Initial Quantification of ClickTag Counts with Cell Ranger (Starvation Data)
In [ ]:
!date
Sat Dec 5 01:21:00 UTC 2020
Download Data¶
In [ ]:
import requests
from tqdm import tnrange, tqdm_notebook
def download_file(doi,ext):
url = 'https://api.datacite.org/dois/'+doi+'/media'
r = requests.get(url).json()
netcdf_url = r['data'][0]['attributes']['url']
r = requests.get(netcdf_url,stream=True)
#Set file name
fname = doi.split('/')[-1]+ext
#Download file with progress bar
if r.status_code == 403:
print("File Unavailable")
if 'content-length' not in r.headers:
print("Did not get file")
else:
with open(fname, 'wb') as f:
total_length = int(r.headers.get('content-length'))
pbar = tnrange(int(total_length/1024), unit="B")
for chunk in r.iter_content(chunk_size=1024):
if chunk:
pbar.update()
f.write(chunk)
return fname
In [ ]:
#70BPbarcodes (ClickTag sequences)
download_file('10.22002/D1.1831','.gz')
#tags1.bam
download_file('10.22002/D1.1815','.gz')
#tags2.bam
download_file('10.22002/D1.1816','.gz')
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`
Out[ ]:
'D1.1816.gz'
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!gunzip *.gz
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!pip install --quiet pysam
!pip install --quiet scanpy
!pip install --quiet fuzzywuzzy
!pip install --quiet biopython
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Building wheel for sinfo (setup.py) ... done
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Import Packages¶
In [ ]:
import pysam
import pickle
import os
import csv
import pandas
import numpy as np
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
from Bio import SeqIO
import time
import copy
import matplotlib.pyplot as plt
import scipy.io
import seaborn as sns
from collections import Counter
from collections import defaultdict
from collections import OrderedDict
from itertools import islice
from itertools import combinations
import pandas as pd
import time
import locale
#import Levenshtein
import re
import scanpy as sc
import multiprocessing
/usr/local/lib/python3.6/dist-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning
warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')
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from google.colab import drive
drive.mount('/drive')
Mounted at /drive
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## Set parameters - below are parameters for 10x 3' v2 chemistry
cell_barcode_length = 16
UMI_length = 10
!mv D1.1831 70BPbarcodes.fa
!mv D1.1815 tags1.bam
!mv D1.1816 tags2.bam
tags = "70BPbarcodes.fa"
CellRangerOut = "tags1.bam"
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def parse_tags(filename):
odict = OrderedDict()
for record in SeqIO.parse(filename, "fasta"):
#odict[row[0].encode('utf-8')] = row[1]
odict[record.name] = str(record.seq)[28:36]
return odict
def worker(procnum, unique_lines_full, start, end, celltags, cellbarcodes, return_list):
"""worker function"""
full_dataframe=pd.DataFrame(index=ab_map.keys(), columns=set([x[:16] for x in unique_lines_full]))
full_dataframe.fillna(0, inplace=True)
#variables
res_table = defaultdict(lambda : defaultdict(int))
n=0
for line in unique_lines_full[start:end]:
cell_barcode = line[0:cell_barcode_length]
UMI = line[cell_barcode_length:cell_barcode_length+UMI_length]
BC_UMI = cell_barcode + UMI
TAG_seq = line[len(BC_UMI):]
tagpositions = [TAG_seq[i:(i+23)] for i in range(0,10)]
fuzzpos = process.extractOne("TCGTCGGCAGCGTCAGATGTGTA", tagpositions)
if fuzzpos[1] > 85:
pos = TAG_seq.find(fuzzpos[0])
fuzzbc = process.extractOne(TAG_seq[pos+23:pos+31], list(ab_map.values()))
if fuzzbc[1] > 85:
best = list(ab_map.keys())[list(ab_map.values()).index(fuzzbc[0])]
BC_UMI_TAG = BC_UMI + best
if BC_UMI_TAG not in UMI_reduce:
#print("got one")
res_table[cell_barcode][best]+=1
full_dataframe.loc[best,cell_barcode]+=1
#print(full_dataframe.loc[best,cell_barcode])
UMI_reduce.add(BC_UMI_TAG)
n += 1
#if(n>5):break
if(n%20000==0):
print(n)
print("elapsed time " + str(time.time()-start))
if procnum < 5:
print('hello_world '+cell_barcode)
print(full_dataframe.loc[:,cell_barcode])
#put res_table into dataframe
"""worker function"""
print('worker '+str(procnum) + '\t' + 'start '+str(start) + '\t' + 'end '+str(end))
print(pd.DataFrame(res_table))
print(' ')
return_list.append(full_dataframe)
return
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ab_map=parse_tags(tags)
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ab_map
Out[ ]:
OrderedDict([('BC_21', 'AGCAGTTA'),
('BC_22', 'CTTGTACC'),
('BC_23', 'GAACCCGG'),
('BC_24', 'TCGTAGAT'),
('BC_25', 'ACGCGGAA'),
('BC_26', 'CGCTATCC'),
('BC_27', 'GTTGCATG'),
('BC_28', 'TAAATCGT'),
('BC_29', 'ATCGCCAT'),
('BC_30', 'CATAAAGG'),
('BC_31', 'TCACGGTA'),
('BC_32', 'CACTCAAC'),
('BC_33', 'GCTGTGTA'),
('BC_34', 'TTGCGTCG'),
('BC_35', 'ATATGAGA'),
('BC_36', 'CACCTCAG'),
('BC_37', 'GCTACTTC'),
('BC_38', 'TGGGAGCT'),
('BC_39', 'ATCCGGCA'),
('BC_40', 'CCGTTATG')])
Count ClickTags lane 1 tags first¶
Will need multiple Colab sessions to complete
In [ ]:
"Create a set for UMI reduction. Fast way to collapse UMIs"
UMI_reduce=set()
#Creaet result table
res_table=defaultdict(lambda : defaultdict(int))
#set counter
n=0
#set number of reads to process
top_n = None
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"""
This section of code processes the entire genome BAM file.
