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VisInteractions.py
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VisInteractions.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 8 08:53:30 2019
@author: rhou
"""
import warnings
warnings.filterwarnings("ignore")
import os, sys
import argparse
import matplotlib
matplotlib.use('agg')
import pandas as pd
import numpy as np
try:
import seaborn as sns
except ImportError:
sys.exit('\n\nError: seaborn module is missing, please install it before proceeding.')
try:
import igraph as ig
except ImportError:
sys.exit('\n\nError: igraph module is missing, please install it before proceeding.')
try:
import networkx as nx
except ImportError:
sys.exit('\n\nError: NetworkX module is missing, please install it before proceeding.')
try:
import pygraphviz as pgv
except ImportError:
sys.exit('\n\nError: PyGraphviz module is missing, please install it before proceeding.')
#filter adjacency matrix
def ChooseTopEdges(adjM, keepTopEdge):
if keepTopEdge == 0:
return adjM
edgeDict = {'s':[],'t':[],'v':[]}
for idx in adjM.index:
for col in adjM.columns:
edgeDict['s'].append(idx)
edgeDict['t'].append(col)
if adjM.loc[idx,col] <=0:
edgeDict['v'].append((-1.0) * adjM.loc[idx,col])
else:
edgeDict['v'].append(adjM.loc[idx,col])
edgeD = pd.DataFrame(edgeDict).sort_values(by=['v'], ascending=False)
edgeD = edgeD.head(keepTopEdge)
nadjM = pd.DataFrame(0.0, index=adjM.index,columns=adjM.index)
for idx in edgeD.index:
nadjM.loc[edgeD.loc[idx,['s']],edgeD.loc[idx,['t']]] = adjM.loc[edgeD.loc[idx,['s']],edgeD.loc[idx,['t']]]
return nadjM
# build delta adjacency matrix
def BuildDeltaAdjM(edgeDF, origlabels, labels, specificityThreshold, weightThreshold, frequencyThreshold, keepTopEdge):
edgeDF['Sending cluster'] = edgeDF['Sending cluster'].astype(str)
edgeDF['Target cluster'] = edgeDF['Target cluster'].astype(str)
adjM1 = pd.DataFrame(0.0, index=origlabels, columns=origlabels)
adjSpecM1 = pd.DataFrame(0.0, index=origlabels, columns=origlabels)
adjCountM1 = pd.DataFrame(0, index=origlabels, columns=origlabels)
adjM2 = pd.DataFrame(0.0, index=origlabels, columns=origlabels)
adjSpecM2 = pd.DataFrame(0.0, index=origlabels, columns=origlabels)
adjCountM2 = pd.DataFrame(0, index=origlabels, columns=origlabels)
for idx in edgeDF.index:
if (edgeDF.loc[idx, 'Ligand detection rate in condition 1']>frequencyThreshold)&(edgeDF.loc[idx, 'Receptor detection rate in condition 1']>frequencyThreshold):
adjM1.loc[str(edgeDF.loc[idx,'Sending cluster']), str(edgeDF.loc[idx,'Target cluster'])] += edgeDF.loc[idx,'Edge expression weight in condition 1']
adjSpecM1.loc[str(edgeDF.loc[idx,'Sending cluster']), str(edgeDF.loc[idx,'Target cluster'])] += edgeDF.loc[idx,'Edge specificity weight in condition 1']
adjCountM1.loc[str(edgeDF.loc[idx,'Sending cluster']), str(edgeDF.loc[idx,'Target cluster'])] += 1
if (edgeDF.loc[idx, 'Ligand detection rate in condition 2']>frequencyThreshold)&(edgeDF.loc[idx, 'Receptor detection rate in condition 2']>frequencyThreshold):
adjM2.loc[str(edgeDF.loc[idx,'Sending cluster']), str(edgeDF.loc[idx,'Target cluster'])] += edgeDF.loc[idx,'Edge expression weight in condition 2']
adjSpecM2.loc[str(edgeDF.loc[idx,'Sending cluster']), str(edgeDF.loc[idx,'Target cluster'])] += edgeDF.loc[idx,'Edge specificity weight in condition 2']
adjCountM2.loc[str(edgeDF.loc[idx,'Sending cluster']), str(edgeDF.loc[idx,'Target cluster'])] += 1
# remove isolated nodes
adjM1.index = labels
adjM1.columns = labels
adjSpecM1.index = labels
adjSpecM1.columns = labels
adjCountM1.index = labels
adjCountM1.columns = labels
ilist1 = adjM1.index[adjM1.max(axis=1)>0]
clist1 = adjM1.columns[adjM1.max(axis=0)>0]
nlist1 = sorted(list(set(ilist1).union(set(clist1))))
adjM2.index = labels
adjM2.columns = labels
adjSpecM2.index = labels
adjSpecM2.columns = labels
adjCountM2.index = labels
adjCountM2.columns = labels
ilist2 = adjM2.index[adjM2.max(axis=1)>0]
clist2 = adjM2.columns[adjM2.max(axis=0)>0]
nlist2 = sorted(list(set(ilist2).union(set(clist2))))
nlist = sorted(list(set(nlist1).union(set(nlist2))))
adjM1 = adjM1.loc[nlist,nlist]
adjSpecM1 = adjSpecM1.loc[nlist,nlist]
adjCountM1 = adjCountM1.loc[nlist,nlist]
adjM2 = adjM2.loc[nlist,nlist]
adjSpecM2 = adjSpecM2.loc[nlist,nlist]
adjCountM2 = adjCountM2.loc[nlist,nlist]
#to average expression
for idx in nlist:
for col in nlist:
if adjCountM1.loc[idx,col] != 0:
adjM1.loc[idx,col] = adjM1.loc[idx,col]/adjCountM1.loc[idx,col]
if adjCountM2.loc[idx,col] != 0:
adjM2.loc[idx,col] = adjM2.loc[idx,col]/adjCountM2.loc[idx,col]
adjMD = adjM2 - adjM1
adjSpecMD = adjSpecM2 - adjSpecM1
adjCountMD = adjCountM2 - adjCountM1
adjM1 = ChooseTopEdges(adjM1, keepTopEdge)
adjSpecM1 = ChooseTopEdges(adjSpecM1, keepTopEdge)
adjCountM1 = ChooseTopEdges(adjCountM1, keepTopEdge)
adjM2 = ChooseTopEdges(adjM2, keepTopEdge)
adjSpecM2 = ChooseTopEdges(adjSpecM2, keepTopEdge)
adjCountM2 = ChooseTopEdges(adjCountM2, keepTopEdge)
adjMD = ChooseTopEdges(adjMD, keepTopEdge)
adjSpecMD = ChooseTopEdges(adjSpecMD, keepTopEdge)
adjCountMD = ChooseTopEdges(adjCountMD, keepTopEdge)
ilist1 = adjM1.index[adjM1.max(axis=1)>0]
clist1 = adjM1.columns[adjM1.max(axis=0)>0]
nlist1 = sorted(list(set(ilist1).union(set(clist1))))
ilist2 = adjM2.index[adjM2.max(axis=1)>0]
clist2 = adjM2.columns[adjM2.max(axis=0)>0]
nlist2 = sorted(list(set(ilist2).union(set(clist2))))
ilist3 = adjMD.index[adjMD.max(axis=1)>0]
clist3 = adjMD.columns[adjMD.max(axis=0)>0]
nlist3 = sorted(list(set(ilist3).union(set(clist3))))
nlist = sorted(list(set(nlist1).union(set(nlist2)).union(set(nlist3))))
adjM1 = adjM1.loc[nlist,nlist]
