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IntronSpliceRatio_final.py
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IntronSpliceRatio_final.py
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import re
import pysam
import pandas as pd
import numpy as np
import scipy.stats as stats
import seaborn as sns
from plotnine import *
from time import strftime,localtime # 20210630 localtime, or ctime: Beijing Time
import argparse
parser = argparse.ArgumentParser(description='To calculate the splicing ratio of introns from cb-RNA-seq data.',
epilog='Author: Fengli Zhao\tzhaofl@sustech.edu.cn 20220301 20211110 20210630 20210618 20210329 20201231 20201222 20201216')
parser.add_argument('--inputs', type=str, required=True, help='Input bam files. Separated by comma.')
parser.add_argument('--geneflt', type=str, help='FPKM matrix file of gene, used to filter the low expressed genes. [No]', default='No')
parser.add_argument('--intron', type=str, help='The intron position file. [TAIR10.LongRNA.intron]', default='TAIR10.LongRNA.intron')
parser.add_argument('--ex_chr', type=str, help='The excluded Chr(s). Else, separated by comma. [No]', default='No')
parser.add_argument('--output', type=str, required=True, help='The name of output file.')
args = parser.parse_args()
infile = args.inputs
geneflt = args.geneflt
intron = args.intron
ex_chr = args.ex_chr
output = args.output
# Format of the 'TAIR10.LongRNA.intron'
#LOC_name Transcript Chr Strand Start End GeneLen Intron_Num Intron_ID
#AT1G01010 AT1G01010.1 Chr1 + 3914 3995 2269 5 1
#AT1G01010 AT1G01010.1 Chr1 + 4277 4485 2269 5 2
#AT1G01010 AT1G01010.1 Chr1 + 4606 4705 2269 5 3
#AT1G01010 AT1G01010.1 Chr1 + 5096 5173 2269 5 4
#AT1G01010 AT1G01010.1 Chr1 + 5327 5438 2269 5 5
###################################################################################################################
def readcount(bam,chrom,start0,end0):
openbam = pysam.AlignmentFile(bam, mode='rb', threads=2)
new_dp = openbam.count_coverage(chrom, start0, end0, quality_threshold = 20)
read = 0; index = 0; ii = 0
while ii < 25:
new = int(new_dp[0][ii]) + int(new_dp[1][ii]) + int(new_dp[2][ii]) + int(new_dp[3][ii])
if ii == 0:
read = new
index = new
else:
if new <= index:
index = new
else:
increase = new - index
read += increase
index = new
ii += 1
openbam.close()
return read
###################################################################################################################
time0 = strftime("%Y-%m-%d %H:%M:%S", localtime())
print('=' * 100)
print('\t[' + time0 + ']\tBeginning to run the IntronSpliceRatio_final.py ...\n')
SS5_dict = {}; SS3_dict = {}; ex_chr_arr = []; FPKM_dict = {}; sample_arr = []
Read_5_str = {}; Read_3_str = {}
bamRE = re.compile(r'(\S+).bam')
if ex_chr != 'No':
ex_chr_arr = ex_chr.split(',')
if geneflt != 'No':
openflt = open(geneflt, 'r')
idx = 0
while True:
lineflt = openflt.readline()
if not lineflt:
break
else:
newarr = lineflt.strip().split('\t')
if idx == 0:
for bamname in newarr[1:]:
nmsamp = bamRE.search(bamname)
samp = nmsamp.group(1)
sample_arr.append(samp)
else:
idxflt = 1
while idxflt < len(newarr):
FPKM_dict.setdefault(newarr[0], {})[sample_arr[idxflt - 1]] = newarr[idxflt]
idxflt += 1
idx += 1
openflt.close()
Array00 = []; Array11 = []; Array22 = []; intron_file_head = ''
bams = infile.split(',')
for bam in bams:
time1 = strftime("%Y-%m-%d %H:%M:%S", localtime())
print('\t\t[' + time1 + ']\tDealling with the ' + bam + ' ...')
