else:
point_size = "0.5"
pch = "."
if method == "scatter":
R("""plot( matrix[,1], matrix[,2], cex=%s, pch="o" %s)""" % (
point_size, extra_options))
if method == "scatter-regression":
R("""plot( matrix[,1], matrix[,2], cex=%s, pch="o" %s)""" % (
point_size, extra_options))
dat = R(
"""dat <- data.frame(x = matrix[,1], y = matrix[,2])""")
R(
"""new <- data.frame(x = seq( min(matrix[,1]), max(matrix[,1]), (max(matrix[,1]) - min(matrix[,1])) / 100))""")
mod = R("""mod <- lm( y ~ x, dat)""")
R("""predict(mod, new, se.fit = TRUE)""")
R("""pred.w.plim <- predict(mod, new, interval="prediction")""")
R("""pred.w.clim <- predict(mod, new, interval="confidence")""")
R(
"""matpoints(new$x,cbind(pred.w.clim, pred.w.plim[,-1]), lty=c(1,2,2,3,3), type="l")""")
R.mtext(
"y = %f * x + %f, r=%6.4f, n=%i" % (mod["coefficients"]["x"],
mod["coefficients"][
"(Intercept)"],
R("""cor( dat )[2]"""),
ndata),
3,
cex=1.0)
elif method == "pairs":
if options.add_diagonal:
R(
"""panel.hist <- function( x,y,... ) { points(x,y,...); abline(0,1); }""")
else:
R(
"""panel.hist <- function( x,y,... ) { points(x,y,...); }""")
# There used to be a argument na_action="na.omit", but
# removed this as there appeared error messages saying
# "na.action is not a graphical parameter" and the
# plots showed occasionally the wrong scale.
# cex=point_size also caused trouble (error message:
# "X11 used font size 8 when 2 was requested" or
# similar)
if options.colours:
R.pairs(matrix,
pch=pch,
col=colours,
main=options.title,
panel="panel.hist",
labels=headers,
cex_labels=2.0)
else:
R.pairs(matrix,
pch=pch,
panel="panel.hist",
main=options.title,
labels=headers,
cex_labels=2.0)
elif method == "boxplot":
extra_options += ",main='%s'" % options.title
# set vertical orientation
if max([len(x) for x in headers]) > 40 / len(headers):
# remove xlabel:
extra_options = re.sub(", xlab='[^']+'", "", extra_options)
extra_options += ", names.arg=headers, las=2"
R(
"""op <- par(mar=c(11,4,4,2))""") # the 10 allows the names.arg below the barplot
R("""boxplot( matrix %s)""" % extra_options)
elif method == "bar" or method == "bar-stacked":
if not options.colours:
extra_options += ", col=rainbow(5)"
# set vertical orientation
if max([len(x) for x in headers]) > 40 / len(headers):
# remove xlabel:
extra_options = re.sub(", xlab='[^']+'", "", extra_options)
extra_options += ", names.arg=headers, las=2"
R(
"""op <- par(mar=c(11,4,4,2))""") # the 10 allows the names.arg below the barplot
R("""barplot(as.matrix(matrix), %s)""" % extra_options)
elif method == "bar-besides":
if not options.colours:
extra_options += ", col=rainbow(%i)" % ndata
# set vertical orientation
if max([len(x) for x in headers]) > 40 / len(headers):
# remove xlabel:
extra_options = re.sub(", xlab='[^']+'", "", extra_options)
extra_options += ", names.arg=headers, las=2"
R(
"""op <- par(mar=c(11,4,4,2))""") # the 10 allows the names.arg below the barplot
R("""barplot(as.matrix(matrix), beside=TRUE %s)""" %
extra_options)
elif method == "scatter+marginal":
if options.title:
# set the size of the outer margins - the title needs to be added at the end
# after plots have been created
R.par(oma=R.c(0, 0, 4, 0))
R("""matrix""")
R("""
x <- matrix[,1];
y <- matrix[,2];
xhist <- hist(x, breaks=20, plot=FALSE);
yhist <- hist(y, breaks=20, plot=FALSE);
top <- max(c(xhist$counts, yhist$counts));
nf <- layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3), respect=TRUE );
par(mar=c(3,3,1,1)) ;
plot(x, y, cex=%s, pch="o" %s) ;
par(mar=c(0,3,1,1)) ;
barplot(xhist$counts, axes=FALSE, ylim=c(0, top), space=0 ) ;
par(mar=c(3,0,1,1)) ;
title(main='%s');
barplot(yhist$counts, axes=FALSE, xlim=c(0, top), space=0, horiz=TRUE ) ;
title(main='%s');
""" % (point_size, extra_options, xlabel, ylabel))
if options.title:
R.mtext(options.title, 3, outer=True, line=1, cex=1.5)
elif method in ("panel", "1_vs_x", "matched"):
if method == "panel":
pairs = []
for x in range(len(headers) - 1):
for y in range(x + 1, len(headers)):
pairs.append((x, y))
elif method == "1_vs_x":
pairs = []
for x in range(1, len(headers)):
pairs.append((0, x))
# print matching columns
elif method == "matched":
pairs = []
for x in range(len(headers) - 1):
for y in range(x + 1, len(headers)):
if headers[x] == headers[y]:
pairs.append((x, y))
break
w = int(math.ceil(math.sqrt(len(pairs))))
h = int(math.ceil(float(len(pairs)) / w))
PosInf = 1e300000
NegInf = -1e300000
xlabel, ylabel = options.labels.split(",")
R("""layout(matrix(seq(1,%i), %i, %i, byrow = TRUE))""" %
(w * h, w, h))
for a, b in pairs:
new_matrix = [x for x in zip(
list(matrix[a].values())[0],
list(matrix[b].values())[0])
if x[0] not in (float("nan"), PosInf, NegInf) and
x[1] not in (float("nan"), PosInf, NegInf)]
try:
R("""plot(matrix[,%i], matrix[,%i], main='%s versus %s', cex=0.5, pch=".", xlab='%s', ylab='%s' )""" % (
a + 1, b + 1, headers[b], headers[a], xlabel, ylabel))
except rpy.RException as msg:
print("could not plot %s versus %s: %s" % (headers[b], headers[a], msg))
if options.hardcopy:
R['dev.off']()
E.info("matrix added as >matrix< in R.")
if not options.hardcopy:
if options.input_filename:
interpreter = code.InteractiveConsole(globals())
interpreter.interact()
else:
E.info(
"can not start new interactive session as input has come from stdin.")