Proccessing is slow due to multiple fuzzy matching steps
Currently configured for 10x v2 and sample tags used in Gehring et. al 2018
The script iterates through the genome BAM, identifies quality barcode sequences with fuzzywuzzy score > 85,
then classifies them.
fuzzpos is a constant sequence just upstream of the barcode
fuzzbc is the barcode sequence extracted for classification
"""
#Load TAGS barcodes
ab_map = parse_tags(tags)
#Create a set for UMI reduction. Fast way to check if it already exists
UMI_reduce = set()
#Create result table
res_table = defaultdict(lambda : defaultdict(int))
res_table_sum = defaultdict(lambda : defaultdict(int))
# set counter
n = 0
top_n = None
unique_lines = set()
start = time.time()
samfile = pysam.AlignmentFile(CellRangerOut, "rb")
for read in samfile.fetch(until_eof=True):
if read.has_tag('CB'):
line = str(read.get_tag('CB'))[:-2] + read.get_tag('UR') + str(read.query_sequence)
unique_lines.add(line)
if top_n:
if top_n < n: break
n += 1
if(n%1000000==0):print(n)
if(n%1000000==0):
print('elapsed time: ' + str(time.time()-start))
samfile.close()
print(str(n) + ' reads loaded')
print(str(len(unique_lines)) + ' unique reads loaded')
print("runtime " + str(time.time() - start))
1000000 elapsed time: 4.597801923751831 2000000 elapsed time: 10.547113418579102 3000000 elapsed time: 16.278574228286743 4000000 elapsed time: 22.130943775177002 5000000 elapsed time: 28.186386346817017 6000000 elapsed time: 33.99790954589844 7000000 elapsed time: 40.27485632896423 8000000 elapsed time: 46.15766453742981 9000000 elapsed time: 51.997294664382935 10000000 elapsed time: 57.882442235946655 10090668 reads loaded 9626566 unique reads loaded runtime 58.40797400474548
In [ ]:
# unique_lines = list(unique_lines)
# #unique_lines
Run for first half of sequences only (in Colab session)¶
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unique_lines = list(unique_lines)
jobs=[]
manager = multiprocessing.Manager()
return_list = manager.list()
n_threads=4 #4
slicestart=0
celltags=list(ab_map.keys())
cellbarcodes=list(set([x[:16] for x in unique_lines]))
for job in range(2):
#divide up the unique_lines by the number of threads
#call the worker function, passing positions in unique_lines as input
sliceend=slicestart+len(unique_lines)//n_threads #len(unique_lines)
p = multiprocessing.Process(target=worker, args=(job, unique_lines,slicestart, sliceend, celltags, cellbarcodes, return_list))
jobs.append(p)
p.start()
slicestart=sliceend
for p in jobs:
p.join()
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hello_world TGTTCCGCATCTCGCT
BC_21 0
BC_22 1
BC_23 0
BC_24 0
BC_25 0
BC_26 0
BC_27 1
BC_28 1
BC_29 0
BC_30 1
BC_31 1
BC_32 1
BC_33 1
BC_34 1
BC_35 1
BC_36 0
BC_37 1
BC_38 2
BC_39 0
BC_40 1
Name: TGTTCCGCATCTCGCT, dtype: int64
worker 0 start 0 end 2406641
hello_world CAGTCCTTCCGCATCT
BC_21 1
BC_22 0
BC_23 0
BC_24 0
BC_25 1
BC_26 0
BC_27 1
BC_28 2
BC_29 0
BC_30 0
BC_31 1
BC_32 1
BC_33 0
BC_34 3
BC_35 0
BC_36 6
BC_37 2
BC_38 1
BC_39 0
BC_40 0
Name: CAGTCCTTCCGCATCT, dtype: int64
worker 1 start 2406641 end 4813282
GTATCTTCACGAAACG GCTCTGTTCAACACTG ... ACGGCCACAGACGCTC CAAGGCCGTTCCACTC
BC_32 2.0 8.0 ... NaN NaN
BC_38 1.0 1.0 ... NaN NaN
BC_29 1.0 365.0 ... NaN NaN
BC_22 3.0 2.0 ... NaN NaN
BC_27 1.0 3.0 ... NaN NaN
BC_36 3.0 5.0 ... NaN NaN
BC_34 4.0 6.0 ... NaN 1.0
BC_23 1.0 NaN ... NaN NaN
BC_28 1.0 NaN ... NaN NaN
BC_30 3.0 297.0 ... NaN NaN
BC_21 1.0 4.0 ... NaN NaN
BC_40 2.0 3.0 ... NaN NaN
BC_37 1.0 4.0 ... NaN NaN
BC_25 4.0 3.0 ... NaN NaN
BC_35 1.0 NaN ... NaN NaN
BC_24 1.0 1.0 ... NaN NaN
BC_31 NaN 4.0 ... NaN NaN
BC_33 NaN 1.0 ... NaN NaN
BC_39 NaN 4.0 ... 1.0 NaN
BC_26 NaN NaN ... NaN NaN
[20 rows x 114025 columns]
GCATGATGTTGTTTGG ATTCTACGTGGCCCTA ... TCGGGACCAAAGTCAA GACACGCCATCAGTCA
BC_29 96.0 1.0 ... NaN NaN
BC_30 84.0 1.0 ... NaN NaN
BC_22 2.0 2.0 ... NaN NaN
BC_26 2.0 2.0 ... NaN 1.0
BC_36 8.0 NaN ... NaN NaN
BC_33 1.0 3.0 ... NaN NaN
