def make_name_dict(data_file,
rose_folder,
names_list=[],
enhancer_type="super"):
"""For each name, check for the presence of an enriched file or allEnhancer table.
These are the files required for enhancer clustering.
"""
data_dict = pipeline_utils.load_data_table(data_file)
# draw the parent folder from the data_file
parent_folder = utils.format_folder(
os.path.dirname(os.path.abspath(data_file)), False)
if parent_folder.count("data_tables") == 1:
parent_folder = parent_folder.replace("data_tables/", "")
print("Using {} as the parent folder".format(parent_folder))
# check to see if a rose folder exists already
if utils.format_folder(rose_folder, False):
rose_exists = True
rose_folder = utils.format_folder(rose_folder, False)
else:
rose_exists = False
rose_folder = utils.format_folder(rose_folder, True)
# check names_list to see if datasets exist
if len(names_list) == 0:
names_list = [
name for name in data_dict if name.upper().count("WCE") == 0
and name.upper().count("INPUT") == 0
]
# if no names_list is given, this filters out WCE
# now check that all of the datasets at a minimum have a rose output OR enriched region file
name_dict = defaultdict(dict)
for name in names_list:
# check if each dataset has a background
background_name = data_dict[name]["background"]
if background_name in data_dict:
name_dict[name]["background"] = True
else:
name_dict[name]["background"] = False
# assumes standard folder structure for enriched file
enriched_file = os.path.join(parent_folder, "macsEnriched",
data_dict[name]["enrichedMacs"])
print("Looking for macs output at {}".format(enriched_file))
try:
open(enriched_file, "r").close()
name_dict[name]["enriched_file"] = enriched_file
except (IOError, FileNotFoundError):
name_dict[name]["enriched_file"] = ""
# roseOutput looks for standard format rose output
# need an allEnhancers table and a region table to proceed
# if the rose folder doesn't exist, don't bother
if rose_exists:
try:
rose_output_files = os.listdir(
os.path.join(rose_folder, "{}_ROSE".format(name)))
if enhancer_type == "super":
enhancer_string = "AllEnhancers.table.txt"
if enhancer_type == "stretch":
enhancer_string = "AllEnhancers_Length.table.txt"
if enhancer_type == "superstretch":
enhancer_string = "AllEnhancers_SuperStretch.table.txt"
all_enhancer_file_list = [
x for x in rose_output_files
if x.count(enhancer_string) == 1 and x[0] != "."
] # no weird hidden or temp files
if all_enhancer_file_list:
name_dict[name]["enhancer_file"] = os.path.join(
rose_folder, "{}_ROSE".format(name),
all_enhancer_file_list[0])
else:
name_dict[name]["enhancer_file"] = ""
except (OSError, FileNotFoundError):
name_dict[name]["enhancer_file"] = ""
else:
name_dict[name]["enhancer_file"] = ""
if (name_dict[name]["enhancer_file"] == ""
and name_dict[name]["enriched_file"] == ""):
print(
"INSUFFICIENT DATA TO RUN ENAHNCER ANALYSIS ON {}. PLEASE MAKE SURE ROSE OUTPUT "
"OR MACS ENRICHED REGION PEAKS FILE EXISTS".format(name))
print(name_dict[name])
sys.exit()
return name_dict
def main():
"""Main function call."""
parser = argparse.ArgumentParser()
# required flags
parser.add_argument(
"-d",
"--data",
dest="data",
default=None,
required=True,
help="Enter a data file for datasets to be processed",
)
parser.add_argument(
"-o",
"--output",
dest="output",
default=None,
required=True,
help="specify an output folder to write results to",
)
# additional args
parser.add_argument(
"-i",
"--input",
dest="input",
required=False,
help=
"Enter a comma separated list of names to analyze. Default will be all datasets",
)
parser.add_argument(
"-n",
"--name",
dest="name",
required=False,
help="Enter a name for the analysis",
)
parser.add_argument(
"-r",
"--rose",
dest="rose",
required=False,
help="Enter a folder to detect or write rose output",
)
parser.add_argument(
"-a",
"--all",
dest="all",
action="store_true",
default=False,
help="flag to run analysis on ALL enhancers (this is much slower)",
)
parser.add_argument(
"-s",
"--stitch",
dest="stitch",
default="",
help=
("specify a fixed stitch distance for all datasets, otherwise will compute stitching "
"automatically on each dataset"),
)
parser.add_argument(
"-e",
"--enhancer-type",
dest="enhancer_type",
default="super",
help=
"specify type of enhancer to analyze: super, stretch, superStretch",
)
parser.add_argument(
"-t",
"--tss",
dest="tss",
default=2500,
help="specify a tss exclusion window. default is 2500bp",
)
parser.add_argument(
"--mask",
dest="mask",
required=False,
help=
"Create a mask set of regions to filter out of analysis. must be .bed or .gff format",
)
args = parser.parse_args()
print(args)
# pull in the data_file and create a data_dict
data_file = args.data
# now the output folder
output_folder = utils.format_folder(
args.output, True) # check and create the output folder
# now the rose folder
if args.rose:
rose_folder = args.rose
else:
rose_folder = os.path.join(output_folder, "rose")
if args.input:
names_list = args.input.split(",")
else:
names_list = []
# get the genome
data_dict = pipeline_utils.load_data_table(data_file)
genome = data_dict[list(data_dict.keys())[0]]["genome"]
# check if using only supers
if args.all:
super_only = False
else:
super_only = True
# get the anlysis name
if args.name:
analysis_name = args.name
else:
analysis_name = "enhancers"
# check for a stitching parameter
if len(str(args.stitch)) > 0:
stitch = str(args.stitch)
else:
stitch = ""
# check for the tss parameter
tss_distance = int(args.tss)
# check enhancer type
enhancer_type = args.enhancer_type.lower()
if ["super", "superstretch", "stretch"].count(enhancer_type) == 0:
print("ERROR: unsupported enhancer type {}".format(enhancer_type))
sys.exit()
# see if there's a mask
if args.mask:
mask_file = args.mask
else:
mask_file = ""
# =====================================================
# =================SUMMARIZE INPUTS====================
# =====================================================
print("WORKING IN GENOME {}".format(genome))
print("DRAWING DATA FROM {} AND ROSE FOLDER {}".format(
data_file, rose_folder))
print("USING {} AS THE OUTPUT FOLDER".format(output_folder))
# =====================================================
# ==============ESTABLISH ALL WORKING FILES============
# =====================================================
print("\n\n\nESTABLISHING WORKING FILES")
name_dict = make_name_dict(data_file, rose_folder, names_list,
enhancer_type)
print(name_dict)
print("STARTING ANALYSIS ON THE FOLLOWING DATASETS:")
print(list(name_dict.keys()))
for name in name_dict:
if len(name_dict[name]["enhancer_file"]) == 0:
print("NO ROSE OUTPUT FOR {}".format(name))
# =====================================================
# ==============LAUNCH ENHANCER MAPPING================
# =====================================================
print("\n\n\nLAUNCHING ENHANCER MAPPING (IF NECESSARY)")
name_dict = launch_enhancer_mapping(
data_file,
name_dict,
output_folder,
rose_folder,
stitch,
tss_distance,
enhancer_type,
mask_file,
)
print(name_dict)
# =====================================================
# ====================GET MEDIAN SIGNAL================
# =====================================================
print("\n\n\nGETTING MEDIAN ENHANCER SIGNAL FROM EACH SAMPLE")
median_dict = make_median_dict(name_dict)
print(median_dict)
# =====================================================
# ====================MERGING ENHANCERS================
# =====================================================
print("\n\n\nIDENTIFYING CONSENSUS ENHANCER REGIONS")
merged_gff_file = os.path.join(
output_folder, "{}_{}_-0_+0.gff".format(genome, analysis_name))
merged_gff_file = merge_collections(name_dict, analysis_name,
merged_gff_file, super_only)
# =====================================================
# ===============MAP TO MERGED REGIONS=================
# =====================================================
print("\n\n\nMAPPING DATA TO CONSENSUS ENHANCER REGIONS")
merged_region_map = map_merged_gff(data_file, name_dict, merged_gff_file,
analysis_name, output_folder, mask_file)
# =====================================================
# ==============CORRECT FOR MEDIAN SIGNAL==============
# =====================================================
print("\n\n\nCREATING ENHANCER SIGNAL TABLE")
signal_table_file = make_enhancer_signal_table(name_dict,
merged_region_map,
median_dict, analysis_name,
genome, output_folder)
# =====================================================
# ===============CALL CLUSTERING R SCRIPT==============
# =====================================================
print("\n\n\nGENERATING CLUSTERING OUTPUT")
cluster_table_file = call_r_script(genome, output_folder, analysis_name,
signal_table_file)
# output should be
# png of cluster gram with rows as genes
# png of cluster gram of samples w/ tree
# ordered table w/ cluster assignment
# similarity matrix for samples
# =====================================================
# =============GENE MAPPING BY CLUSTER=================
# =====================================================
cmd = "ROSE2_geneMapper -g {} -i {}".format(genome, cluster_table_file)
os.system(cmd)
print("FINISHED")
def launch_enhancer_mapping(
data_file,
name_dict,
output_folder,
rose_folder,
stitch,
tss_distance,
enhancer_type,
mask_file="",
):
"""Launches enhancer mapping if needed from enriched region files."""
names_list = list(name_dict.keys())
# check to see if everything is good, if so return True and call it a day
if len([x for x in names_list
if len(name_dict[x]["enhancer_file"]) > 0]) == len(names_list):
print("ENHANCER FILE OUTPUT FOUND FOR ALL DATASETS")
return name_dict
# if not, have to call rose
rose_output_folder = utils.format_folder(rose_folder, True)
queue_list = []
for name in names_list:
# check to see if we need to call rose
if name_dict[name]["enhancer_file"] == "":
# get the enriched file
enriched_file = name_dict[name]["enriched_file"]
# call rose
print("CALLING ROSE FOR {}".format(name))
bash_file_name = pipeline_utils.call_rose2(
data_file,
"",
rose_output_folder,
[name],
[],
enriched_file,
tss_distance,
stitch,
mask=mask_file,
)
print(bash_file_name)
os.system("bash {}".format(bash_file_name))
# add name to queue list
queue_list.append(name)
# define the enhancer type
if enhancer_type == "super":
enhancer_string = "AllEnhancers.table.txt"
if enhancer_type == "stretch":
enhancer_string = "AllEnhancers_Length.table.txt"
if enhancer_type == "superstretch":
enhancer_string = "AllEnhancers_SuperStretch.table.txt"
# now check for completion of datasets
for name in queue_list:
# check for the AllEnhancers table
enhancer_file = os.path.join(
rose_output_folder,
"{}_ROSE".format(name),
"{}_peaks_{}".format(name, enhancer_string),
)
print("CHECKING FOR {} ROSE OUTPUT IN {}".format(name, enhancer_file))
if utils.check_output(enhancer_file, 1, 10):
print("FOUND ENHANCER OUTPUT FOR {}".format(name))
name_dict[name]["enhancer_file"] = enhancer_file
else:
# try finding it w/ a different name
# this will bug out if nothing is there
rose_folder = os.path.join(rose_output_folder,
"{}_ROSE".format(name))
rose_file_list = [
x for x in os.listdir(rose_folder) if x[0] != "."
