Matrix processing
usage: bin/matrix-process.R [-h] -s STEP -i INPUT -c CLASS -b BATCH
--filterout FILTEROUT --imputeout IMPUTEOUT
--normalizeout NORMALIZEOUT --batchremoveout
BATCHREMOVEOUT [--filtercount NUMBER]
[--filtersample NUMBER] [--imputemethod STRING]
[--imputecluster NUMBER] [--imputenum NUMBER]
[--imputecutoff NUMBER] [--imputealpha NUMBER]
[--normmethod STRING] [--normtopk NUMBER]
[--cvthreshold NUMBER] [--removetype STRING]
[--refergenefile STRING] [--batchmethod STRING]
[--batchindex INT] [-p NUMBER]
optional arguments:
-h, --help show this help message and exit
-s STEP, --step STEP which step to run
-i INPUT, --input INPUT
input expression matrix file
-c CLASS, --class CLASS
input class info file
-b BATCH, --batch BATCH
input batch info file
--filterout FILTEROUT
output filter path
--imputeout IMPUTEOUT
output imputation path
--normalizeout NORMALIZEOUT
output normalization file
--batchremoveout BATCHREMOVEOUT
output batchremoved file
--filtercount NUMBER filter by counts of a gene [default = 5]
--filtersample NUMBER
filter by counts of sample above certain counts of a
gene [default = 10]
--imputemethod STRING
the imputation algorithm to use [default =
scimpute_count]
--imputecluster NUMBER
cluster number in scImpute [default = 5]
--imputenum NUMBER number in viper [default = 5000]
--imputecutoff NUMBER
cutoff in viper [default = 0.5]
--imputealpha NUMBER alpha in viper [default = 0.1]
--normmethod STRING the normalization algorithm to use [default = SCNorm]
--normtopk NUMBER top K feature as scale factor [default = 20]
--cvthreshold NUMBER coefficient variance threshold of reference gene,
filter ref gene with CV bigger than [default = 0.5]
--removetype STRING remove some time of RNA for normalization scale factor
calculation [default = miRNA,piRNA]
--refergenefile STRING
reference gene file path [default = None]
--batchmethod STRING the batch removal algorithm to use [default = RUV]
--batchindex INT batch index to select which batch to use [default = 1]
-p NUMBER, --processors NUMBER
Number of processors to use. This option is useful on
multicore *nix or Mac machine only, when performing
multiple runs (nrun > 1) [default = 1]Some parameters:
-s filter imputation normalization batch_removal
--imputemethod scimpute_count,viper_count,null
--normmethod SCnorm,TMM,RLE,CPM,CPM_top,CPM_rm,CPM_refer,null
--batchmetod RUV,Combat,null
--batchindex 1Example:
bin/matrix-process.R -s imputation \
-i output/lulab_hcc/count_matrix/domains_combined.txt \
--filterout output/lulab_hcc/matrix_processing/ \
--imputemethod viper_count \
--imputeout output/lulab_hcc/matrix_processing/ \
--filtercount 5 \
--filtersample 10 \
--imputecluster 5 \
--imputenum 5000 \
--imputecutoff 0.1 \
--imputealpha 0.5 \
-p 4 \
--normalizeout output/lulab_hcc/matrix_processing/ \
--normmethod RLE \
--normtopk 20 \
--removetype miRNA,piRNA \
--cvthreshold 0.5 \
--refergenefile data/matrix_processing/refer_gene_id.txt \
-c data/lulab_hcc/sample_classes.txt \
-b data/lulab_hcc/batch_info.txt \
--batchremoveout output/scirep/matrix_processing/ \
--batchmethod RUV \
--batchindex 1Plots of results
We provide a jupyter notebook to plot the results of matrix processing. notebook
environment
import pandas as pd
import numpy as np
from matplotlib import pyplot
from tqdm import tqdm, tqdm_notebook
import matplotlib.pyplot as plt
import seaborn as sns
import gc, argparse, sys, os, errno
from IPython.core.display import HTML,Image
from functools import reduce
import h5py
%pylab inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm_notebook as tqdm
import scipy
import sklearn
from scipy.stats import pearsonr
import warnings
warnings.filterwarnings('ignore')
from bokeh.io import output_notebook, show
output_notebook()
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import umap
from sklearn.metrics import roc_curve,roc_auc_score,auc
from sklearn.preprocessing import RobustScaler,MinMaxScaler,StandardScaler
from sklearn.neighbors import NearestNeighbors
from bokeh.palettes import Category20c,Set3,Pastel2
from ipywidgets import interact,interactive, FloatSlider,IntSlider, RadioButtons,Dropdown,Tab,Text
from IPython.core.display import HTML,Image
from matplotlib.backends.backend_pdf import PdfPages, PdfFile
from IPython.display import HTML, display, FileLink
from base64 import b64encode, b64decode
from io import StringIO, BytesIO
from contextlib import contextmanagercd ~chenxupeng/projects/exSEEK_training/# setup figure template
figure_template_path = 'bin'
if figure_template_path not in sys.path:
sys.path.append(figure_template_path)
from importlib import reload
import figure_template
# force reload of the module
reload(figure_template)
from figure_template import display_dataframe, embed_pdf_figure, embed_pdf_pages,std_plotfontsize = 6.5
fontscale = 1
fontweight = 'normal'
fonttitle = {'family':'Arial',
'weight' : fontweight,
'size' : fontsize*fontscale}
fontlabel = {'family':'Arial',
'weight' : fontweight,
'size' : fontsize*fontscale}
fontticklabel = {'family':'Arial',
'weight' : fontweight,
'size' : fontsize*fontscale}
fontlegend = {'family':'Arial',
'weight' : fontweight,
#'linewidth':0.5,
'size' : fontsize*fontscale}
fontcbarlabel = {'family':'Arial',
'weight' : fontweight,
#'Rotation' : 270,
#'labelpad' : 25,
'size' : fontsize*fontscale}
fontcbarticklabel = {'family':'Arial',#Helvetica
'weight' : fontweight,
'size' : (fontsize-1)*fontscale}
def std_plot(ax,xlabel=None,ylabel=None,title=None,
legendtitle=None,bbox_to_anchor=None,
labelspacing=0.2,borderpad=0.2,handletextpad=0.2,legendsort=False,markerscale=None,
xlim=None,ylim=None,
xbins=None,ybins=None,
cbar=None,cbarlabel=None,
moveyaxis=False,sns=False,left=True,rotation=None,xticklabel=None,legendscale=True,h=None,l=None,**kwards):
# height = 2 font = 6.5
def autoscale(fig):
if isinstance(fig,matplotlib.figure.Figure):
width,height = fig.get_size_inches()
elif isinstance(fig,matplotlib.axes.Axes):
width,height = fig.figure.get_size_inches()
fontscale = height/2
if width/fontscale > 8:
warnings.warn("Please reset fig's width. When scaling the height to 2 in, the scaled width '%.2f' is large than 8"%(width/fontscale),UserWarning)
return fontscale
class fontprop:
def init(self,fonttitle=None,fontlabel=None,fontticklabel=None,fontlegend=None,fontcbarlabel=None,fontcbarticklabel=None):
self.fonttitle = fonttitle
self.fontlabel = fontlabel
self.fontticklabel = fontticklabel
self.fontlegend = fontlegend
self.fontcbarlabel = fontcbarlabel
self.fontcbarticklabel = fontcbarticklabel
def update(self,fontscale):
self.fonttitle['size'] = self.fonttitle['size']*fontscale
self.fontlabel['size'] = self.fontlabel['size']*fontscale
self.fontticklabel['size'] = self.fontticklabel['size']*fontscale
self.fontlegend['size'] = self.fontlegend['size']*fontscale
self.fontcbarlabel['size'] = self.fontcbarlabel['size']*fontscale
self.fontcbarticklabel['size'] = self.fontcbarticklabel['size']*fontscale
def reset(self,fontscale):
self.fonttitle['size'] = self.fonttitle['size']/fontscale
self.fontlabel['size'] = self.fontlabel['size']/fontscale
self.fontticklabel['size'] = self.fontticklabel['size']/fontscale
self.fontlegend['size'] = self.fontlegend['size']/fontscale
self.fontcbarlabel['size'] = self.fontcbarlabel['size']/fontscale
self.fontcbarticklabel['size'] = self.fontcbarticklabel['size']/fontscale
fontscale = autoscale(ax)
font = fontprop()
font.init(fonttitle,fontlabel,fontticklabel,fontlegend,fontcbarlabel,fontcbarticklabel)
font.update(fontscale)
pyplot.draw()
#plt.figure(linewidth=30.5)
if xlim is not None:
ax.set(xlim=xlim)
if ylim is not None:
ax.set(ylim=ylim)
#pyplot.draw()
if xbins is not None:
locator = MaxNLocator(nbins=xbins)
locator.set_axis(ax.xaxis)
ax.set_xticks(locator())
if ybins is not None:
locator = MaxNLocator(nbins=ybins)
locator.set_axis(ax.yaxis)
ax.set_yticks(locator())
pyplot.draw()
ax.set_xticks(ax.get_xticks())
ax.set_yticks(ax.get_yticks())
ax.set_xlabel(xlabel,fontdict = font.fontlabel,labelpad=(fontsize-1)*fontscale)
ax.set_ylabel(ylabel,fontdict = font.fontlabel,labelpad=(fontsize-1)*fontscale)
if (rotation is not None) & (xticklabel is not None) :
ax.set_xticklabels(xticklabel,fontticklabel,rotation=rotation)
elif (xticklabel is not None) &(rotation is None):
ax.set_xticklabels(xticklabel,fontticklabel)
elif (xticklabel is None) &(rotation is None):
ax.set_xticklabels(ax.get_xticklabels(),fontticklabel)
elif (rotation is not None) & (xticklabel is None):
ax.set_xticklabels(ax.get_xticklabels(),fontticklabel,rotation=rotation)
ax.set_yticklabels(ax.get_yticklabels(),font.fontticklabel)
if moveyaxis is True:
#fontticklabel
ax.spines['left'].set_position(('data',0))
ax.spines['left'].set_visible(left)
ax.spines['right'].set_visible(not left)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_linewidth(0.5*fontscale)
ax.spines['bottom'].set_linewidth(0.5*fontscale)
ax.spines['left'].set_linewidth(0.5*fontscale)
ax.spines['bottom'].set_color('k')
ax.spines['left'].set_color('k')
ax.spines['right'].set_color('k')
ax.tick_params(direction='out', pad=2*fontscale,width=0.5*fontscale)
#ax.spines['bottom']._edgecolor="#000000"
#ax.spines['left']._edgecolor="#000000"
if title is not None:
ax.set_title(title,fontdict = font.fonttitle)
if legendscale is True:
if (h is None)&(l is None):
legend = ax.legend(prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
else:
legend = ax.legend(h,l,prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
if legendtitle is not None:
#if legendloc is None:
# legendloc="best"
legend = ax.legend(title=legendtitle,prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
ax.legend_.get_frame()._linewidth=0.5*fontscale
legend.get_title().set_fontweight('normal')
legend.get_title().set_fontsize(fontscale*fontsize)
if legendsort is True:
# h: handle l:label
h,l = ax.get_legend_handles_labels()
l,h = zip(*sorted(zip(l,h), key=lambda t: int(t[0])))
legend = ax.legend(h,l,title=legendtitle,prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
ax.legend_.get_frame()._linewidth=0.5*fontscale
legend.get_title().set_fontweight('normal')
legend.get_title().set_fontsize(fontscale*fontsize)
if sns is True:
h,l = ax.get_legend_handles_labels()
#l,h = zip(*sorted(zip(l,h), key=lambda t: int(t[0])))
legend = ax.legend(h[1:],l[1:],title=legendtitle,prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
ax.legend_.get_frame()._linewidth=0.5*fontscale
legend.get_title().set_fontweight('normal')
legend.get_title().set_fontsize(fontscale*fontsize)
else:
legend = ax.legend(handles=h,labels=l,title=legendtitle,prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
ax.legend_.get_frame()._linewidth=0.5*fontscale
legend.get_title().set_fontweight('normal')
legend.get_title().set_fontsize(fontscale*fontsize)
if cbar is not None:
#locator, formatter = cbar._get_ticker_locator_formatter()
#ticks, ticklabels, offset_string = cbar._ticker(locator, formatter)
#cbar.ax.spines['top'].set_visible(False)
#cbar.ax.spines['right'].set_visible(False)
#cbar.ax.spines['bottom'].set_visible(False)
#cbar.ax.spines['left'].set_visible(False)
cbar.ax.tick_params(direction='out', pad=3*fontscale,width=0*fontscale,length=0*fontscale)
cbar.set_label(cbarlabel,fontdict = font.fontcbarlabel,Rotation=270,labelpad=fontscale*(fontsize+1))
cbar.ax.set_yticks(cbar.ax.get_yticks())
cbar.ax.set_yticklabels(cbar.ax.get_yticklabels(),font.fontcbarticklabel)
font.reset(fontscale)
return axsavepath = '/home/chenxupeng/projects/exSEEK_training/'+'output/'+'fig3'+'/'
if not os.path.exists(savepath):
os.mkdir(savepath)color
sns.palplot(Pastel2[8])tableau10m = np.array([(114,158,206),(255,158,74),(103,191,92),(237,102,93),(173,139,201),
(168,120,110),(237,151,202),(162,162,162),(205,204,93),(109,204,218)])/255
sns.palplot(tableau10m)sns.palplot(Set3[12])tableau10l5 = np.array([(196,156,148),(247,182,210),(199,199,199),(219,219,141),(158,218,229)])/255
sns.palplot(tableau10l5)tableau20 = np.array([(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)])/255.
