def process2(R1, R2, output, adapter, threads, libpath, mapper, minlen, trim5, counts, rRNA):
file_name = R1.split("/")[-1].split("_")[0]
outdir = os.path.join(output, file_name)
### make directory
if not os.path.exists(outdir):
try:
os.makedirs(outdir)
except Exception as e:
pass
prefix = os.path.join(outdir, file_name)
out_R1_p = prefix + "_R1.fq.gz"
out_R1_u = prefix + "_R1_unpaired.gz"
out_R2_p = prefix + "_R2.fq.gz"
out_R2_u = prefix + "_R2_unpaired.gz"
out_log = prefix + "_trimmomatic.log"
print("\n%s Processing: %s, %s" % (current_time(), R1,R2))
realpath = sys.path[0]
### trimmomatic
subprocess.call("trimmomatic PE -threads %d -phred33 %s %s %s %s %s %s ILLUMINACLIP:%s/../library/adapter/%s:1:30:10:5 SLIDINGWINDOW:4:20 MINLEN:%d HEADCROP:%d 2> %s" % (threads, R1, R2, out_R1_p, out_R1_u, out_R2_p, out_R2_u, realpath, adapter, minlen, trim5, out_log), shell=True)
### Mapping by hisat2
if mapper == 'hisat2':
SummaryFile = prefix + "_hisat_summary.txt"
MapOut = prefix + "_hisat_sort.bam"
subprocess.call("hisat2 -p %d -x %s/genome_tran -1 %s -2 %s -U %s,%s -t --dta --summary-file %s --new-summary|samtools sort -@ %d -m 10G -o %s" % (threads, libpath, out_R1_p, out_R2_p, out_R1_u, out_R2_u, SummaryFile, threads, MapOut), shell=True)
### Mapping by STAR
elif mapper == 'STAR':
STARprefix = prefix + "_STAR_"
subprocess.call("STAR --runThreadN %d --outSAMtype BAM SortedByCoordinate --genomeDir %s --readFilesIn %s %s --readFilesCommand zcat --outFileNamePrefix %s --quantMode GeneCounts --outFilterScoreMinOverLread 0.1 --outFilterMatchNminOverLread 0.1 --outFilterMatchNmin 0 --outFilterMismatchNmax 2" % (threads, libpath, out_R1_p, out_R2_p, STARprefix), shell=True)
MapOut = prefix + "_STAR_Aligned.sortedByCoord.out.bam" ## sorted bam file
### Asemble by stringtie
print("%s Asemble ..." % current_time())
stringtieGTF = prefix + '_stringtie.gtf'
stringtieGene = prefix + '_gene_abund.tab'
subprocess.call("stringtie %s -e -G %s/annotation.gtf -p %d -o %s -A %s" % (MapOut, libpath, threads, stringtieGTF, stringtieGene), shell=True)
### Gene counts
if counts:
countOut = prefix + '_gene_counts.txt'
subprocess.call("featureCounts -a %s/annotation.gtf -o %s %s -t exon -g gene_name -T %d -Q 30 -p" % (libpath, countOut, MapOut, threads), shell=True)
### rRNA
if rRNA:
rapvis_rRNA.rRNA(R1, R2, output, threads)
def merge_profiles(name, output):
while True:
n = GetRunningTasks(name)
if n == 0:
print("%s, Merging profiles ... " % current_time())
files = glob.glob("%s/*/*gene_abund.tab" % (output))
if files:
files = sorted(files)
dict_merge = {}
for f in files:
with open(f) as handle:
for line in islice(handle, 1, None):
line = line.strip().split("\t")
k_map = line[1]
k_RNA = f.split("/")[-2]
count = float(line[8]) # TPM
if k_map in dict_merge:
dict_merge[k_map][k_RNA] = count
else:
tmp_dic = {}
tmp_dic[k_RNA] = count
dict_merge[k_map] = tmp_dic
df = DataFrame(dict_merge).T
df = df.fillna(value=0) ### fill NA to 0
df_sum = DataFrame(df.sum(axis=1), columns=['sum'])
df = df.join(df_sum)
df = df.sort_values(by="sum", ascending=False) ### sort by sum
df.drop(['sum'], axis=1, inplace=True)
merge_out = os.path.join(output, "merge_gene_TPM.txt")
df.to_csv(merge_out,
sep="\t",
header=True,
index=True,
index_label="gene",
float_format="%.2f")
return merge_out
break
else:
print(
"\n### Merge profiles failed, it is not exsit in %s/*/ \n"
% (output))
exit(1)
else:
print("%s, Waitiing for task finished, remaining %d tasks" %
(current_time(), n))
time.sleep(10)
def merge_gene_counts(output):