] # no hidden files
if not rose_file_list:
print("No files found in {}".format(rose_folder))
sys.exit()
enhancer_file = pipeline_utils.get_file(enhancer_string,
rose_file_list,
rose_folder)
name_dict[name]["enhancer_file"] = enhancer_file
return name_dict
def map_merged_gff(data_file, name_dict, merged_gff_file, analysis_name,
output_folder, mask_file):
"""Call rose on the merged_gff_file for all datasets."""
data_dict = pipeline_utils.load_data_table(data_file)
rose_parent_folder = os.path.join(output_folder, "rose")
utils.format_folder(rose_parent_folder, True)
gff_name = os.path.basename(merged_gff_file).split(".")[0]
bash_file_name = os.path.join(output_folder, "rose",
"{}_roseCall.sh".format(analysis_name))
# names_list is just the first dataset
# extrmap will have to have all other datasets + their backgrounds
names_list = list(name_dict.keys())
names_list.sort()
extra_map = []
for name in names_list[1:]:
if name_dict[name]["background"]:
background_name = data_dict[name]["background"]
if background_name in data_dict:
extra_map += [name, background_name]
else:
print(
"ERROR: UNABLE TO FIND LISTED BACKGROUND DATASET {} FOR {}"
.format(background_name, name))
sys.exit()
else:
extra_map += [name]
print(extra_map)
# first check to see if this has already been done
merged_region_map = os.path.join(
output_folder,
"rose",
"{}_ROSE".format(names_list[0]),
"{}_0KB_STITCHED_ENHANCER_REGION_MAP.txt".format(gff_name),
)
print("LOOKING FOR REGION MAP AT {}".format(merged_region_map))
if utils.check_output(merged_region_map, 1, 1):
print("FOUND PREVIOUS REGION MAP")
return merged_region_map
bash_file_name = pipeline_utils.call_rose2(
data_file,
"",
rose_parent_folder,
[names_list[0]],
extra_map,
merged_gff_file,
0,
0,
bash_file_name,
mask=mask_file,
)
bash_command = "bash {}".format(bash_file_name)
os.system(bash_command)
print("Running enhancer mapping command:\n{}".format(bash_command))
if utils.check_output(merged_region_map, 5, 60):
return merged_region_map
else:
print(
"UNABLE TO CALL ROSE ENHANCER MAPPING ON CONSENSUS ENHANCER FILE {}.\nEXITING NOW"
"".format(merged_gff_file))
sys.exit()
def main():
"""Main run function."""
parser = argparse.ArgumentParser()
# required flags
parser.add_argument(
"-b",
"--bam",
dest="bam",
nargs="*",
help="Enter a comma/space separated list of .bam files to be processed.",
required=True,
)
parser.add_argument(
"-i",
"--input",
dest="input",
type=str,
help="Enter .gff or genomic region e.g. chr1:+:1-1000.",
required=True,
)
parser.add_argument(
"-g",
"--genome",
dest="genome",
type=str,
help="specify a genome, HG18,HG19,MM8,MM9,MM10 are currently supported",
required=True,
)
# output flag
parser.add_argument(
"-o",
"--output",
dest="output",
type=str,
help="Enter the output folder.",
required=True,
)
# additional options
parser.add_argument(
"--stretch-input",
dest="stretch_input",
default=None,
type=int,
help=(
"Stretch the input regions to a minimum length in bp, e.g. 10000 (for"
" 10kb)"
),
)
parser.add_argument(
"-c",
"--color",
dest="color",
default=None,
nargs="*",
help=(
"Enter a colon or space separated list of colors e.g. "
"255,0,0:255,125,0, default samples the rainbow"
),
)
parser.add_argument(
"-s",
"--sense",
dest="sense",
default="both",
help="Map to '+','-' or 'both' strands. Default maps to both.",
)
parser.add_argument(
"-e",
"--extension",
dest="extension",
default=200,
help="Extends reads by n bp. Default value is 200bp",
)
parser.add_argument(
"-r",
"--rpm",
dest="rpm",
action="store_true",
default=False,
help="Normalizes density to reads per million (rpm) Default is False",
)
parser.add_argument(
"-y",
"--yScale",
dest="y_scale",
default="relative",
help=(
"Choose either relative or uniform y axis scaling. options = "
"'relative,uniform' Default is relative scaling"
),
)
parser.add_argument(
"-n",
"--names",
dest="names",
default=None,
nargs="*",
help="Enter a comma or space separated list of names for your bams",
)
parser.add_argument(
"-p",
"--plot",
dest="plot",
default="MULTIPLE",
help=(
"Choose either all lines on a single plot or multiple plots. options "
"= 'SINGLE,MULTIPLE,MERGE'"
),
)
parser.add_argument(
"-t",
"--title",
dest="title",
default="",
help=(
"Specify a title for the output plot(s), default will be the "
"coordinate region"
),
)
parser.add_argument(
"-q",
"--skip-cache",
dest="skip_cache",
action="store_true",
default=False,
help="Toggles option to skip loading annotation cache file",
)
parser.add_argument(
"--scale",
dest="scale",
default=None,
nargs="*",
help=(
"Enter a comma or space separated list of scaling factors for your "
"bams. Default is none"
),
)
parser.add_argument(
"--bed",
dest="bed",
nargs="*",
help="Add a comma-delimited or space-delimited list of bed files to plot",
)
parser.add_argument(
"--multi-page",
dest="multi",
action="store_true",
default=False,
help="If flagged will create a new pdf for each region",
)
# DEBUG OPTION TO SAVE TEMP FILES
parser.add_argument(
"--save-temp",
dest="save",
action="store_true",
default=False,
help="If flagged will save temporary files made by bamPlot",
)
args = parser.parse_args()
print(args)
if args.bam and args.input and args.genome and args.output:
# Support a legacy mode where a ',' delimited multiple files
bam_file_list = args.bam
if len(bam_file_list) == 1:
bam_file_list = bam_file_list[0].split(",")
# Make sure these are actually files & readable (!)
for filename in bam_file_list:
assert os.access(filename, os.R_OK)
# bringing in any beds
if args.bed:
bed_file_list = args.bed
if len(bed_file_list) == 1:
bed_file_list = bed_file_list[0].split(",")
print(bed_file_list)
bed_collection = make_bed_collection(bed_file_list)
else:
bed_collection = utils.LocusCollection([], 50)
# Load the input for graphing. One of:
# - A .gff
# - A .bed
# - a specific input region (e.g. chr10:.:93150000-93180000)
valid_sense_options = {"+", "-", "."}
if os.access(args.input, os.R_OK):
if args.input.endswith(".bed"):
# Uniquely graph every input of this bed
parsed_input_bed = utils.parse_table(args.input, "\t")
gff_name = os.path.basename(args.input) # Graph title
gff = None
try:
if parsed_input_bed[0][5] in valid_sense_options:
# This .bed might have a sense parameter
gff = [
[e[0], "", args.input, e[1], e[2], "", e[5], "", ""]
for e in parsed_input_bed
]
except IndexError:
pass
if gff is None:
print(
"Your bed doesn't have a valid sense parameter. Defaulting to both "
"strands, '.'"