BC_34 2.0 NaN ... NaN NaN
BC_31 1.0 NaN ... NaN NaN
BC_37 3.0 NaN ... NaN NaN
BC_25 3.0 4.0 ... NaN NaN
BC_21 2.0 1.0 ... NaN NaN
BC_28 1.0 15.0 ... 1.0 NaN
BC_23 1.0 NaN ... NaN NaN
BC_35 2.0 1.0 ... NaN NaN
BC_38 1.0 2.0 ... NaN NaN
BC_32 3.0 1.0 ... NaN NaN
BC_40 1.0 1.0 ... NaN NaN
BC_27 NaN 13.0 ... NaN NaN
BC_24 NaN NaN ... NaN NaN
BC_39 NaN NaN ... NaN NaN
[20 rows x 113875 columns]
In [ ]:
return_list = list(return_list)
print(return_list)
[ ACGCCAGGTCACTTCC GATCGATTCCTGTACC ... ACTGAACGTCCTCTTG GGATGTTCATGACATC BC_21 1 1 ... 0 1 BC_22 0 0 ... 0 1 BC_23 0 2 ... 0 0 BC_24 0 2 ... 0 2 BC_25 2 0 ... 0 0 BC_26 0 1 ... 0 0 BC_27 1 0 ... 0 2 BC_28 1 0 ... 0 0 BC_29 0 2 ... 0 3 BC_30 1 0 ... 0 3 BC_31 0 1 ... 0 1 BC_32 1 1 ... 0 1 BC_33 3 1 ... 0 0 BC_34 3 4 ... 0 1 BC_35 3 1 ... 0 0 BC_36 0 0 ... 0 1 BC_37 1 1 ... 0 0 BC_38 0 0 ... 0 0 BC_39 0 1 ... 0 0 BC_40 0 0 ... 0 0 [20 rows x 217581 columns], ACGCCAGGTCACTTCC GATCGATTCCTGTACC ... ACTGAACGTCCTCTTG GGATGTTCATGACATC BC_21 1 1 ... 0 1 BC_22 0 0 ... 0 1 BC_23 0 1 ... 1 0 BC_24 0 1 ... 0 3 BC_25 1 2 ... 0 2 BC_26 1 1 ... 0 0 BC_27 0 2 ... 0 1 BC_28 0 1 ... 0 0 BC_29 1 1 ... 0 0 BC_30 1 0 ... 0 0 BC_31 1 1 ... 0 1 BC_32 0 1 ... 0 2 BC_33 0 0 ... 0 0 BC_34 2 3 ... 0 3 BC_35 0 0 ... 0 1 BC_36 1 2 ... 0 1 BC_37 2 0 ... 0 1 BC_38 1 2 ... 0 2 BC_39 1 0 ... 0 0 BC_40 1 0 ... 0 0 [20 rows x 217581 columns]]
In [ ]:
print(slicestart)
print(sliceend)
4813282 4813282
In [ ]:
with open('/drive/MyDrive/listfile_half.data', 'wb') as filehandle:
# store the data as binary data stream
pickle.dump(return_list, filehandle)
Run for second half of sequences + to produce output file (in new Colab session)¶
Previous code take 6+ hours, and cannot be run in a one full 12 hr Colab session
In [ ]:
with open('/drive/MyDrive/listfile_half.data', 'rb') as filehandle:
# read the data as binary data stream
return_list_half = pickle.load(filehandle)
In [ ]:
unique_lines = list(unique_lines)
jobs=[]
manager = multiprocessing.Manager()
return_list = manager.list()
n_threads=4
slicestart=4813282
celltags=list(ab_map.keys())
cellbarcodes=list(set([x[:16] for x in unique_lines]))
for job in range(2):
#divide up the unique_lines by the number of threads
#call the worker function, passing positions in unique_lines as input
sliceend=slicestart+len(unique_lines)//n_threads #len(unique_lines)
p = multiprocessing.Process(target=worker, args=(job, unique_lines,slicestart, sliceend, celltags, cellbarcodes, return_list))
jobs.append(p)
p.start()
slicestart=sliceend
for p in jobs:
p.join()
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hello_world AACCGCGGTCCATCCT
BC_21 3
BC_22 3
BC_23 1
BC_24 4
BC_25 7
BC_26 1
BC_27 2
BC_28 2
BC_29 5
BC_30 4
BC_31 3
BC_32 1
BC_33 4
BC_34 5
BC_35 4
BC_36 1
BC_37 5
BC_38 0
BC_39 3
BC_40 4
Name: AACCGCGGTCCATCCT, dtype: int64
worker 0 start 4813282 end 7219923
TCGGTAAGTAGCCTCG TACGGGCAGTATTGGA ... TGGGAAGTCGTTGCCT ACTGTCCCACTTAAGC
BC_34 1.0 3.0 ... 1.0 NaN
BC_33 1.0 NaN ... NaN NaN
BC_26 NaN 3.0 ... NaN NaN
BC_39 NaN 2.0 ... NaN NaN
BC_36 NaN 3.0 ... NaN NaN
BC_37 NaN 1.0 ... NaN NaN
BC_38 NaN 2.0 ... NaN 1.0
BC_35 NaN 1.0 ... NaN NaN
BC_22 NaN 1.0 ... NaN NaN
BC_25 NaN 1.0 ... NaN NaN
BC_29 NaN 1.0 ... NaN NaN
BC_32 NaN 2.0 ... NaN NaN
BC_31 NaN 1.0 ... NaN NaN
BC_27 NaN 2.0 ... NaN NaN
BC_21 NaN NaN ... NaN NaN
BC_24 NaN NaN ... NaN NaN
BC_23 NaN NaN ... NaN NaN
BC_28 NaN NaN ... NaN NaN
BC_40 NaN NaN ... NaN NaN
BC_30 NaN NaN ... NaN NaN
[20 rows x 114036 columns]
hello_world CAACCAATCAAAGACA
BC_21 3
BC_22 4
BC_23 0
BC_24 3
BC_25 100
BC_26 82
BC_27 24
BC_28 26
BC_29 2
BC_30 1
BC_31 1
BC_32 0
BC_33 0
BC_34 2
BC_35 0
BC_36 1
BC_37 2
BC_38 1
BC_39 1
BC_40 1
Name: CAACCAATCAAAGACA, dtype: int64
worker 1 start 7219923 end 9626564
CCTAGCTAGATCCCAT AACTCAGCAGCAGTTT ... TCAATCTCAATCACAC AGCGGTCGTGATGTCT
BC_29 17.0 5.0 ... NaN NaN
BC_25 43.0 1.0 ... NaN NaN
BC_39 2.0 NaN ... NaN NaN
BC_33 2.0 NaN ... NaN NaN
BC_26 24.0 2.0 ... NaN 1.0
