def main():
args = handle_program_options()
map_header, imap = util.parse_map_file(args.map_fp)
df = pd.read_csv(args.biom_tsv, sep='\t', index_col=0).T
# exclude Sample IDs not in the mapping file
df = df.loc[imap.keys()]
cat_gather = util.gather_categories(imap, map_header, args.group_by)
if len(cat_gather) < 2:
sys.stderr.write("ERROR: Only one category value found. Linear \
Discriminant Analysis requires at least two categories to compare.")
return
color_gather = util.gather_categories(imap, map_header, [args.color_by])
class_map = merge_dicts(*[{sid: cat for sid in cat_gather[cat].sids}
for cat in cat_gather])
class_colors = merge_dicts(*[{class_map[sid]: color
for sid in color_gather[color].sids}
for color in color_gather])
df.insert(0, "Condition", [class_map[entry] for entry in df.index])
if args.save_lda_input:
df.to_csv(args.save_lda_input)
X_lda, y_lda = run_LDA(df)
plot_LDA(X_lda, y_lda, class_colors, out_fp=args.out_fp, dpi=args.dpi,
title=args.plot_title)
def main():
args = prog_options()
try:
biomf = biom.load_table(args.in_biomf)
except IOError as ioe:
sys.exit("Error with input BIOM format file: {}".format(ioe))
else:
biomf_pa = biomf.pa(
inplace=False) # convert to presence/absence BIOM table
obs_ids = biomf_pa.ids("observation")
try:
mheader, mdata = parse_map_file(args.map_fnh)
except IOError as ioe:
sys.exit("Error with input mapping file: {}".format(ioe))
else:
if args.group_by:
sid_cat = gather_categories(mdata, mheader, [args.group_by])
else:
sid_cat = gather_categories(mdata, mheader)
# calculate core
core_calc = {k: set() for k in sid_cat.keys()}
for idx in obs_ids:
for cat, val in sid_cat.iteritems():
obs_count = 0
num_of_samples = len(val.sids)
for sid in val.sids:
try:
assert biomf_pa.get_value_by_ids(idx, sid) == 1
except AssertionError:
continue
else:
obs_count += 1
try:
assert obs_count > round(args.core_pct * num_of_samples)
except AssertionError:
continue
else:
core_calc[cat].add(idx)
# Check if output directory exists, if not, create it
try:
assert os.path.exists(os.path.abspath(args.out_fnh)) is True
except AssertionError:
os.makedirs(os.path.abspath(args.out_fnh))
finally:
for k, v in core_calc.iteritems():
print("{0} core IDs in {1}".format(len(v), k))
idx_filename = os.path.join(os.path.abspath(args.out_fnh),
k + "_80_pct_core_ids.txt")
with open(idx_filename, "w") as of:
of.write("{0}".format("\n".join(sorted(v))))
filtered_biomf = biomf.filter(v, axis="observation", inplace=False)
if args.biom_out:
biom_filename = os.path.join(os.path.abspath(args.out_fnh),
k + "_80_pct_core.biom")
with biom_open(biom_filename, "w") as f:
filtered_biomf.to_hdf5(f, "CORE BIOM")
def color_mapping(sample_map, header, group_column, color_column=None):
"""
Determine color-category mapping. If color_column was specified, then
map the category names to color values. Otherwise, use the brewer colors
to automatically generate a set of colors for the group values.
