def _replot(self):
if self._grDevices.dev_list() != r("NULL"):
self._grDevices.dev_off()
self._graphics.par(bg)
self._graphics.split_screen(r.c(2,1))
self._graphics.split_screen(r.c(1, 2), screen=2)
self._graphics.screen(1)
self._plot_regions()
def convert_hgnc2ensembl(hgnc_id):
init_biomaRt()
v = R.c(hgnc_id)
res = R.getBM(attributes=R.c("ensembl_gene_id"), filters="hgnc_symbol", values=v, mart=__mart)
try:
return R.get("ensembl_gene_id", res)[0]
except:
print 'Error convert_hgnc2ensembl: '+str(hgnc_id)+' not found in database'
return None
def convert_list_ensembl2hgnc(ensembl_id_list):
init_biomaRt()
v = R.c(ensembl_id_list)
res = R.getBM(attributes=R.c("hgnc_symbol"), filters="ensembl_gene_id", values=v, mart=__mart)
try:
return R.get("hgnc_symbol", res)
except:
print 'Error convert_ensembl2hgnc: '+str(ensembl_id)+' not found in database'
return None
def _roc_curve_r(observations, predictions, FDRth=0.05):
"""
:param observations: known truth set
:param predictions: all data
:param FDRth:
:return:
"""
obs_rtbl = numpy2ri.py2ri(observations)
prd_rtbl = numpy2ri.py2ri(predictions)
roc_prm = {'direction': '>'}
RES = pROC.roc(obs_rtbl, prd_rtbl, **roc_prm)
auc = pandas2ri.ri2py(RES.rx2('auc'))[0]
columns = ['threshold', 'ppv', 'sensitivity', 'specificity']
coor_prm = {'ret': r.c('threshold', 'ppv', 'sensitivity', 'specificity')}
COORS = pROC.coords(RES, 'all', **coor_prm)
cords = numpy2ri.ri2py(COORS)
df = pd.DataFrame(cords.T, columns=columns)
FDR5percTh = (df[df.ppv >= (1 - FDRth)])['threshold'].max()
if not np.isnan(FDR5percTh):
index_min = min(df[df.threshold <= FDR5percTh].index.tolist())
else:
index_min = 0
threshold = df.at[index_min, 'threshold']
SENS = df.at[index_min, 'sensitivity']
SPEC = df.at[index_min, 'specificity']
return df, auc, SENS, FDR5percTh
def deaScranDESeq2(counts, conds, comparisons, alpha, scran_clusters=False):
"""Makes a call to DESeq2 with SCRAN to
perform D.E.A. in the given
counts matrix with the given conditions and comparisons.
Returns a list of DESeq2 results for each comparison
"""
results = list()
n_cells = len(counts.columns)
try:
pandas2ri.activate()
deseq2 = RimportLibrary("DESeq2")
scran = RimportLibrary("scran")
multicore = RimportLibrary("BiocParallel")
multicore.register(multicore.MulticoreParam(multiprocessing.cpu_count()-1))
as_matrix = r["as.matrix"]
# Create the R conditions and counts data
r_counts = pandas2ri.py2ri(counts)
cond = robjects.StrVector(conds)
r_call = """
function(r_counts) {
sce = SingleCellExperiment(assays=list(counts=r_counts))
return(sce)
}
"""
r_func = r(r_call)
sce = r_func(as_matrix(r_counts))
if scran_clusters:
r_clusters = scran.quickCluster(as_matrix(r_counts), max(n_cells/10, 10))
min_cluster_size = min(Counter(r_clusters).values())
sizes = list(set([round((min_cluster_size/2) / i) for i in [5,4,3,2,1]]))
sce = scran.computeSumFactors(sce, clusters=r_clusters, sizes=sizes, positive=True)
else:
sizes = list(set([round((n_cells/2) * i) for i in [0.1,0.2,0.3,0.4,0.5]]))
sce = scran.computeSumFactors(sce, sizes=sizes, positive=True)
sce = r.normalize(sce)
dds = r.convertTo(sce, type="DESeq2")
r_call = """
function(dds, conditions){
colData(dds)$conditions = as.factor(conditions)
design(dds) = formula(~ conditions)
return(dds)
}
"""
r_func = r(r_call)
dds = r_func(dds, cond)
dds = r.DESeq(dds)
# Perform the comparisons and store results in list
for A,B in comparisons:
result = r.results(dds, contrast=r.c("conditions", A, B), alpha=alpha)
result = r['as.data.frame'](result)
genes = r['rownames'](result)
result = pandas2ri.ri2py_dataframe(result)
# There seems to be a problem parsing the rownames from R to pandas
# so we do it manually
result.index = genes
results.append(result)
pandas2ri.deactivate()
except Exception as e:
raise e
return results
def _prepare_r_instance(self, data: pd.DataFrame):
if data.index.freq is None:
freq = pd.infer_freq(data.index)
if freq is None:
raise ValueError(
f"The time series index has no valid frequency. Index={data.index}"
)
data.index.freq = freq
print("input", r.c(data.index[0].year, _get_start_epoch(data)))
sts = surveillance.sts(
start=r.c(data.index[0].year, _get_start_epoch(data)),
epoch=robjects.IntVector([
r["as.numeric"](r["as.Date"](d.isoformat()))[0]
for d in data.index.date
]),
# epoch=data.index,
freq=_get_freq(data),
observed=data["n_cases"].values,
epochAsDate=True,
)
return sts
def __init__(self, polyfile=None, findfile=None, bg="white"):
self._graphics = importr("graphics")
self._grDevices = importr("grDevices")
rpy2.interactive.process_revents.start()
self._NA = r("NA")[0]
self._C = lambda seq: r.c(*seq)
self._polyfile = polyfile
self._findfile = findfile
self._replot()
def _call_surveillance_algo(self, disprog_obj, detection_range):
control = r.list(
range=detection_range,
b=self.years_back,
w=self.window_half_width,
reweight=self.reweight,
alpha=self.alpha,
trend=self.trend,
limit54=r.c(self.min_cases_in_past_periods, self.past_period_cutoff),
powertrans=self.power_transform,
)
surv = surveillance.algo_farrington(disprog_obj, control=control)
return surv
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)
def deaDESeq2(counts, conds, comparisons, alpha, size_factors=None):
"""Makes a call to DESeq2 to
perform D.E.A. in the given
counts matrix with the given conditions and comparisons.