nmbam = bamRE.search(bam)
name = nmbam.group(1)
openin = open(intron, 'r')
while True:
line = openin.readline()
if not line:
break
else:
array = line.strip('\n').split('\t')
if line[:3] == 'LOC':
intron_file_head = '\t'.join(array)
else:
if array[2] in ex_chr_arr:
continue
elif geneflt != 'No':
if array[0] in FPKM_dict.keys():
fpkm = FPKM_dict[array[0]]
if name in fpkm.keys():
rna_intron = '_'.join(array[1:])
if float(fpkm[name]) < 1:
SS5_dict.setdefault(rna_intron, {})[name] = 'unknown'
Read_5_str.setdefault(rna_intron, {})[name] = 'uncounted'
SS3_dict.setdefault(rna_intron, {})[name] = 'unknown'
Read_3_str.setdefault(rna_intron, {})[name] = 'uncounted'
else:
num5i = 0; num5e = 0; num3i = 0; num3e = 0
if array[3] == '+':
start1 = int(array[4]); end1 = int(array[4]) + 25
start2 = int(array[4]) - 1 - 25; end2 = int(array[4]) - 1
start3 = int(array[5]) - 25; end3 = int(array[5])
start4 = int(array[5]) + 1; end4 = int(array[5]) + 1 + 25
else:
start1 = int(array[5]) - 25; end1 = int(array[5])
start2 = int(array[5]) + 1; end2 = int(array[5]) + 1 + 25
start3 = int(array[4]); end3 = int(array[4]) + 25
start4 = int(array[4]) - 1 - 25; end4 = int(array[4]) - 1
num5i = readcount(bam, array[2], start1, end1)
num5e = readcount(bam, array[2], start2, end2)
num3i = readcount(bam, array[2], start3, end3)
num3e = readcount(bam, array[2], start4, end4)
Read_5_str.setdefault(rna_intron, {})[name] = str(num5e) + '_' + str(num5i)
Read_3_str.setdefault(rna_intron, {})[name] = str(num3e) + '_' + str(num3i)
if num5e == 0:
SS5_dict.setdefault(rna_intron, {})[name] = 'unknown'
else:
SS5_dict.setdefault(rna_intron, {})[name] = format(num5i/num5e, '.4f')
if num3e == 0:
SS3_dict.setdefault(rna_intron, {})[name] = 'unknown'
else:
SS3_dict.setdefault(rna_intron, {})[name] = format(num3i/num3e, '.4f')
if (num5e != 0 and num3e != 0):
if ((num5i/num5e >= 0 and num5i/num5e <= 1) and (num3i/num3e >= 0 and num3i/num3e <= 1)):
Array00.append(format(num5i/num5e, '.4f')); Array11.append('5SS'); Array22.append(name)
Array00.append(format(num3i/num3e, '.4f')); Array11.append('3SS'); Array22.append(name)
else:
print('Please make sure the sample names same !\n')
else:
rna_intron = '_'.join(array[1:])
num5i = 0; num5e = 0; num3i = 0; num3e = 0
if array[3] == '+':
start1 = int(array[4]); end1 = int(array[4]) + 25
start2 = int(array[4]) - 1 - 25; end2 = int(array[4]) - 1
start3 = int(array[5]) - 25; end3 = int(array[5])
start4 = int(array[5]) + 1; end4 = int(array[5]) + 1 + 25
else:
start1 = int(array[5]) - 25; end1 = int(array[5])
start2 = int(array[5]) + 1; end2 = int(array[5]) + 1 + 25
start3 = int(array[4]); end3 = int(array[4]) + 25
start4 = int(array[4]) - 1 - 25; end4 = int(array[4]) - 1
num5i = readcount(bam, array[2], start1, end1)
num5e = readcount(bam, array[2], start2, end2)
num3i = readcount(bam, array[2], start3, end3)
num3e = readcount(bam, array[2], start4, end4)
Read_5_str.setdefault(rna_intron, {})[name] = str(num5e) + '_' + str(num5i)
Read_3_str.setdefault(rna_intron, {})[name] = str(num3e) + '_' + str(num3i)
if num5e == 0:
SS5_dict.setdefault(rna_intron, {})[name] = 'unknown'
else:
SS5_dict.setdefault(rna_intron, {})[name] = format(num5i/num5e, '.4f')
if num3e == 0:
SS3_dict.setdefault(rna_intron, {})[name] = 'unknown'
else:
SS3_dict.setdefault(rna_intron, {})[name] = format(num3i/num3e, '.4f')
if (num5e != 0 and num3e != 0):
if ((num5i/num5e >= 0 and num5i/num5e <= 1) and (num3i/num3e >= 0 and num3i/num3e <= 1)):
Array00.append(format(num5i/num5e, '.4f')); Array11.append('5SS'); Array22.append(name)
Array00.append(format(num3i/num3e, '.4f')); Array11.append('3SS'); Array22.append(name)
openin.close()
time2 = strftime("%Y-%m-%d %H:%M:%S", localtime())
print('\n\t[' + time2 + ']\tFinished the computing tasks, and begin to export data ...')
