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snakemake1_kb_subsampling.py
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snakemake1_kb_subsampling.py
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import glob
import os
import pandas as pd
import io
import requests
import ntpath
import numpy as np
# for file in ./*/fastqs/full/*S1_L001_R1_001.fastq.gz ; do echo $file; zcat $file | echo $((`wc -l`/4));
# snakemake -j 1 -s kbsub.py --keep-going --rerun-incomplete -pn
# snakemake -j 100 -s kbsub.py --keep-going --rerun-incomplete --latency-wait 50 --cluster "sbatch -A lpachter -t 500 --output=./logs/slurm13_%j.kb" --verbose
# squeue -u edaveiga | grep snakejob | awk '{print $1}' | xargs -n 1 scancel
# kb count -i ~/data/references/mus_musculus-ensembl-96/transcriptome.idx -g ~/data/references/mus_musculus-ensembl-96/transcripts_to_genes.txt -x 10xv3 -o out2 ~/data/10x_genomics_data/heart_1k_v3/heart_1k_v3_R1_concat_1.fastq.gz ~/data/10x_genomics_data/heart_1k_v3/heart_1k_v3_R2_concat_2.fastq.gz
# snakemake -j 300 -s snakemake1_kb_subsampling.py --jobname "{wildcards.dataset_project_id}.{wildcards.dataset_sample_id}.{wildcards.sub}.{jobid}" --keep-going --rerun-incomplete --latency-wait 50 --cluster "sbatch -A lpachter -t 500 --output=./logs/slurm13_%j.kb" --verbose
# --jobname "{wildcards.dataset_project_id}.{wildcards.dataset_sample_id}.{jobid}"
THREAD = 1
REF_PATH = '~/data/references'
# how to call kallisto
KALLISTO = 'kallisto'
#how to call bustools
BUSTOOLS = 'bustools'
# directory with fast r/w if there is one, to speed up kallisto
SCRATCH_DIR = '/central/scratchio/edaveiga/'
# SCRATCH_DIR = 'scratch/'
url="https://docs.google.com/spreadsheets/d/"+ \
"1-2bLIns8r8VRoDenHVk-cQE9feNDnXJXnGZNm70ROrA"+\
"/export?gid="+\
"0" + \
"&format=csv"
metadatas=pd.read_csv(io.StringIO(requests.get(url).content.decode('utf-8')))
def make_t2g_path(wildcards):
DATASET_SAMPLE_ID = wildcards.dataset_sample_id
species = metadatas[metadatas['dataset_sample_id']==DATASET_SAMPLE_ID]['species'].values[0]
if species=='mouse':
T2G = os.path.join(REF_PATH,'mus_musculus-ensembl-96/transcripts_to_genes.txt')
if species=='human':
T2G = os.path.join(REF_PATH,'homo_sapiens-ensembl-96/transcripts_to_genes.txt')
if species=='human-mouse':
T2G = os.path.join(REF_PATH,'mouse_human_mix_ensembl-96/transcripts_to_genes.txt')
return T2G
def make_index_path(wildcards):
DATASET_SAMPLE_ID = wildcards.dataset_sample_id
species = metadatas[metadatas['dataset_sample_id']==DATASET_SAMPLE_ID]['species'].values[0]
if species=='mouse':
INDEX = os.path.join(REF_PATH,'mus_musculus-ensembl-96/transcriptome.idx')
if species=='human':
INDEX = os.path.join(REF_PATH,'homo_sapiens-ensembl-96/transcriptome.idx')
if species=='human-mouse':
INDEX = os.path.join(REF_PATH,'mouse_human_mix_ensembl-96/transcriptome.idx')
return INDEX
def make_whitelist_path(wildcards):
DATASET_SAMPLE_ID = wildcards.dataset_sample_id
TECHNOLOGY = metadatas[metadatas['dataset_sample_id']==DATASET_SAMPLE_ID]['technology'].values[0]
WHITELIST = 'nothing'
if TECHNOLOGY=='10xv3':
WHITELIST = os.path.join(REF_PATH,'10xv3_whitelist.txt')
if TECHNOLOGY=='10xv2':
WHITELIST = os.path.join(REF_PATH,'10xv2_whitelist.txt')
return WHITELIST
def fetch_technology(wildcards):
DATASET_SAMPLE_ID = wildcards.dataset_sample_id
TECHNOLOGY = metadatas[metadatas['dataset_sample_id']==DATASET_SAMPLE_ID]['technology'].values[0]
return TECHNOLOGY
def fetch_read1_filepath(wildcards):
DATASET_SAMPLE_ID = wildcards.dataset_sample_id
DATASET_SAMPLE_PATH = metadatas[metadatas['dataset_sample_id']==DATASET_SAMPLE_ID]['dataset_sample_path'].values[0]
READ1_FILES = metadatas[metadatas['dataset_sample_id']==DATASET_SAMPLE_ID]['concat_read1_file'].values[0].split(',')
#remove trailing spaces
READ1_FILES = [ read1_file.strip() for read1_file in READ1_FILES]
