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Copy pathMethylr2pyPlotter.py
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Methylr2pyPlotter.py
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import pandas as pd
import numpy as np
import rpy2.robjects as robjects
from rpy2.robjects import pandas2ri, numpy2ri
from rpy2.robjects.packages import importr
from rpy2.robjects import r
import seaborn as sns
import matplotlib.pyplot as plt
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure, show
from bokeh.palettes import d3
import bokeh.models as bmo
from bokeh.io import output_notebook
from bokeh.layouts import gridplot
from bokeh.transform import factor_cmap
from bokeh.models import NumeralTickFormatter
class Plotter:
"""Class that will take an Idat_Processor and plot all associated QC and related values.
"""
def __init__(self, IDAT_Processor):
self.IDAT_Processor = IDAT_Processor
def plt_mds(self, dataframe=None, pheno=None, top=1000, n_components=2, group='disease', components=(0,1)):
if dataframe is None:
beta=self.IDAT_Processor.minfi.getBeta(self.IDAT_Processor.RGset)
dataframe=self.IDAT_Processor.ri2py_dataframe(beta, matrix=False).transpose()
if pheno is None:
try:
pheno=self.IDAT_Processor.ri2py_dataframe(self.IDAT_Processor.pheno, matrix=True)
except:
pheno=self.IDAT_Processor.ri2py_dataframe(self.IDAT_Processor.pheno, matrix=False)
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from sklearn.manifold import MDS
if len(dataframe.columns)<=len(dataframe):
#print('Dataframe needed to be transposed')
dataframe=dataframe.transpose()
sorter=dataframe.var(axis=0).sort_values(ascending=False)[:top]
dataframe=dataframe.transpose().reindex(sorter.index).transpose()
from sklearn.impute import SimpleImputer
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
imp.fit(dataframe)
dataframe=pd.DataFrame(imp.transform(dataframe),index = dataframe.index.to_numpy(),
columns=dataframe.columns)
embedding = MDS(n_components=n_components)
X_transformed = embedding.fit_transform(X=dataframe.to_numpy(), y=pheno[group])
X_transformed=pd.DataFrame(X_transformed)
X_transformed[group]=pheno[group].reset_index(drop=True)
X_transformed['ID']=pheno['ID'].to_numpy()
X_transformed['case_ID']=pheno['case_ID'].to_numpy()
X_transformed=X_transformed.rename(columns={components[0]: "a", components[1]: "b"})
output_notebook()
p = figure(
tools="hover,pan,wheel_zoom,save",
toolbar_location="above",
title='MDS Plot',
plot_width=900,
plot_height=700,
)
if len(X_transformed[group].unique())!=2:
palette = d3['Category20'][len(X_transformed[group].unique())]
color_map = bmo.CategoricalColorMapper(factors=X_transformed[group].unique(),
palette=palette)
elif len(X_transformed[group].unique())==2:
colors=['red', 'green']
#from bokeh.palettes import brewer
#colors = brewer["Spectral"][len(X_transformed[group].unique())]