)
# We only take chr/start/stop and ignore everything else.
gff = [
[e[0], "", args.input, e[1], e[2], "", ".", "", ""]
for e in parsed_input_bed
]
else:
# Default to .gff, since that's the original behavior
gff = utils.parse_table(args.input, "\t")
gff_name = os.path.basename(args.input).split(".")[0]
else:
# means a coordinate line has been given e.g. chr1:+:1-100
chrom_line = args.input.split(":")
try:
chrom = chrom_line[0]
sense = chrom_line[1]
except IndexError:
print("Invalid input line or inaccessible file. Try: chr1:.:1-5000")
exit()
assert sense in valid_sense_options
[start, end] = chrom_line[2].split("-")
if chrom[0:3] != "chr":
print("ERROR: UNRECOGNIZED GFF OR CHROMOSOME LINE INPUT")
exit()
gff_line = [chrom, "", args.input, start, end, "", sense, "", ""]
gff_name = "{}_{}_{}_{}".format(chrom, sense, start, end)
gff = [gff_line]
# Consider stretching the regions to a fixed minimum size
if args.stretch_input:
print(
"Stretching inputs to a minimum of: {} bp".format(
str(args.stretch_input)
)
)
min_length = args.stretch_input
stretch_gff = []
for e in gff:
difference = int(e[4]) - int(e[3])
if difference < min_length:
pad = int((min_length - difference) / 2)
stretch_gff.append(
[
e[0],
e[1],
e[2],
int(e[3]) - pad,
int(e[4]) + pad,
e[5],
e[6],
e[7],
e[8],
]
)
else:
stretch_gff.append(e)
gff = stretch_gff
# Sanity test the gff object
assert all([e[6] in valid_sense_options for e in gff]) # All strands are sane
# bring in the genome
genome = args.genome.upper()
if not ["HG18", "HG19", "HG19_RIBO", "HG38", "MM9", "MM10", "RN4", "RN6"].count(
genome
):
print(
"ERROR: UNSUPPORTED GENOME TYPE {}. USE HG19,HG18, RN4, MM9, or MM10".format(
genome,
)
)
parser.print_help()
exit()
# bring in the rest of the options
# output
root_folder = args.output
try:
os.listdir(root_folder)
except OSError:
print("ERROR: UNABLE TO FIND OUTPUT DIRECTORY {}".format(root_folder))
exit()
# Get analysis title
if not args.title:
title = gff_name
else:
title = args.title
# make a temp folder
temp_folder = os.path.join(root_folder, title)
print("CREATING TEMP FOLDER {}".format(temp_folder))
utils.format_folder(temp_folder, create=True)
# colors
if args.color:
color_list = args.color
if len(color_list) == 1:
color_list = color_list[0].split(":")
color_list = [x.split(",") for x in color_list]
if len(color_list) < len(bam_file_list):
print(
"WARNING: FEWER COLORS THAN BAMS SPECIFIED. COLORS WILL BE RECYCLED"
)
# recycling the color list
color_list += color_list * (len(bam_file_list) // len(color_list))
color_list = color_list[: len(bam_file_list)]
else:
# cycles through the colors of the rainbow
color_list = taste_the_rainbow(len(bam_file_list))
# sense
sense = args.sense
extension = int(args.extension)
rpm = args.rpm
scale = args.scale
if scale:
if len(scale) == 1:
scale = scale[0].split(",")
y_scale = args.y_scale.upper()
# names
if args.names:
names = args.names
if len(names) == 1:
names = names[0].split(",")
if len(names) != len(bam_file_list):
print("ERROR: NUMBER OF NAMES AND NUMBER OF BAMS DO NOT CORRESPOND")
parser.print_help()
exit()
else:
names = [os.path.basename(x) for x in bam_file_list]
# plot style
plot_style = args.plot.upper()
if not ["SINGLE", "MULTIPLE", "MERGE"].count(plot_style):
print("ERROR: PLOT STYLE {} NOT AN OPTION".format(plot_style))
parser.print_help()
exit()
# now run!
summary_table_file_name = make_bam_plot_tables(
gff,
genome,
bam_file_list,
color_list,
n_bins,
sense,
extension,
rpm,
temp_folder,
names,
title,
bed_collection,
scale,
)
print("{} is the summary table".format(summary_table_file_name))
# running the R command to plot
multi = args.multi
out_file = os.path.join(root_folder, "{}_plots.pdf".format(title))
r_cmd = call_r_plot(
summary_table_file_name, out_file, y_scale, plot_style, multi
)
# open a bash file
bash_file_name = os.path.join(temp_folder, "{}_Rcmd.sh".format(title))
with open(bash_file_name, "w") as bash_file:
bash_file.write("#!/usr/bin/bash\n")
bash_file.write(r_cmd)
print("Wrote R command to {}".format(bash_file_name))
os.system("bash {}".format(bash_file_name))
# delete temp files
if not args.save:
if utils.check_output(out_file, 1, 10):
# This is super dangerous (!). Add some sanity checks.
assert " " not in temp_folder
assert temp_folder != "/"
shutil.rmtree(temp_folder)
print("Removing temp folder: {}".format(temp_folder))
else:
print("ERROR: NO OUTPUT FILE {} DETECTED".format(out_file))
else:
parser.print_help()
sys.exit()
def main():
"""Main run call."""
debug = False
parser = argparse.ArgumentParser()
# required flags
parser.add_argument(
"-i",
"--i",
dest="input",
required=True,
help=
("Enter a comma separated list of .gff or .bed file of binding sites used to make "
"enhancers"),
)
parser.add_argument(
"-r",
"--rankby",
dest="rankby",
required=True,
help="Enter a comma separated list of bams to rank by",
)
parser.add_argument("-o",
"--out",
dest="out",
required=True,
help="Enter an output folder")
parser.add_argument(
"-g",
"--genome",
dest="genome",
required=True,
help="Enter the genome build (MM9,MM8,HG18,HG19)",
)
# optional flags
parser.add_argument(
"-n",
"--name",
dest="name",
required=False,
help="Provide a name for the analysis otherwise ROSE will guess",
)
parser.add_argument(
"-c",
"--control",
dest="control",
required=False,
help=
("Enter a comma separated list of control bams. Can either provide a single control "
"bam for all rankby bams, or provide a control bam for each individual bam"
),
)
parser.add_argument(
"-s",
"--stitch",
dest="stitch",
default="",
help=
("Enter a max linking distance for stitching. Default will determine optimal stitching"
" parameter"),
)
parser.add_argument(
"-t",
"--tss",
dest="tss",
default=0,
help="Enter a distance from TSS to exclude. 0 = no TSS exclusion",
)
parser.add_argument(
"--mask",
dest="mask",
required=False,
help=
"Mask a set of regions from analysis. Provide a .bed or .gff of masking regions",
)
# RETRIEVING FLAGS
args = parser.parse_args()
# making the out folder if it doesn't exist
out_folder = utils.format_folder(args.out, True)
# figuring out folder schema
gff_folder = utils.format_folder(os.path.join(out_folder, "gff"), True)
mapped_folder = utils.format_folder(os.path.join(out_folder, "mappedGFF"),
True)
# GETTING INPUT FILE(s)
input_list = [
input_file for input_file in args.input.split(",")
if len(input_file) > 1
]
# converting all input files into GFFs and moving into the GFF folder
input_gf_list = []
for input_file in input_list:
# GETTING INPUT FILE
if args.input.split(".")[-1] == "bed":
# CONVERTING A BED TO GFF
input_gff_name = os.path.basename(args.input)[0:-4]
input_gff_file = os.path.join(gff_folder,
"{}.gff".format(input_gff_name))
utils.bed_to_gff(args.input, input_gff_file)
elif args.input.split(".")[-1] == "gff":
# COPY THE INPUT GFF TO THE GFF FOLDER
input_gff_file = args.input
copyfile(
input_gff_file,
os.path.join(gff_folder, os.path.basename(input_gff_file)),
)
else:
print(
"WARNING: INPUT FILE DOES NOT END IN .gff or .bed. ASSUMING .gff FILE FORMAT"
)
# COPY THE INPUT GFF TO THE GFF FOLDER
input_gff_file = args.input
copyfile(
input_gff_file,
os.path.join(gff_folder, os.path.basename(input_gff_file)),
)
input_gf_list.append(input_gff_file)
