def find_cluster_files(cluster_path):
# Return a dictionary of:
# "cdt" : cdt_filename
# "atr" : atr_filename
# "gtr" : gtr_filename
# "kag" : kag_filename
# "kgg" : kgg_filename
# Any of these files can be missing.
import os
from genomicode import filelib
filelib.assert_exists(cluster_path)
opj = os.path.join
cdt = opj(cluster_path, "signal.cdt")
atr = opj(cluster_path, "array_tree.atr")
gtr = opj(cluster_path, "gene_tree.gtr")
kag = opj(cluster_path, "array_cluster.kag")
kgg = opj(cluster_path, "gene_cluster.kgg")
cluster_files = {}
if filelib.exists_nz(cdt):
cluster_files["cdt"] = cdt
if filelib.exists_nz(atr):
cluster_files["atr"] = atr
if filelib.exists_nz(gtr):
cluster_files["gtr"] = gtr
if filelib.exists_nz(kag):
cluster_files["kag"] = kag
if filelib.exists_nz(kgg):
cluster_files["kgg"] = kgg
assert "cdt" in cluster_files, "No clustered file."
return cluster_files
def run(
self, network, antecedents, out_attributes, user_options, num_cores,
outfile):
import subprocess
from genomicode import config
from genomicode import filelib
from Betsy import module_utils
in_data = antecedents
species = out_attributes['ref']
annotate_BIN = config.annotate_vcf
command = ['python', annotate_BIN, in_data.identifier, '-o', outfile,
'-species', species]
process = subprocess.Popen(command,
shell=False,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
process.wait()
error_message = process.communicate()[1]
if 'error' in error_message:
raise ValueError(error_message)
assert filelib.exists_nz(outfile), (
'the output file %s for annot_vcf_file fails' % outfile
)
def run(self, network, in_data, out_attributes, user_options, num_cores,
out_path):
import os
from genomicode import config
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
bowtie2_build = filelib.which_assert(config.bowtie2_build)
ref = alignlib.standardize_reference_genome(in_data.identifier,
out_path,
use_symlinks=True)
# bowtie2-build <ref.fa> <output_stem>
# Makes files:
# <output_stem>.[1234].bt2
# <output_stem>.rev.[12].bt2
sq = parallel.quote
cmd = [
sq(bowtie2_build),
sq(ref.fasta_file_full),
ref.name,
]
parallel.sshell(cmd, path=out_path)
# Check to make sure index was created successfully.
f = os.path.join(out_path, "%s.1.bt2" % ref.name)
assert filelib.exists_nz(f)
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
"""extract the cel files with cc or v3_4"""
import os
import shutil
from Betsy import module_utils
from genomicode import affyio
from genomicode import filelib
in_data = antecedents
directory = module_utils.unzip_if_zip(in_data.identifier)
filenames = os.listdir(directory)
assert filenames, 'The input folder or zip file is empty.'
ver_list = []
if not os.path.exists(outfile):
os.mkdir(outfile)
for filename in filenames:
if filename == '.DS_Store':
pass
else:
fileloc = os.path.join(directory, filename)
cel_v = affyio.guess_cel_version(fileloc)
if cel_v in ['cc1', 'v3', 'v4']:
shutil.copyfile(fileloc, os.path.join(outfile, filename))
ver_list.append(True)
else:
ver_list.append(False)
if True in ver_list:
assert filelib.exists_nz(outfile), (
'the output file %s for extract_CEL_files fails' % outfile)
else:
assert ValueError('There is no cel file in the input.')