Can be given size factors.
Returns a list of DESeq2 results for each comparison
"""
results = list()
try:
pandas2ri.activate()
deseq2 = RimportLibrary("DESeq2")
multicore = RimportLibrary("BiocParallel")
multicore.register(
multicore.MulticoreParam(multiprocessing.cpu_count() - 1))
# Create the R conditions and counts data
r_counts = pandas2ri.py2ri(counts)
cond = robjects.DataFrame({"conditions": robjects.StrVector(conds)})
design = r('formula(~ conditions)')
dds = r.DESeqDataSetFromMatrix(countData=r_counts,
colData=cond,
design=design)
if size_factors is None:
dds = r.DESeq(dds, parallel=True)
else:
assign_sf = r["sizeFactors<-"]
dds = assign_sf(object=dds,
value=robjects.FloatVector(size_factors))
dds = r.estimateDispersions(dds)
dds = r.nbinomWaldTest(dds)
# Perform the comparisons and store results in list
for A, B in comparisons:
result = r.results(dds,
contrast=r.c("conditions", A, B),
alpha=alpha)
result = r['as.data.frame'](result)
genes = r['rownames'](result)
result = pandas2ri.ri2py_dataframe(result)
# There seems to be a problem parsing the rownames from R to pandas
# so we do it manually
result.index = genes
results.append(result)
pandas2ri.deactivate()
except Exception as e:
raise e
return results
def _call_surveillance_algo(self, sts, detection_range):
control = r.list(
**{
"range": detection_range,
"c.ARL": self.glr_test_threshold,
"m0": robjects.NULL,
# Mtilde is set to 1, since that is the only valid value for "epi" and "intercept"
"Mtilde": 1,
"M": self.m,
"change": self.change,
# Role of theta: If NULL then the GLR scheme is used. If not NULL the prespecified value for κ or λ is used in a recursive LR scheme, which is faster."""
"theta": robjects.NULL,
"dir": r.c(*self.direction),
"ret": self.upperbound_statistic,
})
surv = surveillance.glrpois(sts, control=control)
return surv
def __add_GO_info(dictionary):
whichTerms = R.c(dictionary.keys())
qTerms = R.paste(R.paste("'", whichTerms, "'", sep=""), collapse=",")
retVal = R.dbGetQuery(R.GO_dbconn(), R.paste("SELECT ontology, go_id, term, definition FROM go_term WHERE go_id IN (", qTerms, ");", sep=""))
for iter in retVal.iter_row():
go_id = iter.rx2('go_id')[0]
ontology = iter.rx2('ontology')[0]
term = iter.rx2('term')[0]
definition = iter.rx2('definition')[0]
dictionary[go_id]['ontology'] = ontology
dictionary[go_id]['term'] = term
dictionary[go_id]['definition'] = definition
return dictionary
def _call_surveillance_algo(self, sts, detection_range):
control = r.list(
range=detection_range,
b=self.years_back,
w=self.window_half_width,
reweight=self.reweight,
weightsThreshold=self.weights_threshold,
alpha=self.alpha,
trend=self.trend,
trend_threshold=self.trend_threshold,
limit54=r.c(self.min_cases_in_past_periods, self.past_period_cutoff),
powertrans=self.power_transform,
pastWeeksNotIncluded=self.past_weeks_not_included,
thresholdMethod=self.threshold_method,
)
surv = surveillance.farringtonFlexible(sts, control=control)
return surv
def _call_surveillance_algo(self, sts, detection_range):
control = r.list(
**{
"range": detection_range,
"c.ARL": self.glr_test_threshold,
"m0": robjects.NULL,
"alpha": self.alpha,
# Mtilde is set to 1, since that is the only valid value for "epi" and "intercept"
"Mtilde": 1,
"M": self.m,
"change": self.change,
"theta": robjects.NULL,
"dir": r.c(*self.direction),
"ret": self.upperbound_statistic,
"xMax": self.x_max,
})
surv = surveillance.glrnb(sts, control=control)
return surv
def process(outf, dti_f, bval_f, python=False):
"""
Take a list of lists of files DTI and b-val files, returns a
gzip R file with all B0 data arrays stored on it.