"""
group_colors = {}
group_gather = putil.gather_categories(sample_map, header, [group_column])
if color_column is not None:
color_gather = putil.gather_categories(sample_map, header, [color_column])
# match sample IDs between color_gather and group_gather
for group in group_gather:
for color in color_gather:
# allow incomplete assignment of colors, if group sids overlap at
# all with the color sids, consider it a match
if group_gather[group].sids.intersection(color_gather[color].sids):
group_colors[group] = color
else:
bmap = qualitative.Paired[12]
bcolors = itertools.cycle(bmap.hex_colors)
for group in group_gather:
group_colors[group] = bcolors.next()
return group_colors
def main():
args = handle_program_options()
# Read in biom file
try:
shared_biom = biom.load_table(args.input_biom_fp)
except IOError as ie:
sys.exit("\nError reading BIOM file: {}\n".format(ie))
norm_shared_biom = shared_biom.norm(axis="sample", inplace=False)
# Read in mapping file
try:
header, imap = parse_map_file(args.map_fp)
except IOError as ioe:
sys.exit("\nError in metadata mapping filepath: {}\n".format(ioe))
# Samples for each group and get DO values per category
try:
assert args.group_by is None
except AssertionError:
data_gather = gather_categories(imap, header, args.group_by.split(","))
sample_list = [
sid for cat in data_gather.keys() for sid in data_gather[cat].sids
]
else:
sample_list = norm_shared_biom.ids()
doc = calc_doc(norm_shared_biom, sample_list)
try:
assert doc is not None
except AssertionError:
sys.exit("Error in DOC calculations. Please check the modules.")
# Get confidence interval
sl_lowess_regr = get_doc_ci(doc,
args.frac,
args.plot_ci,
sample_list,
num_of_seqs=args.num_iterations)
# Plot the residual figure
plot_residplot(sl_lowess_regr, args.residplot, save=args.save_image)
# Plot DOC
plot_doc(sl_lowess_regr,
args.residplot,
ci=args.plot_ci,
title=args.title,
save=args.save_image)
def main():
args = handle_program_options()
try:
# Load biom format file
biomf = biom.load_table(args.input_biom_fp)
except TypeError as te:
sys.exit(
"The data in the path does not appear to be a BIOM format table. "
"Error: {}.".format(te))
# Determine OTUIDs present in each sample
sample_otus = oc.assign_otu_membership(biomf)
try:
# Parse mapping file
header, imap = util.parse_map_file(args.mapping_file)
except ValueError as ve:
sys.exit("Error: {}.".format(ve))
# Get relevant category information
group_data = util.gather_categories(imap, header, [args.category_column])
# Initialize results dict in group_data with {"otuids": set()} for each category
for group in group_data:
group_data[group].results["otuids"] = set()
# Collect all OTUIDs present in each category
for sid in sample_otus:
group = sample_group(sid, group_data)
group_data[group].results["otuids"].update(sample_otus[sid])
if args.reverse:
# Get shared OTUIDs
shared = shared_otuids(group_data)
# Write out shared OTUIDs results
shared_df = pd.DataFrame.from_dict(shared, orient="index").T
shared_df.to_csv(args.reverse, sep="\t", index=False)
# Create input for unique_otus
group_otuids = {
group: group_data[group].results["otuids"]
for group in group_data
}
# Write out unique OTUIDs to file
write_uniques(args.output_dir, args.prefix, unique_otuids(group_otuids))
def main():
args = handle_program_options()
try:
# Load biom format file
biomf = biom.load_table(args.input_biom_fp)
except TypeError as te:
sys.exit("The data in the path does not appear to be a BIOM format table. "
"Error: {}.".format(te))
# Determine OTUIDs present in each sample
sample_otus = assign_otu_membership(biomf)
try:
# Parse mapping file
header, imap = util.parse_map_file(args.mapping_file)
except ValueError as ve:
sys.exit("Error: {}.".format(ve))
# Get relevant category information
group_data = util.gather_categories(imap, header, [args.category_column])
# Initialize results dict in group_data with {"otuids": set()} for each category
for group in group_data:
group_data[group].results["otuids"] = set()
# Collect all OTUIDs present in each category
for sid in sample_otus:
group = sample_group(sid, group_data)
group_data[group].results["otuids"].update(sample_otus[sid])
if args.reverse:
# Get shared OTUIDs
shared = shared_otuids(group_data)
# Write out shared OTUIDs results
shared_df = pd.DataFrame.from_dict(shared, orient="index").T
shared_df.to_csv(args.reverse, sep="\t", index=False)
else:
# Create input for unique_otus
group_otuids = {group: group_data[group].results["otuids"]
for group in group_data}
# Write out unique OTUIDs to file
write_uniques(args.output_dir, args.prefix, unique_otuids(group_otuids))
def main():
args = handle_program_options()
try:
with open(args.otu_table):
pass
except IOError as ioe:
sys.exit("\nError with BIOM format file:{}\n".format(ioe))
try:
with open(args.pcoa_fp):
pass
except IOError as ioe:
sys.exit("\nError with principal coordinates file:{}\n".format(ioe))
try:
with open(args.mapping):
pass
except IOError as ioe:
sys.exit("\nError with mapping file:{}\n".format(ioe))
if not os.path.exists(args.output_dir):
try:
os.mkdir(args.output_dir)
except OSError as oe:
if os.errno == 2:
msg = ("One or more directories in the path provided for " +
"--output-dir ({}) do not exist. If you are specifying " +
"a new directory for output, please ensure all other " +
"directories in the path currently exist.")