adjM2 = adjM2.loc[nlist,nlist]
adjMD = adjMD.loc[nlist,nlist]
ilist1 = adjSpecM1.index[adjSpecM1.max(axis=1)>0]
clist1 = adjSpecM1.columns[adjSpecM1.max(axis=0)>0]
nlist1 = sorted(list(set(ilist1).union(set(clist1))))
ilist2 = adjSpecM2.index[adjSpecM2.max(axis=1)>0]
clist2 = adjSpecM2.columns[adjSpecM2.max(axis=0)>0]
nlist2 = sorted(list(set(ilist2).union(set(clist2))))
ilist3 = adjSpecMD.index[adjSpecMD.max(axis=1)>0]
clist3 = adjSpecMD.columns[adjSpecMD.max(axis=0)>0]
nlist3 = sorted(list(set(ilist3).union(set(clist3))))
nlist = sorted(list(set(nlist1).union(set(nlist2)).union(set(nlist3))))
adjSpecM1 = adjSpecM1.loc[nlist,nlist]
adjSpecM2 = adjSpecM2.loc[nlist,nlist]
adjSpecMD = adjSpecMD.loc[nlist,nlist]
ilist1 = adjCountM1.index[adjCountM1.max(axis=1)>0]
clist1 = adjCountM1.columns[adjCountM1.max(axis=0)>0]
nlist1 = sorted(list(set(ilist1).union(set(clist1))))
ilist2 = adjCountM2.index[adjCountM2.max(axis=1)>0]
clist2 = adjCountM2.columns[adjCountM2.max(axis=0)>0]
nlist2 = sorted(list(set(ilist2).union(set(clist2))))
ilist3 = adjCountMD.index[adjCountMD.max(axis=1)>0]
clist3 = adjCountMD.columns[adjCountMD.max(axis=0)>0]
nlist3 = sorted(list(set(ilist3).union(set(clist3))))
nlist = sorted(list(set(nlist1).union(set(nlist2)).union(set(nlist3))))
adjCountM1 = adjCountM1.loc[nlist,nlist]
adjCountM2 = adjCountM2.loc[nlist,nlist]
adjCountMD = adjCountMD.loc[nlist,nlist]
nxgW1 = nx.MultiDiGraph(adjM1)
nxgS1 = nx.MultiDiGraph(adjSpecM1)
nxgC1 = nx.MultiDiGraph(adjCountM1)
nxgW2 = nx.MultiDiGraph(adjM2)
nxgS2 = nx.MultiDiGraph(adjSpecM2)
nxgC2 = nx.MultiDiGraph(adjCountM2)
nxgWD = nx.MultiDiGraph(adjMD)
nxgSD = nx.MultiDiGraph(adjSpecMD)
nxgCD = nx.MultiDiGraph(adjCountMD)
return edgeDF, adjM1, adjSpecM1, adjCountM1, nxgW1, nxgS1, nxgC1, adjM2, adjSpecM2, adjCountM2, nxgW2, nxgS2, nxgC2, adjMD, adjSpecMD, adjCountMD, nxgWD, nxgSD, nxgCD
# build adjacency matrix
def BuildAdjM(edgeDF, origlabels, labels, specificityThreshold, weightThreshold, keepTopEdge):
# only keep edges of interest
if 'delta specificity' in edgeDF.columns:
edgeDF = edgeDF.loc[(edgeDF['delta specificity']>specificityThreshold)&(edgeDF['delta weight']>weightThreshold),]
else:
edgeDF = edgeDF.loc[(edgeDF['product of specified']>specificityThreshold)&(edgeDF['original ligand']>weightThreshold)&(edgeDF['original receptor']>weightThreshold),]
edgeDF['sending cluster name'] = edgeDF['sending cluster name'].astype(str)
edgeDF['target cluster name'] = edgeDF['target cluster name'].astype(str)
adjM = pd.DataFrame(0.0, index=origlabels, columns=origlabels)
adjSpecM = pd.DataFrame(0.0, index=origlabels, columns=origlabels)
adjCountM = pd.DataFrame(0, index=origlabels, columns=origlabels)
for idx in edgeDF.index:
if 'delta specificity' in edgeDF.columns:
adjM.loc[str(edgeDF.loc[idx,'sending cluster name']), str(edgeDF.loc[idx,'target cluster name'])] += edgeDF.loc[idx,'delta weight']
adjSpecM.loc[str(edgeDF.loc[idx,'sending cluster name']), str(edgeDF.loc[idx,'target cluster name'])] += edgeDF.loc[idx,'delta specificity']
else:
adjM.loc[str(edgeDF.loc[idx,'sending cluster name']), str(edgeDF.loc[idx,'target cluster name'])] += edgeDF.loc[idx,'product of original']
adjSpecM.loc[str(edgeDF.loc[idx,'sending cluster name']), str(edgeDF.loc[idx,'target cluster name'])] += edgeDF.loc[idx,'product of specified']
adjCountM.loc[str(edgeDF.loc[idx,'sending cluster name']), str(edgeDF.loc[idx,'target cluster name'])] += 1
adjM.index = labels
adjM.columns = labels
adjSpecM.index = labels
adjSpecM.columns = labels
adjCountM.index = labels
adjCountM.columns = labels
ilist = adjM.index[adjM.max(axis=1)>0]
clist = adjM.columns[adjM.max(axis=0)>0]
nlist = sorted(list(set(ilist).union(set(clist))))
adjM = adjM.loc[nlist,nlist]
adjSpecM = adjSpecM.loc[nlist,nlist]
adjCountM = adjCountM.loc[nlist,nlist]
adjM = ChooseTopEdges(adjM, keepTopEdge)
ilist = adjM.index[adjM.max(axis=1)>0]
clist = adjM.columns[adjM.max(axis=0)>0]
nlist = sorted(list(set(ilist).union(set(clist))))
adjM = adjM.loc[nlist,nlist]
adjSpecM = ChooseTopEdges(adjSpecM, keepTopEdge)
ilist = adjSpecM.index[adjSpecM.max(axis=1)>0]
clist = adjSpecM.columns[adjSpecM.max(axis=0)>0]
nlist = sorted(list(set(ilist).union(set(clist))))
adjSpecM = adjSpecM.loc[nlist,nlist]
adjCountM = ChooseTopEdges(adjCountM, keepTopEdge)
ilist = adjCountM.index[adjCountM.max(axis=1)>0]
clist = adjCountM.columns[adjCountM.max(axis=0)>0]
nlist = sorted(list(set(ilist).union(set(clist))))
adjCountM = adjCountM.loc[nlist,nlist]
nxgW = nx.MultiDiGraph(adjM)
nxgS = nx.MultiDiGraph(adjSpecM)
nxgC = nx.MultiDiGraph(adjCountM)
return edgeDF, adjM, adjSpecM, adjCountM, nxgW, nxgS, nxgC
def IgraphFromAdjacency(adjM, layout, labels, cltSizes, clusterDistance):
# insert nodes and edges into the graph object
g = ig.Graph(directed=True)
g.add_vertices(adjM.shape[0])
nNameList = []
nWeightList = []
for nidx in adjM.index:
tidx = labels.index(nidx)
nNameList.append(labels[tidx])
nWeightList.append(cltSizes[tidx])
g.vs["name"] = nNameList
g.vs["weight"] = nWeightList
edgeList = []
edgeWeightList = []
for s in range(len(adjM)):
for t in range(len(adjM)):
if adjM.iloc[s,t] > 0:
edgeList.append((s,t))
edgeWeightList.append(adjM.iloc[s,t])
g.add_edges(edgeList)
g.es['weight'] = edgeWeightList
# set node positions based on the layout
if layout == 'circle' or layout == 'sphere':
pos_list = reversed(g.layout(layout).coords)
else:
# set the seed to make the graph reproducable
np.random.seed(0)
init_coords = np.random.random((len(adjM), 2)).tolist()
try:
pos_list = g.layout(layout, seed=init_coords, weights='weight',).coords
except Exception:
# hack for excepting attribute error for empty graphs...
try:
pos_list = g.layout(layout, seed=init_coords).coords
except Exception:
pos_list = g.layout(layout).coords
posDict = {n: [p[0]*clusterDistance, -p[1]*clusterDistance] for n, p in enumerate(pos_list)}
return posDict
def DrawDeltaHeatmap(readmeStr, typeStr, numStr, tempM1, tempM2, tempMD, tempMF, dataType, resultDir, plotWidth, plotHeight, fontSize, specificityThreshold,weightThreshold,frequencyThreshold,interDB,weightType,plotFormat):
# draw networks of both datasets
newlbls = []
for idx in tempM1.index:
newlbls.append(idx.split('\n')[0])
oldidx = tempM1.index
tempM1.index = newlbls
tempM2.index = newlbls
tempMD.index = newlbls
tempMF.index = newlbls
newcts = []
for col in tempM1.columns:
newcts.append(col.split('\n')[0])
oldcol = tempM1.columns
tempM1.columns = newcts
tempM2.columns = newcts
tempMD.columns = newcts
tempMF.columns = newcts
import matplotlib.colors as colors
BuOnly = colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#0000FF"])
RdOnly = colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#FF0000"])
import matplotlib.pyplot as plt
figWidth = max(tempM1.shape)*fontSize/30
f, ax = plt.subplots(figsize=(figWidth, figWidth))
if numStr == 'int':
if tempM1.max().max() < 500:
g = sns.heatmap(tempM1, square=True, annot=True, fmt=".0f", linewidths=.5, ax=ax, cmap=RdOnly)
else:
g = sns.heatmap(tempM1, square=True, annot=False, linewidths=.5, ax=ax, cmap=RdOnly)
else:
g = sns.heatmap(tempM1, square=True, annot=False, linewidths=.5, ax=ax, cmap=RdOnly)
ax.set_xlabel('Cell-type expressing receptor (receiving)')
ax.set_ylabel('Cell-type expressing ligand (sending)')
fig = g.get_figure()
plotFileName = 'heatmap_cond1_%s-based.%s' % (typeStr, plotFormat)
readmeStr += 'heatmap_cond1_%s-based.%s: cluster-to-cluster communication heatmap in condition 1, the element is %s.\n' % (typeStr, plotFormat, typeStr)
plotFileName = os.path.join(resultDir, plotFileName)
fig.savefig(plotFileName, bbox_inches = "tight")
f, ax = plt.subplots(figsize=(figWidth, figWidth))
if numStr == 'int':
if tempM2.max().max() < 500:
g = sns.heatmap(tempM2, square=True, annot=True, fmt=".0f", linewidths=.5, ax=ax, cmap=BuOnly)
else:
g = sns.heatmap(tempM2, square=True, annot=False, linewidths=.5, ax=ax, cmap=BuOnly)
else:
g = sns.heatmap(tempM2, square=True, annot=False, linewidths=.5, ax=ax, cmap=BuOnly)
ax.set_xlabel('Cell-type expressing receptor (receiving)')
ax.set_ylabel('Cell-type expressing ligand (sending)')
fig = g.get_figure()
plotFileName = 'heatmap_cond2_%s-based.%s' % (typeStr, plotFormat)
readmeStr += 'heatmap_cond2_%s-based.%s: cluster-to-cluster communication heatmap in condition 2, the element is %s.\n' % (typeStr, plotFormat, typeStr)
plotFileName = os.path.join(resultDir, plotFileName)
fig.savefig(plotFileName, bbox_inches = "tight")
import numpy as np
ttempMF = tempMF.copy()
for ttidx in ttempMF.index:
for ttcol in ttempMF.columns:
if tempMD.loc[ttidx,ttcol] < 0:
ttempMF.loc[ttidx,ttcol] = np.log2(ttempMF.loc[ttidx,ttcol]) * -1.0
else:
ttempMF.loc[ttidx,ttcol] = np.log2(ttempMF.loc[ttidx,ttcol])
# draw delta networks
f, ax = plt.subplots(figsize=(figWidth, figWidth))
if numStr == 'int':
#red color for down
g = sns.heatmap(tempMD.astype(float), mask=ttempMF < 1.0, cbar=False, cmap=matplotlib.colors.ListedColormap(['#0000FF']), square=True, annot=True, fmt=".0f", linewidths=.5, ax=ax)
#blue color for up
g = sns.heatmap(tempMD.astype(float), mask=ttempMF > -1.0, cbar=False, cmap=matplotlib.colors.ListedColormap(['#FF0000']), square=True, annot=True, fmt=".0f", linewidths=.5, ax=ax)
#background color
g = sns.heatmap(tempMD.astype(float), mask=(ttempMF > 1.0)|(ttempMF < -1.0), cbar=False, cmap=matplotlib.colors.ListedColormap(['#FFFF00']), square=True, annot=True, fmt=".0f", linewidths=.5, ax=ax)
else:
#red color for down
g = sns.heatmap(tempMD, mask=ttempMF < 1.0, cbar=False, cmap=matplotlib.colors.ListedColormap(['#0000FF']), square=True, annot=True, fmt=".2f", linewidths=.5, ax=ax)
#blue color for up
g = sns.heatmap(tempMD, mask=ttempMF > -1.0, cbar=False, cmap=matplotlib.colors.ListedColormap(['#FF0000']), square=True, annot=True, fmt=".2f", linewidths=.5, ax=ax)
#background color
g = sns.heatmap(tempMD, mask=(ttempMF > 1.0)|(ttempMF < -1.0), cbar=False, cmap=matplotlib.colors.ListedColormap(['#FFFF00']), square=True, annot=True, fmt=".2f", linewidths=.5, ax=ax)
ax.set_xlabel('Cell-type expressing receptor (receiving)')
ax.set_ylabel('Cell-type expressing ligand (sending)')
fig = g.get_figure()
plotFileName = 'heatmap_delta_diff_%s-based.%s' % (typeStr, plotFormat)
readmeStr += 'heatmap_delta_diff_%s-based.%s: cluster-to-cluster communication heatmap in which the element is the difference of %s.\n' % (typeStr, plotFormat, typeStr)