# GETTING THE LIST OF bam_fileS TO PROCESS
# either same number of bams for rankby and control
# or only 1 control #or none!
# bamlist should be all rankby bams followed by control bams
bam_file_list = []
if args.control:
control_bam_list = [
bam for bam in args.control.split(",") if len(bam) > 0
]
rankby_bam_list = [
bam for bam in args.rankby.split(",") if len(bam) > 0
]
if len(control_bam_list) == len(rankby_bam_list):
# case where an equal number of backgrounds are given
bam_file_list = rankby_bam_list + control_bam_list
elif len(control_bam_list) == 1:
# case where a universal background is applied
bam_file_list = rankby_bam_list + control_bam_list * len(
rankby_bam_list)
else:
print(
"ERROR: EITHER PROVIDE A SINGLE CONTROL BAM FOR ALL SAMPLES, OR ONE CONTROL BAM"
" FOR EACH SAMPLE")
sys.exit()
else:
bam_file_list = [bam for bam in args.rankby.split(",") if len(bam) > 0]
# Stitch parameter
if args.stitch == "":
stitch_window = ""
else:
stitch_window = int(args.stitch)
# tss args
tss_window = int(args.tss)
if tss_window != 0:
remove_tss = True
else:
remove_tss = False
# GETTING THE GENOME
genome = args.genome.upper()
print("USING {} AS THE GENOME".format(genome))
# GETTING THE CORRECT ANNOT FILE
try:
annot_file = rose2_utils.genome_dict[genome]
except KeyError:
print("ERROR: UNSUPPORTED GENOMES TYPE {}".format(genome))
sys.exit()
# FINDING THE ANALYSIS NAME
if args.name:
input_name = args.name
else:
input_name = os.path.basename(input_gf_list[0]).split(".")[0]
print("USING {} AS THE ANALYSIS NAME".format(input_name))
print("FORMATTING INPUT REGIONS")
# MAKING THE RAW INPUT FILE FROM THE INPUT GFFs
# use a simpler unique region naming system
if len(input_gf_list) == 1:
input_gff = utils.parse_table(input_gf_list[0], "\t")
else:
input_loci = []
for gff_file in input_gf_list:
print("\tprocessing {}".format(gff_file))
gff = utils.parse_table(gff_file, "\t")
gff_collection = utils.gff_to_locus_collection(gff, 50)
input_loci += gff_collection.get_loci()
input_collection = utils.LocusCollection(input_loci, 50)
input_collection = (input_collection.stitch_collection()
) # stitches to produce unique regions
input_gff = utils.locus_collection_to_gff(input_collection)
formatted_gff = []
# now number things appropriately
for i, line in enumerate(input_gff):
# use the coordinates to make a new id input_name_chr_sense_start_stop
chrom = line[0]
coords = [int(line[3]), int(line[4])]
sense = line[6]
line_id = "{}_{}".format(input_name, str(i + 1)) # 1 indexing
new_line = [
chrom,
line_id,
line_id,
min(coords),
max(coords),
"",
sense,
"",
line_id,
]
formatted_gff.append(new_line)
# name of the master input gff file
master_gff_file = os.path.join(
gff_folder, "{}_{}_ALL_-0_+0.gff".format(genome, input_name))
utils.unparse_table(formatted_gff, master_gff_file, "\t")
print("USING {} AS THE INPUT GFF".format(master_gff_file))
# GET CHROMS FOUND IN THE BAMS
print("GETTING CHROMS IN bam_fileS")
bam_chrom_list = rose2_utils.get_bam_chrom_list(bam_file_list)
print("USING THE FOLLOWING CHROMS")
print(bam_chrom_list)
# LOADING IN THE GFF AND FILTERING BY CHROM
print("LOADING AND FILTERING THE GFF")
input_gff = rose2_utils.filter_gff(master_gff_file, bam_chrom_list)
# LOADING IN THE BOUND REGION REFERENCE COLLECTION
print("LOADING IN GFF REGIONS")
reference_collection = utils.gff_to_locus_collection(input_gff)
print("CHECKING REFERENCE COLLECTION:")
rose2_utils.check_ref_collection(reference_collection)
# MASKING REFERENCE COLLECTION
# see if there's a mask
if args.mask:
mask_file = args.mask
# if it's a bed file
if mask_file.split(".")[-1].upper() == "BED":
mask_gff = utils.bedToGFF(mask_file)
elif mask_file.split(".")[-1].upper() == "GFF":
mask_gff = utils.parse_table(mask_file, "\t")
else:
print("MASK MUST BE A .gff or .bed FILE")
sys.exit()
mask_collection = utils.gff_to_locus_collection(mask_gff)
# now mask the reference loci
reference_loci = reference_collection.get_loci()
filtered_loci = [
locus for locus in reference_loci
if len(mask_collection.get_overlap(locus, "both")) == 0
]
print("FILTERED OUT {} LOCI THAT WERE MASKED IN {}".format(
len(reference_loci) - len(filtered_loci), mask_file))
reference_collection = utils.LocusCollection(filtered_loci, 50)
# NOW STITCH REGIONS
print("STITCHING REGIONS TOGETHER")
stitched_collection, debug_output, stitch_window = rose2_utils.region_stitching(
reference_collection,
input_name,
out_folder,
stitch_window,
tss_window,
annot_file,
remove_tss,
)
# NOW MAKE A STITCHED COLLECTION GFF
print("MAKING GFF FROM STITCHED COLLECTION")
stitched_gff = utils.locus_collection_to_gff(stitched_collection)
print(stitch_window)
print(type(stitch_window))
if not remove_tss:
stitched_gff_file = os.path.join(
gff_folder,
"{}_{}KB_STITCHED.gff".format(input_name,
str(stitch_window // 1000)),
)
stitched_gff_name = "{}_{}KB_STITCHED".format(
input_name, str(stitch_window // 1000))
debug_out_file = os.path.join(
gff_folder,
"{}_{}KB_STITCHED.debug".format(input_name,
str(stitch_window // 1000)),
)
else:
stitched_gff_file = os.path.join(
gff_folder,
"{}_{}KB_STITCHED_TSS_DISTAL.gff".format(
input_name, str(stitch_window // 1000)),
)
stitched_gff_name = "{}_{}KB_STITCHED_TSS_DISTAL".format(
input_name, str(stitch_window // 1000))
debug_out_file = os.path.join(
gff_folder,
"{}_{}KB_STITCHED_TSS_DISTAL.debug".format(
input_name, str(stitch_window // 1000)),
)
# WRITING DEBUG OUTPUT TO DISK
if debug:
print("WRITING DEBUG OUTPUT TO DISK AS {}".format(debug_out_file))
utils.unparse_table(debug_output, debug_out_file, "\t")
# WRITE THE GFF TO DISK
print("WRITING STITCHED GFF TO DISK AS {}".format(stitched_gff_file))
utils.unparse_table(stitched_gff, stitched_gff_file, "\t")
# SETTING UP THE OVERALL OUTPUT FILE
output_file1 = os.path.join(
out_folder, "{}_ENHANCER_REGION_MAP.txt".format(stitched_gff_name))
print("OUTPUT WILL BE WRITTEN TO {}".format(output_file1))
# MAPPING TO THE NON STITCHED (ORIGINAL GFF)
# MAPPING TO THE STITCHED GFF
bam_file_list_unique = list(bam_file_list)
bam_file_list_unique = utils.uniquify(bam_file_list_unique)
# prevent redundant mapping
print("MAPPING TO THE FOLLOWING BAMS:")
print(bam_file_list_unique)
for bam_file in bam_file_list_unique:
bam_file_name = os.path.basename(bam_file)
# MAPPING TO THE STITCHED GFF
mapped_out1_folder = os.path.join(
mapped_folder, "{}_{}_MAPPED".format(stitched_gff_name,
bam_file_name))
mapped_out1_file = os.path.join(
mapped_folder,
"{}_{}_MAPPED".format(stitched_gff_name, bam_file_name),
"matrix.txt",
)
if utils.check_output(mapped_out1_file, 0.2, 0.2):
print("FOUND {} MAPPING DATA FOR BAM: {}".format(
stitched_gff_file, mapped_out1_file))
else:
cmd1 = "bamliquidator_batch --sense . -e 200 --match_bamToGFF -r {} -o {} {}".format(
stitched_gff_file,
mapped_out1_folder,
bam_file,
)
print(cmd1)
os.system(cmd1)
if utils.check_output(mapped_out1_file, 0.2, 5):
print("SUCCESSFULLY MAPPED TO {} FROM BAM: {}".format(
stitched_gff_file, bam_file_name))
else:
print("ERROR: FAILED TO MAP {} FROM BAM: {}".format(
stitched_gff_file, bam_file_name))
sys.exit()
print("BAM MAPPING COMPLETED NOW MAPPING DATA TO REGIONS")
# CALCULATE DENSITY BY REGION
# NEED TO FIX THIS FUNCTION TO ACCOUNT FOR DIFFERENT OUTPUTS OF LIQUIDATOR
rose2_utils.map_collection(
stitched_collection,
reference_collection,
bam_file_list,
mapped_folder,
output_file1,
ref_name=stitched_gff_name,
)
print("FINDING AVERAGE SIGNAL AMONGST BAMS")
meta_output_file = collapse_region_map(output_file1,
input_name + "_MERGED_SIGNAL",
control_bams=args.control)
# now try the merging
print("CALLING AND PLOTTING SUPER-ENHANCERS")
control_name = "NONE"
cmd = "Rscript {} {} {} {} {}".format(
os.path.join(ROOT_DIR, "scripts", "ROSE2_callSuper.R"),
out_folder + "/", # TODO: fix R script so it does not require '/'
meta_output_file,
input_name,
control_name,
)
print(cmd)
os.system(cmd)
# calling the gene mapper
print("CALLING GENE MAPPING")
super_table_file = "{}_SuperEnhancers.table.txt".format(input_name)
# for now don't use ranking bam to call top genes
cmd = "ROSE2_geneMapper -g {} -i {} -f".format(
genome, os.path.join(out_folder, super_table_file))
print(cmd)
os.system(cmd)
stretch_table_file = "{}_StretchEnhancers.table.txt".format(input_name)
cmd = "ROSE2_geneMapper -g {} -i {} -f".format(
genome, os.path.join(out_folder, stretch_table_file))
print(cmd)
os.system(cmd)
superstretch_table_file = "{}_SuperStretchEnhancers.table.txt".format(
input_name)
cmd = "ROSE2_geneMapper.py -g {} -i {} -f".format(genome, out_folder,
superstretch_table_file)
os.system(cmd)
def main():
"""Main run call."""