# Create a map between factor and color.
colormap = {k: colors[i] for i,k in enumerate(X_transformed[group].unique())}
# Create a list of colors for each value that we will be looking at.
X_transformed['colors'] = [colormap[x] for x in X_transformed[group]]
for key in X_transformed[group].unique():
keys=ColumnDataSource(X_transformed[X_transformed[group]==key])
if len(X_transformed[group].unique())!=2:
p.scatter(x='a', y='b', size=10, source=keys ,legend_label=key, color={'field': group, 'transform': color_map})
else:
p.scatter(x='a', y='b', size=10, source=keys ,legend_label=key, color='colors')
p.legend.location = "top_left"
p.legend.click_policy="hide"
p.xaxis.axis_label = 'Principal Component %s' % (components[0]+1)
p.yaxis.axis_label = 'Principal Component %s' % (components[1]+1)
p.hover.tooltips = [("ID", '@ID'), ("category", '@'+group), ("Name", '@case_ID')]
p.title.text = 'MDS Plot'
show(p)
def plt_mu(self, hue=None, thresh=None, RGset=None):
RGset=RGset if RGset else self.IDAT_Processor.RGset
if hue is not None:
_,_, datfr, phenotype =self.IDAT_Processor.getQC(addQC=False,phenotype=hue)
if phenotype is not None:
hue=datfr[phenotype]
else:
return
else:
_,_, datfr =self.IDAT_Processor.getQC(addQC=False, phenotype=None, RGset=None)
hymin=datfr['mMed'].min()
vymin=datfr['uMed'].min()
pheno= robjects.r("pData")(RGset)
try:
datfr['ID']=pd.DataFrame(pandas2ri.ri2py(pheno))['ID'].to_numpy()
except:
datfr['ID']=pd.DataFrame(pandas2ri.ri2py(robjects.r['as'](pheno,'data.frame'))['ID']).to_numpy()
X_transformed=datfr
output_notebook()
p = figure(
tools="hover,pan,wheel_zoom,save",
toolbar_location="above",
title='MU Plot',
plot_width=900,
plot_height=700,
)
if (len(X_transformed[phenotype].unique())>2):
palette = d3['Category20'][len(X_transformed[phenotype].unique())]
color_map = bmo.CategoricalColorMapper(factors=X_transformed[phenotype].unique(),
palette=palette)
elif len(X_transformed[phenotype].unique())==2:
colors=['red', 'green']
#from bokeh.palettes import brewer
#colors = brewer["Spectral"][len(X_transformed[group].unique())]
# Create a map between factor and color.
colormap = {k: colors[i] for i,k in enumerate(X_transformed[phenotype].unique())}
# Create a list of colors for each value that we will be looking at.
X_transformed['colors'] = [colormap[x] for x in X_transformed[phenotype]]
elif len(X_transformed[phenotype].unique())==1:
colors=['red']
#from bokeh.palettes import brewer
#colors = brewer["Spectral"][len(X_transformed[group].unique())]
# Create a map between factor and color.
colormap = {k: colors[i] for i,k in enumerate(X_transformed[phenotype].unique())}
# Create a list of colors for each value that we will be looking at.
X_transformed['colors'] = [colormap[x] for x in X_transformed[phenotype]]
for key in X_transformed[phenotype].unique():
keys=ColumnDataSource(X_transformed[X_transformed[phenotype]==key])
if len(X_transformed[phenotype].unique())>2:
p.scatter(x='mMed', y='uMed', size=10, source=keys ,legend_label=key, color={'field': phenotype, 'transform': color_map})
else:
p.scatter(x='mMed', y='uMed', size=10, source=keys ,legend_label=key, color='colors')
#p.multi_line(xs=[[1, 2, 3], [2, 3, 4]], ys=[[6, 7, 2], [4, 5, 7]],
# color=['red','green'])
p.multi_line(xs=[[thresh,thresh],[hymin,thresh]], ys=[[vymin,thresh],[thresh,thresh]],color='red')
p.legend.location = "top_left"
p.legend.click_policy="hide"
p.hover.tooltips = [ ("category", '@'+phenotype), ("ID", '@ID')]
p.title.text = 'MU Plot'
show(p)
def plt_failedbeads(self,RGset=None, percent=True):