plotFileName = os.path.join(resultDir, plotFileName)
fig.savefig(plotFileName, bbox_inches = "tight")
# draw fold change networks
f, ax = plt.subplots(figsize=(figWidth, figWidth))
#red color for down
g = sns.heatmap(ttempMF, mask=ttempMF < 1.0, cbar=False, cmap=matplotlib.colors.ListedColormap(['#0000FF']), square=True, annot=True, fmt=".2f", linewidths=.5, ax=ax)
#blue color for up
g = sns.heatmap(ttempMF, mask=ttempMF > -1.0, cbar=False, cmap=matplotlib.colors.ListedColormap(['#FF0000']), square=True, annot=True, fmt=".2f", linewidths=.5, ax=ax)
#background color
g = sns.heatmap(ttempMF, mask=(ttempMF > 1.0)|(ttempMF < -1.0), cbar=False, cmap=matplotlib.colors.ListedColormap(['#FFFF00']), square=True, annot=True, fmt=".2f", linewidths=.5, ax=ax)
ax.set_xlabel('Cell-type expressing receptor (receiving)')
ax.set_ylabel('Cell-type expressing ligand (sending)')
fig = g.get_figure()
plotFileName = 'heatmap_fold_change_%s-based.%s' % (typeStr, plotFormat)
readmeStr += 'heatmap_fold_change_%s-based.%s: cluster-to-cluster communication heatmap in which the element is the log2 transformed fold change of %s.\n' % (typeStr, plotFormat, typeStr)
plotFileName = os.path.join(resultDir, plotFileName)
fig.savefig(plotFileName, bbox_inches = "tight")
tempM1.columns = oldcol
tempM1.index = oldidx
tempM2.columns = oldcol
tempM2.index = oldidx
tempMD.columns = oldcol
tempMD.index = oldidx
tempMF.columns = oldcol
tempMF.index = oldidx
return readmeStr
def DrawDeltaGraphvizPlot(readmeStr, typeStr, numStr, nxgS1, adjSpecM1, nxgS2, adjSpecM2, nxgSD, adjSpecMD, dataType, resultDir, plotWidth, plotHeight, fontSize, edgeWidth, colorDict, cltSizeDict, maxClusterSize, wposDict, cltFDdict, labels, specificityThreshold, weightThreshold, frequencyThreshold, interDB, weightType, layout, plotFormat):
# draw networks of both datasets
# convert to a graphviz graph
nxgS1 = nx.nx_agraph.to_agraph(nxgS1)
nxgS1.graph_attr.update(fontname = "Arial")
nxgS1.graph_attr.update(fontsize = fontSize)
nxgS1.graph_attr.update(margin = 0)
nxgS1.graph_attr.update(ratio="compress")
nxgS1.graph_attr.update(label='Network in condition 1\nEdge weight: ' + typeStr)
nxgS1.graph_attr.update(size = "%s,%s" % (plotWidth, plotHeight))
# set edge properties
maxVal = adjSpecM1.max().max()
for ed in nxgS1.edges():
sn = ed[0]
tn = ed[1]
ed.attr['color'] = '#FF0000'
if edgeWidth == 0:
ed.attr['fontsize'] = fontSize
ed.attr['fontname'] = "Arial"
if numStr == 'float':
ed.attr['label'] = '%.2f' % (adjSpecM1.loc[sn, tn])
elif numStr == 'int':
ed.attr['label'] = '%d' % (adjSpecM1.loc[sn, tn])
else:
if adjSpecM1.loc[sn, tn]*edgeWidth/maxVal < 1:
ed.attr['penwidth'] = 1
else:
ed.attr['penwidth'] = int(adjSpecM1.loc[sn, tn]*edgeWidth/maxVal)
# set node color
nxgS1.node_attr['style']='filled,setlinewidth(0)'
idx = 0
maxCltSize = max(cltSizeDict.values())
for lb in labels:
try:
nd=nxgS1.get_node(lb)
except:
continue
nd.attr['shape'] = 'circle'
if maxClusterSize != 0:
radis = cltSizeDict[nd]*maxClusterSize/maxCltSize
nd.attr['width'] = str(round(radis,2))
nd.attr['height'] = str(round(radis,2))
nd.attr['fixedsize'] = 'true'
#newcol = [i+0.5*(1-i) for i in matplotlib.colors.to_rgb(colorDict[nd])]
#nd.attr['fillcolor'] = matplotlib.colors.to_hex(newcol)
nd.attr['fillcolor'] = colorDict[nd]
nd.attr['pin'] = 'true'
nd.attr['pos'] = '%s,%s' % (wposDict[idx][0]*plotWidth/2,wposDict[idx][1]*plotHeight/2)
nd.attr['fontsize'] = fontSize
nd.attr['fontname'] = "Arial"
idx += 1
plotFileName = 'network_cond1_%s-based_layout_%s.%s' % (typeStr, layout, plotFormat)
readmeStr += 'network_cond1_%s-based_layout_xx.%s: cluster-to-cluster communication network in condition 1, the edge weight is %s.\n' % (typeStr, plotFormat, typeStr)
plotFileName = os.path.join(resultDir, plotFileName)
warnings.simplefilter("error")
while True:
try:
nxgS1.draw(plotFileName,prog='fdp')
break
except RuntimeWarning as rw:
errorNodeList = [m[m.find("'")+1:m.find("',")] for m in str(rw).split("Warning:") if 'too small' in m]
if len(errorNodeList)==0:
errorNodeList = [m[m.find("'")+1:m.find("',")] for m in str(rw).split("Warning:") if 'too small' in m]
nxgS1.draw(plotFileName,prog='neato')
break
for nd in nxgS1.nodes():
if str(nd) in errorNodeList:
nd.attr['xlabel'] = str(nd)
nd.attr['label'] = ''
continue
warnings.simplefilter("ignore")
nxgS2 = nx.nx_agraph.to_agraph(nxgS2)
nxgS2.graph_attr.update(fontname = "Arial")
nxgS2.graph_attr.update(fontsize = fontSize)
nxgS2.graph_attr.update(margin = 0)
nxgS2.graph_attr.update(ratio="compress")
nxgS2.graph_attr.update(label='Network in condition 2\nEdge weight: ' + typeStr)
nxgS2.graph_attr.update(size = "%s,%s" % (plotWidth, plotHeight))
# set edge properties
maxVal = adjSpecM2.max().max()
