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VIPInterface_V0.py
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VIPInterface_V0.py
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import requests
import json
import traceback
import sqlite3
#import server.app.decode_fbs as decode_fbs
import scanpy as sc
import anndata as ad
import pandas as pd
import numpy as np
import diffxpy.api as de
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import seaborn as sns
import matplotlib.patches as mpatches
from matplotlib import rcParams
import plotly.graph_objects as go
import plotly.io as plotIO
import base64
import math
import scipy
from io import BytesIO
import sys
import time
import os
import re
import glob
import subprocess
import gc #,psutil
import errno
from pathlib import Path
strExePath = os.path.dirname(os.path.abspath(__file__))
import pprint
ppr = pprint.PrettyPrinter(depth=6,width=500)
import server.compute.diffexp_generic as diffDefault
import pickle
from pyarrow import feather
sys.setrecursionlimit(10000)
sc.settings.verbosity = 2
rcParams.update({'figure.autolayout': True})
api_version = "/api/v0.2"
import threading
jobLock = threading.Lock()
def getLock(lock):
while not lock.acquire():
time.sleep(1.0)
def freeLock(lock):
lock.release()
def route(data,appConfig):
data = initialization(data,appConfig)
#ppr.pprint(appConfig.server_config.single_dataset__datapath)
#ppr.pprint(data)
try:
getLock(jobLock)
setTimeStamp(data)
taskRes = distributeTask(data["method"])(data)
freeLock(jobLock)
gc.collect()
#ppr.pprint("memory usage: rss (%dM) and vms (%dM)"%(int(psutil.Process().memory_info().rss / 1024 **2),
# int(psutil.Process().memory_info().vms / 1024 **2)))
return taskRes
except Exception as e:
freeLock(jobLock)
return 'ERROR @server: '+traceback.format_exc()
import server.app.app as app
class getAdapter(object):
def __init__(self, adapter):
self.adapter = adapter
def __enter__(self):
return self.adapter
def __exit__(self):
return
def initialization(data,appConfig):
# obtain the server host information
data = json.loads(str(data,encoding='utf-8'))
# update the environment information
data.update(VIPenv)
# updatting the hosting data information
if appConfig.is_multi_dataset():
data["url_dataroot"]=appConfig.server_config.multi_dataset__dataroot['d']['base_url']
data['h5ad']=os.path.join(appConfig.server_config.multi_dataset__dataroot['d']['dataroot'], data["dataset"])
else:
data["url_dataroot"]=None
data["dataset"]=None
data['h5ad']=appConfig.server_config.single_dataset__datapath
# setting the plotting options
if 'figOpt' in data.keys():
setFigureOpt(data['figOpt'])
# get the var (gene) and obv index
with app.get_data_adaptor(url_dataroot=data['url_dataroot'],dataset=data['dataset']) as scD:
data['obs_index'] = scD.get_schema()["annotations"]["obs"]["index"]
data['var_index'] = scD.get_schema()["annotations"]["var"]["index"]
return data
def setFigureOpt(opt):
sc.set_figure_params(dpi_save=int(opt['dpi']),fontsize= float(opt['fontsize']),vector_friendly=(opt['vectorFriendly'] == 'Yes'),transparent=(opt['transparent'] == 'Yes'),color_map=opt['colorMap'])
rcParams.update({'savefig.format':opt['img']})
def getObs(data):
selC = list(data['cells'].values())
cNames = ["cell%d" %i for i in selC]
## obtain the category annotation
with app.get_data_adaptor(url_dataroot=data['url_dataroot'],dataset=data['dataset']) as scD:
#scD=data['data_adapter']
#if scD is not None:
selAnno = [data['obs_index']]+data['grp']
dAnno = list(scD.get_obs_keys())
anno = []
sel = list(set(selAnno)&set(dAnno))
if len(sel)>0:
tmp = scD.data.obs.iloc[selC,:].loc[:,sel].astype('str')
tmp.index = cNames
anno += [tmp]
sel = list(set(selAnno)-set(dAnno))
if len(sel)>0:
annotations = scD.dataset_config.user_annotations
if annotations:
labels = annotations.read_labels(scD)
tmp = labels.loc[list(scD.data.obs.loc[selC,data['obs_index']]),sel]
tmp.index = cNames
anno += [tmp]
obs = pd.concat(anno,axis=1)
#ppr.pprint(obs)
## update the annotation Abbreviation
combUpdate = cleanAbbr(data)
if 'abb' in data.keys():
for i in data['grp']:
obs[i] = obs[i].map(data['abb'][i])
return combUpdate, obs
def getObsNum(data):
selC = list(data['cells'].values())
cNames = ["cell%d" %i for i in selC]
## obtain the category annotation
obs = pd.DataFrame()
with app.get_data_adaptor(url_dataroot=data['url_dataroot'],dataset=data['dataset']) as scD:
