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# Import
import cProfile
from distutils import core
from pathlib import Path
import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import seaborn as sns
import os
import pickle
import scipy.stats as stats
import argparse
from adjustText import adjust_text
import matplotlib.backends.backend_pdf
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-b', type = str, help = 'Path to barcodes')
parser.add_argument('-xp', type = str, help = 'Experiment ID')
parser.add_argument('-c', type = str, default = "D:\BCCAncer\FILES\codebook_0_C1E1_van.csv", help = 'Path to codebook')
parser.add_argument('-a', type = str, default = "D:\BCCAncer\FILES\XP2474_4T1_C1E1_new_bulk.csv", help = 'Path to bulk file (abundance file)')
parser.add_argument('-o', type = str, default = "D:\BCCAncer\EXP\XP7174\Correlations\BulkCorr", help = 'Path to save processed files')
parser.add_argument('-is_cambridge', type = bool, default = False, help = 'Are the results for Cambridge datasets?')
parser.add_argument('-drop_blanks', type = bool, default = False, help = 'Remove blanks before correlation')
parser.add_argument('-log', type =bool, default = True, help = 'Do we want to take log while correlation of bulk?')
parser.add_argument("-d", nargs="+", type = float, default=0.65, help = 'Max mean distance threshold for an area of detected barcode')
parser.add_argument("-removeZ", nargs="+", type=int, default=None, help="Z slices to remove from evaluation")
#parser.add_argument('-gene_list', type = str, action='append', required=True, help = 'List of genes which we want to exclude from analysis')
args = parser.parse_args()
if not os.path.exists(args.o):
os.makedirs(args.o)
info_np = np.zeros(shape = (len(args.d), 7))
# read sheet and remove undesired z slices
df = read_sheet(args.b)
if args.removeZ is not None:
removeZ_set = set(args.removeZ)
df = df[~df['z'].isin(removeZ_set)]
pdf = matplotlib.backends.backend_pdf.PdfPages(f'{args.o}/{args.xp}_correlation.pdf')
correlation = Correlation(args.c, df, args.a, args.o, args.xp, args.is_cambridge, pdf)
args.d.sort(reverse = True)
for idx, dist_threshold in enumerate(args.d):
filter_df = correlation.filter_distance(dist_threshold)
gp_df = correlation.groupby(filter_df)
gp_df = correlation.merge_df_cb(gp_df)
tot_counts = gp_df.counts.sum()
blank_counts = gp_df.loc[gp_df['gene_symbol'].isin(['Blank_01', 'Blank_02', 'Blank_03', 'Blank_04', 'Blank_05', 'Blank_06'])].counts.sum()
info_np[idx][0] = dist_threshold
info_np[idx][3] = int(tot_counts)
info_np[idx][4] = int(blank_counts)
info_np[idx][5] = int(len(gp_df))
gp_df = correlation.df_bulk(gp_df)
if args.drop_blanks:
gp_df = correlation.remove_blanks(gp_df)
"""
if args.gene_list:
print('removing')
gp_df = correlation.remove_specific_genes(gp_df)
"""
info_np[idx][-1] = len(gp_df)
gp_df['log_counts'] = np.log2(gp_df['counts']+1)
gp_df['log_tpm'] = np.log2(gp_df['bulk_exp']+0.0001)
gp_df.to_csv(f'{args.o}/{args.xp}_{dist_threshold}.csv')
info_np[idx][1], info_np[idx][2] = correlation.log_correlation(gp_df, dist_threshold)
pdf.close()
info_df = pd.DataFrame(info_np, columns = ['Distance Threshold',\
'Pearson correlation',\
'Spearman correlation',\
'# detected barcodes (including control barcodes)',\
'# detected control (blanks) barcodes',\
'# genes at dist threshold in correlation',\
'# barcodes counted towards correlaton estimation'])
info_df.to_csv(f'{args.o}/info_{args.xp}.csv')
def read_sheet(file):
ext = file.split(".")[-1]
if ext == "csv":
df = pd.read_csv(file)
elif ext == "tsv":
df = pd.read_csv(file, '\t')
elif ext in {"xls", "xlsx", "xlsm", "xlsb"}:
df = pd.read_excel(file)
else:
raise ValueError("Unexpected file extension")
return df
class Correlation:
def __init__(self, codebook, barcodes, bulk, output_dir, name, is_cambridge, pdf_object):
self.codebook = read_sheet(codebook)
self.barcodes = barcodes
self.bulk = read_sheet(bulk)
self.output_dir = output_dir
self.name = name
self.is_cambridge = is_cambridge
self.pdf_object = pdf_object
#self.remove_genes_list = remove_genes_list
def filter_distance(self, distance_threshold):
return self.barcodes.loc[self.barcodes['mean_distance']<distance_threshold]
def groupby(self, df):
return (df.groupby('barcode_id').size().reset_index(name='counts'))
def modify_codebook(self):
self.codebook['barcode_id'] = self.codebook.index
self.codebook.rename(columns={"name": "gene_symbol"}, inplace = True)
def merge_df_cb(self,df):
self.modify_codebook()
return pd.merge(self.codebook, df, how='inner', on='barcode_id')
def df_bulk(self, df):
return pd.merge(self.bulk, df, how='inner', on='gene_symbol')
def remove_blanks(self,df):
return df[df['bulk_exp'] != 0]
"""
def remove_specific_genes(self,df):
c = 0
print(self.remove_genes_list)
for gene in self.remove_genes_list:
print(c, gene)
df = df.loc[df['gene_symbol']!=gene]
#cccprint(df.loc[df['gene_symbol']==gene])
c+=1
return df
"""
def log_correlation(self, df, dist_threshold):
f1, ax = plt.subplots(figsize=(9, 9))
sns.set_palette("deep")
sns.scatterplot(x="log_tpm",y="log_counts",data=df,ax=ax)
if self.is_cambridge == True:
count_type = "C_counts"
else: count_type = "V_counts"
ax.set_title("TPM Correlation for " + self.name, fontsize = 15)
ax.set_xlabel("log2(TPM+1e-4), V_bulk", fontsize = 20)
ax.set_ylabel("log2(# detected counts+1), "+ count_type, fontsize = 20)
def plotlabel(xvar, yvar, label):
ax.text(xvar+0.02, yvar, label)
pearson, _ = stats.pearsonr(df["log_tpm"],df["log_counts"])
spearman, _ = stats.spearmanr(df["log_tpm"],df["log_counts"])
#kendalltau, _ = stats.kendalltau(df["log_tpm"],df["log_counts"])
ax.text(.01, .95, 'Pearson = {:.2f}\nSpearman = {:.2f}'.format(pearson,spearman),transform=ax.transAxes)
#df.apply(lambda x: plotlabel(x['log_tpm'], x['log_counts'], x['gene_symbol']), axis=1)
#plt.axvline(x = 0, color = 'r', label = 'axvline - full height')
self.pdf_object.savefig(f1)
texts = []
for xs,ys,label in zip(df['log_tpm'],df['log_counts'],df['gene_symbol']):
texts.append(ax.text(xs,ys,label))
adjust_text(texts, force_points=0.2, force_text=0.2,expand_points=(1, 1), expand_text=(1, 1), arrowprops=dict(arrowstyle="-", color='black', lw=0.5))
plt.tight_layout()
#plt.savefig(f'{self.output_dir}/{self.name}_{dist_threshold}.pdf')
self.pdf_object.savefig(f1)
return pearson, spearman
if __name__ == '__main__':
#cProfile.run('main()')
main()
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