if RGset is None:
RGset=self.IDAT_Processor.RGset
_,nbeads_df=self.IDAT_Processor.beadCount(RGset=RGset)
nbeads_df=nbeads_df.transpose()
if percent:
#nbeads_df['failure_df']=nbeads_df.isnull().sum(axis=1)/len(nbeads_df)*100
nbeads_df=pd.DataFrame(nbeads_df.isnull().sum(axis=1)/len(nbeads_df.transpose())*100,columns=['failure_df'], index=nbeads_df.index)
else:
#nbeads_df['failure_df']=nbeads_df.isnull().sum(axis=1)
nbeads_df=pd.DataFrame(nbeads_df.isnull().sum(axis=1),columns=['failure_df'], index=nbeads_df.index)
pheno= robjects.r("pData")(RGset)
nbeads_df['ID']=self.IDAT_Processor.ri2py_dataframe(pheno, matrix=True)['ID'].to_numpy()
nbeads_df['disease']=self.IDAT_Processor.ri2py_dataframe(pheno, matrix=True)['disease'].to_numpy()
nbeads_df['case_ID']=self.IDAT_Processor.ri2py_dataframe(pheno, matrix=True)['case_ID'].to_numpy()
p = figure(
tools="hover,pan,wheel_zoom,save",
toolbar_location="above",
plot_width=950,
plot_height=700,
x_range=nbeads_df['ID'].to_numpy(),
)
palette = d3['Category20'][len(nbeads_df['disease'].unique())]
color_map = bmo.CategoricalColorMapper(factors=nbeads_df['disease'].unique(),
palette=palette)
legend_it = []
for key in nbeads_df['disease'].unique():
keys=ColumnDataSource(nbeads_df[nbeads_df['disease']==key])
p.vbar(x='ID', top='failure_df', width=1, line_color="white", legend_label=key,
fill_color={'field': 'disease', 'transform': color_map}, source=keys)
#legend_it.append((key, [p]))
p.xgrid.grid_line_color = None
p.xaxis.axis_label = "Samples"
if percent:
p.yaxis.axis_label = "Percentage"
else:
p.yaxis.axis_label = 'Numbers'
p.x_range.range_padding = 0
p.x_range.group_padding=0
p.xaxis.major_label_text_font_size ='3pt'
p.xaxis.major_label_orientation = 1
p.legend.location = "top_left"
#legend = Legend(items=legend_it, location=(0, -60))
p.legend.click_policy="hide"
#p.add_layout(legend, 'right')
p.hover.tooltips = [ ("category", '@disease'), ("ID", '@ID'), ('case_ID','@case_ID')]
if percent:
p.title.text = 'Percentage of beadcounts below 3 per sample'
else:
p.title.text = 'Number of beadcounts below 3 per sample'
output_notebook()
#p = gridplot([s1, s2,s3], ncols=1, toolbar_location="above")
show(p)
def plt_meandetP(self, detPcut=0.01, SampleCutoff=0.1, log_scale=True, plot='all', RGset=None ):
RGset= RGset if RGset else self.IDAT_Processor.RGset
detP = robjects.r("""function (rgset, detPcut, cutsamples) {
detP <- detectionP(rgset)
numfail <- matrix(colMeans(detP >= detPcut))
rownames(numfail) <- colnames(detP)
colnames(numfail) <- "Failed CpG Fraction."
#print(numfail)
RemainSample <- which(numfail < cutsamples)
if(any(numfail >= cutsamples))
{
rgset_keeps <- rgset[,RemainSample]
detP_keeps <- detectionP(rgset_keeps)
result=list(detP,detP_keeps)
}
else{
result=list(detP)
}
return(result)
}""")(self.IDAT_Processor.RGset, detPcut, SampleCutoff)
pheno= robjects.r("pData")(RGset)
pheno_py=pd.DataFrame(self.IDAT_Processor.ri2py_dataframe(pheno, matrix=True))
detP_bad_py=None
if len(pandas2ri.ri2py(detP))==2:
detP_py=self.IDAT_Processor.ri2py_dataframe(detP[0], matrix=False)
detP_keep_py=self.IDAT_Processor.ri2py_dataframe(detP[1], matrix=False)
detP_py=detP_py.transpose()
detP_py=pd.DataFrame(detP_py.mean(axis=1),columns=['mean'], index=detP_py.index)
detP_keep_py=detP_keep_py.transpose()
detP_keep_py=pd.DataFrame(detP_keep_py.mean(axis=1),columns=['mean'], index=detP_keep_py.index)
if len(np.transpose(pandas2ri.ri2py(detP[0])))!=len(np.transpose(pandas2ri.ri2py(detP[1]))):
bad_ind=[x for x in detP_py.index.tolist() if x not in detP_keep_py.index.tolist()]
