def meta(self, tbl,organism,db_version):
"""Returns the html description from the trackDb file for the specified organism.
"""
try:
trackdb = uscsreader.load_tabledata_dumpfiles(os.path.join(sett["data_dir"][db_version],organism,"trackDb"))
html = trackdb[map(itemgetter('tableName'),trackdb).index(tbl)]['html']
except Exception, e:
return "<h3>(No data found for {}.)</h3>".format(tbl)
def meta(self, tbl, organism, db_version):
"""Returns the html description from the trackDb file for the specified organism.
"""
try:
trackdb = uscsreader.load_tabledata_dumpfiles(
os.path.join(sett["data_dir"][db_version], organism,
"trackDb"))
html = trackdb[map(itemgetter('tableName'),
trackdb).index(tbl)]['html']
except Exception, e:
return "<h3>(No data found for {}.)</h3>".format(tbl)
def get_heatmaps(self, run_id, organism):
""" Returns clustered and PCC matrix if they exist.
'organism': is used to load detailed labels for the GFs.
"""
cherrypy.response.headers['Content-Type'] = 'application/json'
trackdb = uscsreader.load_tabledata_dumpfiles(os.path.join(sett["data_dir"],organism,"trackDb"))
results = {}
path = os.path.join(results_dir, run_id)
matrix_path = os.path.join(path,"matrix.txt")
# Load clustered matrix
matrix_clust_path = os.path.join(path,"clustered.txt")
if os.path.exists(matrix_path):
with open(matrix_path) as f:
results["matrix"] = f.read().replace("\"","")
if os.path.exists(matrix_clust_path):
with open(matrix_clust_path) as f:
d = f.read()
d = d.replace("\"","")
if d[:6] != "ERROR:":
results["matrix_data"] = d.replace("\"","")
# d3 requires "gene_name" to be inserted into the first column
tmp_data = results["matrix_data"].split("\n")
tmp = tmp_data[:] # this copy is passed onto the results page
# insert the description column if it does not exist
tmp_matrix = [x.split("\t") for x in tmp]
if tmp_matrix[0][-1] != "Genomic Feature Description":
tmp_matrix[0] += ["Genomic Feature Description"]
for i in range(1,len(tmp)):
description = [x["longLabel"] for x in trackdb if x["tableName"] == tmp_matrix[i][0]]
if len(description) is not 0: description = description[0]
else: description = ""
tmp_matrix[i] += [description]
results["matrix_data"] = "\n".join(["\t".join(["gene_name",tmp[0]])]+tmp[1:])
results["matrix_data"] = results["matrix_data"]
results["matrix_data_gf_description"] = "\t".join([x[-1] for x in tmp_matrix[1:]])
else: results["matrix_data"] = d[6:]
else:
results["matrix_data"] = "Heatmap will be available after the analysis is complete."
results["matrix_data_gf_description"] = ""
# Pearson's matrix results
matrix_cor_path = os.path.join(path,"pcc_matrix.txt")
if os.path.exists(matrix_cor_path):
with open(matrix_cor_path) as f:
d = f.read()
d = d.replace("\"","")
if d[:6] != "ERROR:":
results["matrix_cor_data"] = d.replace("\"","")
results["matrix_cor"] = d.replace("\"","")
# d3 requires "gene_name" to be inserted into the first column
tmp = results["matrix_cor"].split("\n")
results["matrix_cor"] = "\n".join(["\t".join(["gene_name",tmp[0]])]+tmp[1:])
results["matrix_cor"] = results["matrix_cor"]
else: results["matrix_cor"],results["matrix_cor_data"] = d[6:],""
else:
results["matrix_cor_data"] = ""
results["matrix_cor"] = "PCC heatmap will be available after the clustered matrix is created."