sns.palplot(tableau20)def legendhandle(lists,porm=True,order=0):
'''
input: array,porm palette or marker
palettesorder=0 dataset Category20c
palettesorder=1 batch
return a dic mapping levels of the hue variable to colors
or return a dic mapping levels of the style variable to markers
when use sns function, set palette=dic or markers=dic
'''
if porm == True:
if order == 0:
palette = np.array(Category20c[20]).reshape(4,-1).T.ravel()
if order == 1:
palette = Set3[12]
lists.sort()
dic={}
for i in range(len(lists)):
dic[lists[i]]=palette[i]
return dic
else:
markerlist1 = ['v','^','<','>'] #triangle_down triangle_up triangle_left triangle_left
markerlist2 = ['P','o','X','s'] #plus (filled) circle x (filled) square
#markerlist3 = ['$CPM$','$CPM_top$','$RLE$','$TMM$']
markerlist3 = ['$f$','$g$','$h$','$l$']
markerlist3.sort()
if order == 0:
markers = markerlist2
if order == 1:
markers = markerlist1
if order == 2:
markers = markerlist3
lists.sort()
dic={}
for i in range(len(lists)):
dic[lists[i]]=markers[i]
return dictips = sns.load_dataset("tips")
legendhandle(np.unique(tips['smoker']),True,1)ax = sns.boxplot(x="day", y="total_bill", hue="smoker",data=tips, palette=legendhandle(np.unique(tips['smoker']),True,1))legendhandle(np.unique(tips['smoker']),True,0)tips = sns.load_dataset("tips")
ax = sns.boxplot(x="day", y="total_bill", hue="smoker",data=tips, palette=legendhandle(np.unique(tips['smoker']),True,0))A = ['Norm_RLE', 'Norm_RLE', 'Norm_RLE', 'Norm_RLE', 'Norm_CPM',
'Norm_CPM', 'Norm_CPM', 'Norm_CPM', 'Norm_CPM_top', 'Norm_CPM_top',
'Norm_CPM_top', 'Norm_CPM_top', 'Norm_TMM', 'Norm_TMM', 'Norm_TMM',
'Norm_TMM']Alegendhandle(np.unique(A),False,2)RLE
def plotRLE(mat,batch=None,label=None,logged=False,path=None,filename=None,title=None):
"""
mat: DataFrame, expression matrix
batch: DataFrame, optional, if given, batch.index must be contained in mat.columns
label: One of batch.columns by which the samples are grouped in the figure
"""
log_mat = mat if logged else np.log2(mat+1)
feature_meds = log_mat.apply(np.median,1).tolist()
for i in np.arange(len(feature_meds)):
log_mat.iloc[i] = log_mat.iloc[i] - feature_meds[i]
mat_rle = log_mat
distance = 0
for i in range(mat_rle.shape[1]):
small,large = np.percentile(mat_rle.iloc[:,i], [25, 75])
distance = distance+(large-small)**2
score = distance/mat_rle.shape[1]
stack = mat_rle.stack().reset_index()
stack.rename(columns={stack.columns[2]:"counts", stack.columns[1]: "index"},inplace=True)
#stack['class'] = None
if batch is not None:
batch.index.name = 'index'
batch = batch[label].reset_index()
stack = pd.merge(stack, batch, on=['index'])
fig,ax = plt.subplots(figsize=(2.8,2))
#ax = sns.boxplot(x='index',y='counts',data=stack.sort_values(by=label),fliersize=0,linewidth=0.1,width=1,hue=label,hue_order=np.unique(np.array(stack.loc[:,label])).sort(),dodge=False)
ax = sns.boxplot(x='index',y='counts',data=stack.sort_values(by=label),
fliersize=0,linewidth=0,width=0.8,hue=label,
hue_order=np.unique(np.array(stack.loc[:,label])).sort(),
notch = True,showfliers=False,showmeans=False,showcaps=False,whiskerprops=dict(linewidth=0.5,color='#D8D8D8'),
dodge=False,palette=legendhandle(np.unique(stack.dataset)))
ax.annotate('variation score: %.2f'%score,xy=(mat_rle.shape[1]*0.6,-3),
fontfamily='Arial',fontsize=5.5)
ax.set(xticks=[])
std_plot(ax,'samples','Relative log expression',legendtitle='label',legendsort=False,title=title,ybins=4,bbox_to_anchor=(1.1,1.1),ylim=[-4,4])
else:
fig,ax = plt.subplots(figsize=(3.3,2))
ax = sns.boxplot(x='index',y='counts',data=stack,fliersize=0,linewidth=0.1,width=1,color='g')
ax.set(xticks=[])
std_plot(ax,'samples','RLE',legendtitle='label',legendsort=False,ylim=[-10,10],title=title,ybins=4)
#ax.legend_.remove()
legend = ax.legend(prop=fontlegend,
#labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,bbox_to_anchor=(1.05, 0.75))
ax.legend_.get_frame()._linewidth=0
#ax.legend_.remove()
ax.spines['bottom'].set_visible(False)
fig.tight_layout()
#embed_pdf_figure()
#fig.savefig(path+filename)normlist= ['filter.null.Norm_RLE','filter.null.Norm_CPM','filter.null.Norm_CPM_top',#'filter.null.Norm_null',
#'filter.null.Norm_CPM_top_5','filter.null.Norm_CPM_top_10',
#'filter.null.Norm_CPM_top_20','filter.null.Norm_CPM_top_40',
'filter.null.Norm_TMM']
batchlist = ['Batch_ComBat_1','Batch_null','Batch_RUV','Batch_limma_1']methodlist = []
for i in normlist:
#for j in batchlist:
j=batchlist[1]
methodlist.append(i+'.'+j)
methodlistmethodlist = ['filter.null.Norm_RLE.Batch_null']class_info = pd.read_table('/home/xieyufeng/fig3/data/cfRNA/batch_info.txt',index_col=0)
class_info[class_info.dataset=='lulab_hcc']='GSE123972'class_info = pd.read_table('/home/zhaotianxiao/fig3/batch_info.txt', index_col=0)
class_info[class_info.dataset=='lulab_hcc']='GSE123972'methodlist#titlelist=['CPM','CPM-top','RLE','TMM']
titlelist=['RLE']
j=0
for i in tqdm((methodlist)):
table = pd.read_table('/home/xieyufeng/fig3/output/'+'cfRNA'+'/matrix_processing/'+i+'.mirna_and_domains.txt',
index_col=0)
plotRLE(table,batch=class_info,label='dataset',path=savepath,filename='RLE_leg_big{}.eps'.format(i),title=titlelist[j])
j=j+1def plotRLE(mat,batch=None,label=None,logged=False,path=None,filename=None,title=None):
"""
mat: DataFrame, expression matrix
batch: DataFrame, optional, if given, batch.index must be contained in mat.columns
label: One of batch.columns by which the samples are grouped in the figure
"""
log_mat = mat if logged else np.log2(mat+1)
feature_meds = log_mat.apply(np.median,1).tolist()
for i in np.arange(len(feature_meds)):
log_mat.iloc[i] = log_mat.iloc[i] - feature_meds[i]
mat_rle = log_mat
distance = 0
for i in range(mat_rle.shape[1]):
small,large = np.percentile(mat_rle.iloc[:,i], [25, 75])
distance = distance+(large-small)**2
score = distance/mat_rle.shape[1]
stack = mat_rle.stack().reset_index()
stack.rename(columns={stack.columns[2]:"counts", stack.columns[1]: "index"},inplace=True)
#stack['class'] = None
if batch is not None:
batch.index.name = 'index'
batch = batch[label].reset_index()
stack = pd.merge(stack, batch, on=['index'])
fig,ax = plt.subplots(figsize=(2,2))
#ax = sns.boxplot(x='index',y='counts',data=stack.sort_values(by=label),fliersize=0,linewidth=0.1,width=1,hue=label,hue_order=np.unique(np.array(stack.loc[:,label])).sort(),dodge=False)