for ed in nxgS2.edges():
sn = ed[0]
tn = ed[1]
ed.attr['color'] = '#0000FF'
if edgeWidth == 0:
ed.attr['fontsize'] = fontSize
ed.attr['fontname'] = "Arial"
if numStr == 'float':
ed.attr['label'] = '%.2f' % (adjSpecM2.loc[sn, tn])
elif numStr == 'int':
ed.attr['label'] = '%d' % (adjSpecM2.loc[sn, tn])
else:
if adjSpecM2.loc[sn, tn]*edgeWidth/maxVal < 1:
ed.attr['penwidth'] = 1
else:
ed.attr['penwidth'] = int(adjSpecM2.loc[sn, tn]*edgeWidth/maxVal)
# set node color
nxgS2.node_attr['style']='filled,setlinewidth(0)'
idx = 0
maxCltSize = max(cltSizeDict.values())
for lb in labels:
try:
nd=nxgS2.get_node(lb)
except:
continue
nd.attr['shape'] = 'circle'
if maxClusterSize != 0:
radis = cltSizeDict[nd]*maxClusterSize/maxCltSize
nd.attr['width'] = str(round(radis,2))
nd.attr['height'] = str(round(radis,2))
nd.attr['fixedsize'] = 'true'
#newcol = [i+0.5*(1-i) for i in matplotlib.colors.to_rgb(colorDict[nd])]
#nd.attr['fillcolor'] = matplotlib.colors.to_hex(newcol)
nd.attr['fillcolor'] = colorDict[nd]
nd.attr['pin'] = 'true'
nd.attr['pos'] = '%s,%s' % (wposDict[idx][0]*plotWidth/2,wposDict[idx][1]*plotHeight/2)
nd.attr['fontsize'] = fontSize
nd.attr['fontname'] = "Arial"
idx += 1
plotFileName = 'network_cond2_%s-based_layout_%s.%s' % (typeStr, layout, plotFormat)
readmeStr += 'network_cond2_%s-based_layout_xx.%s: cluster-to-cluster communication network in condition 2, the edge weight is %s.\n' % (typeStr, plotFormat, typeStr)
plotFileName = os.path.join(resultDir, plotFileName)
warnings.simplefilter("error")
while True:
try:
nxgS2.draw(plotFileName,prog='fdp')
break
except RuntimeWarning as rw:
errorNodeList = [m[m.find("'")+1:m.find("',")] for m in str(rw).split("Warning:") if 'too small' in m]
if len(errorNodeList)==0:
errorNodeList = [m[m.find("'")+1:m.find("',")] for m in str(rw).split("Warning:") if 'too small' in m]
nxgS2.draw(plotFileName,prog='neato')
break
for nd in nxgS2.nodes():
if str(nd) in errorNodeList:
nd.attr['xlabel'] = str(nd)
nd.attr['label'] = ''
continue
warnings.simplefilter("ignore")
# set node color
maxCltSize = 0.0
for clt in cltFDdict.keys():
if maxCltSize < abs(cltFDdict[clt]):
maxCltSize = cltFDdict[clt]
cltSizeColorDict = {}
for clt in cltFDdict.keys():
if cltFDdict[clt] > 2:
# xf = cltFDdict[clt] / maxCltSize
# cltSizeColorDict[clt] = (high0[0]+deltah[0]*xf,high0[1]+deltah[1]*xf,high0[2]+deltah[2]*xf)
cltSizeColorDict[clt] = (0,0,1)
elif cltFDdict[clt] < -2:
# xf = cltFDdict[clt] / maxCltSize * -1.0
# cltSizeColorDict[clt] = (low0[0]+deltal[0]*xf,low0[1]+deltal[1]*xf,low0[2]+deltal[2]*xf)
cltSizeColorDict[clt] = (1,0,0)
else:
cltSizeColorDict[clt] = (1, 1, 0)
# draw delta networks
# convert to a graphviz graph
nxgSD = nx.nx_agraph.to_agraph(nxgSD)
nxgSD.graph_attr.update(fontname = "Arial")
nxgSD.graph_attr.update(fontsize = fontSize)
nxgSD.graph_attr.update(margin = 0)
nxgSD.graph_attr.update(ratio="compress")
nxgSD.graph_attr.update(label='Delta Network\nEdge weight: delta ' + typeStr)
nxgSD.graph_attr.update(size = "%s,%s" % (plotWidth, plotHeight))
# set edge properties
maxValP = adjSpecMD.max().max()
maxValN = adjSpecMD.min().min()
if maxValN >= 0:
maxValN = maxValP
for ed in nxgSD.edges():
sn = ed[0]
tn = ed[1]
if adjSpecMD.loc[sn, tn] < 0 and adjSpecM2.loc[sn, tn] > 0 and float(adjSpecM1.loc[sn, tn])/adjSpecM2.loc[sn, tn] > 2:
ed.attr['color'] = '#FF0000'
elif adjSpecMD.loc[sn, tn] > 0 and adjSpecM1.loc[sn, tn] > 0 and float(adjSpecM2.loc[sn, tn])/adjSpecM1.loc[sn, tn] > 2:
ed.attr['color'] = '#0000FF'
else:
ed.attr['color'] = '#FFFF00'
if adjSpecMD.loc[sn, tn] > 0 and ed.attr['color'] != '#FFFF00':#to red
#sclc = float(adjSpecMD.loc[sn, tn])/maxValP*1.0
#ed.attr['color'] = matplotlib.colors.to_hex([1.0, 1.0-sclc, 0.0])
#ed.attr['color'] = matplotlib.colors.to_hex((low0[0]+deltal[0]*sclc,low0[1]+deltal[1]*sclc,low0[2]+deltal[2]*sclc))
pass
elif adjSpecMD.loc[sn, tn] < 0 and ed.attr['color'] != '#FFFF00':
#sclc = float(adjSpecMD.loc[sn, tn])/maxValN*1.0
#ed.attr['color'] = matplotlib.colors.to_hex([1.0-sclc, 1.0, 0.0])
#ed.attr['color'] = matplotlib.colors.to_hex((high0[0]+deltah[0]*sclc,high0[1]+deltah[1]*sclc,high0[2]+deltah[2]*sclc))
pass
if edgeWidth == 0:
ed.attr['fontsize'] = fontSize
ed.attr['fontname'] = "Arial"
if numStr == 'float':
ed.attr['label'] = '%.2f' % (adjSpecMD.loc[sn, tn])
elif numStr == 'int':
ed.attr['label'] = '%d' % (adjSpecMD.loc[sn, tn])
else:
if adjSpecMD.loc[sn, tn] > 0:
if adjSpecMD.loc[sn, tn]*edgeWidth/maxValP < 1:
ed.attr['penwidth'] = 1
else:
ed.attr['penwidth'] = int(float(adjSpecMD.loc[sn, tn])*edgeWidth/maxValP)
else:
if adjSpecMD.loc[sn, tn]*edgeWidth/maxValN < 1:
ed.attr['penwidth'] = 1
else:
ed.attr['penwidth'] = int(float(adjSpecMD.loc[sn, tn])*edgeWidth/maxValN)