pvalue_path = os.path.join(path,"pcc_matrix_pvalue.txt")
if os.path.exists(pvalue_path):
with open(pvalue_path) as f:
d = f.read()
results["matrix_cor_pvalues"] = d.replace("\"","")
# d3 requires "gene_name" to be inserted into the first column
tmp = results["matrix_cor_pvalues"].split("\n")
results["matrix_cor_pvalues"] = "\n".join(["\t".join(["gene_name",tmp[0]])]+tmp[1:])
results["matrix_cor_pvalues"] = results["matrix_cor_pvalues"]
else:
results["matrix_cor_pvalues"] = ""
return simplejson.dumps(results)
def run_hypergeom(fois, gfs, bg_path,outdir,job_name="",zip_run_files=False,bkg_overlaps_path="",gr_data_dir = "" ,run_annotation=True,run_randomization_test=False,padjust="None",pct_score="",organism = ""):
global formatter
global detailed_outpath,matrix_outpath, progress_outpath, curprog, progmax,run_files_dir
if not os.path.exists(os.path.normpath(outdir)): os.mkdir(os.path.normpath(outdir))
fh = logging.FileHandler(os.path.join(outdir,'gr_log.txt'))
fh.setFormatter(formatter)
logger.addHandler(fh)
logger.setLevel(logging.INFO)
run_files_dir = outdir
curprog,progmax = 0,1
logger.info("ORGANIS " + str(organism))
logger.info("P_value adjustment used: {}".format(padjust))
try:
trackdb = []
trackdb_path = os.path.join(gr_data_dir,organism,"trackDb")
if os.path.exists(trackdb_path+".txt.gz") and os.path.exists(trackdb_path + ".sql"):
trackdb = bedfilecreator.load_tabledata_dumpfiles(trackdb_path)
# set output settings
detailed_outpath = os.path.join(outdir, "detailed.txt")
matrix_outpath = os.path.join(outdir,"matrix.txt")
progress_outpath = os.path.join(outdir,".prog")
f = open(matrix_outpath,'wb')
f.close()
f = open(detailed_outpath,'wb')
f.close()
hdlr = logging.FileHandler(logger_path)
formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
hdlr.setFormatter(formatter)
logger.addHandler(hdlr)
logger.propagate = False
# Read in the paths
fois = [line for line in read_lines(fois) if not line.endswith(".tbi")]
gfs = [line for line in read_lines(gfs) if not line.endswith(".tbi")]
# check if there are spaces in invalid parts of the file name
invalid_names = validate_filenames(fois + gfs + [bg_path])
if len(invalid_names) != 0:
logger.error("The following file(s) have invalid file names, cannot have space before/in file extension:\n" + "\n".join(invalid_names))
_write_progress("ERROR: Files have invalid filenames. See log file. Terminating run. See Analysis Log.")
return
if bg_path.endswith(".tbi"):
logger.error("Background has invalid extension (.tbi). Terminating run.")
_write_progress("ERROR: Background has invalid extension (.tbi). Terminating run. See Analysis Log.")
return
# pre-process the FOIs
fois = preprocess_fois(fois,run_files_dir,gr_data_dir,organism)
if len(fois) == 0:
logger.error('No valid FOIs to supplied')
_write_progress("ERROR: No valid FOI files supplied. Terminating run. See Analysis Log.")
return
# Validate FOIs against background. Also get the size of the background (n_bgs)
foi_bg,good_fois = check_background_foi_overlap(bg_path,fois)
write_output("\t".join(map(base_name,good_fois))+"\n", matrix_outpath)
write_output("\t".join(['foi_name', 'foi_obs', 'n_fois', 'bg_obs', 'n_bgs', 'odds_ratio', 'p_val','test_type','p_rand' if run_randomization_test else "",'p_mod' if run_randomization_test else ""]) + "\n",detailed_outpath)
curprog,progmax = 0,len(gfs)
# remove old detailed enrichment result files if they exit
enr_path = os.path.join(run_files_dir,"enrichment")
for f in good_fois:
f_path = os.path.join(enr_path, base_name(f)+'.txt')
if os.path.exists(f_path): os.remove(f_path)
_write_progress("Performing calculations on the background.")
for gf in gfs:
current_gf = base_name(gf)
_write_progress("Performing Hypergeometric analysis for {}".format(base_name(gf)))
write_output("###"+base_name(gf)+"\t"+get_score_strand_settings(gf)+"\t"+get_description(base_name(gf),trackdb)+"###"+"\n",detailed_outpath)
res = get_overlap_statistics(gf,good_fois)
# calculate bg_obs
bg_obs = get_bgobs(bg_path,gf,bkg_overlaps_path)
if bg_obs == None:
logger.error("Skipping {}".format(gf))
continue
n_bgs = foi_bg[0]["indexregions"]
# run the enrichment analysis and output the matrix line for the current gf
write_output("\t".join([base_name(gf)] + [str(p_value(res[i]["intersectregions"],res[i]["queryregions"],bg_obs,n_bgs ,good_fois[i],gf,bg_path,run_randomization_test)) for i in range(len(good_fois))])+"\n",matrix_outpath)
curprog += 1
# Adjust p values for the enrichment files
list_enr = [x for x in os.listdir(enr_path)]
for p in list_enr:
adjust_detailed_pvalue(os.path.join(enr_path, p),padjust)
if len(gfs) >= 1 and len(good_fois) >= 1:
# Adjust the pvalues
adjust_pvalue(matrix_outpath,padjust)
# Cluster the matrix
clust_path = cluster_matrix(matrix_outpath,os.path.join(outdir,"clustered.txt"))
if len(gfs) > 4:
pearsons_cor_matrix(clust_path,outdir)
else:
json_mat = []
json_mat.append({"log": False,"neg": "Underrepresented", "pos": "Overrepresented","name": "Pearsons","alpha":"","matrix": "ERROR:PCC matrix requires at least a 5 X 2 matrix."})
json_mat.append({"log": False,"neg": "Underrepresented", "pos": "Overrepresented","name": "P-value","alpha":"","matrix": "ERROR:PCC matrix requires at least a 5 X 2 matrix."})
with open(os.path.join(outdir,"pcc_matrix.json"),'wb') as f:
simplejson.dump(json_mat,f)
with open(os.path.join(os.path.join(outdir,"pcc_matrix.txt")),"wb") as wb:
wb.write("ERROR:PCC matrix requires at least a 5 X 2 matrix.")