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
from genomicode import shiftscalenorm
import arrayio
from Betsy import read_label_file
from genomicode import filelib
data_node, cls_node = antecedents
if data_node and cls_node:
result, label_line, second_line = read_label_file.read(
cls_node.identifier)
assert len(
result) == 2, 'for shiftscale,there should be only 2 classes'
M = arrayio.read(data_node.identifier)
index1 = result[0][0]
index2 = result[1][0]
M_1 = M.matrix(None, index1)
M_2 = M.matrix(None, index2)
M_y = shiftscalenorm.normalize(M_1, M_2)
for i in range(M_y.dim()[0]):
for j in range(M_y.dim()[1]):
if str(M_y._X[i][j]) == 'nan':
M_y._X[i][j] = M_2._X[i][0]
for j in range(M.nrow()):
for i in range(len(index1)):
M._X[j][index1[i]] = M_y._X[j][i]
f = file(outfile, 'w')
arrayio.tab_delimited_format.write(M, f)
f.close()
assert filelib.exists_nz(outfile), (
'the output file %s for shiftscale fails' % outfile)
return False
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
import os
import subprocess
from genomicode import config
from genomicode import filelib
data_node, cel_node = antecedents
#out_attributes = set_out_attributes(data_node, out_attributes)
phenotype_BIN = config.analyze_phenotype
assert os.path.exists(phenotype_BIN)
assert "geneset_value" in user_options, 'no geneset are provided'
if not os.path.exists(outfile):
os.mkdir(outfile)
command = [
'python', phenotype_BIN, '--phenotype', 'EMT', '--ignore_samples',
'shCDH1,1', '--gene', user_options['geneset_value'], '-o',
outfile + '/EMT', data_node.identifier, cel_node.identifier
]
process = subprocess.Popen(command,
shell=False,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
x = process.communicate()
error_message = x[1]
assert not error_message, error_message
assert filelib.exists_nz(outfile), (
'the output file %s for analyze_phenotype fails' % outfile)
def run(
self, network, antecedents, out_attributes, user_options, num_cores,
outfile):
import subprocess
from Betsy import read_label_file
from Betsy import module_utils
from genomicode import filelib
from genomicode import config
data_node, cls_node = antecedents
if data_node and cls_node:
result, label_line, second_line = read_label_file.read(
cls_node.identifier)
assert len(
result) >= 2, 'for combat,there should be equal or larger than 2 classes'
combat_path = config.combatnorm
combat_BIN = module_utils.which(combat_path)
assert combat_BIN, 'cannot find the %s' % combat_path
command = ['python', combat_BIN, '-f', data_node.identifier, '-o',
outfile, '-label', cls_node.identifier]
process = subprocess.Popen(command,
shell=False,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
error_message = process.communicate()[1]
if error_message:
raise ValueError(error_message)
assert filelib.exists_nz(outfile), (
'the output file %s for combat fails' % outfile
)
return False
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
import subprocess
from genomicode import filelib
from Betsy import module_utils
from genomicode import config
in_data = antecedents
bcftools_BIN = config.bcftools
bcftools_module = module_utils.which(bcftools_BIN)
assert bcftools_module, 'cannot find the %s' % bcftools_BIN
vcfutils_BIN = config.vcfutils
#vcfutils_module = module_utils.which(vcfutils_BIN)
#assert bcftools_module, 'cannot find the %s' % bcftools_BIN
command = [
bcftools_BIN, 'view', in_data.identifier, '|', vcfutils_BIN,
'varFilter', '-D500'
]
#command = ['vcfutils.pl','varFilter','-D100',single_object.identifier]
f = file(outfile, 'w')
try:
process = subprocess.Popen(command,
shell=False,
stdout=f,
stderr=subprocess.PIPE)
finally:
f.close()
error_message = process.communicate()[1]
if 'error' in error_message:
raise ValueError(error_message)
assert filelib.exists_nz(outfile), (
'the output file %s for filter_vcf_file does not exist' % outfile)
def plot_line_keywd(filename, keyword, outfile):
import arrayio
from genomicode import mplgraph
from genomicode import filelib
M = arrayio.read(filename)
header = M.row_names()
label = M._col_names['_SAMPLE_NAME']
lines = []
data = []
legend_name = []
for i in range(M.dim()[0]):
if M.row_names(header[1])[i] == keyword:
data.append(M.slice()[i])
x = "%s (%s)" % (keyword, M.row_names(header[0])[i])
legend_name.append(x)
assert len(data) > 0, 'cannot find the keyword %s in the file %s' % (
keyword, filename)
for i in range(len(data)):
line = [(j, data[i][j]) for j in range(len(data[i]))]
lines.append(line)
params = {
"box_label": label,
"legend": legend_name,
"ylim_min": 0,
"ylabel": "Signal",
"left": 0.1,
}
fig = mplgraph.lineplot(*lines, **params)
fig.savefig(outfile)
assert filelib.exists_nz(outfile), 'the plot_line_keywd fails'
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
"""analyze geneset"""
import subprocess
from Betsy import module_utils
from genomicode import config
from genomicode import filelib
data_node, geneset_node = antecedents
score_geneset_path = config.score_geneset
score_geneset_BIN = module_utils.which(score_geneset_path)
assert score_geneset_BIN, 'cannot find the %s' % score_geneset_path
automatch = out_attributes['automatch']
command = [
'python', score_geneset_BIN, '-o', outfile, '--geneset_file',
geneset_node.identifier, data_node.identifier, '--all'
]
if automatch == 'yes':
command.append('--automatch')
process = subprocess.Popen(command,
shell=False,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
error_message = process.communicate()[1]
if error_message:
raise ValueError(error_message)
assert filelib.exists_nz(outfile), (
'the output file %s for score_pathway_with_geneset fails' %
outfile)
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
import os
import subprocess
from genomicode import config
from genomicode import filelib
#out_attributes = set_out_attributes(in_data, out_attributes)
TCGA_BIN = config.download_tcga
assert 'disease' in user_options
if 'date' in user_options:
x = ['--date', user_options['date']]
else:
x = []
command = [
'python', TCGA_BIN, '--disease', user_options['disease'], '--data',
out_attributes['preprocess'], '--download_only'
] + x
process = subprocess.Popen(command,
shell=False,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
error_message = process.communicate()[1]
if error_message:
raise ValueError(error_message)
result_files = os.listdir(".")