ax = sns.boxplot(x='index',y='counts',data=stack.sort_values(by=label),
fliersize=0,linewidth=0,width=0.8,hue=label,
hue_order=np.unique(np.array(stack.loc[:,label])).sort(),
notch = True,showfliers=False,showmeans=False,showcaps=False,whiskerprops=dict(linewidth=0.5,color='#D8D8D8'),
dodge=False,palette=legendhandle(np.unique(stack.dataset)))
ax.annotate('variation score: %.2f'%score,xy=(mat_rle.shape[1]*0.4,-9),
fontfamily='Arial',fontsize=5.5)
ax.set(xticks=[])
std_plot(ax,'samples','Relative log expression',legendtitle='label',legendsort=False,title=title,ybins=4,bbox_to_anchor=(1.1,1.1))#,ylim=[-4,4])
else:
fig,ax = plt.subplots(figsize=(3.3,2))
ax = sns.boxplot(x='index',y='counts',data=stack,fliersize=0,linewidth=0.1,width=1,color='g')
ax.set(xticks=[])
std_plot(ax,'sample','RLE',legendtitle='label',legendsort=False,ylim=[-10,10],title=title,ybins=4)
#ax.legend_.remove()
legend = ax.legend(prop=fontlegend,
#labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,bbox_to_anchor=(1.05, 0.75))
ax.legend_.get_frame()._linewidth=0
ax.legend_.remove()
ax.spines['bottom'].set_visible(False)
fig.tight_layout()
#embed_pdf_figure()
#fig.savefig(path+filename)table = pd.read_table('/home/shibinbin/projects/exSeek-dev/output/cfRNA/count_matrix/mirna_and_domains.txt',
index_col=0)
plotRLE(table,batch=class_info,label='dataset',path=savepath,filename='RLE_noleg_{}.eps'.format('null'),title='Raw')def plotRLE(ax,mat,batch=None,label=None,logged=False,path=None,filename=None,title=None,ylim=None,ylabel='',color='#E5E5E5'):
"""
mat: DataFrame, expression matrix
batch: DataFrame, optional, if given, batch.index must be contained in mat.columns
label: One of batch.columns by which the samples are grouped in the figure
"""
log_mat = mat if logged else np.log2(mat+1)
feature_meds = log_mat.apply(np.median,1).tolist()
for i in np.arange(len(feature_meds)):
log_mat.iloc[i] = log_mat.iloc[i] - feature_meds[i]
mat_rle = log_mat
distance = 0
for i in range(mat_rle.shape[1]):
small,large = np.percentile(mat_rle.iloc[:,i], [25, 75])
distance = distance+(large-small)**2
score = distance/mat_rle.shape[1]
stack = mat_rle.stack().reset_index()
stack.rename(columns={stack.columns[2]:"counts", stack.columns[1]: "index"},inplace=True)
#stack['class'] = None
if batch is not None:
batch.index.name = 'index'
batch = batch[label].reset_index()
stack = pd.merge(stack, batch, on=['index'])
#ax = sns.boxplot(x='index',y='counts',data=stack.sort_values(by=label),fliersize=0,linewidth=0.1,width=1,hue=label,hue_order=np.unique(np.array(stack.loc[:,label])).sort(),dodge=False)
ax = sns.boxplot(ax=ax,x='index',y='counts',data=stack.sort_values(by=label),
fliersize=0,linewidth=0,width=0.8,hue=label,
hue_order=np.unique(np.array(stack.loc[:,label])).sort(),
notch = True,showfliers=False,showmeans=False,showcaps=False,whiskerprops=dict(linewidth=0.4,color=color),
dodge=False,palette=legendhandle(np.unique(stack.dataset)))
ax.set(xticks=[])
std_plot(ax,'Samples',ylabel,legendtitle='label',legendsort=False,title=title,ybins=4,ylim=ylim)
ax.annotate('$variation\ score$: %.2f'%score,xy=(mat_rle.shape[1]*0.4,ax.get_ylim()[0]*0.9+ax.get_ylim()[1]*0.1),
fontfamily='Arial',fontsize=5.5)
else:
#fig,ax = plt.subplots(figsize=(3,2))
ax = sns.boxplot(ax=ax,x='index',y='counts',data=stack,fliersize=0,linewidth=0.1,width=1,color='g')
ax.set(xticks=[])
std_plot(ax,'sample','RLE',legendtitle='label',legendsort=False,ylim=[-10,10],title=title,ybins=4)
#ax.legend_.remove()
legend = ax.legend(prop=fontlegend,
#labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,bbox_to_anchor=(1.05, 0.75))
ax.legend_.get_frame()._linewidth=0
ax.legend_.remove()
ax.spines['bottom'].set_visible(False)methodlist#titlelist=['RLE','CPM','CPM-top','TMM']
titlelist=['RLE']
fig,axes = plt.subplots(1,len(methodlist)+1,figsize=(5,2))
table = pd.read_table('/home/shibinbin/projects/exSeek-dev/output/cfRNA/count_matrix/mirna_and_domains.txt',
index_col=0)
plotRLE(axes[0],table,batch=class_info,label='dataset',path=savepath,filename='RLE_noleg_{}.eps'.format('null'),title='Raw',ylabel='RLE')
j=1
for i in tqdm((methodlist)):
table = pd.read_table('/home/xieyufeng/fig3/output/'+'cfRNA'+'/matrix_processing/'+i+'.mirna_and_domains.txt',
index_col=0)
if j==1:
plotRLE(axes[j],table,batch=class_info,label='dataset',path=savepath,filename='RLE_leg_big{}.eps'.format(i),title=titlelist[j-1],ylim=[-4,4],ylabel='')
axes[j].set_title('RLE',fontdict = fonttitle)
else:
if i=='filter.null.Norm_RLE.Batch_null':
plotRLE(axes[j],table,batch=class_info,label='dataset',path=savepath,filename='RLE_leg_big{}.eps'.format(i),title=i.split('.')[2],ylim=[-4,4],ylabel='',color='#B7B7B7')
axes[j].set_title('RLE',fontdict = fonttitle,color='r')
else:
plotRLE(axes[j],table,batch=class_info,label='dataset',path=savepath,filename='RLE_leg_big{}.eps'.format(i),title=titlelist[j-1],ylim=[-4,4],ylabel='')
axes[j].set(yticks=[])
axes[j].spines['left'].set_visible(False)
j=j+1
j=j-1
h,l =axes[j].get_legend_handles_labels()
l=np.array(l)
l[l=='GSE94582_NEBNext']='GSE94582_1'
l[l=='GSE94582_Other']='GSE94582_2'
l[l=='GSE94582_TruSeq']='GSE94582_3'
l = l.tolist()
legend = axes[j].legend(h,l,prop=fontlegend,
#labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,bbox_to_anchor=(1.05, 0.8))
axes[j].legend_.get_frame()._linewidth=0
fig.tight_layout()
#fig.savefig(savepath+'demo.eps')
#embed_pdf_figure()heterogeneity
methodlist = ['filter.null.mirna_and_domains.txt',
'filter.null.Norm_RLE.mirna_and_domains.txt',]
#'filter.null.Norm_CPM.mirna_and_domains.txt',
#'filter.null.Norm_CPM_top.mirna_and_domains.txt',
#'filter.null.Norm_TMM.mirna_and_domains.txt']ref = pd.read_table('/home/xieyufeng/exSeek/data/matrix_processing/ref_mirbase_gid.txt',header=-1)
batch_info = pd.read_table('/home/xieyufeng/fig3/data/cfRNA/batch_info.txt')
batch_info.columns=['sample_id','label']
batch_info['label'].iloc[np.where(batch_info.label=='lulab_hcc')]='GSE123972'def heterogeneity(matlist=methodlist,class_info=batch_info,featurenum=4,featurename=None):
def get_box_data(boxPlotter, boxName):
"""
boxName can be either a name "cat" or a tuple ("cat", "hue")
Here we really have to duplicate seaborn code, because there is not direct access to the
box_data in the BoxPlotter class.