openOut = open(output,'w')
openOut.write(intron_file_head)
for bam in sorted(bams):
nmbam = bamRE.search(bam)
name = nmbam.group(1)
openOut.write('\t' + name + '_5SS\t' + name + '_3SS')
for bam in sorted(bams):
nmbam = bamRE.search(bam)
name = nmbam.group(1)
openOut.write('\t' + name + '_Read_5ei_str\t' + name + '_Read_3ei_str')
openOut.write('\n')
SS5_Arr = {}; SS3_Arr = {}
for rna_intron,value5 in SS5_dict.items():
intronRE = re.compile(r'((\w+).\d+)_(\w+)_([+-])_(\d+)_(\d+)_(\d+)_(\d+)_(\d+)')
nmnm = intronRE.search(rna_intron)
rna = nmnm.group(1); gene = nmnm.group(2); chrid = nmnm.group(3); strand = nmnm.group(4); start = nmnm.group(5)
end = nmnm.group(6); gene_len = nmnm.group(7); intron_num = nmnm.group(8); intron_id = nmnm.group(9)
openOut.write(gene + '\t' + rna + '\t' + chrid + '\t' + strand + '\t' + start + '\t' + end + '\t' + gene_len + '\t' + intron_num + '\t' + intron_id)
Array = []; ArrNew = [];
for name in sorted(value5.keys()):
Array.append(str(value5[name]))
SS5_Arr.setdefault(name,[]).append(value5[name])
if rna_intron in SS3_dict.keys():
value3 = SS3_dict[rna_intron]
for name in sorted(value3.keys()):
Array.append(str(value3[name]))
SS3_Arr.setdefault(name,[]).append(value3[name])
readStr_arr = []; readStr_New = []
if rna_intron in Read_5_str.keys():
str5 = Read_5_str[rna_intron]
for name in sorted(str5.keys()):
readStr_arr.append(str5[name])
if rna_intron in Read_3_str.keys():
str3 = Read_3_str[rna_intron]
for name in sorted(str3.keys()):
readStr_arr.append(str3[name])
mid = int(len(Array)/2)
for i in range(mid):
ArrNew.append(Array[i])
ArrNew.append(Array[i + mid])
readStr_New.append(readStr_arr[i])
readStr_New.append(readStr_arr[i + mid])
rario_str = '\t'.join(ArrNew)
read_str = '\t'.join(readStr_New)
openOut.write('\t' + rario_str + '\t' + read_str + '\n')
openOut.close()
SS5_Samp_arr = {}; SS3_Samp_arr = {}; iii = 0
while iii < len(Array00):
if Array11[iii] == '5SS':
SS5_Samp_arr.setdefault(Array22[iii],[]).append(float(Array00[iii]))
else:
SS3_Samp_arr.setdefault(Array22[iii],[]).append(float(Array00[iii]))
iii += 1
print('\n\n\t\t' + '=' * 50)
print('\t\t5\'SS data:\tSample\tNumber\tMean\tMedian')
for name in SS5_Samp_arr.keys():
arr_1 = SS5_Samp_arr[name]
print('\t\t\t\t' + name + '\t' + str(len(arr_1)) + '\t' + str(format(np.mean(arr_1), ".4f")) + '\t' + str(np.median(arr_1)))
print('\n\t\t3\'SS data:\tSample\tNumber\tMean\tMedian')
for name in SS3_Samp_arr.keys():
arr_2 = SS3_Samp_arr[name]
print('\t\t\t\t' + name + '\t' + str(len(arr_2)) + '\t' + str(format(np.mean(arr_2), ".4f")) + '\t' + str(np.median(arr_2)))
print('\t\t' + '=' * 50 + '\n')
print('\t\t' + 'Wilcox test results:' + '\n')
print('\t\t\t' + '5\'SS data:')
samp_Array5 = sorted(list(SS5_Samp_arr.keys()))
samp_array51 = samp_Array5[:-1]; samp_array52 = samp_Array5[1:]
str1_str1 = '\t'.join(samp_array51)
print('\t\t\t\tSample\t' + str1_str1)
for samp52 in samp_array52:
array_tmp = []
array_tmp.append(samp52)
for samp51 in samp_array51:
if samp51 == samp52:
break
else:
array_1 = SS5_Samp_arr[samp51]; array_2 = SS5_Samp_arr[samp52]
t_val = 0; p_val = 1
if len(array_1) == len(array_2):
t_val, p_val = stats.wilcoxon(array_1, array_2)
else:
t_val, p_val = stats.mannwhitneyu(array_1, array_2)
array_tmp.append(str(p_val))
new_str = '\t'.join(array_tmp)
print('\t\t\t\t' + new_str)
print('\n\t\t\t' + '3\'SS data:')