#scD=data['data_adapter']
#if scD is not None:
selAnno = data['grpNum']
dAnno = list(scD.get_obs_keys())
sel = list(set(selAnno)&set(dAnno))
if len(sel)>0:
obs = scD.data.obs.loc[selC,sel]
obs.index = cNames
return obs
def getVar(data):
## obtain the gene annotation
with app.get_data_adaptor(url_dataroot=data['url_dataroot'],dataset=data['dataset']) as scD:
#scD=data['data_adapter']
#if scD is not None:
gInfo = scD.data.var
gInfo.index = list(gInfo[data['var_index']])
gInfo = gInfo.drop([data['var_index']],axis=1)
return gInfo
def collapseGeneSet(data,expr,gNames,cNames,fSparse):
Y = expr
if 'geneGrpColl' in data.keys() and not data['geneGrpColl']=='No' and 'geneGrp' in data.keys() and len(data['geneGrp'])>0:
data['grpLoc'] = []
data['grpID'] = []
if fSparse:
Y = pd.DataFrame.sparse.from_spmatrix(Y,columns=gNames,index=cNames)
for aN in data['geneGrp'].keys():
if data['geneGrpColl']=='mean':
Y = pd.concat([Y,Y[data['geneGrp'][aN]].mean(axis=1).rename(aN)],axis=1,sort=False)
if data['geneGrpColl']=='median':
Y = pd.concat([Y,Y[data['geneGrp'][aN]].median(axis=1).rename(aN)],axis=1,sort=False)
for gene in data['geneGrp'][aN]:
if gene in data['genes']:
data['genes'].remove(gene)
data['genes'] += [aN]
gNames = list(Y.columns)
return Y,gNames
def createData(data):
selC = list(data['cells'].values())
cNames = ["cell%d" %i for i in selC]
## onbtain the expression matrix
gNames = []
expr = []
fSparse = False
X = []
if 'genes' in data.keys():
with app.get_data_adaptor(url_dataroot=data['url_dataroot'],dataset=data['dataset']) as scD:
#scD=data['data_adapter']
#if scD is not None:
if not type(scD.data.X) is np.ndarray:
fSparse = True
if len(data['genes'])>0:
fullG = list(scD.data.var[data['var_index']])
selG = sorted([fullG.index(i) for i in data['genes']]) #when data loaded backed, incremental is required
X = scD.data.X[:,selG]
gNames = [fullG[i] for i in selG] #data['genes']
else:
X = scD.data.X
gNames = list(scD.data.var[data['var_index']])
if 'figOpt' in data.keys() and data['figOpt']['scale'] == 'Yes':
X = sc.pp.scale(X,zero_center=(data['figOpt']['scaleZero'] == 'Yes'),max_value=(float(data['figOpt']['scaleMax']) if data['figOpt']['clipValue']=='Yes' else None))
X = X[selC]
if fSparse:
expr = X
else:
expr = pd.DataFrame(X,columns=gNames,index=cNames)
expr,gNames = collapseGeneSet(data,expr,gNames,cNames,fSparse)
#ppr.pprint("finished expression ...")
## obtain the embedding
embed = {}
if 'layout' in data.keys():
layout = data['layout']
if isinstance(layout,str):
layout = [layout]
if len(layout)>0:
for one in layout:
with app.get_data_adaptor(url_dataroot=data['url_dataroot'],dataset=data['dataset']) as scD:
#scD=data['data_adapter']
#if scD is not None:
embed['X_%s'%one] = pd.DataFrame(scD.data.obsm['X_%s'%one][selC][:,[0,1]],columns=['%s1'%one,'%s2'%one],index=cNames)
#ppr.pprint("finished layout ...")
## obtain the category annotation
combUpdate, obs = getObs(data)
## create a custom annotation category and remove cells which are not in the selected annotation
if combUpdate and len(data['grp'])>1:
newGrp = 'Custom_combine'
combineGrp = list(data['combine'].keys());
obs[newGrp] = obs[combineGrp[0]]
for i in combineGrp:
if not i==combineGrp[0]:
obs[newGrp] += ":"+obs[i]
selC = ~obs[newGrp].str.contains("Other").to_numpy()
expr = expr[selC]
for i in embed.keys():
embed[i] = embed[i][selC]
obs = obs[selC].astype('category')
obs[newGrp].cat.set_categories(data['combineOrder'],inplace=True)
data['grp'] = [newGrp]
obs = obs.astype('category')
## empty selection
if expr.shape[0]==0 or expr.shape[1]==0:
return []
#ppr.pprint("finished obv ...")
return sc.AnnData(expr,obs,var=pd.DataFrame([],index=gNames),obsm={layout:embed[layout].to_numpy() for layout in embed.keys()})
def cleanAbbr(data):
updated = False
if 'abb' in data.keys() and 'combine' in data.keys():
if len(data['combine'])>0:
updated = True
for cate in data['abb'].keys():
if cate in data['combine'].keys():
for anName in data['abb'][cate].keys():
if not anName in data['combine'][cate]:
data['abb'][cate][anName] = "Other";
else:
if not data['abb'][cate][anName]==anName:
data['combineOrder'] = [one.replace(anName,data['abb'][cate][anName]) for one in data['combineOrder']]
else:
data['abb'][cate] = {key:"Other" for key in data['abb'][cate].keys()}
return updated
def errorTask(data):
raise ValueError('Error task!')