# set node color
nxgSD.node_attr['style']='filled,setlinewidth(0)'
idx = 0
maxCltSize = max(cltSizeDict.values())
for lb in labels:
try:
nd=nxgSD.get_node(lb)
except:
continue
nd.attr['shape'] = 'circle'
if maxClusterSize != 0:
radis = cltSizeDict[nd]*maxClusterSize/maxCltSize
nd.attr['width'] = str(round(radis,2))
nd.attr['height'] = str(round(radis,2))
nd.attr['fixedsize'] = 'true'
nd.attr['fillcolor'] = matplotlib.colors.to_hex(cltSizeColorDict[str(nd).split('\n')[0]])
nd.attr['pin'] = 'true'
nd.attr['pos'] = '%s,%s' % (wposDict[idx][0]*plotWidth/2,wposDict[idx][1]*plotHeight/2)
nd.attr['fontsize'] = fontSize
nd.attr['fontname'] = "Arial"
idx += 1
plotFileName = 'network_delta_diff_%s-based_layout_%s.%s' % (typeStr, layout, plotFormat)
readmeStr += 'network_delta_diff_%s-based_layout_xx.%s: cluster-to-cluster communication network in which the edge weight is the difference of %s.\n' % (typeStr, plotFormat, typeStr)
plotFileName = os.path.join(resultDir, plotFileName)
warnings.simplefilter("error")
while True:
try:
nxgSD.draw(plotFileName,prog='fdp')
break
except RuntimeWarning as rw:
errorNodeList = [m[m.find("'")+1:m.find("',")] for m in str(rw).split("Warning:") if 'too small' in m]
if len(errorNodeList)==0:
errorNodeList = [m[m.find("'")+1:m.find("',")] for m in str(rw).split("Warning:") if 'too small' in m]
nxgSD.draw(plotFileName,prog='neato')
break
for nd in nxgSD.nodes():
if str(nd) in errorNodeList:
nd.attr['xlabel'] = str(nd)
nd.attr['label'] = ''
continue
warnings.simplefilter("ignore")
# draw fold change networks
# calculate fold changes
adjSpecM1 = adjSpecM1.astype(float)
adjSpecM2 = adjSpecM2.astype(float)
adjSpecMF = pd.DataFrame(0.0, index=adjSpecMD.index, columns=adjSpecMD.columns)
for idx in adjSpecMD.index:
for col in adjSpecMD.columns:
if adjSpecMD.loc[idx, col] < 0:
if adjSpecM2.loc[idx, col] > 0:
adjSpecMF.loc[idx, col] = adjSpecM1.loc[idx, col]/adjSpecM2.loc[idx, col]
else:
if adjSpecM1.loc[idx, col] > 0:
adjSpecMF.loc[idx, col] = adjSpecM2.loc[idx, col]/adjSpecM1.loc[idx, col]
maxVal = adjSpecMF.max().max()
#set inf as 2 x max fold change
for idx in adjSpecMD.index:
for col in adjSpecMD.columns:
if adjSpecMD.loc[idx, col] < 0:
if adjSpecM2.loc[idx, col] == 0:
adjSpecMF.loc[idx, col] = maxVal * 2
else:
if adjSpecM1.loc[idx, col] == 0:
adjSpecMF.loc[idx, col] = maxVal * 2
maxVal = adjSpecMF.max().max()
# convert to a graphviz graph
nxgSF = nx.MultiDiGraph(adjSpecMF)
nxgSF = nx.nx_agraph.to_agraph(nxgSF)
nxgSF.graph_attr.update(fontname = "Arial")
nxgSF.graph_attr.update(fontsize = fontSize)
nxgSF.graph_attr.update(margin = 0)
nxgSF.graph_attr.update(ratio="adjSpecMD")
nxgSF.graph_attr.update(label='Delta Network\nEdge weight: fold change of ' + typeStr)
nxgSF.graph_attr.update(size = "%s,%s" % (plotWidth, plotHeight))
# set edge properties
for ed in nxgSF.edges():
sn = ed[0]
tn = ed[1]
if adjSpecMD.loc[sn, tn] < 0 and adjSpecM2.loc[sn, tn] > 0 and adjSpecM1.loc[sn, tn]/adjSpecM2.loc[sn, tn] > 2:
ed.attr['color'] = '#FF0000'
elif adjSpecMD.loc[sn, tn] > 0 and adjSpecM1.loc[sn, tn] > 0 and adjSpecM2.loc[sn, tn]/adjSpecM1.loc[sn, tn] > 2:
ed.attr['color'] = '#0000FF'
else:
ed.attr['color'] = '#FFFF00'
#sclc = float(adjSpecMF.loc[sn, tn])/maxVal*1.0
if adjSpecMD.loc[sn, tn] > 0 and ed.attr['color'] != '#FFFF00':#to red
#ed.attr['color'] = matplotlib.colors.to_hex([1.0, 1.0-sclc, 0.0])
#ed.attr['color'] = matplotlib.colors.to_hex((low0[0]+deltal[0]*sclc,low0[1]+deltal[1]*sclc,low0[2]+deltal[2]*sclc))
pass
elif adjSpecMD.loc[sn, tn] < 0 and ed.attr['color'] != '#FFFF00':
#ed.attr['color'] = matplotlib.colors.to_hex([1.0-sclc, 1.0, 0.0])
#ed.attr['color'] = matplotlib.colors.to_hex((high0[0]+deltah[0]*sclc,high0[1]+deltah[1]*sclc,high0[2]+deltah[2]*sclc))
pass
if edgeWidth == 0:
ed.attr['fontsize'] = fontSize
ed.attr['fontname'] = "Arial"
if adjSpecMD.loc[sn, tn] > 0:
ed.attr['label'] = '%.2f' % (adjSpecMF.loc[sn, tn])
else:
ed.attr['label'] = '-%.2f' % (adjSpecMF.loc[sn, tn])
else:
if adjSpecMF.loc[sn, tn]*edgeWidth/maxVal < 1:
ed.attr['penwidth'] = 1
else:
ed.attr['penwidth'] = int(adjSpecMF.loc[sn, tn]*edgeWidth/maxVal)
# set node color
nxgSF.node_attr['style']='filled,setlinewidth(0)'
idx = 0
maxCltSize = max(cltSizeDict.values())
for lb in labels:
try:
nd=nxgSF.get_node(lb)
except:
continue
nd.attr['shape'] = 'circle'
if maxClusterSize != 0:
radis = cltSizeDict[nd]*maxClusterSize/maxCltSize
nd.attr['width'] = str(round(radis,2))
nd.attr['height'] = str(round(radis,2))
nd.attr['fixedsize'] = 'true'
nd.attr['fillcolor'] = matplotlib.colors.to_hex(cltSizeColorDict[str(nd).split('\n')[0]])
nd.attr['pin'] = 'true'
nd.attr['pos'] = '%s,%s' % (wposDict[idx][0]*plotWidth/2,wposDict[idx][1]*plotHeight/2)
nd.attr['fontsize'] = fontSize