BC_35 1.0 1.0 ... NaN NaN
BC_30 12.0 NaN ... NaN NaN
BC_21 4.0 NaN ... NaN NaN
BC_23 1.0 1.0 ... NaN NaN
BC_34 4.0 2.0 ... NaN NaN
BC_27 2.0 NaN ... NaN NaN
BC_36 3.0 1.0 ... 1.0 NaN
BC_38 2.0 1.0 ... NaN NaN
BC_22 1.0 NaN ... NaN NaN
BC_40 2.0 2.0 ... NaN NaN
BC_31 1.0 3.0 ... NaN NaN
BC_37 2.0 NaN ... NaN NaN
BC_24 2.0 1.0 ... NaN NaN
BC_28 NaN 1.0 ... NaN NaN
BC_32 NaN NaN ... NaN NaN
[20 rows x 114000 columns]
In [ ]:
pd.DataFrame(res_table)
Out[ ]:
In [ ]:
final_df=pd.DataFrame(index=ab_map.keys(), columns=set([x[:16] for x in unique_lines]))
final_df.fillna(0,inplace=True)
In [ ]:
final_df = return_list[0].fillna(0) + return_list_half[0].fillna(0)
In [ ]:
final_df.head()
Out[ ]:
| AAACCTGAGAAACCAT | AAACCTGAGAAACCGC | AAACCTGAGAACAACT | AAACCTGAGAACTCGG | AAACCTGAGAAGAAGC | AAACCTGAGAATCTCC | AAACCTGAGACAAAGG | AAACCTGAGACAATAC | AAACCTGAGACAGGCT | AAACCTGAGACCCACC | AAACCTGAGACTACAA | AAACCTGAGACTAGAT | AAACCTGAGACTAGGC | AAACCTGAGAGAACAG | AAACCTGAGAGACTAT | AAACCTGAGAGACTTA | AAACCTGAGAGCCTAG | AAACCTGAGAGCTTCT | AAACCTGAGAGGTTAT | AAACCTGAGAGTAATC | AAACCTGAGAGTCTGG | AAACCTGAGAGTGACC | AAACCTGAGATCACGG | AAACCTGAGATCCCGC | AAACCTGAGATCGATA | AAACCTGAGATGCCAG | AAACCTGAGATGTAAC | AAACCTGAGATGTTAG | AAACCTGAGCAAATCA | AAACCTGAGCAGACTG | AAACCTGAGCAGCGTA | AAACCTGAGCCACTAT | AAACCTGAGCCAGGAT | AAACCTGAGCCATCGC | AAACCTGAGCCCAACC | AAACCTGAGCCCAATT | AAACCTGAGCCCTAAT | AAACCTGAGCCGATTT | AAACCTGAGCCGTCGT | AAACCTGAGCCTATGT | ... | TTTGTCATCGGGAGTA | TTTGTCATCGGTTAAC | TTTGTCATCGTAGGTT | TTTGTCATCGTCACGG | TTTGTCATCGTCCAGG | TTTGTCATCGTGACAT | TTTGTCATCGTGGGAA | TTTGTCATCGTGGTCG | TTTGTCATCGTTGACA | TTTGTCATCGTTTATC | TTTGTCATCTAACCGA | TTTGTCATCTAACGGT | TTTGTCATCTACGAGT | TTTGTCATCTAGAGTC | TTTGTCATCTAGCACA | TTTGTCATCTCAAACG | TTTGTCATCTCAAGTG | TTTGTCATCTCCGGTT | TTTGTCATCTCTGCTG | TTTGTCATCTCTGTCG | TTTGTCATCTGAGTGT | TTTGTCATCTGATTCT | TTTGTCATCTGCAAGT | TTTGTCATCTGCCAGG | TTTGTCATCTGCGACG | TTTGTCATCTGCGTAA | TTTGTCATCTGTACGA | TTTGTCATCTGTCTAT | TTTGTCATCTGTGCAA | TTTGTCATCTGTTGAG | TTTGTCATCTGTTTGT | TTTGTCATCTTAACCT | TTTGTCATCTTAGAGC | TTTGTCATCTTCAACT | TTTGTCATCTTCATGT | TTTGTCATCTTCGAGA | TTTGTCATCTTCTGGC | TTTGTCATCTTGCATT | TTTGTCATCTTGTCAT | TTTGTCATCTTTCCTC | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BC_21 | 0 | 3 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 2 | 3 | 2 | 0 | 0 | 0 | 4 | 0 | 3 | ... | 0 | 0 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 7 | 0 | 4 | 0 | 0 | 0 | 0 | 3 | 2 | 2 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| BC_22 | 0 | 1 | 6 | 4 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 0 | 0 | 1 | 5 | 2 | 2 | 0 | 0 | 0 | 2 | 3 | 0 | ... | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 4 | 0 | 0 | 0 | 1 | 3 | 0 | 6 | 0 | 1 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| BC_23 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | ... | 0 | 0 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| BC_24 | 0 | 1 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 1 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 2 | 5 | 3 | ... | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 2 | 0 | 0 | 0 | 1 | 4 | 0 | 6 | 0 | 3 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| BC_25 | 0 | 1 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 11 | 0 | 0 | 2 | 1 | 1 | 2 | 0 | 0 | 0 | 2 | 0 | 1 | ... | 0 | 0 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 366 | 0 | 1 | 0 | 0 | 0 | 2 | 1 | 4 | 5 | 0 | 4 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 217581 columns
In [ ]:
res_matrix=copy.deepcopy(final_df)
sortedREAP_dtf = copy.deepcopy(res_matrix.sum())
sortedREAP_dtf = sortedREAP_dtf.sort_values(ascending=False)
plt.plot(np.log10(range(len(sortedREAP_dtf))), sortedREAP_dtf.apply(np.log10))
plt.show()
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: RuntimeWarning: divide by zero encountered in log10 after removing the cwd from sys.path.