"""
if python:
import collections
b0s = collections.OrderedDict()
for idx, scan in enumerate(bval_f):
print scan
basename = os.path.basename(scan)
print basename
bval = np.loadtxt(scan)
bval[np.where(bval==np.min(bval))] = 0
im = nb.load(dti_f[idx])
b0_loc = np.where(bval==np.min(bval))[0][0]
dti = im.get_data()[:,:,:,b0_loc]
if python:
b0s[basename] = np.ravel(dti)
else:
ro = numpy2ri(np.ravel(dti+1))
rr = robj.Matrix(ro)
if idx is 0:
myl = r.list(basename=rr)
else:
myl = r.c(myl, r.list(basename=rr))
if python:
import pickle
# write python dict to a file
#mydict = {'a': 1, 'b': 2, 'c': 3}
output = open(outf, 'wb')
pickle.dump(b0s, output)
output.close()
# read python dict back from the file
# pkl_file = open('myfile.pkl', 'rb')
# mydict2 = pickle.load(pkl_file)
# pkl_file.close()
else:
r.assign('bar', myl)
r("save(bar, file='"+outf+"', compress=TRUE)")
def deaDESeq2(counts, conds, comparisons, alpha, size_factors=None):
"""Makes a call to DESeq2 to
perform D.E.A. in the given
counts matrix with the given conditions and comparisons.
Can be given size factors.
Returns a list of DESeq2 results for each comparison
"""
results = list()
try:
pandas2ri.activate()
deseq2 = RimportLibrary("DESeq2")
multicore = RimportLibrary("BiocParallel")
multicore.register(multicore.MulticoreParam(multiprocessing.cpu_count()-1))
# Create the R conditions and counts data
r_counts = pandas2ri.py2ri(counts)
cond = robjects.DataFrame({"conditions": robjects.StrVector(conds)})
design = r('formula(~ conditions)')
dds = r.DESeqDataSetFromMatrix(countData=r_counts, colData=cond, design=design)
if size_factors is None:
dds = r.DESeq(dds, parallel=True, useT=True,
minmu=1e-6, minReplicatesForReplace=np.inf)
else:
assign_sf = r["sizeFactors<-"]
dds = assign_sf(object=dds, value=robjects.FloatVector(size_factors))
dds = r.estimateDispersions(dds)
dds = r.nbinomWaldTest(dds)
# Perform the comparisons and store results in list
for A,B in comparisons:
result = r.results(dds, contrast=r.c("conditions", A, B),
alpha=alpha, parallel=True)
result = r['as.data.frame'](result)
genes = r['rownames'](result)
result = pandas2ri.ri2py_dataframe(result)
# There seems to be a problem parsing the rownames from R to pandas
# so we do it manually
result.index = genes
results.append(result)
pandas2ri.deactivate()
except Exception as e:
raise e
return results
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_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)
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)
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 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 not argv: argv = sys.argv
# setup command line parser
parser = E.OptionParser( version = "%prog version: $Id: cgat_script_template.py 2871 2010-03-03 10:20:44Z andreas $",
usage = globals()["__doc__"] )
parser.add_option("-a", "--gtf-a", dest="gtf_a", type="string",
help="supply a gtf file - will compress uncompressed files" )
parser.add_option("-b", "--gtf-b", dest = "gtf_b", type = "string",
help="supply a second gtf file - will compress uncompressed files")
parser.add_option("-s", "--scripts-dir", dest = "scripts_dir", type = "string",
help="supply a location for accessory scripts")
parser.add_option( "--no-venn", dest = "no_venn", action="store_true",
help="set if no venn is to be drawn")
## add common options (-h/--help, ...) and parse command line
(options, args) = E.Start( parser, argv = argv )
gtf_files = [options.gtf_a, options.gtf_b]
merged_files = []
prefices = []
E.info("merging gtf files")
for gtf in gtf_files:
if gtf.endswith(".gtf.gz"):
outfile = P.snip(gtf, ".gtf.gz") + ".merged.gtf.gz"