sys.exit(msg.format(args.output_dir))
else:
msg = ("An error occurred trying to create the output " +
"directory ({}) with message: {}")
sys.exit(msg.format(args.output_dir, oe.strerror))
# load the BIOM table
biomtbl = biom.load_table(args.otu_table)
# Read unifrac principal coordinates file
unifrac = util.parse_unifrac(args.pcoa_fp)
# Read otu data file
otus = set()
with open(args.otu_ids_fp, "rU") as nciF:
for line in nciF.readlines():
line = line.strip()
otus.add(line)
# Gather categories from mapping file
header, imap = util.parse_map_file(args.mapping)
try:
category_idx = header.index(args.group_by)
except ValueError:
msg = "Error: Specified mapping category '{}' not found."
sys.exit(msg.format(args.group_by))
category_ids = util.gather_categories(imap, header, [args.group_by])
color_map = util.color_mapping(imap, header, args.group_by, args.colors)
rel_abd = get_relative_abundance(biomtbl)
# plot samples based on relative abundance of some OTU ID
for otuid in otus:
otuname = oc.otu_name(biomtbl.metadata(otuid, axis="observation")["taxonomy"])
cat_data = {cat: {"pc1": [], "pc2": [], "size": []}
for cat in category_ids}
for sid in unifrac["pcd"]:
category = cat_data[imap[sid][category_idx]]
try:
size = rel_abd[sid][otuid] * args.scale_by
except KeyError as ke:
print "{} not found in {} sample.".format(ke, sid)
continue
category["pc1"].append(float(unifrac["pcd"][sid][0]))
category["pc2"].append(float(unifrac["pcd"][sid][1]))
category["size"].append(size)
if args.verbose:
print "Saving chart for {}".format(" ".join(otuname.split("_")))
xr, yr = calculate_xy_range(cat_data)
plot_PCoA(cat_data, otuname, unifrac, color_map.keys(),
color_map, xr, yr, args.output_dir,
args.save_as, args.ggplot2_style)
def main():
args = handle_program_options()
try:
with open(args.otu_table):
pass
except IOError as ioe:
sys.exit("\nError with BIOM format file:{}\n".format(ioe))
try:
with open(args.pcoa_fp):
pass
except IOError as ioe:
sys.exit("\nError with principal coordinates file:{}\n".format(ioe))
try:
with open(args.mapping):
pass
except IOError as ioe:
sys.exit("\nError with mapping file:{}\n".format(ioe))
# check that the output dir exists, create it if not
util.ensure_dir(args.output_dir)
# load the BIOM table
biomtbl = biom.load_table(args.otu_table)
# Read unifrac principal coordinates file
unifrac = util.parse_unifrac(args.pcoa_fp)
# Read otu data file
otus = set()
with open(args.otu_ids_fp, "rU") as nciF:
for line in nciF.readlines():
line = line.strip()
otus.add(line)
# Gather categories from mapping file
header, imap = util.parse_map_file(args.mapping)
try:
category_idx = header.index(args.group_by)
except ValueError:
msg = "Error: Specified mapping category '{}' not found."