debug = False
parser = argparse.ArgumentParser()
# required flags
parser.add_argument(
"-i",
"--i",
dest="input",
required=True,
help="Enter a .gff or .bed file of binding sites used to make enhancers",
)
parser.add_argument(
"-r",
"--rankby",
dest="rankby",
required=True,
help="bam_file to rank enhancer by",
)
parser.add_argument(
"-o", "--out", dest="out", required=True, help="Enter an output folder"
)
parser.add_argument(
"-g",
"--genome",
dest="genome",
required=True,
help="Enter the genome build (MM9,MM8,HG18,HG19)",
)
# optional flags
parser.add_argument(
"-b",
"--bams",
dest="bams",
required=False,
help="Enter a comma separated list of additional bam files to map to",
)
parser.add_argument(
"-c",
"--control",
dest="control",
required=False,
help="bam_file to rank enhancer by",
)
parser.add_argument(
"-s",
"--stitch",
dest="stitch",
default="",
help=(
"Enter a max linking distance for stitching. Default will determine optimal stitching"
" parameter"
),
)
parser.add_argument(
"-t",
"--tss",
dest="tss",
default=0,
help="Enter a distance from TSS to exclude. 0 = no TSS exclusion",
)
parser.add_argument(
"--mask",
dest="mask",
required=False,
help="Mask a set of regions from analysis. Provide a .bed or .gff of masking regions",
)
# RETRIEVING FLAGS
args = parser.parse_args()
# making the out folder if it doesn't exist
out_folder = utils.format_folder(args.out, True)
# figuring out folder schema
gff_folder = utils.format_folder(os.path.join(out_folder, "gff"), True)
mapped_folder = utils.format_folder(os.path.join(out_folder, "mapped_gff"), True)
# GETTING INPUT FILE
if args.input.split(".")[-1] == "bed":
# CONVERTING A BED TO GFF
input_gff_name = args.input.split("/")[-1][0:-4]
input_gff_file = os.path.join(gff_folder, "{}.gff".format(input_gff_name))
utils.bed_to_gff(args.input, input_gff_file)
elif args.input.split(".")[-1] == "gff":
# COPY THE INPUT GFF TO THE GFF FOLDER
input_gff_file = args.input
copyfile(
input_gff_file, os.path.join(gff_folder, os.path.basename(input_gff_file))
)
else:
print(
"WARNING: INPUT FILE DOES NOT END IN .gff or .bed. ASSUMING .gff FILE FORMAT"
)
# COPY THE INPUT GFF TO THE GFF FOLDER
input_gff_file = args.input
copyfile(
input_gff_file, os.path.join(gff_folder, os.path.basename(input_gff_file))
)
# GETTING THE LIST OF bam_fileS TO PROCESS
if args.control:
bam_file_list = [args.rankby, args.control]
else:
bam_file_list = [args.rankby]
if args.bams:
bam_file_list += args.bams.split(",")
# bam_file_list = utils.uniquify(bam_file_list) # makes sad when you have the same control
# bam over and over again
# optional args
# Stitch parameter
if args.stitch == "":
stitch_window = ""
else:
stitch_window = int(args.stitch)
# tss args
tss_window = int(args.tss)
if tss_window != 0:
remove_tss = True
else:
remove_tss = False
# GETTING THE BOUND REGION FILE USED TO DEFINE ENHANCERS
print("USING {} AS THE INPUT GFF".format(input_gff_file))
input_name = os.path.basename(input_gff_file).split(".")[0]
# GETTING THE GENOME
genome = args.genome
print("USING {} AS THE GENOME".format(genome))
annot_file = rose2_utils.genome_dict[genome.upper()]
# GET CHROMS FOUND IN THE BAMS
print("GETTING CHROMS IN bam_fileS")
bam_chrom_list = rose2_utils.get_bam_chrom_list(bam_file_list)
print("USING THE FOLLOWING CHROMS")
print(bam_chrom_list)
# LOADING IN THE GFF AND FILTERING BY CHROM
print("LOADING AND FILTERING THE GFF")
input_gff = rose2_utils.filter_gff(input_gff_file, bam_chrom_list)
# LOADING IN THE BOUND REGION REFERENCE COLLECTION
print("LOADING IN GFF REGIONS")
reference_collection = utils.gff_to_locus_collection(input_gff)
print("STARTING WITH {} INPUT REGIONS".format(len(reference_collection)))
print("CHECKING REFERENCE COLLECTION:")
rose2_utils.check_ref_collection(reference_collection)
# MASKING REFERENCE COLLECTION
# see if there's a mask
if args.mask:
mask_file = args.mask
print("USING MASK FILE {}".format(mask_file))
# if it's a bed file
if mask_file.split(".")[-1].upper() == "BED":
mask_gff = utils.bed_to_gff(mask_file)
elif mask_file.split(".")[-1].upper() == "GFF":
mask_gff = utils.parse_table(mask_file, "\t")
else:
print("MASK MUST BE A .gff or .bed FILE")
mask_collection = utils.gff_to_locus_collection(mask_gff)
print("LOADING {} MASK REGIONS".format(str(len(mask_collection))))
# now mask the reference loci
reference_loci = reference_collection.get_loci()
filtered_loci = [
locus
for locus in reference_loci
if len(mask_collection.get_overlap(locus, "both")) == 0
]
print(
"FILTERED OUT {} LOCI THAT WERE MASKED IN {}".format(
str(len(reference_loci) - len(filtered_loci)), mask_file
)
)
reference_collection = utils.LocusCollection(filtered_loci, 50)
# NOW STITCH REGIONS
print("STITCHING REGIONS TOGETHER")
stitched_collection, debug_output, stitch_window = rose2_utils.region_stitching(
reference_collection,
input_name,
out_folder,
stitch_window,
tss_window,
annot_file,
remove_tss,
)
# NOW MAKE A STITCHED COLLECTION GFF
print("MAKING GFF FROM STITCHED COLLECTION")
stitched_gff = utils.locus_collection_to_gff(stitched_collection)
# making sure start/stop ordering are correct
for i in range(len(stitched_gff)):
line = stitched_gff[i]
start = int(line[3])
stop = int(line[4])
if start > stop:
line[3] = stop
line[4] = start
print(stitch_window)
print(type(stitch_window))
if not remove_tss:
stitched_gff_file = os.path.join(
gff_folder,
"{}_{}KB_STITCHED.gff".format(input_name, str(stitch_window // 1000)),
)
stitched_gff_name = "{}_{}KB_STITCHED".format(
input_name, str(stitch_window // 1000)
)
debug_out_file = os.path.join(
gff_folder,
"{}_{}KB_STITCHED.debug".format(input_name, str(stitch_window // 1000)),
)
else:
stitched_gff_file = os.path.join(
gff_folder,
"{}_{}KB_STITCHED_TSS_DISTAL.gff".format(
input_name, str(stitch_window // 1000)
),
)
stitched_gff_name = "{}_{}KB_STITCHED_TSS_DISTAL".format(
input_name, str(stitch_window // 1000)
)
debug_out_file = os.path.join(
gff_folder,
"{}_{}KB_STITCHED_TSS_DISTAL.debug".format(
input_name, str(stitch_window // 1000)
),
)
# WRITING DEBUG OUTPUT TO DISK
if debug:
print("WRITING DEBUG OUTPUT TO DISK AS {}".format(debug_out_file))
utils.unparse_table(debug_output, debug_out_file, "\t")
# WRITE THE GFF TO DISK
print("WRITING STITCHED GFF TO DISK AS {}".format(stitched_gff_file))
utils.unparse_table(stitched_gff, stitched_gff_file, "\t")
# SETTING UP THE OVERALL OUTPUT FILE
output_file1 = os.path.join(
out_folder, "{}_ENHANCER_REGION_MAP.txt".format(stitched_gff_name)
)
print("OUTPUT WILL BE WRITTEN TO {}".format(output_file1))
# MAPPING TO THE NON STITCHED (ORIGINAL GFF)
# MAPPING TO THE STITCHED GFF
bam_file_list_unique = list(bam_file_list)
bam_file_list_unique = utils.uniquify(bam_file_list_unique)
# prevent redundant mapping
print("MAPPING TO THE FOLLOWING BAMS:")
print(bam_file_list_unique)
for bam_file in bam_file_list_unique:
bam_file_name = os.path.basename(bam_file)
# MAPPING TO THE STITCHED GFF
mapped_out1_folder = os.path.join(
mapped_folder, "{}_{}_MAPPED".format(stitched_gff_name, bam_file_name)
)
mapped_out1_file = os.path.join(
mapped_folder,
"{}_{}_MAPPED".format(stitched_gff_name, bam_file_name),
"matrix.txt",
)
if utils.check_output(mapped_out1_file, 0.2, 0.2):
print(
"FOUND {} MAPPING DATA FOR BAM: {}".format(
stitched_gff_file, mapped_out1_file
)
)
else:
cmd1 = "bamliquidator_batch --sense . -e 200 --match_bamToGFF -r {} -o {} {}".format(
stitched_gff_file, mapped_out1_folder, bam_file,
)
print(cmd1)
os.system(cmd1)
if utils.check_output(mapped_out1_file, 0.2, 5):
print(
"SUCCESSFULLY MAPPED TO {} FROM BAM: {}".format(
stitched_gff_file, bam_file_name
)
)
else:
print(
"ERROR: FAILED TO MAP {} FROM BAM: {}".format(
stitched_gff_file, bam_file_name
)
)
sys.exit()
print("BAM MAPPING COMPLETED NOW MAPPING DATA TO REGIONS")
# CALCULATE DENSITY BY REGION
# NEED TO FIX THIS FUNCTION TO ACCOUNT FOR DIFFERENT OUTPUTS OF LIQUIDATOR
rose2_utils.map_collection(
stitched_collection,
reference_collection,
bam_file_list,
mapped_folder,
output_file1,
ref_name=stitched_gff_name,
)
print("CALLING AND PLOTTING SUPER-ENHANCERS")
if args.control:
control_name = os.path.basename(args.control)
else:
control_name = "NONE"
cmd = "Rscript {} {} {} {} {}".format(
os.path.join(ROOT_DIR, "scripts", "ROSE2_callSuper.R"),
out_folder + "/", # TODO: fix R script so it does not require '/'
output_file1,
input_name,
control_name,
)
print(cmd)
os.system(cmd)
# calling the gene mapper
time.sleep(20)
super_table_file = "{}_SuperEnhancers.table.txt".format(input_name)
if args.control:
cmd = "ROSE2_geneMapper -g {} -r {} -c {} -i {}".format(
genome,
args.rankby,
args.control,
os.path.join(out_folder, super_table_file),
)
else:
cmd = "ROSE2_geneMapper -g {} -r {} -i {}".format(
genome, args.rankby, os.path.join(out_folder, super_table_file)
)
os.system(cmd)
stretch_table_file = "{}_StretchEnhancers.table.txt".format(input_name)
if args.control:
cmd = "ROSE2_geneMapper -g {} -r {} -c {} -i {}".format(
genome,
args.rankby,
args.control,
os.path.join(out_folder, stretch_table_file),
)
else:
cmd = "ROSE2_geneMapper -g {} -r {} -i {}".format(
genome, args.rankby, os.path.join(out_folder, stretch_table_file)
)
os.system(cmd)
superstretch_table_file = "{}_SuperStretchEnhancers.table.txt".format(input_name)
if args.control:
cmd = "ROSE2_geneMapper -g {} -r {} -c {} -i {}".format(
genome,
args.rankby,
args.control,
os.path.join(out_folder, superstretch_table_file),
)
else:
cmd = "ROSE2_geneMapper -g {} -r {} -i {}".format(
genome, args.rankby, os.path.join(out_folder, superstretch_table_file)
)
os.system(cmd)
def tf_edge_delta_out(
crc_folder,
bam_list,
analysis_name,
edge_table_path_1,
edge_table_path_2,
group1_list,
group2_list,
output="",
):
"""Calculates changes in group out degree at each predicted motif occurrence (by subpeaks)."""
