#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 6 11:24:49 2018
@author: rhou
"""
import warnings
warnings.filterwarnings("ignore")
import argparse
import pandas as pd
import os, sys, glob
from scipy import io
def CalCords(savefolder, em, visMethod):
cordFile = os.path.join(savefolder, 'cords_%s.csv' % visMethod)
if os.path.exists(cordFile):
cords = pd.read_csv(cordFile, index_col=0, header=0)
return cords
x = em.T.values
from sklearn.preprocessing import StandardScaler
x = StandardScaler().fit_transform(x)
from sklearn.decomposition import PCA
pca = PCA(n_components=10)
principalComponents = pca.fit_transform(x)
if visMethod == 'PCA':
cords = pd.DataFrame(data = principalComponents[:,:2], columns = ['x', 'y'], index = em.columns)
elif visMethod == 'tSNE':
from sklearn.manifold import TSNE
tsneComponents = TSNE(n_components=2).fit_transform(principalComponents)
cords = pd.DataFrame(data = tsneComponents, columns = ['x', 'y'], index = em.columns)
elif visMethod == 'UMAP':
import umap
umapComponents = umap.UMAP(n_components=2).fit_transform(principalComponents)
cords = pd.DataFrame(data = umapComponents, columns = ['x', 'y'], index = em.columns)
cords.index.name = 'barcode'
cords.to_csv(cordFile, index=True, header=True)
return cords
def DrawScatters(savefolder, annoFile, visMethod, cords, annos):
import plotly
import plotly.graph_objs as go
annText = os.path.basename(annoFile).split('.')[0]
for kind in ['cell type', 'top sample']:
if kind not in annos.columns:
continue
annotationList = sorted(list(set(annos.ix[:,kind])))
import seaborn as sns
colorList = sns.hls_palette(n_colors=len(annotationList))
data = []
annoLen = 0
for annoIdx in range(len(annotationList)):
annoNames = annotationList[annoIdx]
if len(annoNames) > annoLen:
annoLen = len(annoNames)
indicesOfAnno = annos[kind]==annoNames
text = []
for idx in annos.index[indicesOfAnno]:
show_text = '%s: %s, barcode: %s' % (kind, annoNames, idx)
text.append(show_text)
trace = go.Scatter(
x = cords.ix[annos.index[indicesOfAnno],'x'],
y = cords.ix[annos.index[indicesOfAnno],'y'],
name = annoNames,
mode = 'markers',
marker=dict(
color='rgb(%s, %s, %s)' % colorList[annoIdx],
size=5,
symbol='circle',
line=dict(
color='rgb(204, 204, 204)',
width=1
),
opacity=0.9
),
text = text,
)
data.append(trace)
if annoLen < 35:
layout = go.Layout(legend=dict(orientation="v"),autosize=True,showlegend=True)
else:
layout = go.Layout(legend=dict(orientation="v"),autosize=True,showlegend=False)
fig = go.Figure(data=data, layout=layout)
fn = os.path.join(savefolder, '%s_%s_%s.html' % (annText, kind.replace(' ', '_'), visMethod))
print('##########saving plot: %s' % fn)
plotly.offline.plot(fig, filename=fn)
#start to visualise test dataset
def main(testFormat, testDS, annoFile, visMethod):
#load test data
print('##########loading test data')
if testFormat == '10x':
fileItem = glob.glob(os.path.join(testDS, "matrix.mtx"))[0]
em = io.mmread(fileItem)
em = em.tocsr().toarray()
if os.path.exists(os.path.join(opt.testDS, 'genes.tsv')):
row = pd.read_table(fileItem[:-10]+"genes.tsv", header=None, index_col=None)
else:
row = pd.read_table(fileItem[:-10]+"features.tsv", header=None, index_col=None)
col = pd.read_table(fileItem[:-10]+"barcodes.tsv", header=None, index_col=None)
em = pd.DataFrame(em, index=row.T.values[1], columns=col.T.values[0])
savefolder = testDS
else:
em = pd.read_csv(testDS, index_col=0, header=0)
savefolder = testDS[:-4]
print('##########reducing dimensions')
cords = CalCords(savefolder, em, visMethod)
annos = pd.read_csv(annoFile, index_col=0, header=0)
commonIdx = set(cords.index).intersection(set(annos.index))
cords = cords.ix[commonIdx,]
annos = annos.ix[commonIdx,]
print('##########darwing the scatter plots in the folder: %s' % savefolder)
DrawScatters(savefolder, annoFile, visMethod, cords, annos)
print('##########DONE!')
if __name__ == "__main__":
#process arguments
parser = argparse.ArgumentParser()
parser.add_argument('--testDS', required=True, help='path to the folder of test dataset if dtype is 10x, otherwise, the path to the file')
parser.add_argument('--dFormat', default='10x', help='10x (default) | csv')
parser.add_argument('--annoFile', required=True, help='path to the annotation file generated by scMatch')
parser.add_argument('--visMethod', default='p', help='p[ca] (default) | t[sne] | u[map]')
opt = parser.parse_args()
#check visMethod
if opt.visMethod.lower() == "p":
visMethod = 'PCA'
elif opt.visMethod.lower() == "t":
visMethod = 'tSNE'
elif opt.visMethod.lower() == "u":
visMethod = 'UMAP'
else:
sys.exit("The visMethod can only be 'p', 't' or 'u'.")
#check dFormat and testDS
if opt.dFormat.lower() == 'csv':
if not os.path.exists(opt.testDS):
sys.exit("The test dataset file does not exist.")
if opt.testDS[-4:].lower() != '.csv':
sys.exit("The test dataset file is not a CSV file.")
elif opt.dFormat.lower() == '10x':
if not os.path.exists(opt.testDS):
sys.exit("The folder of test dataset does not exist.")
if not os.path.exists(os.path.join(opt.testDS, 'matrix.mtx')):
sys.exit("Cannot find 'matrix.mtx' file in the folder of test dataset.")
if not os.path.exists(os.path.join(opt.testDS, 'genes.tsv')):
if not os.path.exists(os.path.join(opt.testDS, 'features.tsv')):
sys.exit("Cannot find 'genes.tsv' or 'features.tsv' file in the folder of test dataset.")
if not os.path.exists(os.path.join(opt.testDS, 'barcodes.tsv')):
sys.exit("Cannot find 'barcodes.tsv' file in the folder of test dataset.")
#check annotation file
if not os.path.exists(opt.annoFile):
sys.exit("Cannot find annotation file for single cells.")
#pass argument check, show input data
print('===================================================')
print('Input data:')
print('The format of the test dataset: %s' % opt.dFormat)
if opt.dFormat == '10x':
print('Test data are in the folder: %s' % opt.testDS)
else:
print('Test data are in the file: %s' % opt.testDS)
print('Annotation file: %s' % opt.annoFile)
print('The visualisation method: %s' % visMethod)
print('===================================================')
#start to visualise test dataset
main(opt.dFormat, opt.testDS, opt.annoFile, visMethod)
#python visAnno.py --dFormat csv --testDS GSE81861_Cell_Line_COUNT.csv --annoFile GSE81861_Cell_Line_COUNT/annotation_result_keep_all_genes/human_Spearman_top_ann.csv