samp_Array3 = sorted(list(SS3_Samp_arr.keys()))
samp_array31 = samp_Array3[:-1]; samp_array32 = samp_Array3[1:]
str1_str1 = '\t'.join(samp_array31)
print('\t\t\t\t\t' + str1_str1)
for samp32 in samp_array32:
array_tmp = []
array_tmp.append(samp32)
for samp31 in samp_array31:
if samp31 == samp32:
continue
else:
array_1 = SS3_Samp_arr[samp31]; array_2 = SS3_Samp_arr[samp32]
t_val = 0; p_val = 1
if len(array_1) == len(array_2):
t_val, p_val = stats.wilcoxon(array_1, array_2)
else:
t_val, p_val = stats.mannwhitneyu(array_1, array_2)
array_tmp.append(str(p_val))
new_str = '\t'.join(array_tmp)
print('\t\t\t\t' + new_str)
time3 = strftime("%Y-%m-%d %H:%M:%S", localtime())
print('\n\t[' + time3 + ']\tBegin to draw pictures ...\n')
df3 = pd.DataFrame({'SS_ratio':Array00, 'Group':Array11, "Sample":Array22})
df3['SS_ratio']=df3.SS_ratio.astype(float)
boxplot_fig = (ggplot(df3, aes(x='Group', y='SS_ratio', fill='Sample'))
+ geom_boxplot(outlier_alpha=0, outlier_size=0, position=position_dodge(0.85))
+ labs(x="", y="Splicing ratio")
+ guides(fill=guide_legend(title=""))
+ theme_matplotlib()
+ theme(legend_position="top")
+ theme(axis_title=element_text(family="arial", size=12), axis_text=element_text(family="arial", size=10), \
legend_title=element_text(family="arial", size=12), legend_text=element_text(family="arial", size=10)))
pdffig3 = output + '_' + '.SS_ratio.flt.boxplot.pdf'
boxplot_fig.save(pdffig3, format='pdf')
for bam in bams:
time4 = strftime("%Y-%m-%d %H:%M:%S", localtime())
print('\t\t[' + time4 + ']\tDraw pictures for ' + bam + ' ...')
nmbam = bamRE.search(bam)
name = nmbam.group(1)
array01 = SS5_Arr[name]
array02 = SS3_Arr[name]
array11 = []; array22 = []; array33 = []; array44 = []; ii = 0
while ii < len(array01):
if (array01[ii] != 'unknown' and array02[ii] != 'unknown'):
array11.append(array01[ii])
array22.append(array02[ii])
if ((float(array01[ii]) >=0 and float(array01[ii]) <= 1) and (float(array02[ii]) >=0 and float(array02[ii]) <= 1)):
array33.append(array01[ii])
array44.append(array02[ii])
ii += 1
df1 = pd.DataFrame({'x':array11, 'y':array22})
df1['x']=df1.x.astype(float); df1['y']=df1.y.astype(float)
sns.set_theme(font='arial')
sns_reg1 = sns.jointplot(x='x', y='y', data=df1, color='#7CBC47', kind='reg', space=0, height=5, ratio=5,
scatter_kws=dict(color='#7CBC47', alpha=0.8, s=3, marker='+'),
line_kws=dict(color='#D31A8A',alpha=1, lw=2),
marginal_kws=dict(bins=20))
# sns_reg1.plot_joint(sns.kdeplot, color="r", zorder=0, levels=6) # 等高线
sns_reg1.set_axis_labels(xlabel='5\'SS ratio', ylabel='3\'SS ratio')
pdffig1 = output + '_' + name + '.SS_ratio.pdf'
sns_reg1.savefig(pdffig1, format='pdf')
df2 = pd.DataFrame({'x':array33, 'y':array44})
df2['x']=df2.x.astype(float); df2['y']=df2.y.astype(float)
sns_reg2 = sns.jointplot(x='x', y='y', data=df2, color='#7CBC47', kind='reg', space=0, height=5, ratio=5,
scatter_kws=dict(color='#7CBC47', alpha=0.8, s=3, marker='+'),
line_kws=dict(color='#D31A8A',alpha=1, lw=2),
marginal_kws=dict(bins=20))
# sns_reg2.plot_joint(sns.kdeplot, color="r", zorder=0, levels=6)
sns_reg2.set_axis_labels(xlabel='5\'SS ratio', ylabel='3\'SS ratio')
pdffig2 = output + '_' + name + '.SS_ratio.flt.pdf'
sns_reg2.savefig(pdffig2, format='pdf')
time5 = strftime("%Y-%m-%d %H:%M:%S", localtime())
print('\n\t[' + time5 + ']\tFinished ! Please check the data and pictures !')
print('=' * 100)