prefices.append(P.snip(gtf, ".gtf.gz"))
merged_files.append(outfile)
statement = '''zcat %s | python %s/gtf2gtf.py --merge-transcripts --log=%s.log | gzip > %s''' % (gtf, options.scripts_dir, outfile, outfile)
P.run()
elif gtf.endswith(".gtf"):
outfile = P.snip(gtf, ".gtf") + ".merged.gtf.gz"
prefices.append(P.snip(gtf,".gtf"))
merged_files.append(outfile)
statement = '''cat %s | python %s/gtf2gtf.py --merge-transcripts --log=%s.log | gzip > %s''' % (gtf, options.scripts_dir, outfile, outfile)
P.run()
else:
raise ValueError("cannot perform merge on %s: is not a gtf file" % gtf)
for prefix in prefices:
if options.gtf_a.find(prefix) != -1:
gtf_a = prefix + ".merged.gtf.gz"
prefix_a = prefix
elif options.gtf_b.find(prefix) != -1:
gtf_b = prefix + ".merged.gtf.gz"
prefix_b = prefix
E.info("intersecting gtf files")
# intersect the resulting merged files
scriptsdir = options.scripts_dir
intersection_out = "_vs_".join([prefix_a, prefix_b]) + ".intersection.gtf.gz"
statement = '''intersectBed -a %(gtf_a)s -b %(gtf_b)s -s -wa
| python %(scriptsdir)s/gtf2gtf.py --merge-transcripts --log=log | gzip > %(intersection_out)s'''
P.run()
if not options.no_venn:
E.info("producing venn diagram for %s vs %s..." % (options.gtf_a, options.gtf_b))
# produce the venn diagram
intersection_file = intersection_out
gtf_a_merged = gtf_a
gtf_b_merged = gtf_b
# create dictionary key
gtf_pair = (gtf_a_merged, gtf_b_merged)
# containers for counts
count_gtf_merged_a = 0
count_gtf_merged_b = 0
count_intersection = 0
# create GTF iterator objects
gtf_iterator_a = GTF.iterator(IOTools.openFile(gtf_pair[0]))
gtf_iterator_b = GTF.iterator(IOTools.openFile(gtf_pair[1]))
gtf_iterator_intersection = GTF.iterator(IOTools.openFile(intersection_file))
# do the counts for each file
E.info("counting entries in %s" % gtf_a)
for entry in gtf_iterator_a:
count_gtf_merged_a += 1
print "counts for gtf-a: ",count_gtf_merged_a
E.info("counting entries in %s" % gtf_b)
for entry in gtf_iterator_b:
count_gtf_merged_b += 1
print "counts for gtf-b: ",count_gtf_merged_b
E.info("counting entries in %s" % intersection_file)
for entry in gtf_iterator_intersection:
count_intersection += 1
print "counts for intersection: ", count_intersection
# this is the important bit - basically take an arbitrary list of numbers to represent the list of lincrna in the refnoncoding set
# then use the intersection count to represent the overlapping section in the lincrna set and add a set of random numbers to this
# set to make up the remaining - non-overlapping set
result = {}
E.info("assembling count lists")
result[gtf_pair] = {"gtf-b" : map(str,xrange(count_gtf_merged_b)) , "gtf-a" : map(str,xrange(count_intersection)) + map(str, [random.random() for i in range(count_intersection,count_gtf_merged_a)] )}
R_source = os.path.join(os.path.abspath(options.scripts_dir), "venn_diagram.R")
R.source(R_source)
prefix_a = prefix_a.replace(".", "_").replace("-", "_")
prefix_b = prefix_b.replace(".", "_").replace("-", "_")
R('''prefix.a <- "%s"''' % prefix_a)
R('''prefix.b <- "%s"''' % prefix_b)
E.info("drawing venn diagram to %s" % (prefix_a + "_vs_" + prefix_b + ".overlap.png"))
R["venn.diagram2"](R.list( A = result[gtf_pair]["gtf-a"], B = result[gtf_pair]["gtf-b"])
, prefix_a + "_vs_" + prefix_b + ".overlap.png"
, **{'cat.cex': 1.5
, 'main.fontfamily': "Arial"
, 'cat.pos':FloatVector((0,0))
, 'cat.fontfamily':"Arial"
, 'main.cex':1.8
, 'height':1000
, 'width':1000
, 'cex':2
, 'fontfamily':"Arial"
, 'lwd':R.c(1,1)
, 'fill':R.c(R.rgb(0,0,0.5,0.5), R.rgb(0.5,0,0,0.5))
, 'category.names':R.c(prefix_a, prefix_b)
, 'margin' : R.c(0.1,0.1,0.1,0.1)
})