detP_bad_py=detP_py.loc[bad_ind]
detP_bad_py=pd.DataFrame(detP_bad_py.mean(axis=1),columns=['mean'], index=detP_bad_py.index)
detP_py['ID']=pheno_py['ID'].to_numpy()
detP_py['disease']=pheno_py['disease'].to_numpy()
detP_py['case_ID']=pheno_py['case_ID'].to_numpy()
detP_keep_py['ID']=pheno_py[pheno_py['ID'].isin(detP_keep_py.index)]['ID'].to_numpy()
detP_keep_py['disease']=pheno_py[pheno_py['ID'].isin(detP_keep_py.index)]['disease'].to_numpy()
detP_keep_py['case_ID']=pheno_py[pheno_py['ID'].isin(detP_keep_py.index)]['case_ID'].to_numpy()
if detP_bad_py is not None:
detP_bad_py['ID']=pheno_py[pheno_py['ID'].isin(detP_bad_py.index)]['ID'].to_numpy()
detP_bad_py['disease']=pheno_py[pheno_py['ID'].isin(detP_bad_py.index)]['disease'].to_numpy()
detP_bad_py['case_ID']=pheno_py[pheno_py['ID'].isin(detP_bad_py.index)]['case_ID'].to_numpy()
elif len(pandas2ri.ri2py(detP))==1:
detP_py=self.IDAT_Processor.ri2py_dataframe(detP[0], matrix=False)
detP_py['ID']=pheno_py['ID'].to_numpy()
detP_py['disease']=pheno_py['disease'].to_numpy()
detP_py['case_ID']=pheno_py['case_ID'].to_numpy()
if plot== 'all' and len(pandas2ri.ri2py(detP))==2:
s1 = figure(
tools="hover,pan,wheel_zoom,save",
toolbar_location="above",
plot_width=950,
plot_height=700,
x_range=detP_py['ID'].to_numpy(),
#y_range=(test_py['mean'].min(), test_py['mean'].max()),
y_axis_type="log"
)
palette = d3['Category20'][len(detP_py['disease'].unique())]
color_map = bmo.CategoricalColorMapper(factors=detP_py['disease'].unique(),
palette=palette)
for key in detP_py['disease'].unique():
keys=ColumnDataSource(detP_py[detP_py['disease']==key])
s1.vbar(x='ID', top='mean', bottom=detP_py['mean'].min(), width=1, line_color="white", legend_label=key,
fill_color={'field': 'disease', 'transform': color_map}, source=keys)
s1.line(x=[0,len(detP_py['ID'].to_numpy())], y=[detPcut,detPcut],color='red')
#p.vbar(x='ID', top='mean', width=1, line_color="white", legend_field='ID',
# fill_color={'field': 'disease', 'transform': color_map}, source=source)
#caption = Label(text='cutoff')
#s2.add_layout(caption, 'above')
s1.xgrid.grid_line_color = None
s1.xaxis.axis_label = "Samplenames"
s1.yaxis.axis_label = "Mean detP"
#s1.y_range.start = 0
s1.x_range.range_padding = 0
s1.x_range.group_padding=0
s1.xaxis.major_label_text_font_size ='3pt'
s1.xaxis.major_label_orientation = 1
s1.legend.location = "top_left"
s1.legend.click_policy="hide"
s1.hover.tooltips = [ ("category", '@disease'), ("ID", '@ID'), ('case_ID','@case_ID')]
s1.title.text = 'All Samples'
s2 = figure(
tools="hover,pan,wheel_zoom,save",
toolbar_location="above",
plot_width=950,
plot_height=700,
x_range=detP_keep_py['ID'].to_numpy(),
#y_range=(test_py['mean'].min(), test_py['mean'].max()),
y_axis_type="log"
)
palette = d3['Category20'][len(detP_keep_py['disease'].unique())]
color_map = bmo.CategoricalColorMapper(factors=detP_keep_py['disease'].unique(),
palette=palette)
for key in detP_keep_py['disease'].unique():
keys=ColumnDataSource(detP_keep_py[detP_keep_py['disease']==key])
s2.vbar(x='ID', top='mean', bottom=detP_keep_py['mean'].min(), width=1, line_color="white", legend_label=key,
fill_color={'field': 'disease', 'transform': color_map}, source=keys)
s2.line(x=[0,len(detP_keep_py['ID'].to_numpy())], y=[detPcut,detPcut],color='red')
s2.xgrid.grid_line_color = None
s2.xaxis.axis_label = "Samplenames"
s2.yaxis.axis_label = "Mean detP"
s2.x_range.range_padding = 0
s2.x_range.group_padding=0
s2.xaxis.major_label_text_font_size ='3pt'
s2.xaxis.major_label_orientation = 1