crc_folder = utils.format_folder(crc_folder, True)
edge_path = merge_edge_tables(
edge_table_path_1,
edge_table_path_2,
os.path.join(crc_folder, "{}_EDGE_TABLE.txt".format(analysis_name)),
)
# make a gff of the edge table
edge_table = utils.parse_table(edge_path, "\t")
edge_gff = []
for line in edge_table[1:]:
gff_line = [
line[2],
"{}_{}".format(line[0], line[1]),
"",
line[3],
line[4],
"",
".",
"",
"{}_{}".format(line[0], line[1]),
]
edge_gff.append(gff_line)
edge_gff_path = os.path.join(crc_folder,
"{}_EDGE_TABLE.gff".format(analysis_name))
utils.unparse_table(edge_gff, edge_gff_path, "\t")
# direct the output to the crc folder
signal_path = os.path.join(
crc_folder, "{}_EDGE_TABLE_signal.txt".format(analysis_name))
all_group_list = group1_list + group2_list
if not utils.check_output(signal_path, 0, 0):
signal_table_list = pipeline_utils.map_regions(
bam_list,
[edge_gff_path],
crc_folder,
crc_folder,
all_group_list,
True,
signal_path,
extend_reads_to=100,
)
print(signal_table_list)
else:
print("Found previous signal table at {}".format(signal_path))
# now bring in the signal table as a dictionary using the locus line as the id
print("making log2 group1 vs group2 signal table at edges")
signal_table = utils.parse_table(signal_path, "\t")
# figure out columns for group1 and group2
group1_columns = [signal_table[0].index(name) for name in group1_list]
group2_columns = [signal_table[0].index(name) for name in group2_list]
group1_signal_vector = []
group2_signal_vector = []
for line in signal_table[1:]:
group1_signal = numpy.mean(
[float(line[col]) for col in group1_columns])
group2_signal = numpy.mean(
[float(line[col]) for col in group2_columns])
group1_signal_vector.append(group1_signal)
group2_signal_vector.append(group2_signal)
group1_median = numpy.median(group1_signal_vector)
group2_median = numpy.median(group2_signal_vector)
print("group1 median signal")
print(group1_median)
print("group2 median signal")
print(group2_median)
# now that we have the median, we can take edges where at least 1 edge is above the median
# and both are above zero and generate a new table w/ the fold change
signal_filtered_path = signal_path.replace(".txt", "_filtered.txt")
if utils.check_output(signal_filtered_path, 0, 0):
print("Found filtered signal table for edges at {}".format(
signal_filtered_path))
signal_table_filtered = utils.parse_table(signal_filtered_path, "\t")
else:
signal_table_filtered = [
signal_table[0] +
["GROUP1_MEAN", "GROUP2_MEAN", "GROUP1_vs_GROUP2_LOG2"]
]
for line in signal_table[1:]:
group1_signal = numpy.mean(
[float(line[col]) for col in group1_columns])
group2_signal = numpy.mean(
[float(line[col]) for col in group2_columns])
if (group1_signal > group1_median or group2_signal > group2_median
) and min(group1_signal, group2_signal) > 0:
delta = numpy.log2(group1_signal / group2_signal)
new_line = line + [group1_signal, group2_signal, delta]
signal_table_filtered.append(new_line)
utils.unparse_table(signal_table_filtered, signal_filtered_path, "\t")
# now get a list of all TFs in the system
tf_list = utils.uniquify(
[line[0].split("_")[0] for line in signal_table_filtered[1:]])
tf_list.sort()
print(tf_list)
out_degree_table = [[
"TF_NAME",
"EDGE_COUNT",
"DELTA_MEAN",
"DELTA_MEDIAN",
"DELTA_STD",
"DELTA_SEM",
]]
for tf_name in tf_list:
print(tf_name)
edge_vector = [
float(line[-1]) for line in signal_table_filtered[1:]
if line[0].split("_")[0] == tf_name
]
edge_count = len(edge_vector)
delta_mean = round(numpy.mean(edge_vector), 4)
delta_median = round(numpy.median(edge_vector), 4)
delta_std = round(numpy.std(edge_vector), 4)
delta_sem = round(stats.sem(edge_vector), 4)
tf_out_line = [
tf_name,
edge_count,
delta_mean,
delta_median,
delta_std,
delta_sem,
]
out_degree_table.append(tf_out_line)
# set final output
if not output:
output_path = os.path.join(
crc_folder, "{}_EDGE_DELTA_OUT.txt".format(analysis_name))
else:
output_path = output
utils.unparse_table(out_degree_table, output_path, "\t")
print(output_path)
return output_path
def main():
"""Main run method for enhancer promoter contribution tool."""
parser = argparse.ArgumentParser()
# required flags
parser.add_argument(
"-b",
"--bam",
dest="bam",
nargs="*",
help="Enter a space separated list of .bam files for the main factor",
required=True,
)
parser.add_argument(
"-i",
"--input",
dest="input",
type=str,
help="Enter .gff or .bed file of regions to analyze",
required=True,
)
parser.add_argument(
"-g",
"--genome",
dest="genome",
type=str,
help=(
"specify a genome, HG18,HG19,HG38,MM8,MM9,MM10,RN6 are currently "
"supported"),
required=True,
)
parser.add_argument(
"-p",
"--chrom-path",
dest="chrom_path",
type=str,
help=("Provide path to a folder with a seperate fasta file for each "
"chromosome"),
required=True,
)
# output flag
parser.add_argument(
"-o",
"--output",
dest="output",
type=str,
help="Enter the output folder.",
required=True,
)
# additional options flags and optional arguments
parser.add_argument(
"-a",
"--activity",
dest="activity",
type=str,
help=("specify a table where first column represents a list of active "
"refseq genes"),
required=False,
)
parser.add_argument(
"-c",
"--control",
dest="control",
nargs="*",
help=("Enter a space separated list of .bam files for background. If "
"flagged, will perform background subtraction"),
required=False,
)
parser.add_argument(
"-t",
"--tss",
dest="tss",
type=int,
help="Define the TSS area +/- the TSS. Default is 1kb",
required=False,
default=1000,
)
parser.add_argument(
"-d",
"--distal",
dest="distal",
type=int,
help="Enter a window to assign distal enhancer signal. Default is 50kb",
required=False,
default=50000,
)
parser.add_argument(
"--other-bams",
dest="other",
nargs="*",
help="enter a space separated list of other bams to map to",
required=False,
)
parser.add_argument(
"--name",
dest="name",
type=str,
help=
("enter a root name for the analysis, otherwise will try to find the "
"name from the input file"),
required=False,
)
parser.add_argument(
"--top",
dest="top",
type=int,
help=
("Run the analysis on the top N genes by total signal. Default is 5000"
),
required=False,
default=5000,
)
parser.add_argument(
"--tads",
dest="tads",
type=str,
help=
("Include a .bed of tad regions to restrict enhancer/gene association"
),
required=False,
default=None,
)
parser.add_argument(
"--mask",
dest="mask",
default=None,
help=(
"Mask a set of regions from analysis. Provide a .bed or .gff of "
"masking regions"),
)
args = parser.parse_args()
print(args)
# =====================================================================================
# ===============================I. PARSING ARGUMENTS==================================
# =====================================================================================
print(
"\n\n#======================================\n#===========I. DATA SUMMARY============\n#="
"=====================================\n")
# top analysis subset
top = args.top
# input genome
genome = args.genome.upper()
print("PERFORMING ANALYSIS ON {} GENOME BUILD".format(genome))
# set of bams
bam_file_list = args.bam
# bring in the input path
input_path = args.input
# try to get the input name or use the name argument
if args.name:
analysis_name = args.name
else:
analysis_name = os.path.basename(input_path).split(".")[0]
print("USING {} AS ANALYSIS NAME".format(analysis_name))
# setting up the output folder
parent_folder = utils.format_folder(args.output, True)
output_folder = utils.format_folder(
os.path.join(parent_folder, analysis_name), True)
print("WRITING OUTPUT TO {}".format(output_folder))
if input_path.split(".")[-1] == "bed":
# type is bed
print("input in bed format, converting to gff")
input_gff = utils.bed_to_gff(input_path)
else:
input_gff = utils.parse_table(input_path, "\t")
# the tss window for proximal signal assignment
tss_window = int(args.tss)
# the distal window for assigning nearby enhancer signal
distal_window = int(args.distal)
# activity path
if args.activity:
activity_path = args.activity
activity_table = utils.parse_table(activity_path, "\t")
ref_col = 0
# try to find the column for refseq id
for i in range(len(
activity_table[2])): # use an internal row in case of header
if str(activity_table[1][i]).count("NM_") or str(
activity_table[1][i]).count("NR_"):
ref_col = i
# now check for header
if not str(activity_table[0][i]).count("NM_") and not str(
activity_table[0][i]).count("NR_"):
print("REMOVING HEADER FROM GENE TABLE:")
print(activity_table[0])
activity_table.pop(0)
gene_list = [line[ref_col] for line in activity_table
] # this needs to be REFSEQ NM ID
print("IDENTIFIED {} ACTIVE GENES".format(len(gene_list)))
else:
gene_list = []
# check if tads are being invoked
if args.tads:
print("LOADING TAD LOCATIONS FROM {}".format(args.tads))
tads_path = args.tads
else:
tads_path = ""
print("LOADING ANNOTATION DATA FOR GENOME {}".format(genome))
genome_dir = args.chrom_path
# making a chrom_dict that is a list of all chroms with sequence
chrom_list = utils.uniquify(
[name.split(".")[0] for name in os.listdir(genome_dir) if name])
# important here to define the window
start_dict, tss_collection, mouse_convert_dict = load_annot_file(
genome,
tss_window,
gene_list,
)
print("FILTERING THE INPUT GFF FOR GOOD CHROMOSOMES")
print(chrom_list)
filtered_gff = [line for line in input_gff if chrom_list.count(line[0])]
print("{} of INITIAL {} REGIONS ARE IN GOOD CHROMOSOMES".format(
str(len(filtered_gff)),
str(len(input_gff)),
))
# =====================================================================================
# ================II. IDENTIFYING TSS PROXIMAL AND DISTAL ELEMENTS=====================
# =====================================================================================
print(