## write footer and output benchmark information.
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$counts)) / 2, c('%s'), fill=c('white','red'))""" % (
"','".join(options.legend)))
if options.title:
R.mtext(options.title, 3, outer=True, line=1, cex=1.5)
if options.loglevel >= 1:
options.stdout.write("## Results for %s\n" % result['method'])
options.stdout.write("%s\t%s\n" % ("key", options.header))
for key in list(result.keys()):
if key == "data.name":
continue
options.stdout.write("\t".join((key, str(result[key]))) + "\n")
stat = Stats.Summary(values1)
for key, value in list(stat.items()):
options.stdout.write("%s1\t%s\n" % (str(key), str(value)))
stat = Stats.Summary(values2)
for key, value in list(stat.items()):
options.stdout.write("%s2\t%s\n" % (str(key), str(value)))
if options.plot:
if options.hardcopy:
R.dev_off()
E.Stop()
import os
import pandas
import numpy
os.environ[
'R_USER'] = '******' #path depends on where you installed Python. Mine is the Anaconda distribution
from rpy2.robjects.packages import importr
from rpy2.robjects import r, pandas2ri
pandas2ri.activate()
# utils = importr('utils')
# utils.install_packages('warbleR')
warbleR = importr('warbleR')
dataframe = pandas.DataFrame(
[['001_K.wav', int(1), int(2), int(3)]],
columns=['sound.files', 'selec', 'start', 'end'])
print(dataframe)
print(warbleR.specan(X=dataframe, bp=r.c(0, 28000)))
# set vertical orientation
if max( [len(x) for x in headers] ) > 40 / len(headers):
# remove xlabel:
extra_options = re.sub( ", xlab='[^']+'", "", extra_options )
extra_options += ", names.arg=headers, las=2"
R("""op <- par(mar=c(11,4,4,2))""") # the 10 allows the names.arg below the barplot
R("""barplot(as.matrix(matrix), beside=TRUE %s)""" % extra_options)
elif method == "scatter+marginal":
if options.title:
# set the size of the outer margins - the title needs to be added at the end
# after plots have been created
R.par(oma=R.c(0,0,4,0) )
R( """matrix""" )
R( """
x <- matrix[,1];
y <- matrix[,2];
xhist <- hist(x, breaks=20, plot=FALSE);
yhist <- hist(y, breaks=20, plot=FALSE);
top <- max(c(xhist$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');
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()
def R_Factor_Analysis( comm_str,
csv_data, csv_colvars, csv_coltypes, fpref,
test_arr, # -> can be NULL for interior calc
Nfac, # -> can be 0 for interior calc
Ntopload, # -> can be 0 for interior calc
flab,
DO_GRAPH,
N_cent = 99, # 'centile'
N_iter = 5000, # 'iterations'
ftype = 'jpeg'):
'''Perform factor analysis using R function factanal(). User can
specify the number of latent factors using the paran() function,
which implements Horn's test.
Returns: Factor scores and loadings'''
# R libraries used here.
paran = importr('paran')
# some stuff about format types
if PARN_OUT_types.__contains__(ftype):
ii = PARN_OUT_types.index(ftype)
OUT_dev = PARN_OUT_devs[ii]
OUT_paran = fpref+'.'+ftype
else:
print "** Error! ",
print "Output file type '%s' is not valid. Select from:" % (ftype)
print "\t",
for x in PARN_OUT_types:
print " '"+x+"' ",
print "\n"
sys.exit(32)
fff = open(fpref+'.log','w')
if comm_str:
fff.write('# '+comm_str+"\n")
# SETUP THE VARIABLE VALUES
Lx,Ly = np.shape(csv_data)
# if user hasn't entered a selection, then use 'em all.
if not(test_arr):
test_arr = list(csv_colvars)
# Get rid of variable columns with 'NA'
test_arr = Cut_ColVars_with_NAs(csv_data, csv_colvars, test_arr)
# check for duplicate columns, which lead to bad singularities
test_arr = CheckForDuplicates( test_arr )
# if user hasn't entered a label, then use:
if not(flab):
flab = 'FACTOR'
# only select variables that are represented in the csv_data headings,
# as well as being either int or float
VARS_inds = []
VARS_names = []
for x in test_arr:
if csv_colvars.__contains__(x):
ii = csv_colvars.index(x)
if [int, float].__contains__(csv_coltypes[ii]):
VARS_inds.append(ii)
VARS_names.append(x)
Nvars = len(VARS_names)
Y = np.zeros((Lx,Nvars), dtype=float)
print "++ Factor analysis contains %s variables:" % (Nvars)
fff.write("\n++ Factor analysis contains %s variables:\n" % (Nvars))
for j in range(Nvars):
jj = VARS_inds[j]
print "\t %s" % (VARS_names[j])
fff.write("\t %s\n" % (VARS_names[j]))
for i in range(Lx):
Y[i,j] = csv_data[i][jj]
i = CorMatCheck(Y, VARS_names)
# SETUP THE NUMBER OF FACTORS
# use eval info to pick number of vars, if user hasn't
if not(Nfac):
print "++ Graphing of parallel analysis (PA) Horn's test is:",
if DO_GRAPH:
print "ON."
else:
print "OFF."
print "++ PA percentile in Horn's test is: ", N_cent
print "++ Number of PA Monte Carlo iterations: ", N_iter
# mostly default values, some user control
PARN = r.paran( Y, iterations=N_iter, centile=N_cent,
quietly=False, status=True, all=True,
cfa=True, graph=DO_GRAPH, color=True,
col=r.c("black","red","blue"), lty=r.c(1,2,3),
lwd=1, legend=True, file=OUT_paran, width=640,
height=640, grdevice=OUT_dev, seed=0)
if DO_GRAPH:
grDevices.dev_off()
print "++ Don't worry about the briefly passing image."
print "\tIt has been saved as: %s\n\n" % ( OUT_paran )
N_PARN_arr = np.array(PARN.rx2('Retained'))
Nfac = int(N_PARN_arr[0])
else:
if Nfac > Nvars:
print "*+ Warning! The user has selected a number of factors larger"
print "\tthan the number of variables (%d > %d)!" % (Nfac, Nvars)
print "\t-> Therefore, we're setting it to be %d," % (Nvars)
print "\t but you might still want to check if anything went awry?"