def distributeTask(aTask):
return {
'SGV':SGV,
'SGVcompare':SGVcompare,
'PGV':PGV,
'PGVcompare':PGVcompare,
'VIOdata':VIOdata,
'HEATplot':pHeatmap,
'HEATdata':HeatData,
'GD':GD,
'DEG':DEG,
'DOT':DOT,
'EMBED':EMBED,
'TRAK':TRACK,
'DUAL':DUAL,
'MARK': MARK,
'MINX':MINX,
'DENS':DENS,
'DENS2D':DENS2D,
'SANK':SANK,
'STACBAR':STACBAR,
'HELLO':HELLO,
'CLI':CLI,
'preDEGname':getPreDEGname,
'preDEGvolcano':getPreDEGvolcano,
'preDEGmulti':getPreDEGbubble,
'preDEGfgsea':getPreDEGfgsea,
'mergeMeta': mergeMeta,
'isMeta': isMeta,
'testVIPready':testVIPready,
'Description':getDesp,
'GSEAgs':getGSEA,
'SPATIAL':SPATIAL,
'saveTest':saveTest,
'getBWinfo':getBWinfo,
'GSP':GSP,
'plotBW':plotBW
}.get(aTask,errorTask)
def HELLO(data):
return 'Hi, connected.'
def iostreamFig(fig):
#getLock(iosLock)
figD = BytesIO()
#ppr.pprint('io located at %d'%int(str(figD).split(" ")[3].replace(">",""),0))
fig.savefig(figD,bbox_inches="tight")
#ppr.pprint(sys.getsizeof(figD))
#ppr.pprint('io located at %d'%int(str(figD).split(" ")[3].replace(">",""),0))
imgD = base64.encodebytes(figD.getvalue()).decode("utf-8")
figD.close()
#ppr.pprint("saved Fig")
#freeLock(iosLock)
if 'matplotlib' in str(type(fig)):
plt.close(fig)#'all'
return imgD
def Msg(msg):
fig = plt.figure(figsize=(5,2))
plt.text(0,0.5,msg)
ax = plt.gca()
ax.axis('off')
return iostreamFig(fig)
def SPATIAL(data):
with app.get_data_adaptor(url_dataroot=data['url_dataroot'],dataset=data['dataset']) as scD:
#scD=data['data_adapter']
#if scD is not None:
#ppr.pprint(vars(scD.data.uns["spatial"]))
spatial=scD.data.uns["spatial"]
if (data['embedding'] == "get_spatial_list"):
return json.dumps({'list':list(spatial)})
library_id=list(spatial)[0]
if (data['embedding'] in list(spatial)):
library_id=data['embedding']
height, width, depth = spatial[library_id]["images"][data['resolution']].shape
embedding = 'X_'+data['embedding']
spatialxy = scD.data.obsm[embedding]
tissue_scalef = spatial[library_id]['scalefactors']['tissue_' + data['resolution'] + '_scalef']
i = data['spots']['spoti_i']
x = 0
y = 1
# from original embedding to (0,1) coordinate system (cellxgene embedding)
scalex = (data['spots']['spot0_x'] - data['spots']['spoti_x']) / (spatialxy[0][x] - spatialxy[i][x])
scaley = (data['spots']['spot0_y'] - data['spots']['spoti_y']) / (spatialxy[0][y] - spatialxy[i][y])
# image is in (-1,0,1) coordinate system, so multiplied by 2
translatex = (spatialxy[i][x]*scalex - data['spots']['spoti_x']) * 2
translatey = (spatialxy[i][y]*scaley - data['spots']['spoti_y']) * 2
scale = 1/tissue_scalef * scalex * 2
# Addtional translate in Y due to flipping of the image if needed
ppr.pprint(scalex)
ppr.pprint(scaley)
ppr.pprint(translatex)
ppr.pprint(translatey)
# from (-1,0,1) (image layer) to (0,1) coordinate system (cellxgene embedding). Overlapping (0,0) origins of both.
translatex = -(1+translatex)
if (translatey > -0.1):
flip = True
translatey = -(1+translatey) + height*scale
else:
flip = False
translatey = -(1+translatey)
returnD = [{'translatex':translatex,'translatey':translatey,'scale':scale}]
dpi=100
figsize = width / float(dpi), height / float(dpi)
fig = plt.figure(figsize=figsize)
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')
if (flip):
ax.imshow(np.flipud(spatial[library_id]["images"][data['resolution']]), interpolation='nearest')
else:
ax.imshow(spatial[library_id]["images"][data['resolution']], interpolation='nearest')
figD = BytesIO()
plt.savefig(figD, dpi=dpi)
ppr.pprint(sys.getsizeof(figD))
imgD = base64.encodebytes(figD.getvalue()).decode("utf-8")
figD.close()
plt.close(fig)
return json.dumps([returnD, imgD])
def MINX(data):
with app.get_data_adaptor(url_dataroot=data['url_dataroot'],dataset=data['dataset']) as scD:
#scD=data['data_adapter']
#if scD is not None:
minV = min(scD.data.X[0])
return '%.1f'%minV
def geneFiltering(adata,cutoff,opt):
## 1. remove cells if the max expression of all genes is lower than the cutoff
if opt==1:
#sT = time.time()
#ix = adata.to_df().apply(lambda x: max(x)>float(cutoff),axis=1)
#ppr.pprint(time.time()-sT)
#sT=time.time()
df = adata.to_df()
ix = df[df>float(cutoff)].count(axis=1)>0
#ppr.pprint(time.time()-sT)
#sT = time.time()
#ix = pd.DataFrame((adata.X>float(cutoff)).sum(1)>0,index=list(adata.obs.index)).iloc[:,0]
#ppr.pprint(time.time()-sT)
adata = adata[ix,]
## 2. Set all expression level smaller than the cutoff to be NaN not for plotting without removing any cells
elif opt==2:
def cutoff(x):
return x if x>float(cutoff) else None
X = adata.to_df()
X=X.applymap(cutoff)
adata = sc.AnnData(X,adata.obs)
return adata
def SGV(data):
# figure width and heights depends on number of unique categories
# characters of category names, gene number
#ppr.pprint("SGV: creating data ...")