s2.legend.location = "top_left"
s2.legend.click_policy="hide"
s2.hover.tooltips = [ ("category", '@disease'), ("ID", '@ID'), ('case_ID','@case_ID')]
s2.title.text = 'Good Samples'
if detP_bad_py is not None:
s3 = figure(
tools="hover,pan,wheel_zoom,save",
toolbar_location="above",
plot_width=950,
plot_height=700,
x_range=detP_bad_py['ID'].to_numpy(),
y_axis_type="log"
)
palette = d3['Category20'][len(detP_bad_py['disease'].unique())]
color_map = bmo.CategoricalColorMapper(factors=detP_bad_py['disease'].unique(),
palette=palette)
for key in detP_bad_py['disease'].unique():
keys=ColumnDataSource(detP_bad_py[detP_bad_py['disease']==key])
s3.vbar(x='ID', top='mean', bottom=detP_bad_py['mean'].min(), width=1, line_color="white", legend_label=key,
fill_color={'field': 'disease', 'transform': color_map}, source=keys)
s3.line(x=[0,len(detP_bad_py['ID'].to_numpy())], y=[detPcut,detPcut],color='red')
s3.xgrid.grid_line_color = None
s3.xaxis.axis_label = "Samplenames"
s3.yaxis.axis_label = "Mean detP"
s3.x_range.range_padding = 0
s3.x_range.group_padding=0
s3.xaxis.major_label_text_font_size ='3pt'
s3.xaxis.major_label_orientation = 1
s3.legend.location = "top_left"
s3.legend.click_policy="hide"
s3.hover.tooltips = [ ("category", '@disease'), ("ID", '@ID'), ('case_ID','@case_ID')]
s3.title.text = 'Good Samples'
output_notebook()
p = gridplot([s1, s2,s3], ncols=1, toolbar_location="above")
show(p)
else:
output_notebook()
p = gridplot([s1, s2], ncols=1, toolbar_location="above")
show(p)
return
if plot== 'all' and len(pandas2ri.ri2py(detP))==1:
p = figure(
tools="hover,pan,wheel_zoom,save",
toolbar_location="above",
plot_width=950,
plot_height=700,
x_range=detP_py['ID'].to_numpy(),
y_axis_type="log"
)
palette = d3['Category20'][len(detP_py['disease'].unique())]
color_map = bmo.CategoricalColorMapper(factors=detP_py['disease'].unique(),
palette=palette)
for key in detP_py['disease'].unique():
keys=ColumnDataSource(detP_py[detP_py['disease']==key])
p.vbar(x='ID', top='mean', bottom=detP_py['mean'].min(), width=1, line_color="white", legend_label=key,
fill_color={'field': 'disease', 'transform': color_map}, source=keys)
p.line(x=[0,len(detP_py['ID'].to_numpy())], y=[detPcut,detPcut],color='red')
p.xgrid.grid_line_color = None
p.xaxis.axis_label = "Samplenames"
p.yaxis.axis_label = "Mean detP"
p.x_range.range_padding = 0
p.x_range.group_padding=0
p.xaxis.major_label_text_font_size ='3pt'
p.xaxis.major_label_orientation = 1
p.legend.location = "top_left"
p.legend.click_policy="hide"
p.hover.tooltips = [ ("category", '@disease'), ("ID", '@ID'), ('case_ID','@case_ID')]
p.title.text = 'All Samples'
output_notebook()
show(p)
return
if plot== 'allsamples':
p = figure(
tools="hover,pan,wheel_zoom,save",
toolbar_location="above",
plot_width=950,
plot_height=700,
x_range=detP_py['ID'].to_numpy(),
y_axis_type="log"
)
palette = d3['Category20'][len(detP_py['disease'].unique())]
color_map = bmo.CategoricalColorMapper(factors=detP_py['disease'].unique(),
palette=palette)
for key in detP_py['disease'].unique():
keys=ColumnDataSource(detP_py[detP_py['disease']==key])
p.vbar(x='ID', top='mean', bottom=detP_py['mean'].min(), width=1, line_color="white", legend_label=key,
fill_color={'field': 'disease', 'transform': color_map}, source=keys)
p.line(x=[0,len(detP_py['ID'].to_numpy())], y=[detPcut,detPcut],color='red')
p.xgrid.grid_line_color = None
p.xaxis.axis_label = "Samplenames"
p.yaxis.axis_label = "Mean detP"
#s1.y_range.start = 0
p.x_range.range_padding = 0
p.x_range.group_padding=0