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
parser = E.OptionParser(
version="%prog version: $Id: r_compare_distributions.py 2782 2009-09-10 11:40:29Z andreas $")
parser.add_option("-m", "--method", dest="method", type="choice",
help="method to use: ks=Kolmogorov-Smirnov, mwu=Mann-WhitneyU, shapiro=Shapiro-Wilk, paired-mwu=paired Mann-WhitneyU, paired-t=paired t-test [default=%default]",
choices=("ks", "mwu", "shapiro", "paired-mwu", "paired-t"))
parser.add_option("-a", "--hardcopy", dest="hardcopy", type="string",
help="write hardcopy to file.", metavar="FILE")
parser.add_option("-1", "--infile1", dest="filename_input1", type="string",
help="input filename for distribution 1.")
parser.add_option("-2", "--infile2", dest="filename_input2", type="string",
help="input filename for distribution 2.")
parser.add_option("--plot-legend", dest="legend", type="string",
help="legend for histograms.""")
parser.add_option("-f", "--infile-map", dest="filename_input_map", type="string",
help="input filename for mapping categories to values.")
parser.add_option("-n", "--norm-test", dest="norm_test", action="store_true",
help="""test if a set of values is normally distributed. Mean and variance
are calculated from the data.""")
parser.add_option("-b", "--num-bins", dest="num_bins", type="int",
help="""number of bins (for plotting purposes only).""")
parser.add_option("--bin-size", dest="bin_size", type="float",
help="""bin size for plot.""")
parser.add_option("--min-value", dest="min_value", type="float",
help="""minimum_value for plot.""")
parser.add_option("--max-value", dest="max_value", type="float",
help="""maximum_value for plot.""")
parser.add_option("--skip-plot", dest="plot", action="store_false",
help="""skipping plotting.""")
parser.add_option("--header-names", dest="header", type="string",
help="""header of value column [default=%default].""")
parser.add_option("--title", dest="title", type="string",
help="""plot title [default=%default].""")
parser.set_defaults(
method="ks",
filename_input1=None,
filename_input2=None,
filename_input_map=None,
legend=None,
norm_test=False,
num_bins=0,
legend_range="2,2",
bin_size=None,
min_value=None,
plot=True,
header="value",
title=None,
)
(options, args) = E.Start(parser,
add_pipe_options=True)
kwargs = {}
xargs = []
for arg in args:
if "=" in arg:
key, value = arg.split("=")
kwargs[key] = value
else:
xargs.append(arg)
if options.legend:
options.legend = options.legend.split(",")
map_category2value = {}
if options.filename_input_map:
map_category2value = IOTools.ReadMap(open(options.filename_input_map, "r"),
map_functions=(str, float))
f = str
else:
f = float
if options.filename_input1:
infile1 = IOTools.openFile(options.filename_input1, "r")
else:
infile1 = sys.stdin
values1, errors1 = IOTools.ReadList(infile1,
map_function=f,
map_category=map_category2value)
if options.filename_input1:
infile1.close()
if errors1 and options.loglevel >= 3:
options.stdlog.write("# errors in input1: %s\n" %
";".join(map(str, errors1)))
if options.norm_test:
mean = R.mean(values1)
stddev = R.sd(values1)
options.stdlog.write("# creating %i samples from normal distribution with mean %f and stddev %f\n" % (
len(values1), mean, stddev))
values2 = R.rnorm(len(values1), mean, stddev)
errors2 = ()
else:
values2, errors2 = IOTools.ReadList(open(options.filename_input2, "r"),
map_function=f,
map_category=map_category2value)
if errors2 and options.loglevel >= 3:
options.stdlog.write("# errors in input2: %s\n" %
";".join(map(str, errors2)))
if options.loglevel >= 1:
options.stdlog.write("# ninput1=%i, nerrors1=%i, ninput2=%i, nerrors2=%i\n" % (len(values1), len(errors1),
len(values2), len(errors2)))
if options.method in ("paired-mwu", "paired-t"):
if len(values1) != len(values2):
raise ValueError(
"number of values must be equal for paired tests.")
if options.hardcopy:
R.png(options.hardcopy, width=1024, height=768)
if options.method == "ks":
result = R.ks_test(values1, values2, *xargs, **kwargs)
elif options.method == "mwu":
result = R.wilcox_test(
values1, values2, paired=False, correct=True, *xargs, **kwargs)
elif options.method == "paired-mwu":
result = R.wilcox_test(
values1, values2, paired=True, correct=True, *xargs, **kwargs)
elif options.method == "paired-t":
result = R.t_test(values1, values2, paired=True, *xargs, **kwargs)
elif options.method == "shapiro":
if len(values1) > 5000:
E.warn(
"shapiro-wilk test only accepts < 5000 values, a random sample has been created.")
values1 = random.sample(values1, 5000)
result = R.shapiro_test(values1, *xargs, **kwargs)
if options.plot:
R.assign("v1", values1)
R.assign("v2", values2)
if options.title:
# set the size of the outer margins - the title needs to be added at the end
# after plots have been created
R.par(oma=R.c(0, 0, 4, 0))
R.layout(R.matrix((1, 2, 3, 4), 2, 2, byrow=True))
R.boxplot(values1, values2, col=('white', 'red'), main="Boxplot")
R("""qqplot( v1, v2, main ='Quantile-quantile plot' ); lines( c(0,1), c(0,1) );""")
# compute breaks:
min_value = min(min(values1), min(values2))
if options.min_value is not None:
min_value = min(min_value, options.min_value)
max_value = max(max(values1), max(values2))
if options.max_value is not None:
max_value = max(max_value, options.max_value)
extra_options = ""
if options.num_bins and not (options.min_value or options.max_value):
extra_options += ", breaks=%i" % options.num_bins
elif options.num_bins and (options.min_value or options.max_value):
bin_size = float((max_value - min_value)) / (options.num_bins + 1)
breaks = [
min_value + x * bin_size for x in range(options.num_bins)]
extra_options += ", breaks=c(%s)" % ",".join(map(str, breaks))
elif options.bin_size is not None:
num_bins = int(((max_value - min_value) / options.bin_size)) + 1
breaks = [
min_value + x * options.bin_size for x in range(num_bins + 1)]
extra_options += ", breaks=c(%s)" % ",".join(map(str, breaks))
R("""h1 <- hist( v1, freq=FALSE, density=20, main='Relative frequency histogram' %s)""" %