nd.attr['fontname'] = "Arial"
idx += 1
plotFileName = 'network_fold_change_%s-based_layout_%s.%s' % (typeStr, layout, plotFormat)
readmeStr += 'network_fold_change_%s-based_layout_xx.%s: cluster-to-cluster communication network in which the edge weight is the fold change of %s.\n' % (typeStr, plotFormat, typeStr)
plotFileName = os.path.join(resultDir, plotFileName)
warnings.simplefilter("error")
while True:
try:
nxgSF.draw(plotFileName,prog='fdp')
break
except RuntimeWarning as rw:
errorNodeList = [m[m.find("'")+1:m.find("',")] for m in str(rw).split("Warning:") if 'too small' in m]
if len(errorNodeList)==0:
errorNodeList = [m[m.find("'")+1:m.find("',")] for m in str(rw).split("Warning:") if 'too small' in m]
nxgSF.draw(plotFileName,prog='neato')
break
for nd in nxgSF.nodes():
if str(nd) in errorNodeList:
nd.attr['xlabel'] = str(nd)
nd.attr['label'] = ''
continue
warnings.simplefilter("ignore")
for idx in adjSpecMD.index:
for col in adjSpecMD.columns:
if adjSpecMD.loc[idx, col] < 0:
if adjSpecM2.loc[idx, col] == 0:
adjSpecMF.loc[idx, col] = adjSpecM1.loc[idx, col] / adjSpecM2.loc[idx, col] * -1
else:
if adjSpecM1.loc[idx, col] == 0:
adjSpecMF.loc[idx, col] = adjSpecM2.loc[idx, col] / adjSpecM1.loc[idx, col]
return readmeStr, adjSpecMF
def DrawChord(readmeStr, typeStr, numStr, tempM, colors, dataType, resultDir, plotWidth, plotHeight, fontSize, specificityThreshold, weightThreshold, frequencyThreshold, interDB, weightType, plotFormat):
import sys
if 2 == sys.version_info[0]:
return readmeStr
from matplotlib.colors import LinearSegmentedColormap
cmap = LinearSegmentedColormap.from_list('Custom', colors, len(colors))
newlbls = []
for idx in tempM.index:
newlbls.append(idx.split('\n')[0])
oldidx = tempM.index
tempM.index = newlbls
newcts = []
for col in tempM.columns:
newcts.append(col.split('\n')[0])
oldcol = tempM.columns
tempM.columns = newcts
import holoviews as hv
from holoviews import dim, opts
hv.extension('matplotlib')
nodeDF = pd.DataFrame({'name':list(newcts)}).reset_index()
nodes = hv.Dataset(nodeDF, 'index')
linkDict = {'source':[],'target':[],'value':[]}
for idx in tempM.index:
sid = int(nodeDF.loc[nodeDF['name']==idx,'index'])
for col in tempM.columns:
if tempM.loc[idx,col] > 0:
tid = int(nodeDF.loc[nodeDF['name']==col,'index'])
linkDict['source'].append(sid)
linkDict['target'].append(tid)
linkDict['value'].append(int(tempM.loc[idx,col]*1000))
links = pd.DataFrame(linkDict)
chd = hv.Chord((links, nodes))
chd = chd.opts(opts.Chord(cmap=cmap, node_color=dim('index').astype(str), edge_color=dim('source').astype(str), labels='name', fontscale=200))
chd = chd.opts(fontsize={'legend': fontSize*4, 'labels': fontSize*4, 'legend_title': fontSize*4}, fig_size=plotWidth*plotHeight*5)
if dataType == '':
plotFileName = 'chord_%s-based.%s' % (typeStr, plotFormat)
readmeStr += 'chord_%s-based.%s: the cluster-to-cluster communication chord diagram in which the edge weight is %s.\n' % (typeStr, plotFormat, typeStr)
else:
plotFileName = '%s_chord_%s-based.%s' % (dataType, typeStr, plotFormat)
readmeStr += 'xxx_chord_%s-based.%s: dynamic cluster-to-cluster communication chord diagram in which the edge weight is %s.\n' % (typeStr, plotFormat, typeStr)
plotFileName = os.path.join(resultDir, plotFileName)
hv.save(chd, plotFileName)
tempM.columns = oldcol
tempM.index = oldidx
return readmeStr
def DrawHeatmap(readmeStr, typeStr, numStr, tempM, dataType, resultDir, plotWidth, plotHeight, fontSize, specificityThreshold, weightThreshold, frequencyThreshold, interDB, weightType, plotFormat):
newlbls = []
for idx in tempM.index:
newlbls.append(idx.split('\n')[0])
oldidx = tempM.index
tempM.index = newlbls
newcts = []
for col in tempM.columns:
newcts.append(col.split('\n')[0])
oldcol = tempM.columns
tempM.columns = newcts
cmap = 'afmhot_r'
import matplotlib.pyplot as plt
f, ax = plt.subplots(figsize=(max(tempM.shape), max(tempM.shape)))
if numStr == 'int':
if tempM.max().max() < 100:
g = sns.heatmap(tempM.astype(int), square=True, annot=True, fmt=".0f", linewidths=.5, ax=ax, cmap=cmap)
else:
g = sns.heatmap(tempM.astype(int), square=True, annot=False, linewidths=.5, ax=ax, cmap=cmap)
else:
g = sns.heatmap(tempM, square=True, annot=False, linewidths=.5, ax=ax, cmap=cmap)
ax.set_xlabel('Cell-type expressing receptor (receiving)')
ax.set_ylabel('Cell-type expressing ligand (sending)')
fig = g.get_figure()
if dataType == '':
plotFileName = 'heatmap_%s-based.%s' % (typeStr, plotFormat)
readmeStr += 'heatmap_%s-based.%s: the cluster-to-cluster communication heatmap in which the element is %s.\n' % (typeStr, plotFormat, typeStr)
else:
plotFileName = '%s_heatmap_%s-based.%s' % (dataType, typeStr, plotFormat)