In [ ]:
"""
Have a look at the data
Columns of the matrix are cells
Rows are tag counts for each cell
Only 8 of twenty possible tags were used in this experiment (2 tags for each of 10 samples)
"""
res_matrix.fillna(0, inplace=True)
res_matrix.loc[:,sortedREAP_dtf.index[6000:6020]]
Out[ ]:
| CATCAAGAGCTCCCAG | CCTTCCCTCCAGAAGG | TCTTCGGCACGAAACG | CTACACCAGACTAAGT | GCACTCTGTCTACCTC | AAACCTGCAAGCCATT | CATGACATCTCAACTT | CTGTGCTTCTCGATGA | TGGCTGGAGCCAGTAG | TGGACGCCATGGGAAC | ACATACGAGTTCCACA | TACCTATCATGCATGT | CACAGGCCAGCCTGTG | CTCGGAGGTAAGTGGC | GTGTGCGAGGTAAACT | CGGACTGAGCCAGAAC | TAGTGGTAGCCTTGAT | AGATTGCCAGACAGGT | TACTCGCGTCTCTCGT | TGGCCAGTCTGCTGTC | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BC_21 | 12 | 3 | 1 | 3 | 3 | 1 | 6 | 6 | 6 | 3 | 3 | 34 | 1 | 3 | 1 | 6 | 3 | 0 | 2 | 1 |
| BC_22 | 2 | 4 | 1 | 3 | 3 | 6 | 4 | 1 | 7 | 1 | 1 | 23 | 0 | 3 | 4 | 4 | 4 | 0 | 3 | 1 |
| BC_23 | 3 | 2 | 1 | 0 | 16 | 1 | 8 | 3 | 2 | 2 | 1 | 3 | 1 | 0 | 0 | 0 | 3 | 2 | 1 | 0 |
| BC_24 | 4 | 3 | 1 | 3 | 37 | 1 | 4 | 3 | 2 | 2 | 1 | 4 | 2 | 1 | 2 | 4 | 1 | 2 | 2 | 0 |
| BC_25 | 8 | 5 | 3 | 2 | 3 | 1 | 12 | 3 | 5 | 0 | 4 | 1 | 5 | 2 | 3 | 3 | 2 | 4 | 7 | 5 |
| BC_26 | 2 | 4 | 1 | 0 | 3 | 2 | 2 | 3 | 4 | 2 | 2 | 1 | 0 | 3 | 1 | 2 | 6 | 3 | 1 | 1 |
| BC_27 | 1 | 3 | 1 | 1 | 2 | 4 | 1 | 0 | 6 | 1 | 0 | 1 | 1 | 2 | 2 | 2 | 3 | 0 | 4 | 34 |
| BC_28 | 2 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 2 | 1 | 1 | 32 |
| BC_29 | 5 | 12 | 40 | 2 | 3 | 3 | 10 | 1 | 7 | 1 | 2 | 3 | 43 | 1 | 0 | 6 | 2 | 3 | 25 | 1 |
| BC_30 | 5 | 11 | 22 | 2 | 0 | 0 | 2 | 2 | 4 | 1 | 0 | 0 | 31 | 0 | 2 | 2 | 3 | 2 | 14 | 0 |
| BC_31 | 8 | 4 | 5 | 3 | 2 | 2 | 1 | 1 | 4 | 2 | 0 | 3 | 4 | 31 | 1 | 4 | 6 | 30 | 5 | 0 |
| BC_32 | 6 | 6 | 2 | 5 | 6 | 5 | 3 | 4 | 5 | 2 | 4 | 2 | 3 | 26 | 2 | 3 | 7 | 34 | 4 | 1 |
| BC_33 | 4 | 1 | 1 | 0 | 1 | 23 | 1 | 0 | 3 | 1 | 1 | 0 | 1 | 1 | 32 | 6 | 3 | 0 | 2 | 2 |
| BC_34 | 9 | 6 | 1 | 0 | 5 | 38 | 10 | 4 | 9 | 6 | 1 | 2 | 0 | 5 | 39 | 9 | 13 | 4 | 4 | 2 |
| BC_35 | 2 | 6 | 2 | 4 | 0 | 3 | 8 | 3 | 7 | 3 | 3 | 2 | 1 | 0 | 0 | 8 | 9 | 1 | 3 | 1 |
| BC_36 | 8 | 6 | 2 | 2 | 3 | 1 | 7 | 4 | 4 | 5 | 6 | 7 | 2 | 5 | 1 | 14 | 15 | 4 | 9 | 5 |
| BC_37 | 7 | 3 | 5 | 2 | 1 | 1 | 4 | 25 | 7 | 28 | 0 | 1 | 1 | 4 | 4 | 8 | 3 | 1 | 2 | 3 |
| BC_38 | 4 | 14 | 2 | 4 | 3 | 3 | 8 | 30 | 7 | 32 | 2 | 4 | 0 | 7 | 1 | 8 | 8 | 0 | 3 | 4 |
| BC_39 | 1 | 0 | 0 | 20 | 1 | 0 | 3 | 1 | 2 | 2 | 28 | 0 | 0 | 1 | 1 | 2 | 2 | 0 | 0 | 1 |
| BC_40 | 3 | 3 | 5 | 40 | 4 | 0 | 0 | 1 | 2 | 2 | 36 | 5 | 0 | 1 | 0 | 3 | 1 | 5 | 4 | 2 |
In [ ]:
res_matrix.to_csv(path_or_buf="jelly3tags1counts.csv")
Analysis for lane 2 ClickTags Following Same Protocol as With lane 1 ClickTags¶
In [ ]:
CellRangerOut = "tags2.bam"
In [ ]:
"Create a set for UMI reduction. Fast way to collapse UMIs"
UMI_reduce=set()
#Creaet result table
res_table=defaultdict(lambda : defaultdict(int))
#set counter
n=0
#set number of reads to process
top_n = None
In [ ]:
"""
This section of code processes the entire genome BAM file. Runtime is about one hour for 3 million reads.