"\n\n#======================================\n#==II. MAPPING TO TSS/DISTAL REGIONS===\n#="
"=====================================\n")
# now we need to split the input region
print("SPLITTING THE INPUT GFF USING A WINDOW OF {}".format(tss_window))
split_gff = split_regions(filtered_gff,
tss_collection,
mask_file=args.mask)
print(len(filtered_gff))
print(len(split_gff))
split_gff_path = os.path.join(output_folder,
"{}_SPLIT.gff".format(analysis_name))
utils.unparse_table(split_gff, split_gff_path, "\t")
print("WRITING TSS SPLIT GFF OUT TO {}".format(split_gff_path))
# now you have to map the bams to the gff
print("MAPPING TO THE SPLIT GFF")
mapped_folder = utils.format_folder(
os.path.join(output_folder, "bam_mapping"), True)
signal_table = map_bams(bam_file_list, split_gff_path, analysis_name,
mapped_folder)
signal_table_path = os.path.join(
output_folder, "{}_signal_table.txt".format(analysis_name))
utils.unparse_table(signal_table, signal_table_path, "\t")
if args.control:
control_bam_file_list = args.control
control_signal_table = map_bams(
control_bam_file_list,
split_gff_path,
analysis_name,
mapped_folder,
)
control_signal_table_path = os.path.join(
output_folder,
"{}_control_signal_table.txt".format(analysis_name),
)
utils.unparse_table(control_signal_table, control_signal_table_path,
"\t")
# now create the background subtracted summarized average table
print("CREATING AN AVERAGE SIGNAL TABLE")
average_table = make_average_table(
output_folder,
analysis_name,
use_background=args.control # TODO: fix to True or False
)
average_table_path = os.path.join(
output_folder, "{}_average_table.txt".format(analysis_name))
utils.unparse_table(average_table, average_table_path, "\t")
# now load up all of the cpg and other parameters to make the actual peak table
# first check if this has already been done
peak_table_path = os.path.join(output_folder,
"{}_PEAK_TABLE.txt".format(analysis_name))
if utils.check_output(peak_table_path, 0.1, 0.1):
print("PEAK TABLE OUTPUT ALREADY EXISTS")
peak_table = utils.parse_table(peak_table_path, "\t")
else:
peak_table = make_peak_table(
param_dict,
split_gff_path,
average_table_path,
start_dict,
gene_list,
genome_dir,
tss_window,
distal_window,
tads_path,
)
utils.unparse_table(peak_table, peak_table_path, "\t")
gene_table = make_gene_table(peak_table, analysis_name)
gene_table_path = os.path.join(output_folder,
"{}_GENE_TABLE.txt".format(analysis_name))
utils.unparse_table(gene_table, gene_table_path, "\t")
# if mouse, need to convert genes over
if genome.count("MM") == 1:
print("CONVERTING MOUSE NAMES TO HUMAN HOMOLOGS FOR GSEA")
converted_gene_table_path = os.path.join(
output_folder,
"{}_GENE_TABLE_CONVERTED.txt".format(analysis_name),
)
converted_gene_table = [gene_table[0]]
for line in gene_table[1:]:
converted_name = mouse_convert_dict[line[0]]
if converted_name:
converted_gene_table.append([converted_name] + line[1:])
utils.unparse_table(converted_gene_table,
converted_gene_table_path, "\t")
gene_table_path = converted_gene_table_path
gene_table = converted_gene_table
# =====================================================================================
# ===================================III. PLOTTING ====================================
# =====================================================================================
print(
"\n\n#======================================\n#===III. PLOTTING ENHANCER/PROMOTER===\n#=="
"====================================\n")
# if there are fewer genes in the gene table than the top genes, only run on all
if len(gene_table) < int(top):
print(
"WARNING: ONLY {} GENES WITH SIGNAL AT EITHER PROMOTERS OR ENHANCERS. NOT ENOUGH TO"
"RUN ANALYSIS ON TOP {}".format(str(len(gene_table) - 1),
str(top)))
top = 0
# now call the R code
print("CALLING R PLOTTING SCRIPTS")
call_r_waterfall(gene_table_path, output_folder, analysis_name, top)
def main():
"""Main run function."""
parser = argparse.ArgumentParser()
# required flags
parser.add_argument(
"-g",
"--genome",
dest="genome",
required=True,
help="Enter the genome build (HG18,HG19,MM9,RN4,RN6) for the project",
)
parser.add_argument(
"-d",
"--data",
dest="data",
required=True,
help="Enter the data file for the project",
)
parser.add_argument(
"-r",
"--rose",
dest="rose",
required=True,
help="Enter a comma separated list of rose folder",
)
parser.add_argument(
"-o",
"--output",
dest="output",
required=True,
help="Enter the output folder for the project",
)
parser.add_argument(
"-n",
"--names",
dest="names",
required=True,
help="Enter a comma separated list of names to go with the datasets",
)
# additional args
parser.add_argument(
"-p",
"--plot",
dest="plot",
action="store_true",
default=False,
help="If flagged, will plot differential regions",
)
parser.add_argument(
"-a",
"--all",
dest="all",
action="store_true",
default=False,
help=
"If flagged, will run analysis for all enhancers and not just supers.",
)
parser.add_argument(
"-m",
"--median",
dest="median",
action="store_true",
default=False,
help="If flagged, will use median enhancer scaling",
)
parser.add_argument(
"-e",
"--enhancer-type",
dest="enhancer_type",
default="super",
help=
"specify type of enhancer to analyze: super, stretch, superStretch",
)
args = parser.parse_args()
print(args)
genome = args.genome.upper()
data_file = args.data
rose_folder_string = args.rose
rose_folder1, rose_folder2 = rose_folder_string.split(",")
parent_folder = utils.format_folder(args.output, True)
name_string = args.names
name1, name2 = name_string.split(",")
merge_name = "{}_{}_merged".format(name1, name2)
# option for median scaling
median_scale = args.median
plot_bam = args.plot
if args.all:
super_only = False
else:
super_only = True
if super_only and plot_bam:
print(
"Running dynamic enhancer analysis on all super enhancers in {} and {} and plotting "
"output to {}".format(name1, name2, parent_folder))
if super_only and not plot_bam:
print(
"Running dynamic enhancer analysis on all super enhancers in {} and {} and writing "
"output to {}".format(name1, name2, parent_folder))
if not super_only and plot_bam:
print(
"Running dynamic enhancer analysis on all enhancers in {} and {} and plotting output "
"to {}. WARNING: Plotting all differential enhancers could take a while"
.format(name1, name2, parent_folder))
if not super_only and not plot_bam:
print(
"Running dynamic enhancer analysis on all enhancers in {} and {} and writing output "
"to {}.".format(name1, name2, parent_folder))
# part 1
print("PART1: analyzing ROSE output from {} and {}".format(name1, name2))
# start with the all enhancer tables from the initial rose calls
rose_folder1 = utils.format_folder(rose_folder1, False)
rose_folder2 = utils.format_folder(rose_folder2, False)
rose_dict1 = make_rose_dict(rose_folder1)
rose_dict2 = make_rose_dict(rose_folder2)
# choosing the type of enhancer to analyze
enhancer_call_type = args.enhancer_type.lower()
if super_only:
print("ANALYZING ENHANCER TYPE: {}".format(enhancer_call_type.upper()))
super_file1 = rose_dict1[enhancer_call_type]
super_file2 = rose_dict2[enhancer_call_type]
all_file1 = rose_dict1["AllEnhancer"]
all_file2 = rose_dict2["AllEnhancer"]
print("\tMERGING ENHANCERS AND CALLING ROSE")
if super_only:
if len(super_file1) == 0:
print("ERROR: UNABLE TO FIND {} FILES IN {}".format(
enhancer_call_type, rose_folder1))
sys.exit()
if len(super_file2) == 0:
print("ERROR: UNABLE TO FIND {} FILES IN {}".format(
enhancer_call_type, rose_folder2))
sys.exit()
rose_output = call_merge_supers(
data_file,
super_file1,
super_file2,
name1,
name2,
merge_name,
genome,
parent_folder,
)
else:
rose_output = call_merge_supers(
data_file,
all_file1,
all_file2,
name1,
name2,
merge_name,
genome,
parent_folder,
)
print("\tCALCULATING ENHANCER DELTA AND MAKING PLOTS")
# part2 is the R script
merged_gff_file = os.path.join(
parent_folder,
"{}_{}_MERGED_REGIONS_-0_+0.gff".format(genome, merge_name))
rcmd = call_delta_r_script(
merged_gff_file,
parent_folder,
data_file,
name1,
name2,
all_file1,
all_file2,
median_scale,
)
print(rcmd)
os.system(rcmd)
time.sleep(30)
call_rose_gene_mapper(merged_gff_file, genome, parent_folder, name1)
# rank the genes
# part 3
# rank the delta
print("PART 3: assinging ranks to differential enhancers")
print("\tASSIGNING SUPER RANK TO MERGED ENHANCERS")
gff_name = "{}_{}_MERGED_REGIONS_-0_+0".format(genome, merge_name)
enhancer_to_gene_file = os.path.join(
parent_folder,
"{}_ROSE".format(name1),
"{}_0KB_STITCHED_ENHANCER_DELTA_ENHANCER_TO_GENE_100KB.txt".format(
gff_name),
)
if utils.check_output(enhancer_to_gene_file):
rank_output = os.path.join(
parent_folder,
"{}_ROSE".format(name1),
"{}_0KB_STITCHED_ENHANCER_DELTA_ENHANCER_TO_GENE_100KB_RANK.txt".
format(gff_name),
)
assign_enhancer_rank(enhancer_to_gene_file, all_file1, all_file2,
name1, name2, rank_output)
else:
print("ERROR: DELTA SCRIPT OR ROSE GENE MAPPER FAILED TO RUN")
sys.exit()
# make the rank plot
print("MAKING RANK PLOTS")
if utils.check_output(rank_output):
rcmd = call_rank_r_script(rank_output, name1, name2, super_file1,
super_file2)
print(rcmd)
os.system(rcmd)
else:
print("ERROR: RANK PLOT SCRIPT FAILED TO RUN")
sys.exit()
time.sleep(30)
print("FINISHING OUTPUT")
finish_rank_output(
data_file,
rank_output,
genome,
parent_folder,
merge_name,
name1,
name2,
1,
100000,
super_only,
plot_bam,
)
def finish_rank_output(
data_file,
rank_output,
genome,
merge_folder,
merge_name,
name1,
name2,
cut_off=1.5,
window=100000,
super_only=True,
plot_bam=True,
):
"""Finish rank output.