p.xaxis.major_label_text_font_size ='3pt'
p.xaxis.major_label_orientation = 1
p.legend.location = "top_left"
p.legend.click_policy="hide"
p.hover.tooltips = [ ("category", '@disease'), ("ID", '@ID'), ('case_ID','@case_ID')]
p.title.text = 'All Samples'
output_notebook()
show(p)
return
if plot== 'goodsamples' and len(pandas2ri.ri2py(detP))==2:
p = figure(
tools="hover,pan,wheel_zoom,save",
toolbar_location="above",
plot_width=950,
plot_height=700,
x_range=detP_keep_py['ID'].to_numpy(),
y_axis_type="log"
)
palette = d3['Category20'][len(detP_keep_py['disease'].unique())]
color_map = bmo.CategoricalColorMapper(factors=detP_keep_py['disease'].unique(),
palette=palette)
for key in detP_keep_py['disease'].unique():
keys=ColumnDataSource(detP_keep_py[detP_keep_py['disease']==key])
p.vbar(x='ID', top='mean', bottom=detP_keep_py['mean'].min(), width=1, line_color="white", legend_label=key,
fill_color={'field': 'disease', 'transform': color_map}, source=keys)
p.line(x=[0,len(detP_keep_py['ID'].to_numpy())], y=[detPcut,detPcut],color='red')
p.xgrid.grid_line_color = None
p.xaxis.axis_label = "Samplenames"
p.yaxis.axis_label = "Mean detP"
p.x_range.range_padding = 0
p.x_range.group_padding=0
p.xaxis.major_label_text_font_size ='3pt'
p.xaxis.major_label_orientation = 1
p.legend.location = "top_left"
p.legend.click_policy="hide"
p.hover.tooltips = [ ("category", '@disease'), ("ID", '@ID'), ('case_ID','@case_ID')]
p.title.text = 'Good Samples'
output_notebook()
show(p)
return
if plot== 'goodsamples' and len(pandas2ri.ri2py(detP))==1:
print('no goodsamples present')
return
if plot== 'badsamples' and detP_bad_py is not None:
p = figure(
tools="hover,pan,wheel_zoom,save",
toolbar_location="above",
plot_width=950,
plot_height=700,
x_range=detP_bad_py['ID'].to_numpy(),
y_axis_type="log"
)
palette = d3['Category20'][len(detP_bad_py['disease'].unique())]
color_map = bmo.CategoricalColorMapper(factors=detP_bad_py['disease'].unique(),
palette=palette)
for key in detP_bad_py['disease'].unique():
keys=ColumnDataSource(detP_bad_py[detP_bad_py['disease']==key])
p.vbar(x='ID', top='mean', bottom=detP_bad_py['mean'].min(), width=1, line_color="white", legend_label=key,
fill_color={'field': 'disease', 'transform': color_map}, source=keys)
p.line(x=[0,len(detP_bad_py['ID'].to_numpy())], y=[detPcut,detPcut],color='red')
p.xgrid.grid_line_color = None
p.xaxis.axis_label = "Samplenames"
p.yaxis.axis_label = "Mean detP"
p.x_range.range_padding = 0
p.x_range.group_padding=0
p.xaxis.major_label_text_font_size ='3pt'
p.xaxis.major_label_orientation = 1
p.legend.location = "top_left"
p.legend.click_policy="hide"
p.hover.tooltips = [ ("category", '@disease'), ("ID", '@ID'), ('case_ID','@case_ID')]
p.title.text = 'Bad Samples'
output_notebook()
show(p)
return
if plot== 'badsamples' and detP_bad_py is None:
print('no badsamples present')
return
else:
print('Please specify which plots you would want: \n'
'either "all", "allsamples", "goodsamples" or "badsamples" may be specified')
return
def plt_curves(self, matrix=None, pheno=None, variant="grouped", group_by=None, nrows=1):
if pheno is None:
pheno=self.IDAT_Processor.pheno_orig_py
import matplotlib.pyplot as plt
import seaborn as sns
import copy
from seaborn import cubehelix_palette
fig, ax = plt.subplots()
from collections import OrderedDict
import matplotlib.pyplot as plt
cmap=sns.color_palette('muted')
if type(matrix)==dict:
if (variant=="grouped"):
for i, (key, matr) in enumerate(matrix.items()):
pheno=pheno[pheno['ID'].isin(matr.columns.to_numpy())]