"""
cat = boxName
i = boxPlotter.group_names.index(cat)
group_data = boxPlotter.plot_data[i]
return group_data
def find_x_position_box(boxPlotter, boxName):
cat = boxName
groupPos = boxPlotter.group_names.index(cat)
return groupPos
classname = np.unique(class_info.label)
classname.sort()
colormap = pd.DataFrame(np.array([classname,tableau10m[:len(np.unique(class_info.label))].tolist()]))
colormap = colormap.T
colormap.columns=['label','color']
class_info = class_info.drop(np.where(class_info.label=='GSE123972')[0]).drop(np.where(class_info.label=='GSE94582')[0]).set_index('sample_id').reset_index()
samplemin = np.unique(class_info.label,return_counts=True)[1].min()
new_class_info = pd.DataFrame([])
for i in unique(class_info.label):
extra_class_info = class_info.iloc[np.where(class_info.label==i)]
new_class_info = new_class_info.append(extra_class_info.sample(n=samplemin))
new_class_info = new_class_info.sort_values(by=['label','sample_id']).set_index('sample_id').reset_index()
flag = 0
plot = pd.DataFrame()
for matname in matlist:
mat = pd.read_table('/home/shibinbin/projects/exSeek-dev/output/cfRNA/matrix_processing/'\
+matname,index_col=0)
mat = mat.loc[:,new_class_info.sample_id]
data = np.log2(mat.iloc[np.where(np.isin([i.split('|')[0] for i in mat.index],ref))]+1)
if flag == 0:
featurelist = pd.DataFrame(data.sum(axis=1))
featurelist.columns=['counts']
ref_del = featurelist.sort_values(by='counts',ascending=False).index[:featurenum].tolist()
data_del = data.loc[ref_del]
stack = pd.DataFrame(data_del.stack())
stack = stack.reset_index()
stack.columns=['feature','sample_id','log2(count+1)']
merge = pd.merge(stack,new_class_info,on=['sample_id'])
merge['state'] = matname
plot = plot.append(merge)
plot['name_state']=[plot.feature.iloc[i].split('|')[0]+'|'+plot.state.iloc[i] for i in range(len(plot.feature))]
#plot = plot.sort_values(by=['name_state'])
plot = plot.set_index('feature').reset_index()
for feature in np.unique(plot.feature):
if (feature.split('|')[0]==featurename)|(featurename==None):
data_sub = plot.iloc[np.where(plot.feature == feature)]
data_sub = data_sub.set_index('feature').reset_index()
#colormap = pd.DataFrame(np.array([np.unique(data_sub.label),np.array(Category20c[20]).reshape(4,-1).T.ravel()[:len(np.unique(data_sub.label))].tolist()]))
#colormap = colormap.T
#colormap.columns=['label','color']
#data_sub = data_sub.merge(colormap)
data_sub = pd.merge(data_sub, colormap, how='left', on=['label'])
fig,ax = plt.subplots(figsize=(3,2))
ylist=[0]*len(matlist)
data_sub_sub={}
merge=pd.DataFrame()
datasetprop=pd.DataFrame()
for label in np.unique(data_sub.label):
data_sub_sub[label] = data_sub.iloc[np.where(data_sub.label == label)]
#data_sub_sub[label].to_csv('./'+label+'.txt',sep='\t')
for i in np.unique(data_sub_sub[label].state):
a = data_sub_sub[label][data_sub_sub[label].state==i]
datasetprop.loc['var',i]=np.var(a['log2(count+1)'],ddof=1)
datasetprop.loc['mean',i]=np.mean(a['log2(count+1)'])
#score['mean']=np.mean(a['log2(count+1)'])
score = pd.DataFrame(datasetprop.stack()).reset_index()
score['dataset']='GSE113994'
merge = merge.append(score)
data_sub_sub[label]['state_sample_id'] = [data_sub_sub[label].state.iloc[i]+'|'+\
str(i) for i in range(len(data_sub_sub[label]))]
sns.pointplot(ax=ax,x="state_sample_id", y="log2(count+1)",palette=data_sub_sub[label].color,hue=data_sub_sub[label].label,
data=data_sub_sub[label],scale=0.2)
#ax.scatter(data_sub_sub.state_sample_id.tolist(),data_sub_sub['log2(count+1)'].tolist(),color=data_sub_sub.color.tolist())
boxPlotter = sns.categorical._BoxPlotter(data=data_sub_sub[label],x='state_sample_id',y='log2(count+1)',hue=data_sub_sub[label].label,
order=None, hue_order=None,
orient=None, width=.8, color=None, palette=None, saturation=.75,
dodge=True, fliersize=5, linewidth=None)
linenum = len(matlist)
start = ax.get_xticks()[0]
binwidth = math.ceil((ax.get_xticks()[0]+ax.get_xticks()[-1])/linenum)
for loc in range(linenum):
box = [boxPlotter.group_names[i] for i in range(start+loc*binwidth,start+(loc+1)*binwidth)]
box_data = []
for i in box:
box_data.append(get_box_data(boxPlotter, i)[0])
ylim = ax.get_ylim()
yRange = ylim[1] - ylim[0]
lineYOffsetAxesCoord = 0.05
lineYOffsetToBoxAxesCoord = 0.06
lineHeightAxesCoord=0.02
yOffset = lineYOffsetAxesCoord*yRange
yOffsetToBox = lineYOffsetToBoxAxesCoord*yRange
ymax = np.array(box_data).max()
y = ymax + yOffsetToBox
if y>=ylist[loc]:
ylist[loc]=y
merge.rename(columns={merge.columns[0]:"prop",
merge.columns[1]: "state",
merge.columns[2]: "value"},inplace=True)
plotprop=pd.DataFrame()
for i in np.unique(merge.state):
b = merge[merge.state==i]
mean = b[b.prop=='mean'].value
mean_var=np.var(mean,ddof=1)
var= b[b.prop=='var'].value
mul = 1
for item in var:
mul *= item
plotprop.loc[i,'mean_var']=mean_var
plotprop.loc[i,'var_mul']=mul
plotprop=plotprop.rename(index={#'filter.null.Norm_CPM.mirna_and_domains.txt':'CPM',
#'filter.null.Norm_CPM_top.mirna_and_domains.txt':'CPM-top',
'filter.null.Norm_RLE.mirna_and_domains.txt':'RLE',
#'filter.null.Norm_TMM.mirna_and_domains.txt':'TMM',
'filter.null.mirna_and_domains.txt':'Raw'})
display(plotprop)
h = lineHeightAxesCoord*yRange
#title = [i.split('.')[2] for i in matlist]
#title = ['Raw' if x == 'mirna_and_domains' else x for x in title]
#title = ['Raw','RLE','CPM','CPM-top','TMM']
title = ['Raw','RLE']
for loc in range(linenum):
lineX, lineY = [start+loc*binwidth,start+(loc+1)*binwidth], [ylist[loc]+h,ylist[loc]+h]
ax.plot(lineX, lineY,color='Black',linewidth='0.5')
ax.annotate(title[loc]+'\n'+'$b$: '+'%.2f'%(plotprop.loc[title[loc],'mean_var'])+' $w$: '+'%.2f'%(plotprop.loc[title[loc],'var_mul']),
xy=(np.mean([start+loc*binwidth,start+(loc+1)*binwidth]), ylist[loc]+h),
xytext=(0, 1), textcoords='offset points',
xycoords='data', ha='center', va='bottom', fontfamily='Arial',fontsize=5.5,
clip_on=False, annotation_clip=False)
ax.spines['bottom'].set_visible(False)
ax.set_xticks([])
#ax.legend(h,l,prop=fontlegend)
#std_plot(ax,'','',legendtitle='label',legendsort=False,title=feature.split('|')[0])
#ax.legend_.remove()
std_plot(ax,'','Normalized counts',legendtitle='label',legendsort=False,title='Heterogeneity of '+feature.split('|')[0]+' expression')
legend = ax.legend(prop=fontlegend,
#labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,bbox_to_anchor=(1, 1.0),framealpha=0,markerscale=5)
ax.legend_.get_frame()._linewidth=0
#ax.legend_.remove()
fig.tight_layout()
#embed_pdf_figure()
#fig.savefig(savepath+'{}_heterogeneity_noleg_2.eps'.format(feature.split('|')[0]))
#heterogeneity()heterogeneity(featurename='hsa-miR-21-5p')heterogeneity(featurenum=8)def heterogeneity(matlist=methodlist,class_info=batch_info,featurenum=4,featurename1=None,featurename2=None):
def get_box_data(boxPlotter, boxName):
"""
boxName can be either a name "cat" or a tuple ("cat", "hue")
Here we really have to duplicate seaborn code, because there is not direct access to the
box_data in the BoxPlotter class.
"""
cat = boxName
i = boxPlotter.group_names.index(cat)
group_data = boxPlotter.plot_data[i]
return group_data
def find_x_position_box(boxPlotter, boxName):
cat = boxName
groupPos = boxPlotter.group_names.index(cat)
return groupPos
classname = np.unique(class_info.label)
classname.sort()
colormap = pd.DataFrame(np.array([classname,tableau10m[:len(np.unique(class_info.label))].tolist()]))
colormap = colormap.T
colormap.columns=['label','color']
class_info = class_info.drop(np.where(class_info.label=='GSE123972')[0]).drop(np.where(class_info.label=='GSE94582')[0]).set_index('sample_id').reset_index()
samplemin = np.unique(class_info.label,return_counts=True)[1].min()
new_class_info = pd.DataFrame([])
for i in unique(class_info.label):
extra_class_info = class_info.iloc[np.where(class_info.label==i)]
new_class_info = new_class_info.append(extra_class_info.sample(n=samplemin))
new_class_info = new_class_info.sort_values(by=['label','sample_id']).set_index('sample_id').reset_index()
flag = 0
plot = pd.DataFrame()
for matname in matlist:
mat = pd.read_table('/home/shibinbin/projects/exSeek-dev/output/cfRNA/matrix_processing/'\
+matname,index_col=0)
mat = mat.loc[:,new_class_info.sample_id]
data = np.log2(mat.iloc[np.where(np.isin([i.split('|')[0] for i in mat.index],ref))]+1)
if flag == 0:
featurelist = pd.DataFrame(data.sum(axis=1))
featurelist.columns=['counts']
ref_del = featurelist.sort_values(by='counts',ascending=False).index[:featurenum].tolist()
data_del = data.loc[ref_del]
stack = pd.DataFrame(data_del.stack())
stack = stack.reset_index()
stack.columns=['feature','sample_id','log2(count+1)']
merge = pd.merge(stack,new_class_info,on=['sample_id'])
merge['state'] = matname
plot = plot.append(merge)
plot['name_state']=[plot.feature.iloc[i].split('|')[0]+'|'+plot.state.iloc[i] for i in range(len(plot.feature))]