result_format = 'tar.gz'
for result_file in result_files:
if result_file.endswith(result_format):
os.rename(result_file, outfile)
assert filelib.exists_nz(outfile), (
'the output file %s for download_tcga fails' % outfile)
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
"""extract the fastq rna seq files"""
import os
import shutil
from genomicode import filelib
from Betsy import module_utils
in_data = antecedents
directory = module_utils.unzip_if_zip(in_data.identifier)
filenames = os.listdir(directory)
assert filenames, 'The input folder or zip file is empty.'
if not os.path.exists(outfile):
os.mkdir(outfile)
format_types = ['fa', 'fastq']
for format_type in format_types:
for filename in filenames:
if filename == '.DS_Store':
continue
fileloc = os.path.join(in_data.identifier, filename)
if fileloc.endswith(format_type + '.gz'):
newfname = os.path.splitext(filename)[0]
new_file = module_utils.gunzip(fileloc)
elif fileloc.endswith(format_type):
new_file = fileloc
newfname = filename
shutil.copyfile(new_file, os.path.join(outfile, newfname))
if fileloc.endswith('.gz'):
os.remove(new_file)
assert filelib.exists_nz(outfile), (
'the output file %s for extract_rna_files_fastq fails' % outfile)
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
import os
import subprocess
from Betsy import module_utils
from genomicode import config
from genomicode import filelib
in_data = antecedents
sortsam_BIN = config.sortsam
assert os.path.exists(sortsam_BIN), 'cannot find the %s' % sortsam_BIN
command = [
'java', '-Xmx5g', '-jar', sortsam_BIN, 'I=' + in_data.identifier,
'O=' + outfile, 'SO=coordinate', 'VALIDATION_STRINGENCY=LENIENT',
'CREATE_INDEX=true'
]
process = subprocess.Popen(command,
shell=False,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
process.wait()
error_message = process.communicate()[1]
if 'error' in error_message:
raise ValueError(error_message)
assert filelib.exists_nz(outfile), (
'the output file %s for sort_sam_file does not exist' % outfile)
def run(
self, network, antecedents, out_attributes, user_options, num_cores,
outfile):
import subprocess
from Betsy import module_utils
from genomicode import config
from genomicode import filelib
in_data = antecedents
scoresig_path = config.scoresig
scoresig_BIN = module_utils.which(scoresig_path)
assert scoresig_BIN, 'cannot find the %s' % scoresig_path
command = ['python', scoresig_BIN, '-r', in_data.identifier, '-m',
in_data.identifier, '-j', '20', '-o', outfile]
process = subprocess.Popen(command,
shell=False,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
error_message = process.communicate()[1]
if error_message:
raise ValueError(error_message)
assert filelib.exists_nz(outfile), (
'the output file %s for run_scoresig does not exists' % outfile
)
def run(
self, network, antecedents, out_attributes, user_options, num_cores,
outfile):
from genomicode import filelib
in_data = antecedents
import arrayio
f_out = file(outfile, 'w')
M = arrayio.read(in_data.identifier)
I_good = []
#get the percentage of gene filter
percent = float(user_options['filter_value']) / 100
for i in range(M.dim()[0]):
missing_count = 0
for j in range(M.dim()[1]):
if M._X[i][j] in [None, 'NA']:
missing_count = missing_count + 1
if float(missing_count) / M.dim()[1] < percent:
I_good.append(i)
M_c = M.matrix(I_good, None)
arrayio.tab_delimited_format.write(M_c, f_out)
f_out.close()
assert filelib.exists_nz(outfile), (
'the output file %s for gene_filter fails' % outfile
)
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
import os
import subprocess
from genomicode import config
from genomicode import filelib
from Betsy import module_utils
in_data = antecedents
directory = module_utils.unzip_if_zip(in_data.identifier)
filenames = os.listdir(directory)
assert filenames, 'The input folder or zip file is empty.'