else:
print "++ The user has selected the number of factors"
print "\tto be %d out of %d." % (Nfac, Nvars)
# RUN THE FACTOR ANALYSIS IN R
FA_out = r.factanal(Y,
factors=Nfac,
scores='regression',
rotation="varimax")
FA_scores =np.array(FA_out.rx2('scores'))
FA_loadings =np.array(FA_out.rx2('loadings'))
# match up highest loadings with the variable names, so we have an
# idea of what's going into the sausage
# how many loadings to output.
# Can be: ALL, 5, or user-entered other
if not(Ntopload):
Ntopload = min(Nvars, 5)
elif Ntopload<0 :
Ntopload = Nvars
else:
Ntopload = min(Nvars, Ntopload)
if Ntopload==Nvars:
strNtopload = "ALL "+str(Nvars)
else:
strNtopload = 'top '+str(Ntopload)+'/'+str(Nvars)
# ordering process
FA_titles = []
print "\n++ Factor loading contributions (%s):" % (strNtopload)
fff.write("\n++ Factor loading contributions (%s):\n" % (strNtopload))
for i in range(Nfac):
P = list(FA_loadings[:,i])
Q = list(VARS_names)
PQ = sorted(zip(P,Q),reverse=1)
str_title = "%s_%02d" % (flab, i+1)
FA_titles.append(str_title)
print "\n\t"+str_title
fff.write("\n\t"+str_title+"\n")
for j in range(Ntopload):
print "\t%20s %12.5f" % (PQ[j][1],PQ[j][0])
fff.write("\t%20s %12.5f\n" % (PQ[j][1],PQ[j][0]))
fff.close()
return FA_scores, FA_titles, VARS_names
def deaScranDESeq2(counts, conds, comparisons, alpha, scran_clusters=False):
"""Makes a call to DESeq2 with SCRAN to
perform D.E.A. in the given
counts matrix with the given conditions and comparisons.
Returns a list of DESeq2 results for each comparison
"""
results = list()
n_cells = len(counts.columns)
try:
pandas2ri.activate()
deseq2 = RimportLibrary("DESeq2")
scran = RimportLibrary("scran")
multicore = RimportLibrary("BiocParallel")
multicore.register(
multicore.MulticoreParam(multiprocessing.cpu_count() - 1))
as_matrix = r["as.matrix"]
# Create the R conditions and counts data
r_counts = pandas2ri.py2ri(counts)
cond = robjects.StrVector(conds)
r_call = """
function(r_counts) {
sce = SingleCellExperiment(assays=list(counts=r_counts))
return(sce)
}
"""
r_func = r(r_call)
sce = r_func(as_matrix(r_counts))
if scran_clusters:
r_clusters = scran.quickCluster(as_matrix(r_counts),
max(n_cells / 10, 10))
min_cluster_size = min(Counter(r_clusters).values())
sizes = list(
set([
round((min_cluster_size / 2) / i) for i in [5, 4, 3, 2, 1]
]))
sce = scran.computeSumFactors(sce,
clusters=r_clusters,
sizes=sizes,
positive=True)
else:
sizes = list(
set([
round((n_cells / 2) * i)
for i in [0.1, 0.2, 0.3, 0.4, 0.5]
]))
sce = scran.computeSumFactors(sce, sizes=sizes, positive=True)
sce = r.normalize(sce)
dds = r.convertTo(sce, type="DESeq2")
r_call = """
function(dds, conditions){
colData(dds)$conditions = as.factor(conditions)
design(dds) = formula(~ conditions)
return(dds)
}
"""
r_func = r(r_call)
dds = r_func(dds, cond)
dds = r.DESeq(dds)
# Perform the comparisons and store results in list
for A, B in comparisons:
result = r.results(dds,
contrast=r.c("conditions", A, B),
alpha=alpha)
result = r['as.data.frame'](result)
genes = r['rownames'](result)
result = pandas2ri.ri2py_dataframe(result)
# There seems to be a problem parsing the rownames from R to pandas
# so we do it manually
result.index = genes
results.append(result)
pandas2ri.deactivate()
except Exception as e:
raise e
return results
def run_all(site_type: str,
gene_sets: Mapping[str, Path] = GENE_SETS,
gene_set_filter: Tuple[int] = (5, 1000),
correct=False,
**kwargs):
"""Runs all active_pathways combinations for given site_type.
Uses pan_cancer/clinvar Active Driver analyses results
and all provided GMT gene sets.
Args:
site_type: site filter which will be passed to ActiveDriver analysis
gene_sets: gene sets to be considered
gene_set_filter: a two-tuple: (min, max) number of genes required
to be in a gene set. If not set, the default of (5, 1000) is used
Results are saved in `output_dir`.