extra_options)
R("""h2 <- hist( v2, freq=FALSE, add=TRUE, density=20, col='red', offset=0.5, angle=135 %s)""" %
extra_options)
if options.legend:
R("""legend( ( max(c(h1$breaks[-1], h2$breaks[-1])) - min(c(h1$breaks[1], h2$breaks[1]) ) ) / 2,
max( max(h1$density), max(h2$density)) / 2, c('%s'), fill=c('white','red'))""" % (
"','".join(options.legend)))
R("""h1 <- hist( v1, freq=TRUE, density=20, main='Absolute frequency histogram' %s)""" %
extra_options)
R("""h2 <- hist( v2, freq=TRUE, add=TRUE, density=20, col='red', offset=0.5, angle=135 %s )""" %
extra_options)
if options.legend:
R("""legend( ( max(c(h1$breaks[-1], h2$breaks[-1])) - min(c(h1$breaks[1], h2$breaks[1]) ) ) / 2,
max( max(h1$counts), max(h2$counts)) / 2, c('%s'), fill=c('white','red'))""" % (
"','".join(options.legend)))
if options.title:
R.mtext(options.title, 3, outer=True, line=1, cex=1.5)
if options.loglevel >= 1:
options.stdout.write("## Results for %s\n" % result['method'])
options.stdout.write("%s\t%s\n" % ("key", options.header))
for key in list(result.keys()):
if key == "data.name":
continue
options.stdout.write("\t".join((key, str(result[key]))) + "\n")
stat = Stats.Summary(values1)
for key, value in list(stat.items()):
options.stdout.write("%s1\t%s\n" % (str(key), str(value)))
stat = Stats.Summary(values2)
for key, value in list(stat.items()):
options.stdout.write("%s2\t%s\n" % (str(key), str(value)))
if options.plot:
if options.hardcopy:
R.dev_off()
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
parser = E.OptionParser(
version=
"%prog version: $Id: r_compare_distributions.py 2782 2009-09-10 11:40:29Z andreas $"
)
parser.add_option(
"-m",
"--method",
dest="method",
type="choice",
help=
"method to use: ks=Kolmogorov-Smirnov, mwu=Mann-WhitneyU, shapiro=Shapiro-Wilk, paired-mwu=paired Mann-WhitneyU, paired-t=paired t-test [default=%default]",
choices=("ks", "mwu", "shapiro", "paired-mwu", "paired-t"))
parser.add_option("-a",
"--hardcopy",
dest="hardcopy",
type="string",
help="write hardcopy to file.",
metavar="FILE")
parser.add_option("-1",
"--infile1",
dest="filename_input1",
type="string",
help="input filename for distribution 1.")
parser.add_option("-2",
"--infile2",
dest="filename_input2",
type="string",
help="input filename for distribution 2.")
parser.add_option("--plot-legend",
dest="legend",
type="string",
help="legend for histograms."
"")
parser.add_option("-f",
"--infile-map",
dest="filename_input_map",
type="string",
help="input filename for mapping categories to values.")
parser.add_option(
"-n",
"--norm-test",
dest="norm_test",
action="store_true",
help=
"""test if a set of values is normally distributed. Mean and variance
are calculated from the data.""")
parser.add_option("-b",
"--num-bins",
dest="num_bins",
type="int",
help="""number of bins (for plotting purposes only).""")
parser.add_option("--bin-size",
dest="bin_size",
type="float",
help="""bin size for plot.""")
parser.add_option("--min-value",
dest="min_value",
type="float",
help="""minimum_value for plot.""")
parser.add_option("--max-value",
dest="max_value",
type="float",
help="""maximum_value for plot.""")
parser.add_option("--skip-plot",
dest="plot",
action="store_false",
help="""skipping plotting.""")
parser.add_option("--header-names",
dest="header",
type="string",
help="""header of value column [default=%default].""")
parser.add_option("--title",
dest="title",
type="string",
help="""plot title [default=%default].""")
parser.set_defaults(
method="ks",
filename_input1=None,
filename_input2=None,
filename_input_map=None,
legend=None,
norm_test=False,
num_bins=0,
legend_range="2,2",
bin_size=None,
min_value=None,
plot=True,
header="value",
title=None,
)
(options, args) = E.Start(parser, add_pipe_options=True)
kwargs = {}
xargs = []
for arg in args:
if "=" in arg:
key, value = arg.split("=")
kwargs[key] = value
else:
xargs.append(arg)
if options.legend:
options.legend = options.legend.split(",")
map_category2value = {}
if options.filename_input_map:
map_category2value = IOTools.ReadMap(open(options.filename_input_map,
"r"),
map_functions=(str, float))
f = str
else:
f = float
if options.filename_input1:
infile1 = IOTools.openFile(options.filename_input1, "r")
else:
infile1 = sys.stdin
values1, errors1 = IOTools.ReadList(infile1,
map_function=f,
map_category=map_category2value)
if options.filename_input1:
infile1.close()
if errors1 and options.loglevel >= 3:
options.stdlog.write("# errors in input1: %s\n" %
";".join(map(str, errors1)))
if options.norm_test:
mean = R.mean(values1)
stddev = R.sd(values1)
options.stdlog.write(
"# creating %i samples from normal distribution with mean %f and stddev %f\n"
% (len(values1), mean, stddev))
values2 = R.rnorm(len(values1), mean, stddev)
errors2 = ()
else:
values2, errors2 = IOTools.ReadList(open(options.filename_input2, "r"),
map_function=f,
map_category=map_category2value)
if errors2 and options.loglevel >= 3:
options.stdlog.write("# errors in input2: %s\n" %
";".join(map(str, errors2)))
if options.loglevel >= 1:
options.stdlog.write(
"# ninput1=%i, nerrors1=%i, ninput2=%i, nerrors2=%i\n" %
(len(values1), len(errors1), len(values2), len(errors2)))
if options.method in ("paired-mwu", "paired-t"):
if len(values1) != len(values2):
raise ValueError(
"number of values must be equal for paired tests.")
if options.hardcopy:
R.png(options.hardcopy, width=1024, height=768)
if options.method == "ks":
result = R.ks_test(values1, values2, *xargs, **kwargs)
elif options.method == "mwu":
result = R.wilcox_test(values1,
values2,
paired=False,
correct=True,
*xargs,
**kwargs)
elif options.method == "paired-mwu":
result = R.wilcox_test(values1,
values2,
paired=True,
correct=True,
*xargs,
**kwargs)
elif options.method == "paired-t":
result = R.t_test(values1, values2, paired=True, *xargs, **kwargs)
elif options.method == "shapiro":
if len(values1) > 5000:
E.warn(
"shapiro-wilk test only accepts < 5000 values, a random sample has been created."