#plot = plot.sort_values(by=['name_state'])
plot = plot.set_index('feature').reset_index()
fig,ax = plt.subplots(figsize=(5,2))
for feature in np.unique(plot.feature):
if feature.split('|')[0]==featurename1:
print(1)
data_sub = plot.iloc[np.where(plot.feature == feature)]
data_sub = data_sub.set_index('feature').reset_index()
#colormap = pd.DataFrame(np.array([np.unique(data_sub.label),np.array(Category20c[20]).reshape(4,-1).T.ravel()[:len(np.unique(data_sub.label))].tolist()]))
#colormap = colormap.T
#colormap.columns=['label','color']
#data_sub = data_sub.merge(colormap)
data_sub = pd.merge(data_sub, colormap, how='left', on=['label'])
ylist=[0]*len(matlist)
data_sub_sub={}
merge=pd.DataFrame()
datasetprop=pd.DataFrame()
for label in np.unique(data_sub.label):
data_sub_sub[label] = data_sub.iloc[np.where(data_sub.label == label)]
#data_sub_sub[label].to_csv('./'+label+'.txt',sep='\t')
for i in np.unique(data_sub_sub[label].state):
a = data_sub_sub[label][data_sub_sub[label].state==i]
datasetprop.loc['var',i]=np.var(a['log2(count+1)'],ddof=1)
datasetprop.loc['mean',i]=np.mean(a['log2(count+1)'])
#score['mean']=np.mean(a['log2(count+1)'])
score = pd.DataFrame(datasetprop.stack()).reset_index()
score['dataset']='GSE113994'
merge = merge.append(score)
data_sub_sub[label]['state_sample_id'] = [data_sub_sub[label].state.iloc[i]+'|'+\
str(i) for i in range(len(data_sub_sub[label]))]
d=data_sub_sub[label]
d.label = [ i+ '|' + featurename1 for i in d.label]
sns.pointplot(ax=ax,x="state_sample_id", y="log2(count+1)",palette=d.color,hue=d.label,
data=d,scale=0.5)
#ax.scatter(data_sub_sub.state_sample_id.tolist(),data_sub_sub['log2(count+1)'].tolist(),color=data_sub_sub.color.tolist())
boxPlotter = sns.categorical._BoxPlotter(data=data_sub_sub[label],x='state_sample_id',y='log2(count+1)',hue=data_sub_sub[label].label,
order=None, hue_order=None,
orient=None, width=.8, color=None, palette=None, saturation=.75,
dodge=True, fliersize=5, linewidth=None)
linenum = len(matlist)
start = ax.get_xticks()[0]
binwidth = math.ceil((ax.get_xticks()[0]+ax.get_xticks()[-1])/linenum)
for loc in range(linenum):
box = [boxPlotter.group_names[i] for i in range(start+loc*binwidth,start+(loc+1)*binwidth)]
box_data = []
for i in box:
box_data.append(get_box_data(boxPlotter, i)[0])
ylim = ax.get_ylim()
yRange = ylim[1] - ylim[0]
lineYOffsetAxesCoord = 0.05
lineYOffsetToBoxAxesCoord = 0.06
lineHeightAxesCoord=0.02
yOffset = lineYOffsetAxesCoord*yRange
yOffsetToBox = lineYOffsetToBoxAxesCoord*yRange
ymax = np.array(box_data).max()
y = ymax + yOffsetToBox
if y>=ylist[loc]:
ylist[loc]=y
merge.rename(columns={merge.columns[0]:"prop",
merge.columns[1]: "state",
merge.columns[2]: "value"},inplace=True)
plotprop=pd.DataFrame()
for i in np.unique(merge.state):
b = merge[merge.state==i]
mean = b[b.prop=='mean'].value
mean_var=np.var(mean,ddof=1)
var= b[b.prop=='var'].value
mul = 1
for item in var:
mul *= item
plotprop.loc[i,'mean_var']=mean_var
plotprop.loc[i,'var_mul']=mul
plotprop=plotprop.rename(index={#'filter.null.Norm_CPM.mirna_and_domains.txt':'CPM',
#'filter.null.Norm_CPM_top.mirna_and_domains.txt':'CPM-top',
'filter.null.Norm_RLE.mirna_and_domains.txt':'RLE',
#'filter.null.Norm_TMM.mirna_and_domains.txt':'TMM',
'filter.null.mirna_and_domains.txt':'Raw'})
#display(plotprop)
h = lineHeightAxesCoord*yRange
#title = [i.split('.')[2] for i in matlist]
#title = ['Raw' if x == 'mirna_and_domains' else x for x in title]
#title = ['Raw','RLE','CPM','CPM-top','TMM']
title = ['Raw','RLE']
for loc in range(linenum):
lineX, lineY = [start+loc*binwidth,start+(loc+1)*binwidth], [ylist[loc]+h,ylist[loc]+h]
ax.plot(lineX, lineY,color='Black',linewidth='0.5')
ax.annotate(title[loc]+'\n'+'$b$: '+'%.2f'%(plotprop.loc[title[loc],'mean_var'])+' $w$: '+'%.2f'%(plotprop.loc[title[loc],'var_mul']),
xy=(np.mean([start+loc*binwidth,start+(loc+1)*binwidth]), ylist[loc]+h),
xytext=(0, 1), textcoords='offset points',
xycoords='data', ha='center', va='bottom', fontfamily='Arial',fontsize=5.5,
clip_on=False, annotation_clip=False)
if feature.split('|')[0]==featurename2:
print(2)
data_sub = plot.iloc[np.where(plot.feature == feature)]
data_sub = data_sub.set_index('feature').reset_index()
#colormap = pd.DataFrame(np.array([np.unique(data_sub.label),np.array(Category20c[20]).reshape(4,-1).T.ravel()[:len(np.unique(data_sub.label))].tolist()]))
#colormap = colormap.T
#colormap.columns=['label','color']
#data_sub = data_sub.merge(colormap)
data_sub = pd.merge(data_sub, colormap, how='left', on=['label'])
ylist=[100]*len(matlist)
data_sub_sub={}
merge=pd.DataFrame()
datasetprop=pd.DataFrame()
for label in np.unique(data_sub.label):
data_sub_sub[label] = data_sub.iloc[np.where(data_sub.label == label)]
#data_sub_sub[label].to_csv('./'+label+'.txt',sep='\t')
for i in np.unique(data_sub_sub[label].state):
a = data_sub_sub[label][data_sub_sub[label].state==i]
datasetprop.loc['var',i]=np.var(a['log2(count+1)'],ddof=1)
datasetprop.loc['mean',i]=np.mean(a['log2(count+1)'])
#score['mean']=np.mean(a['log2(count+1)'])
score = pd.DataFrame(datasetprop.stack()).reset_index()
score['dataset']='GSE113994'
merge = merge.append(score)
data_sub_sub[label]['state_sample_id'] = [data_sub_sub[label].state.iloc[i]+'|'+\
str(i) for i in range(len(data_sub_sub[label]))]
d = data_sub_sub[label]
d.label = [ i+ '|' + featurename2 for i in d.label]
sns.pointplot(ax=ax,x="state_sample_id", y="log2(count+1)",palette=d.color,hue=d.label,
data=d,scale=0.5,linestyles='--',markers='X')
#ax.scatter(data_sub_sub.state_sample_id.tolist(),data_sub_sub['log2(count+1)'].tolist(),color=data_sub_sub.color.tolist())
boxPlotter = sns.categorical._BoxPlotter(data=data_sub_sub[label],x='state_sample_id',y='log2(count+1)',hue=data_sub_sub[label].label,
order=None, hue_order=None,
orient=None, width=.8, color=None, palette=None, saturation=.75,
dodge=True, fliersize=5, linewidth=None)
linenum = len(matlist)
start = ax.get_xticks()[0]
binwidth = math.ceil((ax.get_xticks()[0]+ax.get_xticks()[-1])/linenum)
for loc in range(linenum):
box = [boxPlotter.group_names[i] for i in range(start+loc*binwidth,start+(loc+1)*binwidth)]
box_data = []
for i in box:
box_data.append(get_box_data(boxPlotter, i)[0])
ylim = ax.get_ylim()
yRange = ylim[1] - ylim[0]
lineYOffsetAxesCoord = 0.05
lineYOffsetToBoxAxesCoord = 0.06
lineHeightAxesCoord=0.02
yOffset = lineYOffsetAxesCoord*yRange
yOffsetToBox = lineYOffsetToBoxAxesCoord*yRange
ymin = np.array(box_data).min()
y = ymin - yOffsetToBox
if y<=ylist[loc]:
ylist[loc]=y
merge.rename(columns={merge.columns[0]:"prop",
merge.columns[1]: "state",
merge.columns[2]: "value"},inplace=True)
plotprop=pd.DataFrame()
for i in np.unique(merge.state):
b = merge[merge.state==i]
mean = b[b.prop=='mean'].value
mean_var=np.var(mean,ddof=1)
var= b[b.prop=='var'].value
mul = 1
for item in var:
mul *= item
plotprop.loc[i,'mean_var']=mean_var
plotprop.loc[i,'var_mul']=mul
plotprop=plotprop.rename(index={#'filter.null.Norm_CPM.mirna_and_domains.txt':'CPM',
#'filter.null.Norm_CPM_top.mirna_and_domains.txt':'CPM-top',
'filter.null.Norm_RLE.mirna_and_domains.txt':'RLE',
#'filter.null.Norm_TMM.mirna_and_domains.txt':'TMM',
'filter.null.mirna_and_domains.txt':'Raw'})
#display(plotprop)
h = lineHeightAxesCoord*yRange
#title = [i.split('.')[2] for i in matlist]
#title = ['Raw' if x == 'mirna_and_domains' else x for x in title]
#title = ['Raw','RLE','CPM','CPM-top','TMM']
title = ['Raw','RLE']
for loc in range(linenum):
lineX, lineY = [start+loc*binwidth,start+(loc+1)*binwidth], [ylist[loc]-h,ylist[loc]-h]
ax.plot(lineX, lineY,color='Black',linewidth='0.5')
ax.annotate(title[loc]+'\n'+'$b$: '+'%.2f'%(plotprop.loc[title[loc],'mean_var'])+' $w$: '+'%.2f'%(plotprop.loc[title[loc],'var_mul']),
xy=(np.mean([start+loc*binwidth,start+(loc+1)*binwidth]), ylist[loc]-20*h),
xytext=(0, 1), textcoords='offset points',
xycoords='data', ha='center', va='bottom', fontfamily='Arial',fontsize=5.5,
clip_on=False, annotation_clip=False)
ax.spines['bottom'].set_visible(False)
ax.set_xticks([])
#ax.legend(h,l,prop=fontlegend)
#std_plot(ax,'','',legendtitle='label',legendsort=False,title=feature.split('|')[0])
#ax.legend_.remove()
std_plot(ax,'','Normalized counts',legendtitle='label',legendsort=False,title='Expression heterogeneity of reference genes')
legend = ax.legend(prop=fontlegend,
#labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,bbox_to_anchor=(1, 0.8),framealpha=0,markerscale=2)
ax.legend_.get_frame()._linewidth=0
#ax.legend_.remove()
fig.tight_layout()
#embed_pdf_figure()
#fig.savefig(savepath+'{}_heterogeneity_noleg_2.eps'.format(feature.split('|')[0]))
#heterogeneity()heterogeneity(featurename1='hsa-miR-21-5p',featurename2='hsa-miR-15b-5p')uca_knn
def convert_label_to_int(sample_class):
classes, counts = np.unique(sample_class, return_counts=True)