if not os.path.exists(outfile):
os.mkdir(outfile)
samtools_BIN = config.samtools
assert os.path.exists(
samtools_BIN), 'cannot find the %s' % samtools_BIN
for filename in filenames:
infile = os.path.join(directory, filename)
outname = os.path.splitext(filename)[-2] + '.bam'
outname = os.path.join(outfile, outname)
command = [samtools_BIN, 'view', '-S', '-b', '-o', outname, infile]
process = subprocess.Popen(command,
shell=False,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
process.wait()
error_message = process.communicate()
if 'error' in error_message[1]:
raise ValueError(error_message)
assert filelib.exists_nz(outname), (
'the output file %s for convert_sam_to_bam does not exist' %
outname)
def run(self, network, in_data, out_attributes, user_options, num_cores,
out_path):
from genomicode import config
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
bwa = filelib.which_assert(config.bwa)
ref = alignlib.standardize_reference_genome(in_data.identifier,
out_path,
use_symlinks=True)
# bwa index <out_stem.fa>
# Makes files:
# <out_stem>.fa.amb .ann .bwt .pac .sa
sq = parallel.quote
cmd = [
sq(bwa),
"index",
sq(ref.fasta_file_full),
]
parallel.sshell(cmd, path=out_path)
# Make sure the indexing worked properly.
EXTENSIONS = [".amb", ".ann", ".bwt", ".pac", ".sa"]
for ext in EXTENSIONS:
f = "%s%s" % (ref.fasta_file_full, ext)
assert filelib.exists_nz(f), "Missing: %s" % f
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
from genomicode import filelib
import os
from genomicode import jmath
in_data = antecedents
matrix = [x for x in filelib.read_cols(in_data.identifier)]
matrix = [x[1:] for x in matrix]
matrix = jmath.transpose(matrix)
sample = matrix[0][1:]
data = matrix[1:]
if not os.path.exists(outfile):
os.mkdir(outfile)
for one_data in data:
value = one_data[1:]
value = [float(i) for i in value]
pair = [(value[i], sample[i]) for i in range(len(value))]
pair.sort()
gene_value = [i[0] for i in pair]
label = [i[1] for i in pair]
ylabel = one_data[0]
from genomicode import mplgraph
fig = mplgraph.barplot(gene_value,
box_label=label,
xtick_rotation=90,
xlabel='sample',
ylabel=ylabel)
output = os.path.join(outfile, ylabel)
fig.savefig(output + '.png')
assert filelib.exists_nz(outfile), (
'the output file %s for plot_geneset_score_bar fails' % outfile)
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
import os
import subprocess
import arrayio
from Betsy import module_utils
from genomicode import filelib
from genomicode import config
in_data = antecedents
bfrm_path = config.bfrmnorm
bfrm_BIN = module_utils.which(bfrm_path)
assert bfrm_BIN, 'cannot find the %s' % bfrm_path
num_factor = 1
#num_factor = 10
if 'num_factors' in user_options.keys():
num_factor = int(user_options['num_factors'])
assert num_factor >= 1, 'the num_factor should be >=1'
# What is single_object?
#M = arrayio.read(single_object.identifier)
M = arrayio.read(in_data.identifier)
col_num = M.ncol()
assert num_factor <= col_num, (
'the num_factor should be less than %d' % col_num)
tmp = 'tmp_dir'
command = [
'python', bfrm_BIN, in_data.identifier, '-f',
str(num_factor), '-o', tmp
]
process = subprocess.Popen(command,
shell=False,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
error_message = process.communicate()[1]
if error_message:
raise ValueError(error_message)
assert filelib.exists_nz(tmp), (
'the output dir %s for bfrm_normalize fails' % tmp)
assert filelib.exists_nz(os.path.join(tmp, 'normalized.gct')), (
'the output gct file for bfrm_normalize fails')
out = os.path.join(tmp, 'normalized.gct')
M = arrayio.read(out)
M_new = arrayio.convert(M, to_format=arrayio.pcl_format)
f = file(outfile, 'w')
arrayio.tab_delimited_format.write(M_new, f)
f.close()
def run(
self, network, antecedents, out_attributes, user_options, num_cores,
out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
MAX_RAM = 64 # maximum amount of ram to use in Gb.