)
values1 = random.sample(values1, 5000)
result = R.shapiro_test(values1, *xargs, **kwargs)
if options.plot:
R.assign("v1", values1)
R.assign("v2", values2)
if options.title:
# set the size of the outer margins - the title needs to be added at the end
# after plots have been created
R.par(oma=R.c(0, 0, 4, 0))
R.layout(R.matrix((1, 2, 3, 4), 2, 2, byrow=True))
R.boxplot(values1, values2, col=('white', 'red'), main="Boxplot")
R("""qqplot( v1, v2, main ='Quantile-quantile plot' ); lines( c(0,1), c(0,1) );"""
)
# compute breaks:
min_value = min(min(values1), min(values2))
if options.min_value is not None:
min_value = min(min_value, options.min_value)
max_value = max(max(values1), max(values2))
if options.max_value is not None:
max_value = max(max_value, options.max_value)
extra_options = ""
if options.num_bins and not (options.min_value or options.max_value):
extra_options += ", breaks=%i" % options.num_bins
elif options.num_bins and (options.min_value or options.max_value):
bin_size = float((max_value - min_value)) / (options.num_bins + 1)
breaks = [
min_value + x * bin_size for x in range(options.num_bins)
]
extra_options += ", breaks=c(%s)" % ",".join(map(str, breaks))
elif options.bin_size is not None:
num_bins = int(((max_value - min_value) / options.bin_size)) + 1
breaks = [
min_value + x * options.bin_size for x in range(num_bins + 1)
]
extra_options += ", breaks=c(%s)" % ",".join(map(str, breaks))
R("""h1 <- hist( v1, freq=FALSE, density=20, main='Relative frequency histogram' %s)"""
% extra_options)
R("""h2 <- hist( v2, freq=FALSE, add=TRUE, density=20, col='red', offset=0.5, angle=135 %s)"""
% extra_options)
if options.legend:
R("""legend( ( max(c(h1$breaks[-1], h2$breaks[-1])) - min(c(h1$breaks[1], h2$breaks[1]) ) ) / 2,
max( max(h1$density), max(h2$density)) / 2, c('%s'), fill=c('white','red'))"""
% ("','".join(options.legend)))
R("""h1 <- hist( v1, freq=TRUE, density=20, main='Absolute frequency histogram' %s)"""
% extra_options)
R("""h2 <- hist( v2, freq=TRUE, add=TRUE, density=20, col='red', offset=0.5, angle=135 %s )"""
% extra_options)
if options.legend:
R("""legend( ( max(c(h1$breaks[-1], h2$breaks[-1])) - min(c(h1$breaks[1], h2$breaks[1]) ) ) / 2,
max( max(h1$counts), max(h2$counts)) / 2, c('%s'), fill=c('white','red'))"""
% ("','".join(options.legend)))
if options.title:
R.mtext(options.title, 3, outer=True, line=1, cex=1.5)
if options.loglevel >= 1:
options.stdout.write("## Results for %s\n" % result['method'])
options.stdout.write("%s\t%s\n" % ("key", options.header))
for key in list(result.keys()):
if key == "data.name":
continue
options.stdout.write("\t".join((key, str(result[key]))) + "\n")
stat = Stats.Summary(values1)
for key, value in list(stat.items()):
options.stdout.write("%s1\t%s\n" % (str(key), str(value)))
stat = Stats.Summary(values2)
for key, value in list(stat.items()):
options.stdout.write("%s2\t%s\n" % (str(key), str(value)))
if options.plot:
if options.hardcopy:
R.dev_off()
E.Stop()
def main(argv=None):
if argv is None:
argv = sys.argv
parser = E.OptionParser(
version=
"%prog version: $Id: r_table2scatter.py 2782 2009-09-10 11:40:29Z andreas $"
)
parser.add_option(
"-c",
"--columns",
dest="columns",
type="string",
help=
"columns to take from table. Choices are 'all', 'all-but-first' or a ','-separated list of columns."
)
parser.add_option(
"--logscale",
dest="logscale",
type="string",
help="log-transform one or both axes [default=%Default].")
parser.add_option("-a",
"--hardcopy",
dest="hardcopy",
type="string",
help="write hardcopy to file [default=%default].",
metavar="FILE")
parser.add_option("-f",
"--file",
dest="input_filename",
type="string",
help="filename with table data [default=%default].",
metavar="FILE")
parser.add_option("-2",
"--file2",
dest="input_filename2",
type="string",
help="additional data file [default=%default].",
metavar="FILE")
parser.add_option(
"-s",
"--stats",
dest="statistics",
type="choice",
choices=("correlation", "spearman", "pearson", "count"),
help="statistical quantities to compute [default=%default]",
action="append")
parser.add_option("-p",
"--plot",
dest="plot",
type="choice",
choices=("scatter", "pairs", "panel", "bar",
"bar-stacked", "bar-besides", "1_vs_x",
"matched", "boxplot", "scatter+marginal",
"scatter-regression"),
help="plots to plot [default=%default]",
action="append")
parser.add_option(
"-t",
"--threshold",
dest="threshold",
type="float",
help="min threshold to use for counting method [default=%default].")
parser.add_option(
"-o",
"--colours",
dest="colours",
type="int",
help="column with colour information [default=%default].")
parser.add_option(
"-l",
"--plot-labels",
dest="labels",
type="string",
help="column labels for x and y in matched plots [default=%default].")
parser.add_option("-d",
"--add-diagonal",
dest="add_diagonal",
action="store_true",
help="add diagonal to plot [default=%default].")
parser.add_option("-e",
"--plot-legend",
dest="legend",
type="int",
help="column with legend [default=%default].")
parser.add_option("-r",
"--options",
dest="r_options",
type="string",
help="R plotting options [default=%default].")
parser.add_option("--format",
dest="format",
type="choice",
choices=("full", "sparse"),
help="output format [default=%default].")
parser.add_option("--title",
dest="title",
type="string",
help="""plot title [default=%default].""")
parser.add_option("",
"--xrange",
dest="xrange",
type="string",
help="x viewing range of plot [default=%default].")
parser.add_option("",
"--yrange",
dest="yrange",
type="string",
help="y viewing range of plot[default=%default].")
parser.add_option("--allow-empty-file",
dest="fail_on_empty",
action="store_false",
help="do not fail on empty input [default=%default].")
parser.add_option("--fail-on-empty",
dest="fail_on_empty",
action="store_true",
help="fail on empty input [default=%default].")