readmeStr += 'xxx_heatmap_%s-based.%s: dynamic cluster-to-cluster communication heatmap in which the element is %s.\n' % (typeStr, plotFormat, typeStr)
plotFileName = os.path.join(resultDir, plotFileName)
fig.savefig(plotFileName, bbox_inches = "tight")
tempM.columns = oldcol
tempM.index = oldidx
return readmeStr
def DrawGraphvizPlot(readmeStr, typeStr, numStr, nxgS, adjSpecM, dataType, resultDir, plotWidth, plotHeight, fontSize, edgeWidth, colorDict, cltSizeDict, maxClusterSize, wposDict, labels, specificityThreshold, weightThreshold, frequencyThreshold, interDB, weightType, layout, plotFormat):
# convert to a graphviz graph
nxgS = nx.nx_agraph.to_agraph(nxgS)
# draw whole network
nxgS.graph_attr.update(fontname = "Arial")
nxgS.graph_attr.update(fontsize = fontSize)
nxgS.graph_attr.update(margin = 0)
nxgS.graph_attr.update(ratio="fill")
nxgS.graph_attr.update(label='Edge weight: ' + typeStr)
nxgS.graph_attr.update(size = "%s,%s" % (plotWidth, plotHeight))
# set edge properties
maxVal = adjSpecM.max().max()
for ed in nxgS.edges():
sn = ed[0]
tn = ed[1]
#newcol = [i+0.5*(1-i) for i in matplotlib.colors.to_rgb(colorDict[sn])]
#ed.attr['color'] = matplotlib.colors.to_hex(newcol)
ed.attr['color'] = colorDict[sn]
if edgeWidth == 0:
ed.attr['fontsize'] = fontSize
ed.attr['fontname'] = "Arial"
if numStr == 'float':
ed.attr['label'] = '%.2f' % (adjSpecM.loc[sn, tn])
elif numStr == 'int':
ed.attr['label'] = '%d' % (adjSpecM.loc[sn, tn])
else:
if adjSpecM.loc[sn, tn]*edgeWidth/maxVal < 1:
ed.attr['penwidth'] = 1
else:
ed.attr['penwidth'] = int(adjSpecM.loc[sn, tn]*edgeWidth/maxVal)
# set node color
nxgS.node_attr['style']='filled,setlinewidth(0)'
idx = 0
maxCltSize = max(cltSizeDict.values())
for lb in adjSpecM.index:
try:
nd=nxgS.get_node(lb)
except:
continue
nd.attr['shape'] = 'circle'
if maxClusterSize != 0:
radis = cltSizeDict[nd]*maxClusterSize/maxCltSize
nd.attr['width'] = str(round(radis,2))
nd.attr['height'] = str(round(radis,2))
nd.attr['fixedsize'] = 'true'
#newcol = [i+0.5*(1-i) for i in matplotlib.colors.to_rgb(colorDict[nd])]
#nd.attr['fillcolor'] = matplotlib.colors.to_hex(newcol)
nd.attr['fillcolor'] = colorDict[nd]
nd.attr['pin'] = 'true'
nd.attr['pos'] = '%s,%s' % (wposDict[idx][0]*plotWidth/2,wposDict[idx][1]*plotHeight/2)
nd.attr['fontsize'] = fontSize
nd.attr['fontname'] = "Arial"
idx += 1
if dataType == '':
plotFileName = 'network_%s-based_layout_%s.%s' % (typeStr, layout, plotFormat)
readmeStr += 'network_%s-based_layout_xx.%s: the cluster-to-cluster communication network in which the edge weight is %s.\n' % (typeStr, plotFormat, typeStr)
else:
plotFileName = '%s_network_%s-based_layout_%s.%s' % (dataType, typeStr, layout, plotFormat)
readmeStr += 'xxx_network_%s-based_layout_xx.%s: dynamic cluster-to-cluster communication network in which the edge weight is %s.\n' % (typeStr, plotFormat, typeStr)
plotFileName = os.path.join(resultDir, plotFileName)
warnings.simplefilter("error")
while True:
try:
nxgS.draw(plotFileName,prog='fdp')
break
except RuntimeWarning as rw:
errorNodeList = [m[m.find("'")+1:m.find("',")] for m in str(rw).split("Warning:") if 'too small' in m]
if len(errorNodeList)==0:
errorNodeList = [m[m.find("'")+1:m.find("',")] for m in str(rw).split("Warning:") if 'too small' in m]
nxgS.draw(plotFileName,prog='neato')
break
for nd in nxgS.nodes():
if str(nd) in errorNodeList:
nd.attr['xlabel'] = str(nd)
nd.attr['label'] = ''
continue
warnings.simplefilter("ignore")
return readmeStr
def BuildDeltaInterClusterNetwork(origlabels, labels, cltSizes, ccolorList, edgeDF, specificityThreshold, weightThreshold, frequencyThreshold, keepTopEdge, interDB, weightType, layout, plotFormat, plotWidth, plotHeight, fontSize, edgeWidth, maxClusterSize, clusterDistance, resultDir, dataType=''):
readmeStr = '\n'
compT = pd.read_excel(os.path.join(resultDir,'..','cluster_comparison.xlsx'), index_col=None,header=0)
compT = compT.sort_values(by=['Cluster','Source'])
compT = compT.set_index('Cluster')
cltFDdict = {}
maxFC = 0
inflist = []
for idx in range(0,len(compT),2):
if compT.iloc[idx,1] * compT.iloc[idx+1,1] > 0:
if compT.iloc[idx,1] > compT.iloc[idx+1,1]:
tempFC = compT.iloc[idx,1]/compT.iloc[idx+1,1]
cltFDdict[compT.index[idx]] = -1.0*tempFC
elif compT.iloc[idx,1] < compT.iloc[idx+1,1]:
tempFC = compT.iloc[idx+1,1]/compT.iloc[idx,1]
cltFDdict[compT.index[idx]] = 1.0*tempFC
else:
tempFC = 1
cltFDdict[compT.index[idx]] = 1.0*tempFC
if maxFC < tempFC:
maxFC = tempFC
elif compT.iloc[idx,1] * compT.iloc[idx+1,1] == 0:
inflist.append(idx)
#set inf as 2 x max fold change
maxFC = maxFC *2
for idx in inflist:
if compT.iloc[idx,1] != 0:
cltFDdict[compT.index[idx]] = -1.0 * maxFC
else:
cltFDdict[compT.index[idx]] = 1.0 * maxFC
# color scheme
colors = sns.color_palette(ccolorList,len(labels)).as_hex()