sys.exit(msg.format(args.group_by))
category_ids = util.gather_categories(imap, header, [args.group_by])
color_map = util.color_mapping(imap, header, args.group_by, args.colors)
rel_abd = bc.relative_abundance(biomtbl)
rel_abd = bc.arcsine_sqrt_transform(rel_abd)
# plot samples based on relative abundance of some OTU ID
for otuid in otus:
otuname = oc.otu_name(biomtbl.metadata(otuid, axis="observation")["taxonomy"])
cat_data = {cat: {"pc1": [], "pc2": [], "size": []}
for cat in category_ids}
for sid in unifrac["pcd"]:
category = cat_data[imap[sid][category_idx]]
try:
size = rel_abd[sid][otuid] * args.scale_by
except KeyError as ke:
print("{} not found in {} sample.".format(ke, sid))
continue
category["pc1"].append(float(unifrac["pcd"][sid][0]))
category["pc2"].append(float(unifrac["pcd"][sid][1]))
category["size"].append(size)
if args.verbose:
print("Saving chart for {}".format(" ".join(otuname.split("_"))))
xr, yr = calculate_xy_range(cat_data)
plot_PCoA(cat_data, otuname, unifrac, color_map.keys(),
color_map, xr, yr, args.output_dir,
args.save_as, args.ggplot2_style)
def main():
args = handle_program_options()
try:
with open(args.coord_fp):
pass
except IOError as ioe:
err_msg = "\nError in input principal coordinates filepath (-i): {}\n"
sys.exit(err_msg.format(ioe))
try:
with open(args.map_fp):
pass
except IOError as ioe:
err_msg = "\nError in input metadata mapping filepath (-m): {}\n"
sys.exit(err_msg.format(ioe))
with open(args.coord_fp) as F:
pcd = F.readlines()
pcd = [line.split("\t") for line in pcd]
map_header, imap = util.parse_map_file(args.map_fp)
data_gather = util.gather_categories(imap, map_header,
args.group_by.split(","))
categories = OrderedDict([(condition, {"pc1": [], "pc2": [], "pc3": []})
for condition in data_gather.keys()])
bcolors = itertools.cycle(Set3_12.hex_colors)
if not args.colors:
colors = [bcolors.next() for _ in categories]
else:
colors = util.color_mapping(imap, map_header,
args.group_by, args.colors)
colors = colors.values()
parsed_unifrac = util.parse_unifrac(args.coord_fp)
pco = args.pc_order
if args.dimensions == 3:
pco.append(3)
pc1v = parsed_unifrac["varexp"][pco[0] - 1]
pc2v = parsed_unifrac["varexp"][pco[1] - 1]
if args.dimensions == 3:
pc3v = parsed_unifrac["varexp"][pco[2] - 1]
for sid, points in parsed_unifrac["pcd"].items():
for condition, dc in data_gather.items():
if sid in dc.sids:
cat = condition
break
categories[cat]["pc1"].append((sid, points[pco[0] - 1]))
categories[cat]["pc2"].append((sid, points[pco[1] - 1]))
if args.dimensions == 3:
categories[cat]["pc3"].append((sid, points[pco[2] - 1]))
axis_str = "PC{} (Percent Explained Variance {:.3f}%)"
# initialize plot
fig = plt.figure(figsize=args.figsize)
if args.dimensions == 3:
ax = fig.add_subplot(111, projection="3d")
ax.view_init(elev=args.z_angles[1], azim=args.z_angles[0])
ax.set_zlabel(axis_str.format(3, pc3v), labelpad=args.label_padding)
if args.z_limits:
ax.set_zlim(args.z_limits)
else:
ax = fig.add_subplot(111)
# plot data
for i, cat in enumerate(categories):
if args.dimensions == 3:
ax.scatter(xs=[e[1] for e in categories[cat]["pc1"]],
ys=[e[1] for e in categories[cat]["pc2"]],
zs=[e[1] for e in categories[cat]["pc3"]],
zdir="z", c=colors[i], s=args.point_size, label=cat,
edgecolors="k")
else:
ax.scatter([e[1] for e in categories[cat]["pc1"]],
[e[1] for e in categories[cat]["pc2"]],
c=colors[i], s=args.point_size, label=cat, edgecolors="k")