adata = createData(data)
#ppr.pprint("SGV: data created ...")
adata = geneFiltering(adata,data['cutoff'],1)
if len(adata)==0:
raise ValueError('No cells in the condition!')
a = list(set(list(adata.obs[data['grp'][0]])))
ncharA = max([len(x) for x in a])
w = len(a)/4+1
h = ncharA/6+2.5
ro = math.acos(10/max([15,ncharA]))/math.pi*180
##
fig = plt.figure(figsize=[w,h])
sc.pl.violin(adata,data['genes'],groupby=data['grp'][0],ax=fig.gca(),show=False)
fig.autofmt_xdate(bottom=0.2,rotation=ro,ha='right')
return iostreamFig(fig)
def SGVcompare(data):
adata = createData(data)
#adata = geneFiltering(adata,data['cutoff'],1)
if len(adata)==0:
raise ValueError('No cells in the condition!')
# plot in R
strF = ('%s/SGV%f.csv' % (data["CLItmp"],time.time()))
X=pd.concat([adata.to_df(),adata.obs[data['grp']]],axis=1,sort=False)
X[X.iloc[:,0]>=float(data['cellCutoff'])].to_csv(strF,index=False)
strCMD = " ".join(["%s/Rscript"%data['Rpath'],strExePath+'/violin.R',strF,str(data['cutoff']),data['figOpt']['img'],str(data['figOpt']['fontsize']),str(data['figOpt']['dpi']),data['Rlib']])
#ppr.pprint(strCMD)
res = subprocess.run([strExePath+'/violin.R',strF,str(data['cutoff']),data['figOpt']['img'],str(data['figOpt']['fontsize']),str(data['figOpt']['dpi']),data['Rlib']],capture_output=True)#
img = res.stdout.decode('utf-8')
os.remove(strF)
if 'Error' in res.stderr.decode('utf-8'):
raise SyntaxError("in R: "+res.stderr.decode('utf-8'))
return img
def VIOdata(data):
adata = createData(data)
adata = geneFiltering(adata,data['cutoff'],1)
if len(adata)==0:
raise ValueError('No cells in the condition!')
return pd.concat([adata.to_df(),adata.obs], axis=1, sort=False).to_csv()
def unique(seq):
seen = set()
seen_add = seen.add
return [x for x in seq if not (x in seen or seen_add(x))]
def updateGene(data):
grpID = []
grpLoc=[]
allG = []
if 'geneGrp' in data.keys():
for aN in data['geneGrp'].keys():
grpLoc += [(len(allG),len(allG)+len(data['geneGrp'][aN])-1)]
allG += data['geneGrp'][aN]
grpID += [aN]
data['genes'] = unique(allG+data['genes'])
data['grpLoc'] = grpLoc
data['grpID'] = grpID
def PGV(data):
# figure width and heights depends on number of unique categories
# characters of category names, gene number #pecam1 pdpn
updateGene(data)
#ppr.pprint("PGV: creating data ...")
adata = createData(data)
#ppr.pprint("PGV: data created ...")
adata = geneFiltering(adata,data['cutoff'],1)
if adata.shape[0]==0 or adata.shape[1]==0:
return Msg('No cells in the condition!')
a = list(set(list(adata.obs[data['grp'][0]])))
ncharA = max([len(x) for x in a])
w = max([3,ncharA/8])+len(data['genes'])/2+1.5
h = len(a)+0.5
swapAx = False
##
if data['by']=='Columns':
a = w
w = h
h = a
swapAx = True
if 'split_show' in data['figOpt']['scanpybranch']: #.dev140+ge9cbc5f
vp = sc.pl.stacked_violin(adata,data['genes'],groupby=data['grp'][0],return_fig=True,figsize=(w,h),swap_axes=swapAx,var_group_positions=data['grpLoc'],var_group_labels=data['grpID'])
vp.add_totals().style(yticklabels=True, cmap=data['color']).show()
#vp.add_totals().show()
fig = vp#plt.gcf()
else:
fig = plt.figure(figsize=[w,h])
axes = sc.pl.stacked_violin(adata,data['genes'],groupby=data['grp'][0],show=False,ax=fig.gca(),swap_axes=swapAx,
var_group_positions=data['grpLoc'],var_group_labels=data['grpID'])
return iostreamFig(fig)
def PGVcompare(data):
adata = createData(data)
#adata = geneFiltering(adata,data['cutoff'],1)
sc.pp.filter_cells(adata,min_counts=float(data['cutoff']))
if len(adata)==0:
raise ValueError('No cells in the condition!')