parser.set_defaults(hardcopy=None,
input_filename="",
input_filename2=None,
columns="all",
logscale=None,
statistics=[],
plot=[],
threshold=0.0,
labels="x,y",
colours=None,
diagonal=False,
legend=None,
title=None,
xrange=None,
yrange=None,
r_options="",
fail_on_empty=True,
format="full")
(options, args) = E.Start(parser)
if len(args) == 1 and not options.input_filename:
options.input_filename = args[0]
if options.columns not in ("all", "all-but-first"):
options.columns = [int(x) - 1 for x in options.columns.split(",")]
if options.colours:
options.colours -= 1
if options.legend:
options.legend -= 1
table = {}
headers = []
# read data matrix
if options.input_filename:
lines = IOTools.openFile(options.input_filename, "r").readlines()
else:
# note: this will not work for interactive viewing, but
# creating hardcopy plots works.
lines = sys.stdin.readlines()
lines = [x for x in lines if x[0] != "#"]
if len(lines) == 0:
if options.fail_on_empty:
raise IOError("no input")
E.warn("empty input")
E.Stop()
return
matrix, headers, colours, legend = readTable(lines,
"matrix",
take_columns=options.columns,
headers=True,
colours=options.colours,
row_names=options.legend)
if options.input_filename2:
# read another matrix (should be of the same format.
matrix2, headers2, colours2, legend2 = readTable(
lines,
"matrix2",
take_columns=options.columns,
headers=True,
colours=options.colours,
row_names=options.legend)
R.assign("headers", headers)
ndata = R("""length( matrix[,1] )""")[0]
if options.loglevel >= 1:
options.stdlog.write("# read matrix: %ix%i\n" % (len(headers), ndata))
if colours:
R.assign("colours", colours)
for method in options.statistics:
if method == "correlation":
cor = R.cor(matrix, use="pairwise.complete.obs")
writeMatrix(sys.stdout, cor, headers=headers, format="%5.2f")
elif method == "pearson":
options.stdout.write("\t".join(("var1", "var2", "coeff", "passed",
"pvalue", "n", "method",
"alternative")) + "\n")
for x in range(len(headers) - 1):
for y in range(x + 1, len(headers)):
try:
result = R("""cor.test( matrix[,%i], matrix[,%i] )""" %
(x + 1, y + 1))
except rpy.RPyException as msg:
E.warn(
"correlation not computed for columns %i(%s) and %i(%s): %s"
% (x, headers[x], y, headers[y], msg))
options.stdout.write(
"%s\t%s\t%s\t%s\t%s\t%i\t%s\t%s\n" %
(headers[x], headers[y], "na", "na", "na", 0, "na",
"na"))
else:
options.stdout.write(
"%s\t%s\t%6.4f\t%s\t%e\t%i\t%s\t%s\n" %
(headers[x], headers[y],
result.rx2('estimate').rx2('cor')[0],
Stats.getSignificance(
float(result.rx2('p.value')[0])),
result.rx2('p.value')[0],
result.rx2('parameter').rx2('df')[0],
result.rx2('method')[0],
result.rx2('alternative')[0]))
elif method == "spearman":
options.stdout.write("\t".join(("var1", "var2", "coeff", "passed",
"pvalue", "method",
"alternative")) + "\n")
for x in range(len(headers) - 1):
for y in range(x + 1, len(headers)):
result = R(
"""cor.test( matrix[,%i], matrix[,%i], method='spearman')"""
% (x + 1, y + 1))
options.stdout.write(
"%s\t%s\t%6.4f\t%s\t%e\t%i\t%s\t%s\n" %
(headers[x], headers[y], result['estimate']['rho'],
Stats.getSignificance(float(result['p.value'])),
result['p.value'], result['parameter']['df'],
result['method'], result['alternative']))
elif method == "count":
# number of shared elements > threshold
m, r, c = MatlabTools.ReadMatrix(open(options.input_filename, "r"),
take=options.columns,
headers=True)
mask = numpy.greater(m, options.threshold)
counts = numpy.dot(numpy.transpose(mask), mask)
writeMatrix(options.stdout, counts, headers=c, format="%i")
if options.plot:
# remove columns that are completely empty
if "pairs" in options.plot:
colsums = R('''colSums( is.na(matrix ))''')
take = [x for x in range(len(colsums)) if colsums[x] != ndata]
if take:
E.warn("removing empty columns %s before plotting" % str(take))
matrix = R.subset(matrix, select=[x + 1 for x in take])
R.assign("""matrix""", matrix)
headers = [headers[x] for x in take]
if legend:
legend = [headers[x] for x in take]
if options.r_options:
extra_options = ", %s" % options.r_options
else:
extra_options = ""
if options.legend is not None and len(legend):
extra_options += ", legend=c('%s')" % "','".join(legend)
if options.labels:
xlabel, ylabel = options.labels.split(",")
extra_options += ", xlab='%s', ylab='%s'" % (xlabel, ylabel)
else:
xlabel, ylabel = "", ""
if options.colours:
extra_options += ", col=colours"
if options.logscale:
extra_options += ", log='%s'" % options.logscale
if options.xrange:
extra_options += ", xlim=c(%f,%f)" % tuple(
map(float, options.xrange.split(",")))
if options.yrange:
extra_options += ", ylim=c(%f,%f)" % tuple(
map(float, options.yrange.split(",")))
if options.hardcopy:
if options.hardcopy.endswith(".eps"):
R.postscript(options.hardcopy)
elif options.hardcopy.endswith(".png"):
R.png(options.hardcopy, width=1024, height=768, type="cairo")
elif options.hardcopy.endswith(".jpg"):
R.jpg(options.hardcopy, width=1024, height=768, type="cairo")
for method in options.plot:
if ndata < 100:
point_size = "1"
pch = "o"
elif ndata < 1000:
point_size = "1"
pch = "o"
else:
point_size = "0.5"
pch = "."
if method == "scatter":
R("""plot( matrix[,1], matrix[,2], cex=%s, pch="o" %s)""" %
(point_size, extra_options))
if method == "scatter-regression":
R("""plot( matrix[,1], matrix[,2], cex=%s, pch="o" %s)""" %
(point_size, extra_options))
dat = R(
"""dat <- data.frame(x = matrix[,1], y = matrix[,2])""")
R("""new <- data.frame(x = seq( min(matrix[,1]), max(matrix[,1]), (max(matrix[,1]) - min(matrix[,1])) / 100))"""
)
mod = R("""mod <- lm( y ~ x, dat)""")
R("""predict(mod, new, se.fit = TRUE)""")
R("""pred.w.plim <- predict(mod, new, interval="prediction")"""
)
R("""pred.w.clim <- predict(mod, new, interval="confidence")"""
)
R("""matpoints(new$x,cbind(pred.w.clim, pred.w.plim[,-1]), lty=c(1,2,2,3,3), type="l")"""
)
R.mtext("y = %f * x + %f, r=%6.4f, n=%i" %
(mod["coefficients"]["x"],
mod["coefficients"]["(Intercept)"],
R("""cor( dat )[2]"""), ndata),
3,
cex=1.0)
elif method == "pairs":
if options.add_diagonal:
R("""panel.hist <- function( x,y,... ) { points(x,y,...); abline(0,1); }"""
)
else:
R("""panel.hist <- function( x,y,... ) { points(x,y,...); }"""
)