READ1_FILEPATHS = [os.path.join(DATASET_SAMPLE_PATH, read_filename) for read_filename in READ1_FILES]
return READ1_FILEPATHS
def fetch_read2_filepath(wildcards):
DATASET_SAMPLE_ID = wildcards.dataset_sample_id
DATASET_SAMPLE_PATH = metadatas[metadatas['dataset_sample_id']==DATASET_SAMPLE_ID]['dataset_sample_path'].values[0]
READ2_FILES = metadatas[metadatas['dataset_sample_id']==DATASET_SAMPLE_ID]['concat_read2_file'].values[0].split(',')
#remove trailing spaces
READ2_FILES = [ read2_file.strip() for read2_file in READ2_FILES]
READ2_FILEPATHS = [os.path.join(DATASET_SAMPLE_PATH, read_filename) for read_filename in READ2_FILES]
return READ2_FILEPATHS
def fetch_subsampling_depths(wildcards):
DATASET_SAMPLE_ID = wildcards.dataset_sample_id
subsampling_depths = metadatas[metadatas['dataset_sample_id']==DATASET_SAMPLE_ID]['subsampling_depths'].values[0]
subsampling_depths = [int(x) for x in subsampling_depths.split(',')]
return subsampling_depths
final_subsampled_fastqs = []
final_count_matrices = []
final_genecount_mtx = []
final_bus_txt =[]
final_files = []
for dataset_sample_id in metadatas[metadatas['process']==1]['dataset_sample_id']:
dataset_project_id = metadatas[metadatas['dataset_sample_id']==dataset_sample_id]['dataset_project_id']
for sub_string in metadatas[metadatas['dataset_sample_id']==dataset_sample_id]['subsampling_depths']:
subsampling_depths = [int(x) for x in sub_string.split(',')]
for subdepth in subsampling_depths:
final_files.append(str('subsampling/' + dataset_project_id.values[0] + '/' + dataset_sample_id + '/kbsub_'+str(subdepth)+'/output/counts_unfiltered/adata.h5ad'))
print( '==========================================================')
print(final_files)
print('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')
# print(str(dataset_project_id + '/' + dataset_sample_id + '/subsampled_'+str(sub)+'/counts_unfiltered/adata.h5ad'))
# print(final_h5ad[0])
rule all:
input:
final_files
rule subsample:
input:
READ1_FILEPATH = fetch_read1_filepath,
READ2_FILEPATH = fetch_read2_filepath
params:
SUBSAMPLING=lambda wildcards: f'{wildcards.sub}',
SUB_DIR_TMP = os.path.join(SCRATCH_DIR, '{dataset_project_id}/{dataset_sample_id}/tmp_{sub}/'),
output:
R1 = os.path.join(SCRATCH_DIR, '{dataset_project_id}/{dataset_sample_id}/tmp_{sub}/subsampledreads_{sub}_R1.fq'),
R2 = os.path.join(SCRATCH_DIR, '{dataset_project_id}/{dataset_sample_id}/tmp_{sub}/subsampledreads_{sub}_R2.fq')
shell:
"""
if (({params.SUBSAMPLING}==0)); then
cp {input.READ1_FILEPATH} {output.R1}
cp {input.READ2_FILEPATH} {output.R2}
fi
if (({params.SUBSAMPLING}!=0)); then
mkdir -p {params.SUB_DIR_TMP}
cd {params.SUB_DIR_TMP}
seqtk sample -2 -s100 {input.READ1_FILEPATH} {params.SUBSAMPLING} > {output.R1} && \
seqtk sample -2 -s100 {input.READ2_FILEPATH} {params.SUBSAMPLING} > {output.R2}
fi
"""
rule run_kb:
input:
R1 = os.path.join(SCRATCH_DIR, '{dataset_project_id}/{dataset_sample_id}/tmp_{sub}/subsampledreads_{sub}_R1.fq'),
R2 = os.path.join(SCRATCH_DIR, '{dataset_project_id}/{dataset_sample_id}/tmp_{sub}/subsampledreads_{sub}_R2.fq'),
params:
DATASET_SAMPLE_ID = '{dataset_sample_id}',
INDEX=make_index_path,
TECHNOLOGY = fetch_technology,
WHITELIST=make_whitelist_path,
T2G=make_t2g_path,
KB_OUT_DIR=directory('subsampling/{dataset_project_id}/{dataset_sample_id}/kbsub_{sub}/'),
output:
INSPECT_FILE='subsampling/{dataset_project_id}/{dataset_sample_id}/kbsub_{sub}/output/counts_unfiltered/adata.h5ad',
shell:
"""
echo "deeeeeeeeeeeeerp"
echo "kb count --h5ad -i {params.INDEX} -g {params.T2G} -x {params.TECHNOLOGY} --overwrite --verbose --keep-tmp -t {THREAD} -o output {input.R1} {input.R2}"
echo "faaaaaaaaaart"
mkdir -p {params.KB_OUT_DIR}
cd {params.KB_OUT_DIR}
kb count --h5ad -i {params.INDEX} -g {params.T2G} -x {params.TECHNOLOGY} --overwrite --verbose --keep-tmp -t {THREAD} -o output {input.R1} {input.R2} && \
rm {input.R1} && rm {input.R2}
"""