classes = np.argmax(sample_class.reshape((-1, 1)) == classes.reshape((1, -1)), axis=1)
return classes
def unsupervised_clustering_accuracy(y, y_pred):
from sklearn.utils.linear_assignment_ import linear_assignment
assert len(y_pred) == len(y)
u = np.unique(np.concatenate((y, y_pred)))
n_clusters = len(u)
mapping = dict(zip(u, range(n_clusters)))
reward_matrix = np.zeros((n_clusters, n_clusters), dtype=np.int64)
for y_pred_, y_ in zip(y_pred, y):
if y_ in mapping:
reward_matrix[mapping[y_pred_], mapping[y_]] += 1
cost_matrix = reward_matrix.max() - reward_matrix
ind = linear_assignment(cost_matrix)
return sum([reward_matrix[i, j] for i, j in ind]) * 1.0 / y_pred.size, ind
def uca_scores(X,y, prediction_algorithm='knn'):
from sklearn.metrics import adjusted_rand_score as ARI
from sklearn.metrics import normalized_mutual_info_score as NMI
from sklearn.metrics import silhouette_score
from sklearn.mixture import GaussianMixture as GMM
from sklearn.cluster import KMeans
cluster_num = np.unique(y).shape[0]
if prediction_algorithm == 'knn':
labels_pred = KMeans(cluster_num, n_init=200).fit_predict(X)
elif prediction_algorithm == 'gmm':
gmm = GMM(cluster_num)
gmm.fit(X)
labels_pred = gmm.predict(X)
labels = y
#asw_score = silhouette_score(X, labels)
#nmi_score = NMI(labels, labels_pred)
#ari_score = ARI(labels, labels_pred)
labels_int = convert_label_to_int(labels)
uca_score = unsupervised_clustering_accuracy(labels_int, labels_pred)[0]
return uca_score
def get_uca_score(data,sampleclass,method_PCA = True,prediction_algorithm='knn'):
X = np.log2(data + 0.001).T
X = StandardScaler().fit_transform(X)
if method_PCA == True:
transform = PCA()
else:
transform = TSNE()
X_pca = transform.fit_transform(X)
X_, y_ = X_pca, sampleclass.loc[data.columns.values].values.ravel()
#knn_score_ = knn_score(X_, y_)
uca_score = uca_scores(X_, y_, prediction_algorithm)
return uca_scoredef knn_score(X, y, K=10):
N = X.shape[0]
assert K < N
nn = NearestNeighbors(K)
nn.fit(X)
distances, indices = nn.kneighbors(X, K + 1)
neighbor_classes = np.take(y, indices[:, 1:])
same_class_fractions = np.sum(neighbor_classes == y[:, np.newaxis], axis=1)
classes, counts = np.unique(y, return_counts=True)
classes = np.argmax(y.reshape((-1, 1)) == classes.reshape((1, -1)), axis=1)
counts = np.take(counts, classes)
mean_r = K/(N - 1)*counts
max_r = np.minimum(K, counts)
#print (same_class_fractions.shape,mean_r.shape,max_r.shape)
#scores = (np.mean(same_class_fractions) - mean_r)/(max_r - mean_r)
scores = (same_class_fractions - mean_r)/(max_r - mean_r)
#print(scores)
return scores.mean()
def get_knn_score(data,sampleclass,method_PCA = True,prediction_algorithm='knn'):
X = np.log2(data + 0.001).T
X = StandardScaler().fit_transform(X)
if method_PCA == True:
transform = PCA()
else:
transform = TSNE()
X_pca = transform.fit_transform(X)
X_, y_ = X_pca, sampleclass.loc[data.columns.values].values.ravel()
knn_score_ = knn_score(X_, y_)
return knn_score_methodlist = []
for i in normlist:
for j in batchlist:
methodlist.append(i+'.'+j)
methodlistbatch_info = pd.read_table('/home/xieyufeng/fig3/data/cfRNA/batch_info.txt',index_col=0)
batch_info = pd.read_table('/home/zhaotianxiao/fig3/batch_info.txt', index_col=0)
batch_info[batch_info.dataset=='lulab_hcc']='GSE123972'
sampleclass = batch_info.iloc[:,0]
knn_list=[]
for i in tqdm(methodlist):
table = pd.read_table('/home/xieyufeng/fig3/output/'+'cfRNA'+'/matrix_processing/'+i+'.mirna_and_domains.txt',
index_col=0)
knn_list.append(get_knn_score(table,sampleclass))
knn_summary = pd.DataFrame(data={'preprocess_method':methodlist,'knn_score':list(knn_list)})
knn_summary = knn_summary.set_index('preprocess_method')class_info = pd.read_table('/home/xieyufeng/fig3/data/cfRNA/sample_classes.txt',index_col=0)
sampleclass = class_info
uca_list=[]
for i in tqdm(methodlist):
table = pd.read_table('/home/xieyufeng/fig3/output/'+'cfRNA'+'/matrix_processing/'+i+'.mirna_and_domains.txt',
index_col=0)
uca_list.append(get_uca_score(table,sampleclass))
uca_summary = pd.DataFrame(data={'preprocess_method':methodlist,'uca_score':list(uca_list)})
uca_summary = uca_summary.set_index('preprocess_method')get_uca_score(table,sampleclass)from scipy.stats import pearsonr
pearsonr(uca_summary,knn_summary)merge = pd.concat([knn_summary,uca_summary],axis=1)
merge['impute'] = [method.split('.')[1] for method in merge.index]
merge['normalization'] = [method.split('.')[2] for method in merge.index]
merge['batch'] = [method.split('.')[3] for method in merge.index]
sizelist=[10,50,200]
impute_list = np.unique(merge['impute'])
merge['imputation_size'] = merge['impute']
for i in np.arange(len(impute_list)):
where = np.where(merge['imputation_size']==impute_list[i])
for j in where:
merge['imputation_size'].iloc[j]=sizelist[i]
merge.knn_score =1-merge.knn_scoremerge = merge.drop(merge.iloc[np.where(np.array([i.split('.')[-1] for i in merge.index]) == 'Batch_RUVn_1')[0]].index)fig,ax=plt.subplots(figsize=(6,4))
ax = sns.scatterplot(x='uca_score',y='knn_score',data = merge,hue='batch',style='normalization',
markers=legendhandle(np.unique(merge.normalization),False,1),
palette=legendhandle(np.unique(merge.batch),True,1),s=100)
#"PCC score:{:.2f}".format(pearsonr(uca_summary,knn_summary)[0][0]))
h,l=ax.get_legend_handles_labels()
l = np.array(l)
l[l=='batch']='batch removal method'
l[l=='Batch_ComBat_1']='ComBat'
l[l=='Batch_null']='null'
l[l=='Batch_RUV']='RUV'
l[l=='Batch_limma_1']='limma'
l[l=='normalization']='normalization method'
l[l=='Norm_RLE']='RLE'
l[l=='Norm_CPM']='CPM'
l[l=='Norm_CPM_top']='CPM-top'
l[l=='Norm_TMM']='TMM'
l = l.tolist()
#ax.legend_.remove()
std_plot(ax,'UCA score','mkNN score',h=h,l=l,markerscale=1.5,bbox_to_anchor=(1.05, 0.9))
ax.legend_.get_frame()._linewidth=0
fig.tight_layout()
#fig.savefig(savepath+'uca_knn_binbin_leg.eps')
#embed_pdf_figure()understand UCA
def convert_label_to_int(sample_class):
classes, counts = np.unique(sample_class, return_counts=True)
classes = np.argmax(sample_class.reshape((-1, 1)) == classes.reshape((1, -1)), axis=1)
return classes
def unsupervised_clustering_accuracy(y, y_pred):
from sklearn.utils.linear_assignment_ import linear_assignment
assert len(y_pred) == len(y)
u = np.unique(np.concatenate((y, y_pred)))
n_clusters = len(u)
mapping = dict(zip(u, range(n_clusters)))
reward_matrix = np.zeros((n_clusters, n_clusters), dtype=np.int64)
for y_pred_, y_ in zip(y_pred, y):
if y_ in mapping:
reward_matrix[mapping[y_pred_], mapping[y_]] += 1
cost_matrix = reward_matrix.max() - reward_matrix
ind = linear_assignment(cost_matrix)
return sum([reward_matrix[i, j] for i, j in ind]) * 1.0 / y_pred.size, ind
def uca_scores(X,y, prediction_algorithm='knn'):
from sklearn.metrics import adjusted_rand_score as ARI
from sklearn.metrics import normalized_mutual_info_score as NMI
from sklearn.metrics import silhouette_score
from sklearn.mixture import GaussianMixture as GMM
from sklearn.cluster import KMeans
cluster_num = np.unique(y).shape[0]
if prediction_algorithm == 'knn':
labels_pred = KMeans(cluster_num, n_init=200).fit_predict(X)
print(labels_pred)
print(np.unique(labels_pred,return_counts=True))
elif prediction_algorithm == 'gmm':
gmm = GMM(cluster_num)
gmm.fit(X)
labels_pred = gmm.predict(X)
labels = y
#asw_score = silhouette_score(X, labels)
#nmi_score = NMI(labels, labels_pred)
#ari_score = ARI(labels, labels_pred)
labels_int = convert_label_to_int(labels)
uca_score = unsupervised_clustering_accuracy(labels_int, labels_pred)[0]
return uca_score,unsupervised_clustering_accuracy(labels_int, labels_pred)[1]
def get_uca_score(data,sampleclass,method_PCA = True,prediction_algorithm='knn'):
X = np.log2(data + 0.001).T
X = StandardScaler().fit_transform(X)
if method_PCA == True:
transform = PCA()
else:
transform = TSNE()
X_pca = transform.fit_transform(X)
X_, y_ = X_pca, sampleclass.loc[data.columns.values].values.ravel()
#knn_score_ = knn_score(X_, y_)
uca_score,ind = uca_scores(X_, y_, prediction_algorithm)get_uca_score(table,sampleclass)labels = sampleclass.loc[table.columns.values].values.ravel()
print(convert_label_to_int(labels))
print(np.unique(convert_label_to_int(labels),return_counts=True))def uca_scores(X,y, prediction_algorithm='knn'):
from sklearn.metrics import adjusted_rand_score as ARI
from sklearn.metrics import normalized_mutual_info_score as NMI
from sklearn.metrics import silhouette_score
from sklearn.mixture import GaussianMixture as GMM
from sklearn.cluster import KMeans
cluster_num = np.unique(y).shape[0]
if prediction_algorithm == 'knn':
labels_pred = KMeans(cluster_num, n_init=200).fit_predict(X)
elif prediction_algorithm == 'gmm':
gmm = GMM(cluster_num)
gmm.fit(X)
labels_pred = gmm.predict(X)
labels = y
#asw_score = silhouette_score(X, labels)
#nmi_score = NMI(labels, labels_pred)
#ari_score = ARI(labels, labels_pred)