strF = ('%s/PGVcompare%f.csv' % (data["CLItmp"],time.time()))
X=pd.concat([adata.to_df(),adata.obs[data['grp']]],axis=1,sort=False).to_csv(strF,index_label="cellID")
# plot in R
strCMD = " ".join(["Rscript",strExePath+'/complex_vlnplot_multiple.R',strF,','.join(data['genes']),data['grp'][0],data['grp'][1],str(data['width']),str(data['height']),data['figOpt']['img'],str(data['figOpt']['fontsize']),str(data['figOpt']['dpi']),data['Rlib']])
res = subprocess.run(strCMD,shell=True,capture_output=True)
img = res.stdout.decode('utf-8')
os.remove(strF)
if 'Error' in res.stderr.decode('utf-8'):
raise SyntaxError("in R: "+res.stderr.decode('utf-8'))
return img
def silentRM(strF):
try:
os.remove(strF)
except OSError as e:
if e.errno != errno.ENOENT:
raise
def pHeatmap(data):
# figure width is depends on the number of categories was choose to show
# and the character length of each category term
# if the number of element in a category is smaller than 10, "Set1" or "Set3" is choosen
# if the number of element in a category is between 10 and 20, default is choosen
# if the number of element in a category is larger than 20, husl is choosen
#Xsep = createData(data,True)
#adata = sc.AnnData(Xsep['expr'],Xsep['obs'])
#sT = time.time()
adata = createData(data)
if len(data['grpNum'])>0:
adata.obs = pd.concat([adata.obs,getObsNum(data)],axis=1)
data['grp'] += data['addGrp']
#Xdata = pd.concat([adata.to_df(),adata.obs], axis=1, sort=False).to_csv()
#ppr.pprint('HEAT data reading cost %f seconds' % (time.time()-sT) )
#sT = time.time()
h = 8
w = len(data['genes'])/3+0.3 + 2
heatCol=data['color']
Zscore=None
heatCenter=None
colTitle="Expression"
if data['plotMethod']=='sns':
exprOrder = True
if data['order'][0]!="Expression":
exprOrder = False;
adata = adata[adata.obs.sort_values(data['order']).index,]
#s = adata.obs[data['order']]
#ix = sorted(range(len(s)), key=lambda k: s[k])
#adata = adata[ix,]
colCounter = 0
colName =['Set1','Set3']
grpCol = list()
grpLegend = list()
grpWd = list()
grpLen = list()
for gID in data['grp']:
grp = adata.obs[gID]
Ugrp = grp.unique()
if len(Ugrp)<10:
lut = dict(zip(Ugrp,sns.color_palette(colName[colCounter%2],len(Ugrp)).as_hex()))
colCounter += 1
elif len(Ugrp)<20:
lut = dict(zip(Ugrp,sns.color_palette(n_colors=len(Ugrp)).as_hex()))
else:
lut = dict(zip(Ugrp,sns.color_palette("husl",len(Ugrp)).as_hex()))
grpCol.append(grp.map(lut))
grpLegend.append([mpatches.Patch(color=v,label=k) for k,v in lut.items()])
grpWd.append(max([len(x) for x in Ugrp]))#0.02*fW*max([len(x) for x in Ugrp])
grpLen.append(len(Ugrp)+2)
if data['norm']=='zscore':
Zscore=1
#heatCol="vlag"
heatCenter=0
colTitle="Z-score"
#ppr.pprint('HEAT data preparing cost %f seconds' % (time.time()-sT) )
#sT = time.time()
try:
g = sns.clustermap(adata.to_df(),
method="ward",row_cluster=exprOrder,z_score=Zscore,cmap=heatCol,center=heatCenter,
row_colors=pd.concat(grpCol,axis=1).astype('str'),yticklabels=False,xticklabels=True,
figsize=(w,h),colors_ratio=0.05,
cbar_pos=(.3, .95, .55, .02),
cbar_kws={"orientation": "horizontal","label": colTitle,"shrink": 0.5})
except Exception as e:
return 'ERROR: Z score calculation failed for 0 standard diviation. '+traceback.format_exc() # 'ERROR @server: {}, {}'.format(type(e),str(e))
#ppr.pprint('HEAT plotting cost %f seconds' % (time.time()-sT) )
#sT = time.time()
g.ax_col_dendrogram.set_visible(False)
#g.ax_row_dendrogram.set_visible(False)
plt.setp(g.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
grpW = [1.02]
grpH = [1.2]
cumulaN = 0
cumulaMax = 0
characterW=1/40 # a character is 1/40 of heatmap width
characterH=1/40 # a character is 1/40 of heatmap height
for i in sorted(range(len(grpLen)),key=lambda k:grpLen[k]):#range(5):#
cumulaN += grpLen[i]
if cumulaN>(10+1/characterH):
grpW.append(grpW[-1]+cumulaMax)
grpH = [1.2]
cumulaN =0
cumulaMax=0
leg = g.ax_heatmap.legend(handles=grpLegend[i],frameon=True,title=data['grp'][i],loc="upper left",
bbox_to_anchor=(grpW[-1],grpH[-1]),fontsize=5)#grpW[i],0.5,0.3
#leg = g.ax_heatmap.legend(handles=grpLegend[0],frameon=True,title=data['grp'][0],loc="upper left",