print("%s, Merging Gene Counts ... " % current_time())
files = glob.glob("%s/*/*gene_counts.txt" % (output))
try:
files = sorted(files)
dict_merge = {}
for f in files:
with open(f) as handle:
for line in islice(handle, 2, None):
line = line.strip().split("\t")
k_map = line[0]
k_RNA = f.split("/")[-2]
count = int(line[6]) # count
if k_map in dict_merge:
dict_merge[k_map][k_RNA] = count
else:
tmp_dic = {}
tmp_dic[k_RNA] = count
dict_merge[k_map] = tmp_dic
df = DataFrame(dict_merge).T
df = df.fillna(value=0) ### fill NA to 0
df_sum = DataFrame(df.sum(axis=1), columns=['sum'])
df = df.join(df_sum)
df = df[df['sum'] > 0]
df = df.sort_values(by="sum", ascending=False) ### sort by sum
df.drop(['sum'], axis=1, inplace=True)
merge_out2 = os.path.join(output, "merge_gene_counts.txt")
df.to_csv(merge_out2,
sep="\t",
header=True,
index=True,
index_label="gene",
float_format="%.0f")
#return merge_out2
except Exception as e:
print("\n### Merge Gene Counts failed, it is not exsit in %s/*/ \n" %
(output))
def gene_dis(fi, output, libpath):
print("%s, Caculating gene expression pattern ... " % current_time())
data = pd.read_table(fi, header=0)
prefix = os.path.join(output, 'merge_gene_TPM')
###
data_melt = data.melt('gene', var_name='sample')
data_melt = data_melt.query('value>0')
data_melt.index = data_melt['gene']
### Gene species by gene type
gene_type = {}
with open("%s/gene_type.txt" % libpath) as f:
x = str(data_melt.index[0])
if x.startswith("ENS"):
for line in f:
line = line.strip().split("\t")
gene_type[line[1]] = line[3]
else:
for line in f:
line = line.strip().split("\t")
gene_type[line[2]] = line[3]
gene_type = pd.Series(gene_type, name='gene_type', dtype="string")
type_list = ["protein_coding", "pseudogene", "lincRNA", "antisense"]
for i in range(0, len(gene_type.index)):
if gene_type[i] in type_list:
pass
elif re.search("pseudogene", gene_type[i]):
gene_type[i] = 'pseudogene'
else:
gene_type[i] = 'others'
# Categories
data_melt2 = pd.merge(data_melt,
gene_type,
how='left',
sort=False,
right_index=True,
left_index=True)
cat_type = CategoricalDtype(categories=data.columns[1:], ordered=True)
data_melt2['sample'] = data_melt2['sample'].astype(cat_type)
# set width and height
width = int(data.shape[0])
height = 6
fontsize = 15
if width >= 8:
width = math.log(width, 2) * 2 ### adjust the width of barplot
else:
width = width / 1.5
aspect = width / width
#aspect = int(data.shape[1])
#if aspect >3:
#aspect = np.log(aspect) - 1
#if aspect >1:
# aspect = np.log(aspect)
#else :
# aspect = aspect
colors = list(reversed(sns.color_palette()[0:5]))
hue_order = [
"others", "pseudogene", "antisense", "lincRNA", "protein_coding"
]
sns.displot(data_melt2,
x="sample",
hue="gene_type",
hue_order=hue_order,
palette=colors,
multiple="stack",
shrink=.8,
height=height,
aspect=aspect)
plt.xticks(rotation=90)
plt.xlabel('Samples', fontsize=fontsize)
plt.ylabel('Gene species', fontsize=fontsize)
out_box = prefix + "_species_type.pdf"
plt.savefig(out_box, bbox_inches='tight')
plt.close()
'''
### Gene species
sns.displot(data_melt, x="sample", shrink=.8, height=height, aspect=aspect)
plt.xticks(rotation=90)
plt.xlabel('Samples', fontsize=fontsize)
plt.ylabel('Gene numbers', fontsize=fontsize)
out_box = prefix + "_species.pdf"
plt.savefig(out_box, bbox_inches='tight')
plt.close()
'''
### Gene species by expression interval
values = pd.cut(
data_melt['value'], [0, 1, 5, 10, 50, 100, 1000, 1000000],
labels=['0~1', '1~5', '5~10', '10~50', '50~100', '100~1000', '>1000'])