# There used to be a argument na_action="na.omit", but
# removed this as there appeared error messages saying
# "na.action is not a graphical parameter" and the
# plots showed occasionally the wrong scale.
# cex=point_size also caused trouble (error message:
# "X11 used font size 8 when 2 was requested" or
# similar)
if options.colours:
R.pairs(matrix,
pch=pch,
col=colours,
main=options.title,
panel="panel.hist",
labels=headers,
cex_labels=2.0)
else:
R.pairs(matrix,
pch=pch,
panel="panel.hist",
main=options.title,
labels=headers,
cex_labels=2.0)
elif method == "boxplot":
extra_options += ",main='%s'" % options.title
# set vertical orientation
if max([len(x) for x in headers]) > 40 / len(headers):
# remove xlabel:
extra_options = re.sub(", xlab='[^']+'", "", extra_options)
extra_options += ", names.arg=headers, las=2"
R("""op <- par(mar=c(11,4,4,2))"""
) # the 10 allows the names.arg below the barplot
R("""boxplot( matrix %s)""" % extra_options)
elif method == "bar" or method == "bar-stacked":
if not options.colours:
extra_options += ", col=rainbow(5)"
# set vertical orientation
if max([len(x) for x in headers]) > 40 / len(headers):
# remove xlabel:
extra_options = re.sub(", xlab='[^']+'", "", extra_options)
extra_options += ", names.arg=headers, las=2"
R("""op <- par(mar=c(11,4,4,2))"""
) # the 10 allows the names.arg below the barplot
R("""barplot(as.matrix(matrix), %s)""" % extra_options)
elif method == "bar-besides":
if not options.colours:
extra_options += ", col=rainbow(%i)" % ndata
# set vertical orientation
if max([len(x) for x in headers]) > 40 / len(headers):
# remove xlabel:
extra_options = re.sub(", xlab='[^']+'", "", extra_options)
extra_options += ", names.arg=headers, las=2"
R("""op <- par(mar=c(11,4,4,2))"""
) # the 10 allows the names.arg below the barplot
R("""barplot(as.matrix(matrix), beside=TRUE %s)""" %
extra_options)
elif method == "scatter+marginal":
if options.title:
# set the size of the outer margins - the title needs to be added at the end
# after plots have been created
R.par(oma=R.c(0, 0, 4, 0))
R("""matrix""")
R("""
x <- matrix[,1];
y <- matrix[,2];
xhist <- hist(x, breaks=20, plot=FALSE);
yhist <- hist(y, breaks=20, plot=FALSE);
top <- max(c(xhist$counts, yhist$counts));
nf <- layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3), respect=TRUE );
par(mar=c(3,3,1,1)) ;
plot(x, y, cex=%s, pch="o" %s) ;
par(mar=c(0,3,1,1)) ;
barplot(xhist$counts, axes=FALSE, ylim=c(0, top), space=0 ) ;
par(mar=c(3,0,1,1)) ;
title(main='%s');
barplot(yhist$counts, axes=FALSE, xlim=c(0, top), space=0, horiz=TRUE ) ;
title(main='%s');
""" % (point_size, extra_options, xlabel, ylabel))
if options.title:
R.mtext(options.title, 3, outer=True, line=1, cex=1.5)
elif method in ("panel", "1_vs_x", "matched"):
if method == "panel":
pairs = []
for x in range(len(headers) - 1):
for y in range(x + 1, len(headers)):
pairs.append((x, y))
elif method == "1_vs_x":
pairs = []
for x in range(1, len(headers)):
pairs.append((0, x))
# print matching columns
elif method == "matched":
pairs = []
for x in range(len(headers) - 1):
for y in range(x + 1, len(headers)):
if headers[x] == headers[y]:
pairs.append((x, y))
break
w = int(math.ceil(math.sqrt(len(pairs))))
h = int(math.ceil(float(len(pairs)) / w))
PosInf = 1e300000
NegInf = -1e300000
xlabel, ylabel = options.labels.split(",")
R("""layout(matrix(seq(1,%i), %i, %i, byrow = TRUE))""" %
(w * h, w, h))
for a, b in pairs:
new_matrix = [
x for x in zip(
list(matrix[a].values())[0],
list(matrix[b].values())[0])
if x[0] not in (float("nan"), PosInf, NegInf)
and x[1] not in (float("nan"), PosInf, NegInf)
]
try:
R("""plot(matrix[,%i], matrix[,%i], main='%s versus %s', cex=0.5, pch=".", xlab='%s', ylab='%s' )"""
% (a + 1, b + 1, headers[b], headers[a], xlabel,
ylabel))
except rpy.RException as msg:
print("could not plot %s versus %s: %s" %
(headers[b], headers[a], msg))
if options.hardcopy:
R['dev.off']()
E.info("matrix added as >matrix< in R.")
if not options.hardcopy:
if options.input_filename:
interpreter = code.InteractiveConsole(globals())
interpreter.interact()
else:
E.info(
"can not start new interactive session as input has come from stdin."