else:
with open(os.path.join(outdir,"clustered.txt"),"wb") as wb:
wb.write("ERROR:Clustered matrix requires at least a 2 X 2 matrix.")
if run_annotation:
annot_outdir = os.path.join(outdir,"annotations")
if not os.path.exists(annot_outdir): os.mkdir(annot_outdir)
curprog,progmax = 0,len(fois)
for f in fois:
_write_progress("Running Annotation Analysis for {}.".format(base_name(f)))
with open(os.path.join(annot_outdir,base_name(f) + ".txt"),"wb") as wr:
anot = get_annotation(f,gfs).split("\n")
anot[0] = anot[0].replace("Region\t\t","Region\t")
wr.write("Region"+"\t"+"\t".join(base_name(x) for x in reversed(anot[0].split("\t")[1:])) + "\tTotal") # annotationAnalysis column order is reverse of input order
for ind, a in enumerate(anot[1:]):
if a.strip() != "":
cur_row = a.split("\t")
wr.write("\n" + str(ind) + "|"+"\t".join(cur_row + [str(sum([int(x) for x in cur_row[1:] if x != ""]))]))
curprog += 1
if zip_run_files:
_write_progress("Preparing run files for download")
_zip_run_files(fois,gfs,bg_path,outdir,job_name)
curprog,progmax = 1,1
_write_progress("Analysis Completed")
except Exception, e:
logger.error( traceback.print_exc())
write_output(traceback.format_exc(),logger_path)
_write_progress("Run crashed. See end of log for details.")
def get_heatmaps(self, run_id, organism):
""" Returns clustered and PCC matrix if they exist.
'organism': is used to load detailed labels for the GFs.
"""
global results_dir, uploads_dir, sett
cherrypy.response.headers['Content-Type'] = 'application/json'
trackdb = uscsreader.load_tabledata_dumpfiles(
os.path.join(sett["data_dir"], organism, "trackDb"))
results = {}
path = os.path.join(results_dir, run_id)
matrix_path = os.path.join(path, "matrix.txt")
# Load clustered matrix
matrix_clust_path = os.path.join(path, "clustered.txt")
if os.path.exists(matrix_path):
with open(matrix_path) as f:
results["matrix"] = f.read().replace("\"", "")
if os.path.exists(matrix_clust_path):
with open(matrix_clust_path) as f:
d = f.read()
d = d.replace("\"", "")
if d[:6] != "ERROR:":
results["matrix_data"] = d.replace("\"", "")
# d3 requires "gene_name" to be inserted into the first column
tmp_data = results["matrix_data"].split("\n")
tmp = tmp_data[:] # this copy is passed onto the results page
# insert the description column if it does not exist
tmp_matrix = [x.split("\t") for x in tmp]
if tmp_matrix[0][-1] != "Genomic Feature Description":
tmp_matrix[0] += ["Genomic Feature Description"]
for i in range(1, len(tmp)):
description = [
x["longLabel"] for x in trackdb
if x["tableName"] == tmp_matrix[i][0]
]
if len(description) is not 0:
description = description[0]
else:
description = ""
tmp_matrix[i] += [description]
results["matrix_data"] = "\n".join(
["\t".join(["gene_name", tmp[0]])] + tmp[1:])
results["matrix_data"] = results["matrix_data"]
results["matrix_data_gf_description"] = "\t".join(
[x[-1] for x in tmp_matrix[1:]])
else:
results["matrix_data"] = d[6:]
else:
results[
"matrix_data"] = "Heatmap will be available after the analysis is complete."