Clean up the rank output table. Make a gff of all of the gained/lost supers beyond a certain
cut_off w/ a window. Make a list of gained genes and lost genes. Make a bed of gained loss.
"""
data_dict = pipeline_utils.load_data_table(data_file)
# making sure window and cut_off are int/float
cut_off = float(cut_off)
window = int(window)
genome = genome.upper()
# make the output folder
output_folder = utils.format_folder(os.path.join(merge_folder, "output"),
True)
# bring in the old rank table
rank_enhancer_table = utils.parse_table(rank_output, "\t")
# make a new formatted table
header = rank_enhancer_table[0]
header[-4] = "DELTA RANK"
header[-3] = "IS_SUPER"
formatted_rank_table = [header]
# the gffs
gained_gff = []
lost_gff = []
gained_window_gff = []
lost_window_gff = []
if super_only:
enhancer_type = "SUPERS"
else:
enhancer_type = "ENHANCERS"
# the beds
if super_only:
gained_track_header = (
'track name="{} {} only SEs" description="{} super enhancers that are found only in '
'{} vs {}" itemRGB=On color=255,0,0'.format(
genome, name2, genome, name2, name1))
gained_bed = [[gained_track_header]]
conserved_track_header = (
'track name="{} {} and {} SEs" description="{} super enhancers that are found in both'
' {} vs {}" itemRGB=On color=0,0,0'.format(genome, name1, name2,
genome, name1, name2))
conserved_bed = [[conserved_track_header]]
lost_track_header = (
'track name="{} {} only SEs" description="{} super enhancers that are found only in '
'{} vs {}" itemRGB=On color=0,255,0'.format(
genome, name1, genome, name1, name2))
lost_bed = [[lost_track_header]]
else:
gained_track_header = (
'track name="{} {} only enhancers" description="{} enhancers that are found only in '
'{} vs {}" itemRGB=On color=255,0,0'.format(
genome, name2, genome, name2, name1))
gained_bed = [[gained_track_header]]
conserved_track_header = (
'track name="{} {} and {} enhancers" description="{} enhancers that are found in both'
' {} vs {}" itemRGB=On color=0,0,0'.format(genome, name1, name2,
genome, name1, name2))
conserved_bed = [[conserved_track_header]]
lost_track_header = (
'track name="{} {} only enhancers" description="{} enhancers that are found only in '
'{} vs {}" itemRGB=On color=0,255,0'.format(
genome, name1, genome, name1, name2))
lost_bed = [[lost_track_header]]
# the genes
gene_table = [[
"GENE",
"ENHANCER_ID",
"ENHANCER_CHROM",
"ENHANCER_START",
"ENHANCER_STOP",
header[6],
header[7],
header[8],
"STATUS",
]]
for line in rank_enhancer_table[1:]:
# fixing the enhancer ID
line[0] = line[0].replace("_lociStitched", "")
formatted_rank_table.append(line)
# getting the genes
gene_list = []
gene_list += line[9].split(",")
gene_list += line[10].split(",")
gene_list += line[11].split(",")
gene_list = [x for x in gene_list if len(x) > 0]
gene_list = utils.uniquify(gene_list)
gene_string = ",".join(gene_list)
bed_line = [line[1], line[2], line[3], line[0], line[-4]]
# for gained
if float(line[6]) > cut_off:
gff_line = [
line[1],
line[0],
"",
line[2],
line[3],
"",
".",
"",
gene_string,
]
gff_window_line = [
line[1],
line[0],
"",
int(line[2]) - window,
int(line[3]) + window,
"",
".",
"",
gene_string,
]
gained_gff.append(gff_line)
gained_window_gff.append(gff_window_line)
gene_status = name2
gained_bed.append(bed_line)
# for lost
elif float(line[6]) < (-1 * cut_off):
gff_line = [
line[1],
line[0],
"",
line[2],
line[3],
"",
".",
"",
gene_string,
]
gff_window_line = [
line[1],
line[0],
"",
int(line[2]) - window,
int(line[3]) + window,
"",
".",
"",
gene_string,
]
lost_gff.append(gff_line)
lost_window_gff.append(gff_window_line)
gene_status = name1
lost_bed.append(bed_line)
# for conserved
else:
gene_status = "CONSERVED"
conserved_bed.append(bed_line)
# now fill in the gene Table
for gene in gene_list:
gene_table_line = [
gene,
line[0],
line[1],
line[2],
line[3],
line[6],
line[7],
line[8],
gene_status,
]
gene_table.append(gene_table_line)
# concat the bed
full_bed = gained_bed + conserved_bed + lost_bed
# start writing the output
# there's the two gffs, the bed,the formatted table, the gene table
# formatted table
formatted_filename = os.path.join(
output_folder,
"{}_{}_MERGED_{}_RANK_TABLE.txt".format(genome, merge_name,
enhancer_type),
)
utils.unparse_table(formatted_rank_table, formatted_filename, "\t")
# gffs
gff_folder = utils.format_folder(output_folder + "gff/", True)
gff_filename_gained = os.path.join(
gff_folder,
"{}_{}_{}_ONLY_{}_-0_+0.gff".format(genome, merge_name, name2.upper(),
enhancer_type),
)
gff_filename_window_gained = os.path.join(
gff_folder,
"{}_{}_{}_ONLY_{}_-{}KB_+{}KB.gff".format(
genome,
merge_name,
name2.upper(),
enhancer_type,
str(window // 1000),
str(window // 1000),
),
)
gff_filename_lost = os.path.join(
gff_folder,
"{}_{}_{}_ONLY_{}_-0_+0.gff".format(genome, merge_name, name1.upper(),
enhancer_type),
)
gff_filename_window_lost = os.path.join(
gff_folder,
"{}_{}_{}_ONLY_{}_-{}KB_+{}KB.gff".format(
genome,
merge_name,
name1.upper(),
enhancer_type,
str(window // 1000),
str(window // 1000),
),
)
utils.unparse_table(gained_gff, gff_filename_gained, "\t")
utils.unparse_table(gained_window_gff, gff_filename_window_gained, "\t")
utils.unparse_table(lost_gff, gff_filename_lost, "\t")
utils.unparse_table(lost_window_gff, gff_filename_window_lost, "\t")
# bed
bed_filename = os.path.join(
output_folder, "{}_{}_MERGED_{}.bed".format(genome, merge_name,
enhancer_type))
utils.unparse_table(full_bed, bed_filename, "\t")
# gene_table
gene_filename = os.path.join(
output_folder,
"{}_{}_MERGED_{}_GENE_TABLE.txt".format(genome, merge_name,
enhancer_type),
)
utils.unparse_table(gene_table, gene_filename, "\t")
# finally, move all of the plots to the output folder
copyfile(
glob.glob(os.path.join(merge_folder, "{}_ROSE".format(name1),
"*.pdf"))[0],
os.path.join(
output_folder,
"{}_{}_MERGED_{}_DELTA.pdf".format(genome, merge_name,
enhancer_type),
),
)
copyfile(
glob.glob(
os.path.join(merge_folder, "{}_ROSE".format(name1),
"*RANK_PLOT.png"))[0],
os.path.join(
output_folder,
"{}_{}_MERGED_{}_RANK_PLOT.png".format(genome, merge_name,
enhancer_type),
),
)
# now execute the bamPlot_turbo commands
if plot_bam:
bam1 = data_dict[name1]["bam"]
bam2 = data_dict[name2]["bam"]
bam_string = "{} {}".format(bam1, bam2)
name_string = "{} {}".format(name1, name2)
color_string = "0,0,0:100,100,100"
if len(gained_gff) > 0:
# gained command
plot_title = "{}_ONLY_SE".format(name2)
cmd = (
"bamPlot_turbo -g {} -b {} -i {} -o {} -n {} -c {} -t {} -r -y UNIFORM -p "
"MULTIPLE".format(
genome,
bam_string,
gff_filename_gained,
output_folder,
name_string,
color_string,
plot_title,
))
os.system(cmd)
# gained window command
plot_title = "{}_ONLY_SE_{}KB_WINDOW".format(
name2, str(window // 1000))
cmd = (
"bamPlot_turbo -g {} -b {} -i {} -o {} -n {} -c {} -t {} -r -y UNIFORM -p "
"MULTIPLE".format(
genome,
bam_string,
gff_filename_window_gained,
output_folder,
name_string,
color_string,
plot_title,
))
os.system(cmd)
if len(lost_gff) > 0:
# lost command
plot_title = "{}_ONLY_SE".format(name1)
cmd = (
"bamPlot_turbo -g {} -b {} -i {} -o {} -n {} -c {} -t {} -r -y UNIFORM -p "
"MULTIPLE".format(
genome,
bam_string,
gff_filename_lost,
output_folder,
name_string,
color_string,
plot_title,
))
os.system(cmd)
# lost command
plot_title = "{}_ONLY_SE_{}KB_WINDOW".format(
name1, str(window // 1000))
cmd = (
"bamPlot_turbo -g {} -b {} -i {} -o {} -n {} -c {} -t {} -r -y UNIFORM -p "
"MULTIPLE".format(
genome,
bam_string,
gff_filename_window_lost,
output_folder,
name_string,
color_string,
plot_title,
))
os.system(cmd)
return