)
E.Stop()
# set vertical orientation
if max( [len(x) for x in headers] ) > 40 / len(headers):
# remove xlabel:
extra_options = re.sub( ", xlab='[^']+'", "", extra_options )
extra_options += ", names.arg=headers, las=2"
R("""op <- par(mar=c(11,4,4,2))""") # the 10 allows the names.arg below the barplot
R("""barplot(as.matrix(matrix), beside=TRUE %s)""" % extra_options)
elif method == "scatter+marginal":
if options.title:
# set the size of the outer margins - the title needs to be added at the end
# after plots have been created
R.par(oma=R.c(0,0,4,0) )
R( """matrix""" )
R( """
x <- matrix[,1];
y <- matrix[,2];
xhist <- hist(x, breaks=20, plot=FALSE);
yhist <- hist(y, breaks=20, plot=FALSE);
top <- max(c(xhist$counts, yhist$counts));
nf <- layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3), respect=TRUE );
par(mar=c(3,3,1,1)) ;
plot(x, y, cex=%s, pch="o" %s) ;
par(mar=c(0,3,1,1)) ;
barplot(xhist$counts, axes=FALSE, ylim=c(0, top), space=0 ) ;
par(mar=c(3,0,1,1)) ;
title(main='%s');
if max([len(x) for x in headers]) > 40 / len(headers):
# remove xlabel:
extra_options = re.sub(", xlab='[^']+'", "", extra_options)
extra_options += ", names.arg=headers, las=2"
R("""op <- par(mar=c(11,4,4,2))"""
) # the 10 allows the names.arg below the barplot
R("""barplot(as.matrix(matrix), beside=TRUE %s)""" %
extra_options)
elif method == "scatter+marginal":
if options.title:
# set the size of the outer margins - the title needs to be added at the end
# after plots have been created
R.par(oma=R.c(0, 0, 4, 0))
R("""matrix""")
R("""
x <- matrix[,1];
y <- matrix[,2];
xhist <- hist(x, breaks=20, plot=FALSE);
yhist <- hist(y, breaks=20, plot=FALSE);
top <- max(c(xhist$counts, yhist$counts));
nf <- layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3), respect=TRUE );
par(mar=c(3,3,1,1)) ;
plot(x, y, cex=%s, pch="o" %s) ;
par(mar=c(0,3,1,1)) ;
barplot(xhist$counts, axes=FALSE, ylim=c(0, top), space=0 ) ;
par(mar=c(3,0,1,1)) ;
title(main='%s');
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-g", "--gtf-file", dest="gtf", type="string",
help="GTF containing gene annotations")
parser.add_option("-s", "--sort", dest="sort", type="choice",
default="length",
choices=sort_choices,
help="Property to sort rows by. Choices are %s"
% ", ".join(sort_choices))
parser.add_option("-b", "--bin-size", dest="bin_size", type="int",
default=25,
help="Size of window over which to sum reads")
parser.add_option("-u", "--upstream-window", dest="us_win", type="int",
default=500,
help="Amount of sequence upstream of alignment point (less introns)")
parser.add_option("-d", "--downstream-window", dest="ds_win", type="int",
default=None,
help="Amount of sequence downstream of alignment point (default longest segment)")
parser.add_option("-a", "--align-at", dest="align_at", type="choice",
default="start",
choices=align_choices,
help="Where to align genes/transcripts at. Choices are %s"
% ", ".join(align_choices))
parser.add_option("-H", "--height", dest="height", type="int",
default=None,
help="Number of rows in output matrix/heigh of plot in px")
parser.add_option("-w", "--width", dest="width", type="int",
default=None,
help="Number of columns in output/width of plot in px"
"default based on bin size")
parser.add_option("-n", "--normalize", dest="normalize", type="choice",
default="none",
choices=norm_choices,
help="Row normalization to apply. Choices are: %s"
% ", ".join(norm_choices))
parser.add_option("-r", "--renormalize", dest="renormalize", type="choice",
default="none",
choices=norm_choices,
help="Row normalization to apply after row/column compression")
parser.add_option("--no-plot", dest="plot", action="store_false",
default=True,
help="Do not output plot - compute matrix only")
parser.add_option("--use-matrix", dest="use_matrix", type="string",
default=None,
help="Use existing matrix")
parser.add_option("--annotations", dest="annotations", type="choice",
action="append",
choices=annotation_choices,
help="Add annotations to the output plot")
parser.add_option("--reverse-strand", dest="rstrand", action="store_true",
default=False,
help="Find reads on reverse strand")
parser.add_option("-f", "--feature", dest="feature", type="choice",
choices=["gene", "transcript"],
default="gene",
help="use genes or transcripts")
parser.add_option("--quantile", dest="quantile", type="float",
default=0.99,
help="Quantile to use in quantile normalization")
parser.add_option("-o", "--outfile-prefix", dest="outfile_pattern", type="string",
default=None,
help="base of names for output files")
parser.add_option("-c", "--crop", dest="crop", type="string",
default=None,
help="crop view to a certain range on the xaxis. Specify like"
"-500:1000")
parser.add_option("--format", dest="format", type="string",
default="png",
help="Output format, use valid R graphics device")
parser.add_option("--plus-wig", dest="plus_wig", type="string",
help="Use this wig for plus strand info rather than bam file")
parser.add_option("--minus-wig", dest="minus_wig", type="string",
help="Use this wig for minus strand info rather than bam file")
parser.add_option("--bed", dest="bed", type="string",
help="Use this bed for signal(must be indexed)")
parser.add_option("--norm-mat", dest="norm_mat", type="string",
help="Use this matrix for normalizing (e.g. RNA data")
parser.add_option("--sort-order-file", dest="sort_file", type="string",
default=None,
help="Two column file containing gene names in the first
column and a numeric value to sort on in the second")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
if options.plot and (options.height is None):
options.height = 100
if options.gtf:
f = IOTools.openFile(options.gtf)
if options.feature == "gene":
gtf_iterator = GTF.flat_gene_iterator(GTF.iterator(f))
else:
gtf_iterator = GTF.transcript_iterator(GTF.iterator(f))
lengths = dict()
utr3_lengths = dict()
utr5_lengths = dict()
first_exon_lengths = dict()
for transcript in gtf_iterator:
lengths[transcript[0].transcript_id] = sum(
[e[1] - e[0] for e in GTF.asRanges(transcript, "exon")])
exons = GTF.asRanges(transcript, "exon")
utrs = GTF.asRanges(transcript, "UTR")
coding = Intervals.truncate(exons, utrs)
coding.sort()
utr5 = [utr for utr in utrs if utr[1] <= coding[0][0]]
utr3 = [utr for utr in utrs if utr[0] >= coding[-1][-1]]
if transcript[0].strand == "-":
utr3, utr5 = utr5, utr3
if transcript[0].strand == "+" or len(exons) == 1:
first_exon_lengths[transcript[0].transcript_id] = \
exons[0][1] - exons[0][0]
else:
first_exon_lengths[transcript[0].transcript_id] = \