results["matrix_data_gf_description"] = ""
# Pearson's matrix results
matrix_cor_path = os.path.join(path, "pcc_matrix.txt")
if os.path.exists(matrix_cor_path):
with open(matrix_cor_path) as f:
d = f.read()
d = d.replace("\"", "")
if d[:6] != "ERROR:":
results["matrix_cor_data"] = d.replace("\"", "")
results["matrix_cor"] = d.replace("\"", "")
# d3 requires "gene_name" to be inserted into the first column
tmp = results["matrix_cor"].split("\n")
results["matrix_cor"] = "\n".join(
["\t".join(["gene_name", tmp[0]])] + tmp[1:])
results["matrix_cor"] = results["matrix_cor"]
else:
results["matrix_cor"], results["matrix_cor_data"] = d[
6:], ""
else:
results["matrix_cor_data"] = ""
results[
"matrix_cor"] = "PCC heatmap will be available after the clustered matrix is created."
pvalue_path = os.path.join(path, "pcc_matrix_pvalue.txt")
if os.path.exists(pvalue_path):
with open(pvalue_path) as f:
d = f.read()
results["matrix_cor_pvalues"] = d.replace("\"", "")
# d3 requires "gene_name" to be inserted into the first column
tmp = results["matrix_cor_pvalues"].split("\n")
results["matrix_cor_pvalues"] = "\n".join(
["\t".join(["gene_name", tmp[0]])] + tmp[1:])
results["matrix_cor_pvalues"] = results["matrix_cor_pvalues"]
else:
results["matrix_cor_pvalues"] = ""
return simplejson.dumps(results)
def run_hypergeom(fois,
gfs,
bg_path,
outdir,
job_name="",
zip_run_files=False,
bkg_overlaps_path="",
gr_data_dir="",
run_annotation=True,
run_randomization_test=False,
padjust="None",
pct_score="",
organism=""):
global formatter
global detailed_outpath, matrix_outpath, progress_outpath, curprog, progmax, run_files_dir
if not os.path.exists(os.path.normpath(outdir)):
os.mkdir(os.path.normpath(outdir))
fh = logging.FileHandler(os.path.join(outdir, 'gr_log.txt'))
fh.setFormatter(formatter)
logger.addHandler(fh)
logger.setLevel(logging.INFO)
run_files_dir = outdir
curprog, progmax = 0, 1
logger.info("ORGANIS " + str(organism))
logger.info("P_value adjustment used: {}".format(padjust))
try:
trackdb = []
trackdb_path = os.path.join(gr_data_dir, organism, "trackDb")
if os.path.exists(trackdb_path +
".txt.gz") and os.path.exists(trackdb_path + ".sql"):
trackdb = bedfilecreator.load_tabledata_dumpfiles(trackdb_path)
# set output settings
detailed_outpath = os.path.join(outdir, "detailed.txt")
matrix_outpath = os.path.join(outdir, "matrix.txt")
progress_outpath = os.path.join(outdir, ".prog")
f = open(matrix_outpath, 'wb')
f.close()
f = open(detailed_outpath, 'wb')
f.close()
hdlr = logging.FileHandler(logger_path)
formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
hdlr.setFormatter(formatter)
logger.addHandler(hdlr)
logger.propagate = False
# Read in the paths
fois = [line for line in read_lines(fois) if not line.endswith(".tbi")]
gfs = [line for line in read_lines(gfs) if not line.endswith(".tbi")]
# check if there are spaces in invalid parts of the file name
invalid_names = validate_filenames(fois + gfs + [bg_path])
if len(invalid_names) != 0:
logger.error(
"The following file(s) have invalid file names, cannot have space before/in file extension:\n"
+ "\n".join(invalid_names))
_write_progress(
"ERROR: Files have invalid filenames. See log file. Terminating run. See Analysis Log."
)
return
if bg_path.endswith(".tbi"):
logger.error(
"Background has invalid extension (.tbi). Terminating run.")
_write_progress(
"ERROR: Background has invalid extension (.tbi). Terminating run. See Analysis Log."
)
return
# pre-process the FOIs
fois = preprocess_fois(fois, run_files_dir, gr_data_dir, organism)
if len(fois) == 0:
logger.error('No valid FOIs to supplied')
_write_progress(
"ERROR: No valid FOI files supplied. Terminating run. See Analysis Log."