bam_node, ref_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
jobs = [] # list of (in_filename, log_filename, out_filename)
for in_filename in bam_filenames:
p, f = os.path.split(in_filename)
s, ext = os.path.splitext(f)
log_filename = os.path.join(out_path, "%s.log" % s)
out_filename = os.path.join(out_path, f)
x = in_filename, log_filename, out_filename
jobs.append(x)
# java -Xmx5g -jar /usr/local/bin/GATK/GenomeAnalysisTK.jar
# -T SplitNCigarReads -R ../hg19.fa -I $i -o $j
# -rf ReassignOneMappingQuality -RMQF 255 -RMQT 60
# -U ALLOW_N_CIGAR_READS
# Start with 5 Gb RAM.
commands = make_commands(jobs, ref.fasta_file_full, 5)
nc = mlib.calc_max_procs_from_ram(5, upper_max=num_cores)
parallel.pshell(commands, max_procs=nc)
metadata["commands"] = commands
metadata["num_procs"] = nc
# If any of the analyses didn't finish, try again with more
# RAM.
jobs2 = []
for x in jobs:
in_filename, log_filename, out_filename = x
if filelib.exists_nz(out_filename):
continue
jobs2.append(x)
if jobs2:
commands = make_commands(jobs2, ref.fasta_file_full, MAX_RAM)
nc = mlib.calc_max_procs_from_ram(MAX_RAM, upper_max=num_cores)
parallel.pshell(commands, max_procs=nc)
metadata["commands"] += commands
# Make sure the analysis completed successfully.
out_filenames = [x[-1] for x in jobs]
filelib.assert_exists_nz_many(out_filenames)
return metadata
def plot_line_keywds(filename, keywords, outfile):
import arrayio
from genomicode import mplgraph
from genomicode import filelib
M = arrayio.read(filename)
header = M.row_names()
label = M._col_names['_SAMPLE_NAME']
outfiles = []
for keyword in keywords:
out = keyword + '.png'
lines = []
data = []
legend_name = []
for i in range(M.dim()[0]):
if M.row_names(header[1])[i] == keyword:
data.append(M.slice()[i])
legend_name.append(M.row_names(header[0])[i])
assert len(data) > 0, 'cannot find the keywords %s in the file %s' % (
keywords, filename)
for i in range(len(data)):
line = [(j, data[i][j]) for j in range(len(data[i]))]
lines.append(line)
params = {
"box_label": label,
"legend": legend_name,
"ylim_min": 0,
"ylabel": keyword,
"left": 0.1,
}
fig = mplgraph.lineplot(*lines, **params)
fig.savefig(out)
outfiles.append(out)
import Image
img_w_list = []
img_h_list = []
imgs = []
for i in range(len(outfiles)):
img = Image.open(outfiles[i], 'r')
img_w, img_h = img.size
img_w_list.append(img_w)
img_h_list.append(img_h)
imgs.append(img)
total_w = max(img_w_list) + 30
total_h = sum(img_h_list) + 10
background = Image.new('RGBA', (total_w, total_h), (255, 255, 255, 255))
bg_w, bg_h = background.size
offset_w = (bg_w - max(img_w_list)) / 2
offset_h_list = []
for i in range(len(img_h_list)):
offset_h = bg_h - sum(img_h_list[i:])
offset_h_list.append(offset_h)
for img, offset_h in zip(imgs, offset_h_list):
background.paste(img, (offset_w, offset_h))
background.save(outfile)
assert filelib.exists_nz(outfile), 'the plot_line_keywds fails'
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
import shutil
from genomicode import filelib
in_data = antecedents
#out_attributes = set_out_attributes(in_data, out_attributes)
shutil.copyfile(in_data.identifier, outfile)
assert filelib.exists_nz(outfile), (
'the output file %s for convert_postprocess_impute fails' %
outfile)
def run(
self, network, antecedents, out_attributes, user_options, num_cores,
outfile):
from genomicode import filelib
import os
import arrayio
from genomicode import config
from genomicode import arrayplatformlib
in_data = antecedents
mapfile = config.HumanHT_12_to_HG_u133_Plus_2
assert os.path.exists(mapfile), 'mapping file %s does not exist' % mapfile
result = []
for d in filelib.read_row(mapfile, header=True):
if int(d.Distance) <= 1000 and d.Match == 'Best for Both':
result.append((d.Affymetrix_Probe_Set_ID, d.Illumina_Probe_ID))
M = arrayio.read(in_data.identifier)
#platform_list = arrayplatformlib.identify_all_platforms_of_matrix(M)