exons[-1][1] - exons[-1][0]
utr3_lengths[transcript[0].transcript_id] = sum(
[e[1] - e[0] for e in utr3])
utr5_lengths[transcript[0].transcript_id] = sum(
[e[1] - e[0] for e in utr5])
lengths = pandas.Series(lengths)
utr3_lengths = pandas.Series(utr3_lengths)
utr5_lengths = pandas.Series(utr5_lengths)
first_exon_lengths = pandas.Series(first_exon_lengths)
else:
options.sort = "none"
options.annotations = None
if options.plus_wig:
getter = iCLIP.make_getter(plus_wig=options.plus_wig,
minus_wig=options.minus_wig)
elif options.bed:
getter = iCLIP.make_getter(bedfile=options.bed)
else:
try:
getter = iCLIP.make_getter(bamfile=args[0])
except IOError:
E.error("Cannot open bamfile %s" % args[0])
return(1)
except IndexError:
getter = None
if options.use_matrix:
raw_matrix = pandas.read_csv(options.use_matrix,
sep="\t",
index_col=0)
raw_matrix.columns = raw_matrix.columns.astype("int")
else:
raw_matrix = get_matrix(getter, lengths, options)
if options.crop:
crop_from, crop_to = map(int, options.crop.split(":"))
raw_matrix = raw_matrix.loc[:, crop_from:crop_to]
if options.norm_mat:
norm_matrix = pandas.read_csv(options.norm_mat,
sep="\t",
index_col=0)
norm_matrix.columns = norm_matrix.columns.astype("int")
if options.crop:
norm_matrix = norm_matrix.loc[:, crop_from:crop_to]
if all(norm_matrix.columns == raw_matrix.columns) and \
all(raw_matrix.index.isin(norm_matrix.index.values)):
norm_matrix = norm_matrix.loc[raw_matrix.index]
norm_matrix = norm_matrix.replace(
0, norm_matrix[norm_matrix > 0].min().min())
raw_matrix = raw_matrix/norm_matrix
norm_matrix = None
else:
raise ValueError("Incompatible normalisation matrix")
normalized_matrix = normalize(raw_matrix, options.normalize,
quantile=options.quantile)
if options.sort == "length":
sorter = lengths
elif options.sort == "3utr":
sorter = utr3_lengths
elif options.sort == "5utr":
sorter = utr5_lengths
elif options.sort == "first-exon":
sorter = first_exon_lengths
elif options.sort == "manual":
sorter = pandas.read_csv(options.sort_file, sep="\t",
index_col=0, usecols=[0, 1])
sorter = sorter[sorter.columns[0]]
elif options.sort == "none":
sorter = pandas.Series(range(raw_matrix.shape[0]),
index=raw_matrix.index[::-1])
sorter = sorter[sorter.index.isin(normalized_matrix.index)]
sorter = sorter.sort_values(ascending=False)
sorted_matrix = normalized_matrix.loc[sorter.index.values]
compress_matrix = iCLIP.compress_matrix(sorted_matrix,
ncols=options.width,
nrows=options.height)
renormalized_matrix = normalize(compress_matrix, options.renormalize,
quantile=options.quantile)
if renormalized_matrix is raw_matrix and options.use_matrix is not None:
E.info("Input and output matrices are identical, no matrix output")
else:
if options.outfile_pattern:
mat_outfile = IOTools.openFile(
options.outfile_pattern + ".matrix.tsv.gz", "w")
else:
mat_outfile = options.stdout
renormalized_matrix.to_csv(mat_outfile, sep="\t")
if options.plot:
try:
from rpy2.robjects import r as R
from rpy2 import robjects as ro
except:
E.info("No rpy2. Not plotting image")
return(0)
from rpy2.robjects.numpy2ri import numpy2ri
ro.conversion.py2ri = numpy2ri
ro.numpy2ri.activate()
if options.outfile_pattern:
plot_outfile = options.outfile_pattern + ".png"
else:
plot_outfile = "bam2heatmap_out.png"
c = R["c"]
R[options.format](plot_outfile,
width=renormalized_matrix.shape[1] + 72,
height=renormalized_matrix.shape[0] + 72,
unit="px",
res=72)
R.par(mai=c(1, 0.5, 0, 0.5))
cols = R["colorRampPalette"](c("white", "blue"))(50)
bases = renormalized_matrix.columns.values.astype("int")
groups = renormalized_matrix.index.values.astype("int")
mat = renormalized_matrix.as_matrix()
mat[mat >= 1] = 1
R.image(bases, groups, R.t(mat),
zlim=c(0, 1),
raster=True,
col=cols,
xlab="Base",
yaxt="n")
def _sort_and_compress_annotation(anno):
sorted_anno = anno.loc[sorter.index]
comp_anno = iCLIP.compress_matrix(
sorted_anno, renormalized_matrix.shape[0])
return comp_anno
if options.annotations:
ends = _sort_and_compress_annotation(lengths)
starts = pandas.Series(0, index=renormalized_matrix.index)
if options.align_at == "end":
starts, ends = -1 * ends, starts
if "start" in options.annotations:
R.lines(starts.values, starts.index.values, col="black", pch=".")
if "end" in options.annotations:
R.lines(ends.values, ends.index.values,
pch=".", col="black")
if "5utr" in options.annotations:
utr5s = _sort_and_compress_annotation(utr5_lengths)
utr5s = starts + utr5s
R.lines(utr5s.values, utr5s.index.values, col="orange", pch=".")
if "3utr" in options.annotations:
utr3s = _sort_and_compress_annotation(utr3_lengths)
utr3s = ends - utr3s
R.lines(utr3s.values, utr3s.index.values, col="orange", pch=".")
R["dev.off"]()
# write footer and output benchmark information.
E.Stop()
def plot_qc_reads(qc_df):
"""
Plot number of reads part of a pipeline QC file.
"""
# Record NA values as 0
qc_df = qc_df.fillna(0)#.set_index("sample")
cols = ["sample",
"num_reads",
"num_mapped",
"num_unique_mapped",
"num_junctions"]
qc_df = qc_df[cols]
melted_qc = pandas.melt(qc_df, id_vars=["sample"])
qc_r = conversion_pydataframe(melted_qc)
labels = tuple(["num_reads",
"num_mapped",
"num_unique_mapped",
"num_junctions"])
labels = robj.StrVector(labels)
variable_i = qc_r.names.index('variable')
qc_r[variable_i] = robj.FactorVector(qc_r[variable_i],
levels = labels)
ggplot2.theme_set(ggplot2.theme_bw(12))
scales = importr("scales")
r_opts = r.options(scipen=4)
p = ggplot2.ggplot(qc_r) + \
ggplot2.geom_point(aes_string(x="sample", y="value")) + \
ggplot2.scale_y_continuous(trans=scales.log10_trans(),
breaks=scales.trans_breaks("log10",
robj.r('function(x) 10^x')),
labels=scales.trans_format("log10",
robj.r('math_format(10^.x)'))) + \
r.xlab("CLIP-Seq samples") + \
r.ylab("No. reads") + \
ggplot2.coord_flip() + \
ggplot2.facet_wrap(Formula("~ variable"), ncol=1) + \
theme(**{"panel.grid.major.x": element_blank(),
"panel.grid.minor.x": element_blank(),
"panel.grid.major.y": theme_line(size=0.5,colour="grey66",linetype=3)})
p.plot()
return
r.par(mfrow=np.array([1,2]))
num_samples = len(qc_df.num_reads)
r.par(bty="n", lwd=1.7, lty=2)
r_opts = r.options(scipen=4)
r.options(r_opts)
r.dotchart(convert_to_r_matrix(qc_df[["num_reads",
"num_mapped",
"num_unique_mapped"]]),
xlab="No. reads",
lcolor="black",
pch=19,
gcolor="darkblue",
cex=0.8)
r.par(bty="n")
r.dotchart(convert_to_r_matrix(qc_df[["num_ribosub_mapped",
"num_ribo",
"num_junctions"]]),
xlab="No. reads",
lcolor="black",
pch=19,
gcolor="darkblue",
cex=0.8)