Proccessing is slow due to multiple fuzzy matching steps
Currently configured for 10x v2 and sample tags used in Gehring et. al 2018
The script iterates through the genome BAM, identifies quality barcode sequences with fuzzywuzzy score > 85,
then classifies them.
fuzzpos is a constant sequence just upstream of the barcode
fuzzbc is the barcode sequence extracted for classification
"""
#Load TAGS barcodes
ab_map = parse_tags(tags)
#Create a set for UMI reduction. Fast way to check if it already exists
UMI_reduce = set()
#Create result table
res_table = defaultdict(lambda : defaultdict(int))
res_table_sum = defaultdict(lambda : defaultdict(int))
# set counter
n = 0
top_n = None
unique_lines = set()
start = time.time()
samfile = pysam.AlignmentFile(CellRangerOut, "rb")
for read in samfile.fetch(until_eof=True):
if read.has_tag('CB'):
line = str(read.get_tag('CB'))[:-2] + read.get_tag('UR') + str(read.query_sequence)
unique_lines.add(line)
if top_n:
if top_n < n: break
n += 1
if(n%1000000==0):print(n)
if(n%1000000==0):
print('elapsed time: ' + str(time.time()-start))
samfile.close()
print(str(n) + ' reads loaded')
print(str(len(unique_lines)) + ' unique reads loaded')
print("runtime " + str(time.time() - start))
1000000 elapsed time: 4.673456192016602 2000000 elapsed time: 10.896527528762817 3000000 elapsed time: 16.912776470184326 4000000 elapsed time: 23.32162356376648 5000000 elapsed time: 29.383440494537354 6000000 elapsed time: 35.499902963638306 7000000 elapsed time: 41.95974850654602 8000000 elapsed time: 48.08058547973633 9000000 elapsed time: 54.21342086791992 10000000 elapsed time: 60.32341408729553 10090668 reads loaded 9626566 unique reads loaded runtime 60.868274211883545
Run for first half of sequences only (in Colab session)¶
In [ ]:
unique_lines = list(unique_lines)
jobs=[]
manager = multiprocessing.Manager()
return_list = manager.list()
n_threads=4 #4
slicestart=0
celltags=list(ab_map.keys())
cellbarcodes=list(set([x[:16] for x in unique_lines]))
for job in range(2):
#divide up the unique_lines by the number of threads
#call the worker function, passing positions in unique_lines as input
sliceend=slicestart+len(unique_lines)//n_threads #len(unique_lines)
p = multiprocessing.Process(target=worker, args=(job, unique_lines,slicestart, sliceend, celltags, cellbarcodes, return_list))
jobs.append(p)
p.start()
slicestart=sliceend
for p in jobs:
p.join()
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hello_world TGTTCCGCATCTCGCT
BC_21 0
BC_22 1
BC_23 0
BC_24 0
BC_25 0
BC_26 0
BC_27 1
BC_28 1
BC_29 0
BC_30 1
BC_31 1
BC_32 1
BC_33 1
BC_34 1
BC_35 1
BC_36 0
BC_37 1
BC_38 2
BC_39 0
BC_40 1
Name: TGTTCCGCATCTCGCT, dtype: int64
worker 0 start 0 end 2406641
hello_world CAGTCCTTCCGCATCT
BC_21 1
BC_22 0
BC_23 0
BC_24 0
BC_25 1
BC_26 0
BC_27 1
BC_28 2
BC_29 0
BC_30 0
BC_31 1
BC_32 1
BC_33 0
BC_34 3
BC_35 0
BC_36 6
BC_37 2
BC_38 1
BC_39 0
BC_40 0
Name: CAGTCCTTCCGCATCT, dtype: int64
worker 1 start 2406641 end 4813282
GTATCTTCACGAAACG GCTCTGTTCAACACTG ... ACGGCCACAGACGCTC CAAGGCCGTTCCACTC
BC_32 2.0 8.0 ... NaN NaN
BC_38 1.0 1.0 ... NaN NaN
BC_29 1.0 365.0 ... NaN NaN
BC_22 3.0 2.0 ... NaN NaN
BC_27 1.0 3.0 ... NaN NaN
BC_36 3.0 5.0 ... NaN NaN
BC_34 4.0 6.0 ... NaN 1.0
BC_23 1.0 NaN ... NaN NaN
BC_28 1.0 NaN ... NaN NaN
BC_30 3.0 297.0 ... NaN NaN
BC_21 1.0 4.0 ... NaN NaN
BC_40 2.0 3.0 ... NaN NaN
BC_37 1.0 4.0 ... NaN NaN
BC_25 4.0 3.0 ... NaN NaN
BC_35 1.0 NaN ... NaN NaN
BC_24 1.0 1.0 ... NaN NaN
BC_31 NaN 4.0 ... NaN NaN
BC_33 NaN 1.0 ... NaN NaN
BC_39 NaN 4.0 ... 1.0 NaN
BC_26 NaN NaN ... NaN NaN
[20 rows x 114025 columns]
GCATGATGTTGTTTGG ATTCTACGTGGCCCTA ... TCGGGACCAAAGTCAA GACACGCCATCAGTCA
BC_29 96.0 1.0 ... NaN NaN
BC_30 84.0 1.0 ... NaN NaN
BC_22 2.0 2.0 ... NaN NaN
BC_26 2.0 2.0 ... NaN 1.0
BC_36 8.0 NaN ... NaN NaN
BC_33 1.0 3.0 ... NaN NaN
BC_34 2.0 NaN ... NaN NaN
BC_31 1.0 NaN ... NaN NaN
BC_37 3.0 NaN ... NaN NaN
BC_25 3.0 4.0 ... NaN NaN
BC_21 2.0 1.0 ... NaN NaN
BC_28 1.0 15.0 ... 1.0 NaN
BC_23 1.0 NaN ... NaN NaN