# bbox_to_anchor=(1.02,1-i*0.25),fontsize=5)#grpW[i],0.5,0.
cumulaMax = max([cumulaMax,grpWd[i]*characterW])
grpH.append(grpH[-1]-grpLen[i]*characterH)
leg.get_title().set_fontsize(6)#min(grpSize)+2
g.ax_heatmap.add_artist(leg)
#ppr.pprint('HEAT post plotting cost %f seconds' % (time.time()-sT) )
return iostreamFig(g)#json.dumps([iostreamFig(g),Xdata])#)#
elif data['plotMethod']=='cHeatmap':
characterW=1/40
grpW = list()
grpN = list()
for gID in data['grp']:
grp = adata.obs[gID]
Ugrp = grp.unique()
grpN.append(1+len(Ugrp))
grpW.append(max([len(x) for x in Ugrp]))
legendCol = math.ceil(sum(grpN)/40)
w=w+characterW*sum(sorted(grpW,reverse=True)[0:legendCol])
strF = ('%s/HEAT%f.csv' % (data["CLItmp"],time.time()))
D = adata.to_df()
if data['norm']=='zscore':
for one in D.columns:
D[one] = (D[one]-D[one].mean())/D[one].std()
colTitle="Z-score"
D = pd.concat([D,adata.obs],axis=1,sort=False)
D.to_csv(strF,index=False)
if len(data['gAnno'])>0:
gAnno = pd.DataFrame(data['gAnno'])
gAnno.to_csv(strF.replace("HEAT","HEATgene"),index=False)
elif data['gAnnoDef']:
gAnno = pd.read_csv("%s/proteinatlas_protein_class.csv"%strExePath,index_col=0,header=0)
gNames = {i:i.upper() for i in data['genes']}
gAnno = gAnno.loc[list(set(gNames.values()) & set(gAnno.index)),:].iloc[:,1:]
for one in [i for i in gNames.values() if i not in gAnno.index]:
gAnno.loc[one,:] = np.nan
gAnno = gAnno.loc[gNames.values(),:]
gAnno.index=gNames.keys()
gAnno.fillna("N",inplace=True)
gAnno.to_csv(strF.replace("HEAT","HEATgene"))
#ppr.pprint(gAnno)
## plot in R
cmd = "%s/complexHeatmap.R %s %s %s %s %s %s %s %s %s %s %s %s %s %s '%s'"%(strExePath,strF,','.join(data['genes']),colTitle,','.join(data['order']),str(data['width']),str(data['height']),heatCol,
str(data['legendRow']),str(data['fontadj']),
data['figOpt']['img'],str(data['figOpt']['fontsize']),str(data['figOpt']['dpi']),str(data['swapAxes']),data['figOpt']['vectorFriendly'],data['Rlib'])
#ppp = pprint.PrettyPrinter(depth=6,width=300)
#ppp.pprint(cmd)
#return SyntaxError("in R: ")
res = subprocess.run(cmd,check=True,shell=True,capture_output=True)#
img = res.stdout.decode('utf-8')
os.remove(strF)
#silentRM(strF.replace("HEAT","HEATgene"))
if 'Error' in res.stderr.decode('utf-8'):
raise SyntaxError("in R: "+res.stderr.decode('utf-8'))
return img
else:
raise ValueError('Unknown heatmap plotting method (%s)!'%data['plotMethod'])
def HeatData(data):
adata = createData(data)
Xdata = pd.concat([adata.to_df(),adata.obs], axis=1, sort=False).to_csv()
return Xdata
def GD(data):
adata = None;
for one in data['cells'].keys():
#sT = time.time()
oneD = data.copy()
oneD.update({'cells':data['cells'][one],
'genes':[],
'grp':[]})
D = createData(oneD)
#ppr.pprint("one grp aquire data cost %f seconds" % (time.time()-sT))
D.obs['cellGrp'] = one
if adata is None:
adata = D
else:
#sT =time.time()
adata = adata.concatenate(D)
#ppr.pprint("Concatenate data cost %f seconds" % (time.time()-sT))
if adata is None:
return Msg("No cells were satisfied the condition!")
##
adata.obs.astype('category')
cutOff = 'geneN_cutoff'+data['cutoff']
#sT = time.time()
#adata.obs[cutOff] = adata.to_df().apply(lambda x: sum(x>float(data['cutoff'])),axis=1)
#ppr.pprint(time.time()-sT)
#sT = time.time()
#df = adata.to_df()
#adata.obs[cutOff] = df[df>float(data['cutoff'])].count(axis=1)
#ppr.pprint(time.time()-sT)
sT = time.time()
adata.obs[cutOff] = (adata.X >float(data['cutoff'])).sum(1)
ppr.pprint(time.time()-sT)
##
w = 3
if len(data['cells'])>1:
w += 3
fig = plt.figure(figsize=[w,4])
sc.pl.violin(adata,cutOff,groupby='cellGrp',ax=fig.gca(),show=False,rotation=0,size=2)
return iostreamFig(fig)
def getGSEA(data):
strGSEA = '%s/gsea/'%strExePath
return json.dumps(sorted([os.path.basename(i).replace(".symbols.gmt","") for i in glob.glob(strGSEA+"*.symbols.gmt")]))
def DEG(data):
adata = None
genes = data['genes']
data['genes'] = []
comGrp = 'cellGrp'
if 'combine' in data.keys():
if data['DEmethod']=='default':
combUpdate, obs = getObs(data)
if combUpdate and len(data['grp'])>1:
obs[comGrp] = obs[data['grp'][0]]
for i in data['grp']:
if i!=data['grp'][0]:
obs[comGrp] += ":"+obs[i]
mask = [obs[comGrp].isin([data['comGrp'][i]]) for i in [0,1]]
else:
data['figOpt']['scale'] = 'No'
adata = createData(data)
comGrp = data['grp'][0]
adata = adata[adata.obs[comGrp].isin(data['comGrp'])]
else:
mask = [pd.Series(range(data['cellN'])).isin(data['cells'][one].values()) for one in data['comGrp']]
for one in data['comGrp']:
oneD = data.copy()
oneD['cells'] = data['cells'][one]
oneD['genes'] = []
oneD['grp'] = []
oneD['figOpt']['scale']='No'
#oneD = {'cells':data['cells'][one],
# 'genes':[],
# 'grp':[],
# 'figOpt':{'scale':'No'},
# 'url':data['url']}
D = createData(oneD)
D.obs[comGrp] = one
if adata is None:
adata = D
else:
adata = adata.concatenate(D)
if data['DEmethod']=='default':
if sum(mask[0]==True)<10 or sum(mask[1]==True)<10:
raise ValueError('Less than 10 cells in a group!')