platform_list = arrayplatformlib.score_all_platforms_of_matrix(M)
illu_id = None
probe_id = None
for platform in platform_list:
if 'HumanHT_12' in platform:
illu_id = M._row_names[platform[0]]
if 'HG_U133_Plus_2' in platform:
probe_id = M._row_names[platform[0]]
if not illu_id or not probe_id:
return None
index = []
for i in range(M.nrow()):
if (probe_id[i], illu_id[i]) in result:
index.append(i)
if len(index) > 0:
M_new = M.matrix(index, None)
f = file(outfile, 'w')
arrayio.tab_delimited_format.write(M_new, f)
f.close()
assert filelib.exists_nz(outfile), (
'the output file %s for best_match_both fails' % outfile
)
else:
return None
def _make_config_file(config_filename, skip_depth_filter=False):
import os
from genomicode import filelib
from Betsy import module_utils as mlib
strelka_path = mlib.get_config("strelka", assert_exists=True)
src_config = os.path.join(strelka_path, "etc",
"strelka_config_bwa_default.ini")
filelib.exists_nz(src_config)
lines = open(src_config).readlines()
assert lines
# Edit configure options.
for i in range(len(lines)):
x = lines[i]
x = x.strip()
line = x
# Make sure skip_depth_filter is correct.
# isSkipDepthFilters should be set to 1 to skip depth
# filtration for whole exome or other targeted sequencing data
#
# sSkipDepthFilters = 0
if line.startswith("isSkipDepthFilters"):
# isSkipDepthFilters = 0
x = line.split()
assert len(x) == 3
assert x[1] == "="
assert x[2] in ["0", "1"]
if skip_depth_filter:
x[2] = "1"
else:
x[2] = "0"
line = " ".join(x)
lines[i] = line
lines = [x + "\n" for x in lines] # replace newline that was stripped.
open(config_filename, 'w').writelines(lines)
def run(
self, network, antecedents, out_attributes, user_options, num_cores,
outfile):
"""convert the cel file with ccl or v3_4 to v3_4"""
import shutil
from genomicode import filelib
in_data = antecedents
#new_parameters = set_out_attributes(in_data, out_attributes)
shutil.copytree(in_data.identifier, outfile)
assert filelib.exists_nz(outfile), (
'the output file %s for detect_CEL_version' % outfile
)
def run(
self, network, antecedents, out_attributes, user_options, num_cores,
outfile):
import os
from genomicode import jmath
from genomicode import filelib
in_data = antecedents
cwd = os.getcwd()
R = jmath.start_R()
R('require(limma,quietly=TRUE)')
R('library(marray)')
os.chdir(in_data.identifier)
try:
R('dir<-getwd()')
R('files<-list.files(dir)')
R('x.read<-read.Agilent(files)')
finally:
os.chdir(cwd)
R('xnorm.loc <- maNorm(x.read, norm = "loess")')
R('x.norm <- maNormScale(xnorm.loc, norm = "p")')
tmpfile = 'tmp.txt'
jmath.R_equals(tmpfile, 'tmpfile')
R('write.marray(x.norm,tmpfile)')
f = open(tmpfile, 'r')
text = f.readlines()
firstline = text[0].split()
f.close()
firstindex = firstline.index('"ProbeName"')
if '"Sequence"' in firstline:
secondindex = firstline.index('"Sequence"')
else:
secondindex = firstline.index('"ControlType"')
sample = range(secondindex + 1, len(firstline))
f = open(outfile, 'w')
for i in text:
line = i.split()
f.write(line[firstindex] + '\t')
for j in sample:
f.write(line[j] + '\t')
f.write('\n')
f.close()
os.remove(tmpfile)
assert filelib.exists_nz(outfile), (
'the output file %s for preprocess_agilent fails' % outfile
)
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
import os
import shutil
from genomicode import filelib
in_data = antecedents
result_files = os.listdir(in_data.identifier)
for result_file in result_files:
if '-controls' in result_file:
goal_file = os.path.join(in_data.identifier, result_file)
shutil.copyfile(goal_file, outfile)
assert filelib.exists_nz(outfile), (
'the output file %s for illu_control fails' % outfile)
def add_snpeff_to_svm(svm_file, snpeff_file, outfile):
import shutil
from genomicode import filelib
from genomicode import SimpleVariantMatrix
from genomicode import AnnotationMatrix
if not filelib.exists_nz(snpeff_file):
shutil.copy2(svm_file, outfile)