BC_35 2.0 1.0 ... NaN NaN
BC_38 1.0 2.0 ... NaN NaN
BC_32 3.0 1.0 ... NaN NaN
BC_40 1.0 1.0 ... NaN NaN
BC_27 NaN 13.0 ... NaN NaN
BC_24 NaN NaN ... NaN NaN
BC_39 NaN NaN ... NaN NaN
[20 rows x 113875 columns]
In [ ]:
return_list = list(return_list)
print(return_list)
[ ACGCCAGGTCACTTCC GATCGATTCCTGTACC ... ACTGAACGTCCTCTTG GGATGTTCATGACATC BC_21 1 1 ... 0 1 BC_22 0 0 ... 0 1 BC_23 0 2 ... 0 0 BC_24 0 2 ... 0 2 BC_25 2 0 ... 0 0 BC_26 0 1 ... 0 0 BC_27 1 0 ... 0 2 BC_28 1 0 ... 0 0 BC_29 0 2 ... 0 3 BC_30 1 0 ... 0 3 BC_31 0 1 ... 0 1 BC_32 1 1 ... 0 1 BC_33 3 1 ... 0 0 BC_34 3 4 ... 0 1 BC_35 3 1 ... 0 0 BC_36 0 0 ... 0 1 BC_37 1 1 ... 0 0 BC_38 0 0 ... 0 0 BC_39 0 1 ... 0 0 BC_40 0 0 ... 0 0 [20 rows x 217581 columns], ACGCCAGGTCACTTCC GATCGATTCCTGTACC ... ACTGAACGTCCTCTTG GGATGTTCATGACATC BC_21 1 1 ... 0 1 BC_22 0 0 ... 0 1 BC_23 0 1 ... 1 0 BC_24 0 1 ... 0 3 BC_25 1 2 ... 0 2 BC_26 1 1 ... 0 0 BC_27 0 2 ... 0 1 BC_28 0 1 ... 0 0 BC_29 1 1 ... 0 0 BC_30 1 0 ... 0 0 BC_31 1 1 ... 0 1 BC_32 0 1 ... 0 2 BC_33 0 0 ... 0 0 BC_34 2 3 ... 0 3 BC_35 0 0 ... 0 1 BC_36 1 2 ... 0 1 BC_37 2 0 ... 0 1 BC_38 1 2 ... 0 2 BC_39 1 0 ... 0 0 BC_40 1 0 ... 0 0 [20 rows x 217581 columns]]
In [ ]:
print(slicestart)
print(sliceend)
4813282 4813282
In [ ]:
with open('/drive/MyDrive/listfile_half_tags2.data', 'wb') as filehandle:
# store the data as binary data stream
pickle.dump(return_list, filehandle)
Run for second half of sequences + to produce output file (in new Colab session)¶
Previous code take 6+ hours, and cannot be run in a one full 12 hr Colab session
In [ ]:
with open('/drive/MyDrive/listfile_half_tags2.data', 'rb') as filehandle:
# read the data as binary data stream
return_list_half = pickle.load(filehandle)
In [ ]:
unique_lines = list(unique_lines)
jobs=[]
manager = multiprocessing.Manager()
return_list = manager.list()
n_threads=4
slicestart=4813282
celltags=list(ab_map.keys())
cellbarcodes=list(set([x[:16] for x in unique_lines]))
for job in range(2):
#divide up the unique_lines by the number of threads
#call the worker function, passing positions in unique_lines as input
sliceend=slicestart+len(unique_lines)//n_threads #len(unique_lines)
p = multiprocessing.Process(target=worker, args=(job, unique_lines,slicestart, sliceend, celltags, cellbarcodes, return_list))
jobs.append(p)
p.start()
slicestart=sliceend
for p in jobs:
p.join()
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hello_world AACCGCGGTCCATCCT
BC_21 3
BC_22 3
BC_23 1
BC_24 4
BC_25 7
BC_26 1
BC_27 2
BC_28 2
BC_29 5
BC_30 4
BC_31 3
BC_32 1
BC_33 4
BC_34 5
BC_35 4
BC_36 1
BC_37 5
BC_38 0
BC_39 3
BC_40 4
Name: AACCGCGGTCCATCCT, dtype: int64
worker 0 start 4813282 end 7219923
TCGGTAAGTAGCCTCG TACGGGCAGTATTGGA ... TGGGAAGTCGTTGCCT ACTGTCCCACTTAAGC
BC_34 1.0 3.0 ... 1.0 NaN
BC_33 1.0 NaN ... NaN NaN
BC_26 NaN 3.0 ... NaN NaN
BC_39 NaN 2.0 ... NaN NaN
BC_36 NaN 3.0 ... NaN NaN
BC_37 NaN 1.0 ... NaN NaN
BC_38 NaN 2.0 ... NaN 1.0
BC_35 NaN 1.0 ... NaN NaN
BC_22 NaN 1.0 ... NaN NaN
BC_25 NaN 1.0 ... NaN NaN
BC_29 NaN 1.0 ... NaN NaN
BC_32 NaN 2.0 ... NaN NaN
BC_31 NaN 1.0 ... NaN NaN
BC_27 NaN 2.0 ... NaN NaN
BC_21 NaN NaN ... NaN NaN
BC_24 NaN NaN ... NaN NaN
BC_23 NaN NaN ... NaN NaN
BC_28 NaN NaN ... NaN NaN
BC_40 NaN NaN ... NaN NaN
BC_30 NaN NaN ... NaN NaN
[20 rows x 114036 columns]
hello_world CAACCAATCAAAGACA
BC_21 3
BC_22 4
BC_23 0
BC_24 3
BC_25 100
BC_26 82
BC_27 24
BC_28 26
BC_29 2
BC_30 1
BC_31 1
BC_32 0
BC_33 0
BC_34 2
BC_35 0
BC_36 1
BC_37 2
BC_38 1
BC_39 1
BC_40 1
Name: CAACCAATCAAAGACA, dtype: int64
worker 1 start 7219923 end 9626564
CCTAGCTAGATCCCAT AACTCAGCAGCAGTTT ... TCAATCTCAATCACAC AGCGGTCGTGATGTCT
BC_29 17.0 5.0 ... NaN NaN
BC_25 43.0 1.0 ... NaN NaN
BC_39 2.0 NaN ... NaN NaN
BC_33 2.0 NaN ... NaN NaN
BC_26 24.0 2.0 ... NaN 1.0
BC_35 1.0 1.0 ... NaN NaN
BC_30 12.0 NaN ... NaN NaN
BC_21 4.0 NaN ... NaN NaN
BC_23 1.0 1.0 ... NaN NaN
BC_34 4.0 2.0 ... NaN NaN
BC_27 2.0 NaN ... NaN NaN
BC_36 3.0 1.0 ... 1.0 NaN
BC_38 2.0 1.0 ... NaN NaN
BC_22 1.0 NaN ... NaN NaN
BC_40 2.0 2.0 ... NaN NaN