with app.get_data_adaptor(url_dataroot=data['url_dataroot'],dataset=data['dataset']) as scD:
#scD=data['data_adapter']
#if scD is not None:
#res = diffDefault.diffexp_ttest(scD,mask[0].to_numpy(),mask[1].to_numpy(),scD.data.shape[1])# shape[cells as rows, genes as columns]
res = scD.compute_diffexp_ttest(mask[0].to_numpy(),mask[1].to_numpy(),scD.data.shape[1]-1,0.01)
gNames = list(scD.data.var[data['var_index']])
deg = pd.DataFrame(res['positive']+res['negative'],columns=['gID','log2fc','pval','qval'])
deg = deg.drop_duplicates(ignore_index=True)
gName = pd.Series([gNames[i] for i in deg['gID']],name='gene')
deg = pd.concat([deg,gName],axis=1).loc[:,['gene','log2fc','pval','qval']]
else:
if not 'AnnData' in str(type(adata)):
raise ValueError('No data extracted by user selection')
adata.obs.astype('category')
nm = None
if data['DEmethod']=='wald':
nm = 'nb'
if data['DEmethod']=='wald':
res = de.test.wald(adata,formula_loc="~1+"+comGrp,factor_loc_totest=comGrp)
elif data['DEmethod']=='t-test':
res = de.test.t_test(adata,grouping=comGrp)
elif data['DEmethod']=='rank':
res = de.test.rank_test(adata,grouping=comGrp)
else:
raise ValueError('Unknown DE methods:'+data['DEmethod'])
#res = de.test.two_sample(adata,comGrp,test=data['DEmethod'],noise_model=nm)
deg = res.summary()
deg['log2fc'] = -1 * deg['log2fc']
deg = deg.sort_values(by=['qval']).loc[:,['gene','log2fc','pval','qval']]
#del adata
## plot in R
#strF = ('/tmp/DEG%f.csv' % time.time())
strF = ('%s/DEG%f.csv' % (data["CLItmp"],time.time()))
deg.to_csv(strF,index=False)
#ppr.pprint([strExePath+'/volcano.R',strF,'"%s"'%';'.join(genes),data['figOpt']['img'],str(data['figOpt']['fontsize']),str(data['figOpt']['dpi']),str(data['logFC']),data['comGrp'][1],data['comGrp'][0]])
res = subprocess.run([strExePath+'/volcano.R',strF,';'.join(data['genes']),data['figOpt']['img'],str(data['figOpt']['fontsize']),str(data['figOpt']['dpi']),str(data['logFC']),data['comGrp'][1],data['comGrp'][0],str(data['sigFDR']),str(data['sigFC']),str(data['labelSize']),str(data['dotSize']),str(data['ymin']),str(data['ymax']),data['figOpt']['vectorFriendly'],data['Rlib']],capture_output=True)#
if 'Error' in res.stderr.decode('utf-8'):
raise SyntaxError("in volcano.R: "+res.stderr.decode('utf-8'))
img = res.stdout.decode('utf-8')
# GSEA
GSEAimg=""
GSEAtable=pd.DataFrame()
if data['gsea']['enable']:
res = subprocess.run([strExePath+'/fgsea.R',
strF,
'%s/gsea/%s.symbols.gmt'%(strExePath,data['gsea']['gs']),
str(data['gsea']['gsMin']),
str(data['gsea']['gsMax']),
str(data['gsea']['padj']),
data['gsea']['up'],
data['gsea']['dn'],
str(data['gsea']['collapse']),
data['figOpt']['img'],
str(data['figOpt']['fontsize']),
str(data['figOpt']['dpi']),
data['Rlib']],capture_output=True)#
if 'Error' in res.stderr.decode('utf-8'):
raise SyntaxError("in fgsea.R: "+res.stderr.decode('utf-8'))
GSEAimg = res.stdout.decode('utf-8')
GSEAtable = pd.read_csv(strF)
GSEAtable['leadingEdge'] = GSEAtable['leadingEdge'].apply(lambda x:'|'.join(x.split('|')[:10]))
os.remove(strF)
#####
gInfo = getVar(data)
deg.index = deg['gene']
deg = pd.concat([deg,gInfo],axis=1,sort=False)
#return deg.to_csv()
if not data['topN']=='All':
deg = deg.iloc[range(int(data['topN'])),]
#deg.loc[:,'log2fc'] = deg.loc[:,'log2fc'].apply(lambda x: '%.2f'%x)
#deg.loc[:,'pval'] = deg.loc[:,'pval'].apply(lambda x: '%.4E'%x)
#deg.loc[:,'qval'] = deg.loc[:,'qval'].apply(lambda x: '%.4E'%x)
#ppr.pprint(GSEAtable)
#ppr.pprint(GSEAtable.sort_values('pval'))
return json.dumps([deg.to_csv(index=False),img,GSEAtable.to_csv(index=False),GSEAimg])#json.dumps([deg.values.tolist(),img])
def specificity_score(adata=None, ctype_col:str=None, ctypes:list=None, ctype_sets:list=None, glist:list=None):
if not (adata and ctype_col):
raise ValueError('No adata or No ctype_col_name provided!')