return
# Read the annotations.
header = None # includes Chrom, Pos, Ref, Alt
coord2d = {}
for d in filelib.read_row(snpeff_file, header=1):
if header is None:
header = d._header
coord = d.Chrom, d.Pos, d.Ref, d.Alt
coord2d[coord] = d
svm = SimpleVariantMatrix.read_as_am(svm_file)
CHROM = svm.header2annots["______Chrom"]
POS = svm.header2annots["______Pos"]
REF = svm.header2annots["______Ref"]
ALT = svm.header2annots["______Alt"]
snpeff_header = header[4:]
snpeff_matrix = [] # Row major.
for i in range(len(CHROM)):
coord = CHROM[i], POS[i], REF[i], ALT[i]
row = [""] * len(snpeff_header)
d = coord2d.get(coord)
if d:
row = d._cols[4:]
assert len(row) == len(snpeff_header)
snpeff_matrix.append(row)
assert len(snpeff_matrix) == len(CHROM)
# AnnotationMatrix is column major.
snpeff_annots = []
for j in range(len(snpeff_header)):
x = [snpeff_matrix[i][j] for i in range(len(snpeff_matrix))]
snpeff_annots.append(x)
# Convert the headers to SVM format.
snpeff_header = ["SnpEff______%s" % x for x in snpeff_header]
# Make the new SimpleVariantMatrix.
headers = svm.headers[:4] + snpeff_header + svm.headers[4:]
x = [svm.header2annots[x] for x in svm.headers_h]
all_annots = x[:4] + snpeff_annots + x[4:]
merged = AnnotationMatrix.create_from_annotations(
headers, all_annots, headerlines=svm.headerlines)
SimpleVariantMatrix.write_from_am(outfile, merged)
def run(
self, network, antecedents, out_attributes, user_options, num_cores,
outfile):
from genomicode import mplgraph
from genomicode import filelib
in_data = antecedents
matrix = [x for x in filelib.read_cols(in_data.identifier)]
header = matrix[0]
index = header.index('Confidence')
matrix = matrix[1:]
confidence = [float(i[index]) for i in matrix]
sample = [i[0] for i in matrix]
if confidence == [''] * len(matrix) or 'Correct?' in header:
index = header.index('Predicted_class')
class_value = [i[index] for i in matrix]
label_dict = dict()
label_list = []
i = -1
for label in class_value:
if label not in label_dict.keys():
i = i + 1
label_dict[label] = i
label_list.append(label_dict[label])
yticks = label_dict.keys()
ytick_pos = [label_dict[i] for i in label_dict.keys()]
fig = mplgraph.barplot(label_list,
box_label=sample,
ylim=(-0.5, 1.5),
ytick_pos=ytick_pos,
yticks=yticks,
xtick_rotation='vertical',
ylabel='Prediction',
xlabel='Sample')
fig.savefig(outfile)
else:
fig = mplgraph.barplot(confidence,
box_label=sample,
ylim=(-1.5, 1.5),
xtick_rotation='vertical',
ylabel='Prediction',
xlabel='Sample')
fig.savefig(outfile)
assert filelib.exists_nz(outfile), (
'the output file %s for plot_prediction_bar fails' % outfile
)
def run(
self, network, antecedents, out_attributes, user_options, num_cores,
outfile):
from genomicode import quantnorm
import arrayio
from genomicode import filelib
in_data = antecedents
M = arrayio.read(in_data.identifier)
Y = quantnorm.normalize(M)
f = file(outfile, 'w')
Y_c = arrayio.convert(Y, to_format=arrayio.pcl_format)
arrayio.tab_delimited_format.write(Y_c, f)
f.close()
assert filelib.exists_nz(outfile), (
'the output file %s for quantile fails' % outfile
)