# Script to annotate PCoA sample points.
if args.annotate_points:
for x, y in zip(categories[cat]["pc1"], categories[cat]["pc2"]):
ax.annotate(
x[0], xy=(x[1], y[1]), xytext=(-10, -15),
textcoords="offset points", ha="center", va="center",
)
# customize plot options
if args.x_limits:
ax.set_xlim(args.x_limits)
if args.y_limits:
ax.set_ylim(args.y_limits)
ax.set_xlabel(axis_str.format(pco[0], float(pc1v)), labelpad=args.label_padding)
ax.set_ylabel(axis_str.format(pco[1], float(pc2v)), labelpad=args.label_padding)
leg = plt.legend(loc="best", scatterpoints=3, frameon=True, framealpha=1)
leg.get_frame().set_edgecolor('k')
# Set the font characteristics
font = {"family": "normal", "weight": "bold", "size": args.font_size}
mpl.rc("font", **font)
if args.title:
ax.set_title(args.title)
if args.ggplot2_style and not args.dimensions == 3:
gu.ggplot2_style(ax)
# save or display result
if args.out_fp:
fig.savefig(args.out_fp, facecolor="white", edgecolor="none", bbox_inches="tight",
pad_inches=0.2)
else:
plt.show()
def main():
args = handle_program_options()
try:
with open(args.otu_table):
pass
except IOError as ioe:
sys.exit(
"\nError with OTU_Sample abundance data file:{}\n"
.format(ioe)
)
try:
with open(args.mapping):
pass
except IOError as ioe:
sys.exit(
"\nError with mapping file:{}\n"
.format(ioe)
)
# input data
biomf = biom.load_table(args.otu_table)
map_header, imap = util.parse_map_file(args.mapping)
# rewrite tree file with otu names
if args.input_tree:
with open(args.input_tree) as treF, open(args.output_tre, "w") as outF:
tree = treF.readline()
if "'" in tree:
tree = tree.replace("'", '')
outF.write(newick_replace_otuids(tree, biomf))
oid_rows = {id_: md["taxonomy"]
for val, id_, md in biomf.iter(axis="observation")}
# calculate analysis results
categories = None
if args.map_categories is not None:
categories = args.map_categories.split(",")
# set transform if --stabilize_variance is specfied
tform = bc.arcsine_sqrt_transform if args.stabilize_variance else None
groups = util.gather_categories(imap, map_header, categories)
for group in groups.values():
if args.analysis_metric in ["MRA", "NMRA"]:
results = bc.MRA(biomf, group.sids, transform=tform)
elif args.analysis_metric == "raw":
results = bc.transform_raw_abundance(biomf, sampleIDs=group.sids,
sample_abd=False)
group.results.update({oc.otu_name(oid_rows[oid]): results[oid]
for oid in results})
# write iTol data set file
with open(args.output_itol_table, "w") as itolF:
if args.analysis_metric == "raw":
itolF.write("DATASET_GRADIENT\nSEPARATOR TAB\n")
itolF.write("DATASET_LABEL\tLog Total Abundance\n")
itolF.write("COLOR\t#000000\n")
itolF.write("LEGEND_TITLE\tLog Total Abundance\n")
itolF.write("LEGEND_SHAPES\t1\n")
itolF.write("LEGEND_COLORS\t#000000\n")
itolF.write("LEGEND_LABELS\tLog Total Abundance\n")
itolF.write("COLOR_MIN\t#FFFFFF\n")
itolF.write("COLOR_MAX\t#000000\n")
else:
itolF.write("DATASET_MULTIBAR\nSEPARATOR TAB\n")
itolF.write("DATASET_LABEL\tNMRA\n")
itolF.write("FIELD_COLORS\t{}\n".format("\t".join(["#ff0000"
for _ in range(len(groups))])))
itolF.write("FIELD_LABELS\t" + "\t".join(groups.keys())+"\n")
itolF.write("LEGEND_TITLE\tNMRA\n")
itolF.write("LEGEND_SHAPES\t{}\n".format("\t".join(["1"
for _ in range(len(groups))])))
itolF.write("LEGEND_COLORS\t{}\n".format("\t".join(["#ff0000"
for _ in range(len(groups))])))
itolF.write("LEGEND_LABELS\t" + "\t".join(groups.keys())+"\n")
itolF.write("WIDTH\t300\n")
itolF.write("DATA\n")
all_otus = frozenset({oc.otu_name(md["taxonomy"])
for val, id_, md in
biomf.iter(axis="observation")})
for oname in all_otus:
row = ["{name}"] # \t{s:.2f}\t{ns:.2f}\n"
row_data = {"name": oname}
msum = 0
for name, group in groups.iteritems():
row.append("{{{}:.5f}}".format(name))
if oname in group.results:
row_data[name] = group.results[oname]
else:
row_data[name] = 0.0
msum += row_data[name]
# normalize avg relative abundance data
if args.analysis_metric == "NMRA" and msum > 0:
row_data.update({key: data/msum
for key, data in row_data.items()
if key != "name"})
itolF.write("\t".join(row).format(**row_data) + "\n")
def test_gather_categories(self):
"""
Testing gather_category function from iTol.py. If successful, the
function will be moved to util.py.