if not (ctypes and ctype_sets):
ctypes = adata.obs[ctype_col].unique().tolist()
ctype_dict = {_: set([_]) for _ in ctypes}
else:
if len(ctypes)!=len(ctype_sets):
raise ValueError("Please check your cell type partition!")
if len(ctypes)>1:
sumset = ctype_sets[0]
for nextset in ctype_sets[1:]:
if sumset.intersection(nextset):
raise ValueError("Self-defined cell type partitions have overlap!!")
else:
sumset = sumset.union(nextset)
ctype_dict = {ctypes[i]: ctype_sets[i] for i in range(len(ctypes))}
if glist and len(glist):
adata = adata[:, adata.var_names.isin(set(glist))]
if adata.shape[-1]==0:
raise ValueError("No gene found! Pls check your gene list!")
adata_dict = {ctype:adata[adata.obs[ctype_col].isin(ctype_dict[ctype])] for ctype in ctypes}
mean_exp_dict = {ctype: np.squeeze(np.asarray(adata_dict[ctype].X.sum(axis=0))) / adata_dict[ctype].n_obs for ctype in ctypes}
df = pd.DataFrame(data=mean_exp_dict, index=adata_dict[ctypes[0]].var_names)
df['all'] = df.sum(axis=1)
for col in df.columns:
df[col] = df[col]/df['all']
# assign nan for genes not found
# gene in glist order
if glist and len(glist): df = df.reindex(glist)
return df
def restoreX(d):
if d.max()<50:
d1 = np.exp(d)
d2 = np.exp2(d)
if abs(d1-d1.round()).sum()<abs(d2-d2.round()).sum():
d = d1
ppr.pprint("...exp is taken")
else:
d = d2
ppr.pprint("...exp2 is taken")
if d.min()>0.1 and (d==d.min()).sum()>100:
d = d-d.min()
ppr.pprint("...min value %f is taken"%d.min())
return(d)
def GSP(data):
data['figOpt']['scale']='No'
D = createData(data)
if scipy.sparse.issparse(D.X):
D.X.data = restoreX(D.X.data)
else:
D.X = restoreX(D.X)
grp = data['grp'][0]
X = specificity_score(adata=D,ctype_col=grp)
if 'all' in X.columns:
Xshow=X.drop('all',axis=1)
else:
Xshow = X
# plot depends on the gene number
if X.shape[0]==1:
XT = Xshow.T
gene = XT.columns[0]
XT[grp] = XT.index
fig = plt.figure()
#ax = XT.plot.bar(x=grp,y=gene)
ax = sns.barplot(data=XT,x=grp,y=gene)
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)#rotation=45, horizontalalignment='right'
img = iostreamFig(fig)
elif X.shape[0]<20:
fig = plt.figure(figsize=[0.8*Xshow.shape[1],0.5*Xshow.shape[0]+0.1*max([len(x) for x in Xshow.columns])])
ax = sns.heatmap(Xshow,annot=True,fmt=".1f",cmap=sns.cubehelix_palette(as_cmap=True))
img = iostreamFig(fig)
else:
g = sns.clustermap(
Xshow.sample(min(1000,X.shape[0])),
method="ward",col_cluster=False,
yticklabels=False,xticklabels=True)
img = iostreamFig(g)
X.insert(0,"gene",X.index)
return json.dumps([X.to_csv(index=False),img])
def DOT(data):
#ppr.pprint("DOT, starting ...")
updateGene(data)
# Dot plot, The dotplot visualization provides a compact way of showing per group, the fraction of cells expressing a gene (dot size) and the mean expression of the gene in those cell (color scale). The use of the dotplot is only meaningful when the counts matrix contains zeros representing no gene counts. dotplot visualization does not work for scaled or corrected matrices in which zero counts had been replaced by other values, see http://scanpy-tutorials.readthedocs.io/en/multiomics/visualizing-marker-genes.html
data['figOpt']['scale'] = 'No';
#ppr.pprint("DOT: creating data ...")
adata = createData(data)
#ppr.pprint("DOT: data created!")
if len(adata)==0:
return Msg('No cells in the condition!')
#return adata
grp = adata.obs[data['grp'][0]].unique()
if len(grp)<10:
col = np.array(sns.color_palette('Set1',len(grp)).as_hex())
elif len(grp)<20:
col = np.array(sns.color_palette(n_colors=len(grp)).as_hex())
else:
col = np.array(sns.color_palette("husl",len(grp)).as_hex())
adata.uns[data['grp'][0]+'_colors'] = col
#ppr.pprint(sc.__version__)
if 'split_show' in data['figOpt']['scanpybranch']:#.dev140+ge9cbc5f
dp = sc.pl.dotplot(adata,data['genes'],groupby=data['grp'][0],expression_cutoff=float(data['cutoff']),mean_only_expressed=(data['mean_only_expressed'] == 'Yes'),
var_group_positions=data['grpLoc'],var_group_labels=data['grpID'],
return_fig=True)#
dp = dp.add_totals(size=1.2).legend(show_size_legend=True,width=float(data['legendW'])).style(cmap=data['color'], dot_edge_color='black', dot_edge_lw=1, size_exponent=1.5)
dp.show()
fig = dp.get_axes()['mainplot_ax'].figure