)
return
# Validate FOIs against background. Also get the size of the background (n_bgs)
foi_bg, good_fois = check_background_foi_overlap(bg_path, fois)
write_output("\t".join(map(base_name, good_fois)) + "\n",
matrix_outpath)
write_output(
"\t".join([
'foi_name', 'foi_obs', 'n_fois', 'bg_obs', 'n_bgs',
'odds_ratio', 'p_val', 'test_type',
'p_rand' if run_randomization_test else "",
'p_mod' if run_randomization_test else ""
]) + "\n", detailed_outpath)
curprog, progmax = 0, len(gfs)
# remove old detailed enrichment result files if they exit
enr_path = os.path.join(run_files_dir, "enrichment")
for f in good_fois:
f_path = os.path.join(enr_path, base_name(f) + '.txt')
if os.path.exists(f_path): os.remove(f_path)
_write_progress("Performing calculations on the background.")
for gf in gfs:
current_gf = base_name(gf)
_write_progress("Performing Hypergeometric analysis for {}".format(
base_name(gf)))
write_output(
"###" + base_name(gf) + "\t" + get_score_strand_settings(gf) +
"\t" + get_description(base_name(gf), trackdb) + "###" + "\n",
detailed_outpath)
res = get_overlap_statistics(gf, good_fois)
# calculate bg_obs
bg_obs = get_bgobs(bg_path, gf, bkg_overlaps_path)
if bg_obs == None:
logger.error("Skipping {}".format(gf))
continue
n_bgs = foi_bg[0]["indexregions"]
# run the enrichment analysis and output the matrix line for the current gf
write_output(
"\t".join([base_name(gf)] + [
str(
p_value(res[i]["intersectregions"], res[i]
["queryregions"], bg_obs, n_bgs, good_fois[i],
gf, bg_path, run_randomization_test))
for i in range(len(good_fois))
]) + "\n", matrix_outpath)
curprog += 1
# Adjust p values for the enrichment files
list_enr = [x for x in os.listdir(enr_path)]
for p in list_enr:
adjust_detailed_pvalue(os.path.join(enr_path, p), padjust)
if len(gfs) >= 1 and len(good_fois) >= 1:
# Adjust the pvalues
adjust_pvalue(matrix_outpath, padjust)
# Cluster the matrix
clust_path = cluster_matrix(matrix_outpath,
os.path.join(outdir, "clustered.txt"))
if len(gfs) > 4:
pearsons_cor_matrix(clust_path, outdir)
else:
json_mat = []
json_mat.append({
"log":
False,
"neg":
"Underrepresented",
"pos":
"Overrepresented",
"name":
"Pearsons",
"alpha":
"",
"matrix":
"ERROR:PCC matrix requires at least a 5 X 2 matrix."
})
json_mat.append({
"log":
False,
"neg":
"Underrepresented",
"pos":
"Overrepresented",
"name":
"P-value",
"alpha":
"",
"matrix":
"ERROR:PCC matrix requires at least a 5 X 2 matrix."
})
with open(os.path.join(outdir, "pcc_matrix.json"), 'wb') as f:
simplejson.dump(json_mat, f)
with open(os.path.join(os.path.join(outdir, "pcc_matrix.txt")),
"wb") as wb:
wb.write(
"ERROR:PCC matrix requires at least a 5 X 2 matrix.")
else:
with open(os.path.join(outdir, "clustered.txt"), "wb") as wb:
wb.write(
"ERROR:Clustered matrix requires at least a 2 X 2 matrix.")
if run_annotation:
annot_outdir = os.path.join(outdir, "annotations")
if not os.path.exists(annot_outdir): os.mkdir(annot_outdir)
curprog, progmax = 0, len(fois)
for f in fois:
_write_progress("Running Annotation Analysis for {}.".format(
base_name(f)))
with open(os.path.join(annot_outdir,
base_name(f) + ".txt"), "wb") as wr:
anot = get_annotation(f, gfs).split("\n")
anot[0] = anot[0].replace("Region\t\t", "Region\t")
wr.write(
"Region" + "\t" + "\t".join(
base_name(x)
for x in reversed(anot[0].split("\t")[1:])) +
"\tTotal"
) # annotationAnalysis column order is reverse of input order
for ind, a in enumerate(anot[1:]):
if a.strip() != "":
cur_row = a.split("\t")
wr.write(
"\n" + str(ind) + "|" + "\t".join(cur_row + [
str(
sum([
int(x)
for x in cur_row[1:] if x != ""
]))
]))
curprog += 1
if zip_run_files:
_write_progress("Preparing run files for download")
_zip_run_files(fois, gfs, bg_path, outdir, job_name)
curprog, progmax = 1, 1
_write_progress("Analysis Completed")
except Exception, e:
logger.error(traceback.print_exc())
write_output(traceback.format_exc(), logger_path)
_write_progress("Run crashed. See end of log for details.")