group_list=pheno[group_by].unique().tolist()
matr=copy.deepcopy(matr.transpose())
c=len(matrix)/nrows
if nrows >= 1:
ind=i+1
plt.subplot(nrows, c, ind)
if nrows > 1:
if ind==nrows:
ind=0
for val in group_list:
sns.distplot(matr[np.array(pheno[group_by]==val)].mean(), label=val, hist=False)
plt.xlabel('curve-values')
plt.ylabel('density')
plt.title(key)
handles, labels = plt.gca().get_legend_handles_labels()
by_label = OrderedDict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.tight_layout()
plt.show()
elif(variant=="single"):
for i, (key, matr) in enumerate(matrix.items()):
pheno=pheno[pheno['ID'].isin(matr.columns.to_numpy())]
matr=copy.deepcopy(matr)
c=len(matrix)/nrows
if nrows >= 1:
ind=i+1
plt.subplot(nrows, c, ind)
if nrows > 1:
if ind==nrows:
ind=0
colors = ['#2300A8', '#00A658','#1f77b4', '#ff7f0e',
'#d62728', '#9467bd', '#8c564b',
'#e377c2', '#7f7f7f', '#bcbd22', '#17becf','yellow','#070d0d', '#8ffe09']
colordict={}
for items, values in zip(pheno[group_by].unique().tolist(), colors):
colordict[items]=values
for col in matr.columns:
label=pheno[group_by][pheno['ID']==col].to_numpy()[0]
if len(matr.columns) <= 30:
sns.distplot(
matr[col], hist=False, rug=False, label=label,
color=colordict[label])
else:
sns.distplot(
matr[col], hist=False, rug=False,label=label, color=colordict[label])
plt.xlabel('curve-values')
plt.ylabel('density')
plt.title(key)
handles, labels = plt.gca().get_legend_handles_labels()
by_label = OrderedDict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.tight_layout()
plt.show()
else:
print('Please provide dataframes in key value dict manner, where the key is the label of the plot and the values is the '
'dataframe')
def plot_qc_minfi_report(self, output_dir, diseasegroup='disease', samplenames='ID'):
check_list=[diseasegroup, samplenames]
for val in check_list:
if val not in pandas2ri.ri2py(robjects.r['as'](self.IDAT_Processor.pheno,'data.frame')).columns.tolist():
print('The pheno sheet does not contain a '+diseasegroup+' or '+samplenames+' column you specified \n'
'These are the available column names:')
print(pandas2ri.ri2py(robjects.r['as'](self.IDAT_Processor.pheno,'data.frame')).columns.tolist())
return
self.IDAT_Processor.minfi.qcReport(self.IDAT_Processor.RGset,
sampNames=pandas2ri.ri2py(self.IDAT_Processor.pheno)[samplenames],
sampGroups=pandas2ri.ri2py(self.IDAT_Processor.pheno)[diseasegroup],
pdf = "{}/qcReport.pdf".format(output_dir))
def screeplot(self, RGset=None, nmax=10):
if RGset:
RGset=RGset
else:
RGset=self.IDAT_Processor.RGset
pcs,nmax = robjects.r("""function (RGset, nmax) {
library(matrixStats)
screeplot <- function(RGset=RGset, nmax=nmax){
extr<-minfi:::.extractFromRGSet450k(RGset)
controlMatrix <- minfi:::.buildControlMatrix450k(extr)
pc <- prcomp(controlMatrix)
nmax <- ifelse(nmax > nrow(controlMatrix), nrow(controlMatrix), nmax)
#barplot(summary(pc)$importance[2,1:nmax], ylab="Proportion of Variance", main="Scree Plot", col="#7cb4c9")
#print(summary(pc)$importance[2,1:nmax])
result=list(pc=summary(pc)$importance[2,1:nmax], nmax=nmax)
return(result)
}
pcs<-screeplot(RGset=RGset, nmax=nmax)
result=list(pcs$pc,pcs$nmax)
return(result)
}""")(RGset, nmax)
m=numpy2ri.ri2py(pcs)
df=pd.DataFrame(np.array(m)).transpose()
x = df.columns.tolist()
x=[str(element+1) for element in x]
y1 = df.to_numpy()[0].tolist()
source = ColumnDataSource(data=dict(x=x, y=y1))
p = figure(
tools="hover,pan,wheel_zoom,save",
toolbar_location="above",
plot_width=950,
plot_height=700,
x_range=x
)
palette = d3['Category20'][len(x)]