BC_31 1.0 3.0 ... NaN NaN
BC_37 2.0 NaN ... NaN NaN
BC_24 2.0 1.0 ... NaN NaN
BC_28 NaN 1.0 ... NaN NaN
BC_32 NaN NaN ... NaN NaN
[20 rows x 114000 columns]
In [ ]:
pd.DataFrame(res_table)
Out[ ]:
In [ ]:
final_df=pd.DataFrame(index=ab_map.keys(), columns=set([x[:16] for x in unique_lines]))
final_df.fillna(0,inplace=True)
In [ ]:
final_df = return_list[0].fillna(0) + return_list_half[0].fillna(0)
In [ ]:
final_df.head()
Out[ ]:
| AAACCTGAGAAACCAT | AAACCTGAGAAACCGC | AAACCTGAGAACAACT | AAACCTGAGAACTCGG | AAACCTGAGAAGAAGC | AAACCTGAGAATCTCC | AAACCTGAGACAAAGG | AAACCTGAGACAATAC | AAACCTGAGACAGGCT | AAACCTGAGACCCACC | AAACCTGAGACTACAA | AAACCTGAGACTAGAT | AAACCTGAGACTAGGC | AAACCTGAGAGAACAG | AAACCTGAGAGACTAT | AAACCTGAGAGACTTA | AAACCTGAGAGCCTAG | AAACCTGAGAGCTTCT | AAACCTGAGAGGTTAT | AAACCTGAGAGTAATC | AAACCTGAGAGTCTGG | AAACCTGAGAGTGACC | AAACCTGAGATCACGG | AAACCTGAGATCCCGC | AAACCTGAGATCGATA | AAACCTGAGATGCCAG | AAACCTGAGATGTAAC | AAACCTGAGATGTTAG | AAACCTGAGCAAATCA | AAACCTGAGCAGACTG | AAACCTGAGCAGCGTA | AAACCTGAGCCACTAT | AAACCTGAGCCAGGAT | AAACCTGAGCCATCGC | AAACCTGAGCCCAACC | AAACCTGAGCCCAATT | AAACCTGAGCCCTAAT | AAACCTGAGCCGATTT | AAACCTGAGCCGTCGT | AAACCTGAGCCTATGT | ... | TTTGTCATCGGGAGTA | TTTGTCATCGGTTAAC | TTTGTCATCGTAGGTT | TTTGTCATCGTCACGG | TTTGTCATCGTCCAGG | TTTGTCATCGTGACAT | TTTGTCATCGTGGGAA | TTTGTCATCGTGGTCG | TTTGTCATCGTTGACA | TTTGTCATCGTTTATC | TTTGTCATCTAACCGA | TTTGTCATCTAACGGT | TTTGTCATCTACGAGT | TTTGTCATCTAGAGTC | TTTGTCATCTAGCACA | TTTGTCATCTCAAACG | TTTGTCATCTCAAGTG | TTTGTCATCTCCGGTT | TTTGTCATCTCTGCTG | TTTGTCATCTCTGTCG | TTTGTCATCTGAGTGT | TTTGTCATCTGATTCT | TTTGTCATCTGCAAGT | TTTGTCATCTGCCAGG | TTTGTCATCTGCGACG | TTTGTCATCTGCGTAA | TTTGTCATCTGTACGA | TTTGTCATCTGTCTAT | TTTGTCATCTGTGCAA | TTTGTCATCTGTTGAG | TTTGTCATCTGTTTGT | TTTGTCATCTTAACCT | TTTGTCATCTTAGAGC | TTTGTCATCTTCAACT | TTTGTCATCTTCATGT | TTTGTCATCTTCGAGA | TTTGTCATCTTCTGGC | TTTGTCATCTTGCATT | TTTGTCATCTTGTCAT | TTTGTCATCTTTCCTC | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BC_21 | 0 | 3 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 2 | 3 | 2 | 0 | 0 | 0 | 4 | 0 | 3 | ... | 0 | 0 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 7 | 0 | 4 | 0 | 0 | 0 | 0 | 3 | 2 | 2 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| BC_22 | 0 | 1 | 6 | 4 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 4 | 0 | 0 | 1 | 5 | 2 | 2 | 0 | 0 | 0 | 2 | 3 | 0 | ... | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 4 | 0 | 0 | 0 | 1 | 3 | 0 | 6 | 0 | 1 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| BC_23 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | ... | 0 | 0 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| BC_24 | 0 | 1 | 0 | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 1 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 2 | 5 | 3 | ... | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 2 | 0 | 0 | 0 | 1 | 4 | 0 | 6 | 0 | 3 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| BC_25 | 0 | 1 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 11 | 0 | 0 | 2 | 1 | 1 | 2 | 0 | 0 | 0 | 2 | 0 | 1 | ... | 0 | 0 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 366 | 0 | 1 | 0 | 0 | 0 | 2 | 1 | 4 | 5 | 0 | 4 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 217581 columns
In [ ]:
res_matrix=copy.deepcopy(final_df)
sortedREAP_dtf = copy.deepcopy(res_matrix.sum())
sortedREAP_dtf = sortedREAP_dtf.sort_values(ascending=False)
plt.plot(np.log10(range(len(sortedREAP_dtf))), sortedREAP_dtf.apply(np.log10))
plt.show()
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: RuntimeWarning: divide by zero encountered in log10 after removing the cwd from sys.path.
In [ ]:
"""
Have a look at the data
Columns of the matrix are cells
Rows are tag counts for each cell
Only 8 of twenty possible tags were used in this experiment (2 tags for each of 4 samples)
"""
res_matrix.fillna(0, inplace=True)
res_matrix.loc[:,sortedREAP_dtf.index[5000:5200]]
In [ ]:
res_matrix.to_csv(path_or_buf="jelly3tags2counts.csv")