data_melt = data_melt.copy() ### For SettingWithCopyWarning
data_melt['ExpressionInterval'] = values
#data_melt.loc[:,'ExpressionInterval'] = values
sns.displot(data_melt,
x="sample",
hue="ExpressionInterval",
multiple="stack",
shrink=.8,
height=height,
aspect=aspect)
plt.xticks(rotation=90)
plt.xlabel('Samples', fontsize=fontsize)
plt.ylabel('Gene species', fontsize=fontsize)
out_box = prefix + "_species_EI.pdf"
plt.savefig(out_box, bbox_inches='tight')
plt.close()
### expression density
#data_melt['log2value'] = np.log2(data_melt['value'])
#sns.displot(data=data_melt, x="log2value", kind="kde", hue='sample', height=4, aspect=1.4, common_norm=False)
sns.kdeplot(data=data_melt,
x="value",
hue='sample',
log_scale=True,
common_norm=False)
plt.xlabel('log10(TPM)', fontsize=15)
out_box = prefix + "_density.pdf"
plt.savefig(out_box, bbox_inches='tight')
plt.close()
'-trim5',
default=0,
type=int,
metavar='N',
help='remove N bases from the begining of each read (default:0)')
parser.add_argument('--counts',
action='store_true',
help='Get gene counts')
parser.add_argument('--rRNA',
action='store_true',
help='whether mapping to rRNA(Human)')
parser.add_argument('-v',
'--version',
action='version',
version='%(prog)s 0.0.2')
args = parser.parse_args()
print("\n%s ..... Start RNAseq processing" % (current_time()))
start_time = time.time()
process(args.input, args.output, args.adapter, args.threads,
args.libraryPath, args.mapper, args.minlen, args.trim5,
args.counts, args.rRNA)
###
end_time = time.time()
run_time = round((end_time - start_time) / 60, 5)
print("\n%s ..... Finished all. Used time: %s m\n" %
(current_time(), run_time))
def SubmitTask(fi, output, adapter, threads, libpath, mapper, tasks, name,
minlen, trim5, queue, counts, rRNA):
'''
submit tasks to the server
'''
### get the data with fastq format
files = []
fAll = glob.glob("%s/*" % fi)
for f in fAll:
if f.endswith('fastq') or f.endswith('fastq.gz') or f.endswith(
'fq.gz') or f.endswith('fq'):
files.append(f)
files = sorted(files)
f_index = list(np.arange(0, len(files), 2))
f_num = 0
for i in f_index:
while True:
### get task number
n = GetRunningTasks(name)
### check task number
if n >= tasks:
time.sleep(10)
print("%s, Submitted Tasks: %d, total: %d" %
(current_time(), f_num, len(f_index)))
else:
f_num += 1 # samples number
R1 = files[i]
R2 = files[i + 1]
tmp = "tmp.sh"
f = open(tmp, 'w')
f.write("#!/bin/bash\n")
f.write("#$ -o %s.o\n" % name)
f.write("#$ -e %s.e\n" % name)
jobName = name + '.' + str(f_num)
f.write("#$ -N %s\n" % jobName)
f.write("source ~/.bashrc\n")
f.write("source ~/.bash_profile\n")
realpath = sys.path[0]
'''
f.write("python %s/rapvis_process.py -f1 %s -f2 %s -o %s -a %s -p %d -lib %s -m %s --minlen %d --trim5 %d\n" %(realpath, R1, R2, output, adapter, threads, libpath, mapper, minlen, trim5))
if rRNA:
f.write("python %s/rapvis_rRNA.py -f1 %s -f2 %s -o %s -p %d\n" % (realpath, R1, R2, output, threads))
'''
rRNA = '--rRNA' if rRNA else ''
counts = '--counts' if counts else ''
f.write(
"python %s/rapvis_process.py -R1 %s -R2 %s -o %s -a %s -p %d -lib %s -m %s --minlen %d --trim5 %d %s %s\n"
% (realpath, R1, R2, output, adapter, threads, libpath,
mapper, minlen, trim5, counts, rRNA))
f.close()
subprocess.call("qsub -cwd -q %s %s" % (queue, tmp),
shell=True)
#subprocess.call("qsub -cwd -l node=4 -q %s %s" % (queue, tmp), shell=True)
#subprocess.call("qsub -cwd -l mem_free=150G -q %s %s" % (queue, tmp), shell=True)
subprocess.call("rm %s" % tmp, shell=True)
time.sleep(1)
break