labels_int = convert_label_to_int(labels)
uca_score = unsupervised_clustering_accuracy(labels_int, labels_pred)[0]
return uca_score,unsupervised_clustering_accuracy(labels_int, labels_pred)[1]
def get_uca_score(data,sampleclass,method_PCA = True,prediction_algorithm='knn'):
X = np.log2(data + 0.001).T
X = StandardScaler().fit_transform(X)
if method_PCA == True:
transform = PCA()
else:
transform = TSNE()
X_pca = transform.fit_transform(X)
X_, y_ = X_pca, sampleclass.loc[data.columns.values].values.ravel()
#knn_score_ = knn_score(X_, y_)
uca_score,ind = uca_scores(X_, y_, prediction_algorithm)
return ind
get_uca_score(table,sampleclass)understand mkNN
first alignment score
其中$k$是最近邻算法（k nearest-neighbors, kNN）的前$k$个最近邻，$\overline{x}$是样本周围的样本同属一个批次的数量的平均，$N$表示样本数。 当两个批次样本完全分开时，$k=\overline{x}$，$\text{Alignment Score}=0$；当两个批次样本完全混杂时，比例因子$\frac{1}{k-\frac{k}{N}}$作用下，$\text{Alignment Score}$接近1。 exSEEK提出了适用于多种批次的mkNN指标，该指标由由史斌斌首次提出。
mkNN
其中，$b$表示批次，$B$为批次数量，$N_b$是批次$b$下样本的数量。批次效应越明显，该指标越接近0。
IFrame('https://drive.google.com/file/d/1yWvw3fwWeSSrBgmhz_uaC4oQ0wltkIge/preview',
width=800,height=600)PCA
def PCA_plot_with_uca_score_sns(ax,data,sampleclass,batchinfo, method = 'PCA'):
X = log_transform(data).T
X = StandardScaler().fit_transform(X)
if method == 'PCA':
transform = PCA()
elif method == 'tSNE':
transform = TSNE()
elif method == 'UMAP':
transform = umap.UMAP(n_neighbors=5,min_dist=0.3,metric='correlation')
X_pca = transform.fit_transform(X)
plot_table = pd.DataFrame(X_pca[:,:2])
plot_table.index = data.columns
plot_table = pd.concat((plot_table,sampleclass.loc[plot_table.index],batchinfo.loc[plot_table.index]),axis=1)
plot_table.columns = ['Dimension 1','Dimension 2','class','batch']
plot_table = plot_table.sort_values(by='batch')
classnum = np.unique(plot_table.iloc[:,2]).shape[0]
sns.scatterplot(ax=ax,data=plot_table,x="Dimension 1", y="Dimension 2",
palette=legendhandle(np.unique(plot_table.batch)) , hue="batch",style='class',s=50,linewidth=0.01)
#plt.figure(linewidth=30.5)
#legend.get_title().set_fontweight('normal')
#legend.get_title().set_fontsize(6.5)
#ax.legend(bbox_to_anchor = (1, 1))
#ax.spines['right'].set_visible(False)
#ax.spines['top'].set_visible(False)
#uca_score = get_clustering_score(data, sampleclass)
#ax.set_title(method_type + ': ' +'UCA = {:.3f}'.format(uca_score) +', ' + 'kBET = {:.3f}'.format(kbet_score))
#ax.annotate('UCA score: {:.6f}'.format(uca_score),xy=(1,0),xycoords='data',size=6.5)
#print('Alignment score: {}'.format(knn_score(X_pca, sampleclass.loc[data.columns.values].values.ravel() )))
def log_transform(data, small = 0.01):
return np.log2(data + small)fontsize = 6.5
fontscale = 1
fontweight = 'normal'
fonttitle = {'family':'Arial',
'weight' : fontweight,
'size' : fontsize*fontscale}
fontlabel = {'family':'Arial',
'weight' : fontweight,
'size' : fontsize*fontscale}
fontticklabel = {'family':'Arial',
'weight' : fontweight,
'size' : fontsize*fontscale}
fontlegend = {'family':'Arial',
'weight' : fontweight,
#'linewidth':0.5,
'size' : fontsize*fontscale}
fontcbarlabel = {'family':'Arial',
'weight' : fontweight,
#'Rotation' : 270,
#'labelpad' : 25,
'size' : fontsize*fontscale}
fontcbarticklabel = {'family':'Arial',#Helvetica
'weight' : fontweight,
'size' : (fontsize-1)*fontscale}
def std_plot(ax,xlabel=None,ylabel=None,title=None,
legendtitle=None,bbox_to_anchor=None,
labelspacing=0.2,borderpad=0.2,handletextpad=0.2,legendsort=False,markerscale=None,
xlim=None,ylim=None,
xbins=None,ybins=None,
cbar=None,cbarlabel=None,
moveyaxis=False,sns=False,left=True,rotation=None,xticklabel=None,legendscale=True,h=None,l=None,**kwards):
# height = 2 font = 6.5
def autoscale(fig):
if isinstance(fig,matplotlib.figure.Figure):
width,height = fig.get_size_inches()
elif isinstance(fig,matplotlib.axes.Axes):
width,height = fig.figure.get_size_inches()
fontscale = height/3
if width/fontscale > 8:
warnings.warn("Please reset fig's width. When scaling the height to 2 in, the scaled width '%.2f' is large than 8"%(width/fontscale),UserWarning)
return fontscale
class fontprop:
def init(self,fonttitle=None,fontlabel=None,fontticklabel=None,fontlegend=None,fontcbarlabel=None,fontcbarticklabel=None):
self.fonttitle = fonttitle
self.fontlabel = fontlabel
self.fontticklabel = fontticklabel
self.fontlegend = fontlegend
self.fontcbarlabel = fontcbarlabel
self.fontcbarticklabel = fontcbarticklabel
def update(self,fontscale):
self.fonttitle['size'] = self.fonttitle['size']*fontscale
self.fontlabel['size'] = self.fontlabel['size']*fontscale
self.fontticklabel['size'] = self.fontticklabel['size']*fontscale
self.fontlegend['size'] = self.fontlegend['size']*fontscale
self.fontcbarlabel['size'] = self.fontcbarlabel['size']*fontscale
self.fontcbarticklabel['size'] = self.fontcbarticklabel['size']*fontscale
def reset(self,fontscale):
self.fonttitle['size'] = self.fonttitle['size']/fontscale
self.fontlabel['size'] = self.fontlabel['size']/fontscale
self.fontticklabel['size'] = self.fontticklabel['size']/fontscale
self.fontlegend['size'] = self.fontlegend['size']/fontscale
self.fontcbarlabel['size'] = self.fontcbarlabel['size']/fontscale
self.fontcbarticklabel['size'] = self.fontcbarticklabel['size']/fontscale
fontscale = autoscale(ax)
font = fontprop()
font.init(fonttitle,fontlabel,fontticklabel,fontlegend,fontcbarlabel,fontcbarticklabel)
font.update(fontscale)
pyplot.draw()
#plt.figure(linewidth=30.5)
if xlim is not None:
ax.set(xlim=xlim)
if ylim is not None:
ax.set(ylim=ylim)
#pyplot.draw()
if xbins is not None:
locator = MaxNLocator(nbins=xbins)
locator.set_axis(ax.xaxis)
ax.set_xticks(locator())
if ybins is not None:
locator = MaxNLocator(nbins=ybins)
locator.set_axis(ax.yaxis)
ax.set_yticks(locator())
pyplot.draw()
ax.set_xticks(ax.get_xticks())
ax.set_yticks(ax.get_yticks())
ax.set_xlabel(xlabel,fontdict = font.fontlabel,labelpad=(fontsize-1)*fontscale)
ax.set_ylabel(ylabel,fontdict = font.fontlabel,labelpad=(fontsize-1)*fontscale)
if (rotation is not None) & (xticklabel is not None) :
ax.set_xticklabels(xticklabel,fontticklabel,rotation=rotation)
elif (xticklabel is not None) &(rotation is None):
ax.set_xticklabels(xticklabel,fontticklabel)
elif (xticklabel is None) &(rotation is None):
ax.set_xticklabels(ax.get_xticklabels(),fontticklabel)
elif (rotation is not None) & (xticklabel is None):
ax.set_xticklabels(ax.get_xticklabels(),fontticklabel,rotation=rotation)
ax.set_yticklabels(ax.get_yticklabels(),font.fontticklabel)
if moveyaxis is True:
#fontticklabel
ax.spines['left'].set_position(('data',0))
ax.spines['left'].set_visible(left)
ax.spines['right'].set_visible(not left)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_linewidth(0.5*fontscale)
ax.spines['bottom'].set_linewidth(0.5*fontscale)
ax.spines['left'].set_linewidth(0.5*fontscale)
ax.spines['bottom'].set_color('k')
ax.spines['left'].set_color('k')
ax.spines['right'].set_color('k')
ax.tick_params(direction='out', pad=2*fontscale,width=0.5*fontscale)
#ax.spines['bottom']._edgecolor="#000000"
#ax.spines['left']._edgecolor="#000000"
if title is not None:
ax.set_title(title,fontdict = font.fonttitle)
if legendscale is True:
if (h is None)&(l is None):
legend = ax.legend(prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
else:
legend = ax.legend(h,l,prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
if legendtitle is not None:
#if legendloc is None:
# legendloc="best"
legend = ax.legend(title=legendtitle,prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
ax.legend_.get_frame()._linewidth=0.5*fontscale
legend.get_title().set_fontweight('normal')
legend.get_title().set_fontsize(fontscale*fontsize)
if legendsort is True:
# h: handle l:label
h,l = ax.get_legend_handles_labels()
l,h = zip(*sorted(zip(l,h), key=lambda t: int(t[0])))
legend = ax.legend(h,l,title=legendtitle,prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
ax.legend_.get_frame()._linewidth=0.5*fontscale
legend.get_title().set_fontweight('normal')
legend.get_title().set_fontsize(fontscale*fontsize)
if sns is True:
h,l = ax.get_legend_handles_labels()
#l,h = zip(*sorted(zip(l,h), key=lambda t: int(t[0])))
legend = ax.legend(h[1:],l[1:],title=legendtitle,prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
ax.legend_.get_frame()._linewidth=0.5*fontscale
legend.get_title().set_fontweight('normal')
legend.get_title().set_fontsize(fontscale*fontsize)
else:
legend = ax.legend(handles=h,labels=l,title=legendtitle,prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
ax.legend_.get_frame()._linewidth=0.5*fontscale
legend.get_title().set_fontweight('normal')
legend.get_title().set_fontsize(fontscale*fontsize)
if cbar is not None:
#locator, formatter = cbar._get_ticker_locator_formatter()
#ticks, ticklabels, offset_string = cbar._ticker(locator, formatter)
#cbar.ax.spines['top'].set_visible(False)