p.vbar(x='x', top='y', width=1, line_color="white", source=source, legend_field="x",
fill_color=factor_cmap('x', palette=palette, factors=x))
p.xgrid.grid_line_color = None
p.xaxis.axis_label = "Principile components"
p.yaxis.axis_label = "Proportion of explained variance"
p.yaxis.formatter = NumeralTickFormatter(format='0 %')
p.legend.orientation = "horizontal"
p.legend.location = "top_center"
p.legend.title = 'Principle components'
p.hover.tooltips = [ ("Explained variance", '@y')]
p.title.text = 'Screeplot'
output_notebook()
show(p)
def plt_covariates(self, pheno=None, matrix=None, pcs=2, ids=False):
if not matrix or not pheno:
print('Please input data')
return
cxy = robjects.r("""function (pcs, matrix, pheno,ids) {
if (ids!=FALSE)rownames(pheno)<-pheno[,ids]
keep<-intersect(colnames(matrix),rownames(pheno))
pheno<- pheno[keep,]
matrix<- matrix[,keep]
#pheno <- pheno[rownames(pheno) %in% colnames(matrix) ,]
df <- apply(pheno, 2, function(x) as.numeric(factor(x)))
keep <- apply(df, 2, sd) > 0
df <- df[ , keep]
library(irlba)
#pc <- prcomp(t(matrix), rank=pcs)
pc <- prcomp_irlba(t(matrix), n=pcs)
cxy <- round(cor(pc$x, scale(df)),2)
return(cxy)
}""")(pcs, matrix, pheno,ids)
corr=self.IDAT_Processor.ri2py_dataframe(cxy, matrix=False)
f, ax = plt.subplots(figsize=(10, 7))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(220, 10, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
ax=sns.heatmap(corr.transpose(), cmap=cmap, vmax=1, vmin=-1, center=0, annot=True,
linewidths=.5, cbar_kws={"shrink": .5, 'label': "Pearson's r"})
ax.set_title("Heatmap of correlations")
bottom, top = ax.get_ylim()
ax.set_ylim(bottom + 0.5, top - 0.5)
plt.show()
def plot_qc_metrics(self, output_dir):
"""Plot QC results from ENmix pipeline and possible minfi. Still experimental.
Parameters
----------
output_dir
Where to store plots."""
self.IDAT_Processor.enmix.plotCtrl(self.IDAT_Processor.RGset)
grdevice = importr("grDevices")
geneplotter = importr("geneplotter")
base = importr('base')
anno=self.IDAT_Processor.minfi.getAnnotation(self.IDAT_Processor.RGset)
anno_py = pandas2ri.ri2py(robjects.r['as'](anno,'data.frame'))
beta_py = pandas2ri.ri2py(self.IDAT_Processor.beta)
beta1=numpy2ri.py2ri(beta_py[anno_py["Type"]=="I"])
beta2=numpy2ri.py2ri(beta_py[anno_py["Type"]=="II"])
grdevice.jpeg(output_dir+'/dist.jpg',height=900,width=600)
base.par(mfrow=robjects.vectors.IntVector([3,2]))
self.IDAT_Processor.enmix.multidensity(self.IDAT_Processor.beta, main="Multidensity")
self.IDAT_Processor.enmix.multifreqpoly(self.IDAT_Processor.beta, xlab="Beta value")
self.IDAT_Processor.enmix.multidensity(beta1, main="Multidensity: Infinium I")
self.IDAT_Processor.enmix.multifreqpoly(beta1, main="Multidensity: Infinium I", xlab="Beta value")
self.IDAT_Processor.enmix.multidensity(beta2, main="Multidensity: Infinium II")
self.IDAT_Processor.enmix.multifreqpoly(beta2, main="Multidensity: Infinium II", xlab="Beta value")
grdevice.dev_off()
self.IDAT_Processor.minfi.qcReport(self.IDAT_Processor.RGset, pdf = "{}/qcReport.pdf".format(output_dir))
self.IDAT_Processor.minfi.mdsPlot(self.IDAT_Processor.RGset)
self.IDAT_Processor.minfi.densityPlot(self.IDAT_Processor.RGset, main='Beta', xlab='Beta')
def plot_original_qc(self, output_dir):
"""Plot QC results from ENmix pipeline and possible minfi. Still experimental.
Parameters
----------
output_dir
Where to store plots."""
self.IDAT_Processor.preprocessRAW()
self.IDAT_Processor.return_beta()
self.IDAT_Processor.get_beta()
self.IDAT_Processor.plot_qc_metrics(output_dir)