Returns:
Mapping of directories with newly computed ActivePathways results
"""
data_table = importr('data.table')
paths = {}
kwargs['geneset.filter'] = IntVector(gene_set_filter)
for analysis in [
active_driver.pan_cancer_analysis, active_driver.clinvar_analysis
]:
for gene_set in gene_sets:
path = output_dir / analysis.name / gene_set / site_type
# remove the old results (if any)
rmtree(path, ignore_errors=True)
# recreate dir
path.mkdir(parents=True)
path = path.absolute()
ad_result = analysis(site_type)
print(
f'Preparing active pathways: {analysis.name} for {len(ad_result["all_gene_based_fdr"])} genes'
)
print(f'Gene sets/background: {gene_set}')
gene_sets_path = gene_sets[gene_set]
if callable(gene_sets_path):
gene_sets_path = gene_sets_path()
result = run_active_pathways(ad_result,
str(gene_sets_path),
cytoscape_dir=path,
correct=correct,
**kwargs)
data_table.fwrite(result,
str(path / 'pathways.tsv'),
sep='\t',
sep2=r.c('', ',', ''))
paths[(analysis, gene_set)] = path
return paths
def draw_r(regions, points, **kwargs):
# initialize the environment
from rpy2.interactive import process_revents
from rpy2.robjects import r
from rpy2.robjects.packages import importr
NA = r("NA")[0]
RGB = lambda rgb: r.rgb(*rgb, maxColorValue=256)
C = lambda seq: r.c(*seq)
OOB = 40
graphics = importr("graphics")
grDevices = importr("grDevices")
process_revents.start()
graphics.par(bg="white")
graphics.split_screen(r.c(2, 1))
graphics.split_screen(r.c(1, 2), screen=2)
graphics.screen(1)
# prepare the regions for plotting
ul, lr = regions.box()
xlim = r.c(ul[0], lr[0])
ylim = r.c(lr[1], ul[1])
# create the main plot window
graphics.plot(r.c(), r.c(), main=regions.name(), type="p", pch="+",
xlim=xlim, ylim=ylim, xlab="", ylab="",
xaxp=r.c(0, lr[0], lr[0]/200), yaxp=r.c(0, lr[1], lr[1]/200),
bg="white")
# plot the polygons in the order given
order = sorted(regions.polys(), key=lambda p: p.area, reverse=True)
for poly in order:
xs, ys = zip(*poly.boundary[0].coords)
color = regions.color(poly.name(), default=NA)
cr, cg, cb = r.col2rgb(color)
rgb = r.rgb(cr, cg, cb, alpha=128, maxColorValue=255)
graphics.polygon(C(xs), C(ys), col=rgb)
# plot the grid
graphics.abline(v=r.c(OOB, lr[0]-OOB), lty=2)
graphics.abline(h=r.seq(0, lr[1], 200), col="lightgray", lty=2)
graphics.abline(v=r.seq(0, lr[0], 200), col="lightgray", lty=2)
# plot the points
xs, ys, names = zip(*[(pt[0].x, pt[0].y, pt[1]) for pt in points])
colors = [RGB(points.Color(name)) for name in names]
graphics.points(C(xs), C(ys), xlab="", ylab="", pch="+", col=C(colors))
# save as a png
if "png" in kwargs and kwargs['png']:
grDevices.dev_print(grDevices.png, file=kwargs['png'], width=lr[0],
height=lr[1])
# derive legend contents: colors, counts, names
tid_counts = {}
uniq_tids = []
for n in names:
if n not in uniq_tids:
tid_counts[n] = 0
uniq_tids.append(n)
tid_counts[n] += 1
uniq_colors = [RGB(points.Color(tid)) for tid in uniq_tids]
uniq_names = [("%d\t%s" % (i, IDs.TileID[i])) for i in uniq_tids]
name_counts = [("%d\t%s: %d" % (k, IDs.TileID[k], v)) for (k,v) in \
tid_counts.items()]
# display the colors legend
legend_args = dict(y_intersp=0.7, cex=0.7)
graphics.screen(3)
graphics.legend("center", title="Tile Colors", legend=C(uniq_names),
col=C(uniq_colors), pch="+", pt_cex=1, **legend_args)
# display the counts legend
graphics.screen(4)
graphics.legend("center", title="Tile Counts", legend=C(name_counts),
**legend_args)
# sleep until the window is closed
while grDevices.dev_list() != r("NULL"):
time.sleep(0.1)
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if not argv:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(
version=
"%prog version: $Id: cgat_script_template.py 2871 2010-03-03 10:20:44Z andreas $",
usage=globals()["__doc__"])
parser.add_option(
"-a",
"--first-gtf-file",
dest="gtf_a",
type="string",
help="supply a gtf file - will compress uncompressed files")
parser.add_option(
"-b",
"--second-gtf-file",
dest="gtf_b",
type="string",
help="supply a second gtf file - will compress uncompressed files")
parser.add_option("-s",
"--scripts-dir",
dest="scripts_dir",
type="string",
help="supply a location for accessory scripts")