#cbar.ax.spines['right'].set_visible(False)
#cbar.ax.spines['bottom'].set_visible(False)
#cbar.ax.spines['left'].set_visible(False)
cbar.ax.tick_params(direction='out', pad=3*fontscale,width=0*fontscale,length=0*fontscale)
cbar.set_label(cbarlabel,fontdict = font.fontcbarlabel,Rotation=270,labelpad=fontscale*(fontsize+1))
cbar.ax.set_yticks(cbar.ax.get_yticks())
cbar.ax.set_yticklabels(cbar.ax.get_yticklabels(),font.fontcbarticklabel)
font.reset(fontscale)
return axsample_class = pd.read_table('/home/xieyufeng/fig3/data/cfRNA/sample_classes.txt', index_col=0)
batch_info = pd.read_table('/home/xieyufeng/fig3/data/cfRNA/batch_info.txt', index_col=0)
batch_info[batch_info.dataset=='lulab_hcc']='GSE123972'sample_class[sample_class.label=='Normal']='HD'
sample_class[sample_class.label!='HD']='HCC'batch_info = pd.read_table('/home/zhaotianxiao/fig3/batch_info.txt', index_col=0)
batch_info[batch_info.dataset=='lulab_hcc']='GSE123972'kbet_table = pd.read_table('/home/xieyufeng/fig3/output/cfRNA/select_preprocess_method/kbet_score/mirna_and_domains/summary.txt', index_col = 0)
uca_table = pd.read_table('/home/xieyufeng/fig3/output/cfRNA/select_preprocess_method/uca_score/mirna_and_domains/summary.txt', index_col = 0)kbet_table = pd.read_table('/home/shibinbin/projects/exSeek-dev/output/cfRNA/select_preprocess_method/kbet_score/mirna_and_domains/summary.txt', index_col = 0)
uca_table = pd.read_table('/home/shibinbin/projects/exSeek-dev/output/cfRNA/select_preprocess_method/uca_score/mirna_and_domains/summary.txt', index_col = 0)knn_summary = pd.read_csv('/home/shibinbin/projects/exSeek-dev/output/cfRNA/select_preprocess_method/knn_score/mirna_and_domains/summary.txt',sep='\t')
knn_summary = knn_summary.set_index('preprocess_method')fontsizemethod = 'filter.null.Norm_RLE.Batch_limma_1'
data = pd.read_table('/home/xieyufeng/fig3/output/cfRNA/matrix_processing/'+method+'.mirna_and_domains.txt',
index_col = 0)
fig, (ax,lax) = plt.subplots(ncols=2, gridspec_kw={"width_ratios":[4,1]},figsize=(8.5,6))
PCA_plot_with_uca_score_sns(ax,data,sample_class, batch_info,method='PCA')
h,l=ax.get_legend_handles_labels()
for loc in range(len(l)):
if l[loc] == 'GSE94582_NEBNext':
l[loc] = 'GSE94582_1'
elif l[loc] == 'GSE94582_Other':
l[loc] = 'GSE94582_2'
elif l[loc] == 'GSE94582_TruSeq':
l[loc] = 'GSE94582_3'
std_plot(ax,'Dimension 1','Dimension 2',
title='RLE with Limma',
xbins=4,ybins=5,h=h,l=l,bbox_to_anchor=(0.9,0.8),markerscale=1.5)
ax.legend_.remove()
lax.axis("off")
std_plot(lax,h=h,l=l,bbox_to_anchor=(1,0.8),markerscale=2,labelspacing=0.3)
lax.legend_.get_frame()._linewidth=0
fig.tight_layout()
#fig.savefig(savepath+'RLE with Limma.eps')
#embed_pdf_figure()
print('UCA = {:.3f}'.format(uca_summary.loc[method].values[0]) +', ' + 'mkNN = {:.3f}'.format(1-knn_summary.loc[method].values[0]))knn_summarymethod = 'filter.null.Norm_RLE.Batch_null'
data = pd.read_table('/home/xieyufeng/fig3/output/cfRNA/matrix_processing/'+method+'.mirna_and_domains.txt',
index_col = 0)
fig, ax = plt.subplots(figsize=(6.1,6))
PCA_plot_with_uca_score_sns(ax,data,sample_class, batch_info,method='PCA')
std_plot(ax,'Dimension 1','Dimension 2',title='RLE',xbins=4,ybins=5)
ax.legend_.remove()
fig.tight_layout()
#fig.savefig(savepath+'RLE with Null_noleg.eps')
#embed_pdf_figure()
method = 'filter.null.Norm_RLE.Batch_null'
#print('UCA = {:.3f}'.format(uca_summary.loc[method].values[0]) +', ' + 'mkNN = {:.3f}'.format(1-knn_summary.loc[method].values[0]))variance explained
def var_ex(mat,anno_info):
from scipy.stats import f
def list201(array):
dataframe = pd.DataFrame()
for i in np.unique(array):
dataframe[i] = array==i
return dataframe
rsquared_mat = pd.DataFrame()
bms = pd.DataFrame()
wms = pd.DataFrame()
fvalue = pd.DataFrame()
p = pd.DataFrame()
rsquared_cutoff=pd.DataFrame()
tss_all = (np.var(mat.T)*mat.shape[1]).tolist()
var_list = anno_info.columns
for var in var_list:
anno = anno_info[var]
if len(np.unique(anno))<=1:
warnings.warn("ignoring '%s' with fewer than 2 unique levels"%var,UserWarning)
keep = ~anno.isna()
if np.all(keep):
tss = tss_all
else:
anno = anno[keep]
mat = mat.loc[:,keep]
tss = np.array(np.var(mat.T)*mat.shape[1])
anno2class = list201(anno)
wss = 0
for i in anno2class.columns:
mat_select=mat.iloc[:,np.where(anno2class[i])[0]]
wss = wss + np.array(np.var(mat_select.T)*mat_select.shape[1])
#display(wss)
rsquared_mat[var] = 1-wss/tss
bms[var] = (tss-wss)/(anno2class.shape[1]-1)
wms[var] = wss/(len(anno)-anno2class.shape[1])
fvalue[var] = bms[var]/wms[var]
p[var] = [1-f.cdf(i,anno2class.shape[1]-1,len(anno)-anno2class.shape[1]) for i in fvalue[var]]
rsquared_cutoff[var] = [1-1/(f.isf(0.05, anno2class.shape[1]-1, len(anno)-anno2class.shape[1])*\
(anno2class.shape[1]-1)/(len(anno)-anno2class.shape[1])+1)]
return rsquared_mat,rsquared_cutoff,pbatchinfo_path ="/home/xieyufeng/fig3/data/cfRNA/batch_info.txt"
batchinfo_path ="/home/xieyufeng/fig3/data/cfRNA/batch_info.txt"
classinfo_path = "/home/xieyufeng/fig3/data/cfRNA/sample_classes.txt"
mat1_path="/home/xieyufeng/fig3/output/cfRNA/matrix_processing/filter.null.Norm_RLE.Batch_null.mirna_and_domains.txt"
mat2_path="/home/xieyufeng/fig3/output/cfRNA/matrix_processing/filter.null.Norm_RLE.Batch_limma_1.mirna_and_domains.txt"mat1 = pd.read_csv(mat1_path,sep='\t')
mat2 = pd.read_csv(mat2_path,sep='\t')
batch_info = pd.read_csv(batchinfo_path,sep='\t')
batch_info = pd.read_table('/home/zhaotianxiao/fig3/batch_info.txt')
sample_info = pd.read_csv(classinfo_path,sep='\t')
anno_info = pd.merge(batch_info,sample_info,on=['sample_id'])
anno_info = anno_info.set_index('sample_id')
anno_info = anno_info.loc[mat1.columns]
#anno_info = anno_info.reset_index()
rsquared_mat1,rsquared_cutoff1,p1 = var_ex(mat1,anno_info)
anno_info = anno_info.loc[mat2.columns]
rsquared_mat2,rsquared_cutoff2,p2 = var_ex(mat2,anno_info)import matplotlib.gridspec as gridspec
def r2mat21class(rsquared_mat1=None,rsquared_mat2=None,rsquared_cutoff=rsquared_cutoff1,p1=None,p2=None):
fig =plt.figure(figsize=(6,4))
gs = gridspec.GridSpec(2, rsquared_mat1.shape[1],height_ratios=[4,1])
#fig,(axes,lax)=plt.subplots(2,rsquared_mat1.shape[1],gridspec_kw={"height_ratios":[4,1]},figsize=(6,4))
lax = fig.add_subplot(gs[1, :])
pyplot.draw()
for i in range(len(rsquared_mat1.columns)):
axes = fig.add_subplot(gs[0, i])
var = rsquared_mat1.columns[i]
plot_mat = pd.DataFrame([rsquared_mat1[var],rsquared_mat2[var]]).T
plot_mat.columns=['before batch removal','after batch removal']
cutoff = rsquared_cutoff[var].iloc[0]
#axes[i].set_xscale('log',subsx=[-2,-1,0,1,2])
#axes[i].hist(plot_mat.before,500,density=1)
sns.kdeplot(plot_mat['before batch removal'],ax=axes,c='#80b1d3')#,bw=0.001,kernel='gau')
sns.kdeplot(plot_mat['after batch removal'],ax=axes,c='#fb8072')#,bw=0.001)
axes.axvline(x=cutoff,linestyle='--',linewidth=0.5,c='k')
axes.set_xticks([-2,-1,0,1,2])#,cutoff])
axes.set_xticklabels([0.01,0.1,1,10,100])#,'%.1f'%math.pow(10,cutoff)])
ymax,ymin = max(axes.get_yticks()),min(axes.get_yticks())
axes.annotate('%.2f'%math.pow(10,cutoff),xy=(cutoff+0.1,0.05*ymin+0.95*ymax),fontfamily='Arial',fontsize=6.5*autoscale(fig))
axes.legend(title='state',prop=fontlegend)
if i==0:
if var=='dataset':
std_plot(axes,'Variance explained%','Density',legendtitle='state',legendsort=False,title='Batches',xlim=[-2,2],bbox_to_anchor=(1, 0.75))
elif var=='label':
std_plot(axes,'Variance explained%','Density',legendtitle='state',legendsort=False,title='Cancer/Normal',xlim=[-2,2],bbox_to_anchor=(1, 0.75))
else:
if var=='dataset':
std_plot(axes,'Variance explained%','',legendtitle='state',legendsort=False,title='Batches',xlim=[-2,2],bbox_to_anchor=(1,-0.2))
elif var=='label':
std_plot(axes,'Variance explained%','',legendtitle='state',legendsort=False,title='Cancer/Normal',xlim=[-2,2],bbox_to_anchor=(1,-0.3),ncol=2)
axes.legend_.get_frame()._linewidth=0
#axes[i].legend(title='s',prop=fontlegend)
p_mat = pd.DataFrame([p1[var],p2[var]]).T
p_mat.columns=['before','after']
#display(p_mat)
#table = axes[i].table(cellText=np.array([np.int_(np.sum(p_mat<0.05)),
# ['%.2f'%i for i in (np.sum(p_mat<0.05)/len(p_mat))]]),
# colLabels=['before','after'],rowLabels=['amount','percentage'],
# colWidths=[0.3,0.3],
# bbox=[0,0,0.5,0.35])
#table.set_fontsize(6.5)
if i != len(rsquared_mat1.columns)-1:
axes.legend_.remove()
#plt.subplots_adjust(left=0.4, bottom=0.4)
#axes[-1].axis('off')
lax.axis("off")
h,l=axes.get_legend_handles_labels()
axes.legend_.remove()
std_plot(lax,h=h,l=l,bbox_to_anchor=(1,1),markerscale=2,labelspacing=0.3,ncol=2)
fig.tight_layout()
#fig.savefig(savepath+'variance_explained.eps')
#embed_pdf_figure()
r2mat21class(np.log10(rsquared_mat1*100),np.log10(rsquared_mat2*100),np.log10(rsquared_cutoff1*100),p1,p2)p_mat = pd.DataFrame([p1.label,p2.label]).T
p_mat.columns=['before','after']
np.sum(p_mat<0.01)Last updated