parser.add_option("--no-venn",
dest="no_venn",
action="store_true",
help="set if no venn is to be drawn")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
gtf_files = [options.gtf_a, options.gtf_b]
merged_files = []
prefices = []
E.info("merging gtf files")
for gtf in gtf_files:
if gtf.endswith(".gtf.gz"):
outfile = IOTools.snip(gtf, ".gtf.gz") + ".merged.gtf.gz"
prefices.append(IOTools.snip(gtf, ".gtf.gz"))
merged_files.append(outfile)
statement = '''zcat %s | python %s/gtf2gtf.py --method=merge-transcripts --log=%s.log | gzip > %s''' % (
gtf, options.scripts_dir, outfile, outfile)
P.execute(statement)
elif gtf.endswith(".gtf"):
outfile = IOTools.snip(gtf, ".gtf") + ".merged.gtf.gz"
prefices.append(IOTools.snip(gtf, ".gtf"))
merged_files.append(outfile)
statement = '''cat %s | python %s/gtf2gtf.py --method=merge-transcripts --log=%s.log | gzip > %s''' % (
gtf, options.scripts_dir, outfile, outfile)
E.execute(statement)
else:
raise ValueError("cannot perform merge on %s: is not a gtf file" %
gtf)
for prefix in prefices:
if options.gtf_a.find(prefix) != -1:
gtf_a = prefix + ".merged.gtf.gz"
prefix_a = prefix
elif options.gtf_b.find(prefix) != -1:
gtf_b = prefix + ".merged.gtf.gz"
prefix_b = prefix
E.info("intersecting gtf files")
# intersect the resulting merged files
scriptsdir = options.scripts_dir
intersection_out = "_vs_".join([prefix_a, prefix_b
]) + ".intersection.gtf.gz"
statement = '''intersectBed -a %(gtf_a)s -b %(gtf_b)s -s -wa
| python %(scriptsdir)s/gtf2gtf.py --method=merge-transcripts --log=log | gzip > %(intersection_out)s'''
P.run()
if not options.no_venn:
E.info("producing venn diagram for %s vs %s..." %
(options.gtf_a, options.gtf_b))
# produce the venn diagram
intersection_file = intersection_out
gtf_a_merged = gtf_a
gtf_b_merged = gtf_b
# create dictionary key
gtf_pair = (gtf_a_merged, gtf_b_merged)
# containers for counts
count_gtf_merged_a = 0
count_gtf_merged_b = 0
count_intersection = 0
# create GTF iterator objects
gtf_iterator_a = GTF.iterator(IOTools.openFile(gtf_pair[0]))
gtf_iterator_b = GTF.iterator(IOTools.openFile(gtf_pair[1]))
gtf_iterator_intersection = GTF.iterator(
IOTools.openFile(intersection_file))
# do the counts for each file
E.info("counting entries in %s" % gtf_a)
for entry in gtf_iterator_a:
count_gtf_merged_a += 1
print("counts for gtf-a: ", count_gtf_merged_a)
E.info("counting entries in %s" % gtf_b)
for entry in gtf_iterator_b:
count_gtf_merged_b += 1
print("counts for gtf-b: ", count_gtf_merged_b)
E.info("counting entries in %s" % intersection_file)
for entry in gtf_iterator_intersection:
count_intersection += 1
print("counts for intersection: ", count_intersection)
# this is the important bit - basically take an arbitrary list of numbers to represent the list of lincrna in the refnoncoding set
# then use the intersection count to represent the overlapping section in the lincrna set and add a set of random numbers to this
# set to make up the remaining - non-overlapping set
result = {}
E.info("assembling count lists")
result[gtf_pair] = {
"gtf-b":
list(map(str, range(count_gtf_merged_b))),
"gtf-a":
list(map(str, range(count_intersection))) + list(
map(str, [
random.random()
for i in range(count_intersection, count_gtf_merged_a)
]))
}
R_source = os.path.join(os.path.abspath(options.scripts_dir),
"venn_diagram.R")
R.source(R_source)
prefix_a = prefix_a.replace(".", "_").replace("-", "_")
prefix_b = prefix_b.replace(".", "_").replace("-", "_")
R('''prefix.a <- "%s"''' % prefix_a)
R('''prefix.b <- "%s"''' % prefix_b)
E.info("drawing venn diagram to %s" %
(prefix_a + "_vs_" + prefix_b + ".overlap.png"))
R["venn.diagram2"](R.list(A=result[gtf_pair]["gtf-a"],
B=result[gtf_pair]["gtf-b"]),
prefix_a + "_vs_" + prefix_b + ".overlap.png", **{
'cat.cex':
1.5,
'main.fontfamily':
"Arial",
'cat.pos':
FloatVector((0, 0)),
'cat.fontfamily':
"Arial",
'main.cex':
1.8,
'height':
1000,
'width':
1000,
'cex':
2,
'fontfamily':
"Arial",
'lwd':
R.c(1, 1),
'fill':
R.c(R.rgb(0, 0, 0.5, 0.5),
R.rgb(0.5, 0, 0, 0.5)),
'category.names':
R.c(prefix_a, prefix_b),
'margin':
R.c(0.1, 0.1, 0.1, 0.1)
})
# write footer and output benchmark information.
E.Stop()