:return: Returns OK if test goals were achieved, otherwise raises
error.
"""
DataCategory = namedtuple("DataCategory", "sids results")
result = ut.gather_categories(self.map_data, self.map_header)
result1 = ut.gather_categories(self.map_data, self.map_header,
["Treatment"]) # one category given
result2 = ut.gather_categories(self.map_data, self.map_header,
["Smoking=Control"]) # incorrect condition given
result3 = ut.gather_categories(self.map_data, self.map_header,
["Treatment=Fast"]) # correct condition given
result4 = ut.gather_categories(self.map_data, self.map_header,
["Treatment", "Smoking"]) # 2 categories given
result5 = ut.gather_categories(self.map_data, self.map_header,
["Treatment", "Smoking=Never_Smoker"]) # 1 category 1 condition
result6 = ut.gather_categories(self.map_data, self.map_header,
["Treatment=Control", "Smoking=Current_Smoker"]) # 1 category 1 condition
result7 = ut.gather_categories(self.map_data, self.map_header,
["Smoking=Current_Smoker", "Smoking=Never_Smoker"]) # 2 conditions given
result8 = ut.gather_categories(self.map_data, self.map_header,
["Smoking=Never_Smoker", "Treatment",
"Gender=Female"]) # more than 2 categories - mix
result9 = ut.gather_categories(self.map_data, self.map_header,
categories="Nationality:Peru")
# Testing if the function calculates without any categories.
manual = {"default": DataCategory({"PC.354", "PC.355", "PC.356", "PC.481",
"PC.593", "PC.607", "PC.634", "PC.635",
"PC.636"}, {})}
self.assertDictEqual(
result, manual,
msg="With no category or condition given, gather_categories() did not return "
"all SampleIDs as expected."
)
# Testing if the function accurately calculates for one category
manual1 = {"Control": DataCategory({"PC.355", "PC.356", "PC.354",
"PC.481", "PC.593"}, {}),
"Fast": DataCategory({"PC.634", "PC.635", "PC.636", "PC.607"}, {})}
self.assertDictEqual(
result1, manual1,
msg="With one category given, gather_categories() did not return per "
"category SampleIDs as expected."
)
# Testing if the function accurately calculates for incorrect condition given
self.assertDictEqual(
result2, manual,
msg="With incorrect condition given, gather_categories() did not return "
"all SampleIDs by default as expected."
)
# Testing if the function accurately calculates for correct one condition given
manual3 = {"Fast": DataCategory({"PC.634", "PC.635", "PC.636", "PC.607"}, {})}
self.assertDictEqual(
result3, manual3,
msg="With one correct condition given, gather_categories() did not return "
"SampleIDs for the condition given, as expected."
)
# Testing if the function accurately calculates for correct one condition given
manual4 = {"Control_Current_Smoker": DataCategory({"PC.355", "PC.356"}, {}),
"Control_Never_Smoker": DataCategory({"PC.354", "PC.481", "PC.593"}, {}),
"Fast_Current_Smoker": DataCategory({"PC.634", "PC.635", "PC.636"}, {}),
"Fast_Never_Smoker": DataCategory({"PC.607"}, {})}
self.assertDictEqual(
result4, manual4,
msg="With multiple categories given, gather_categories() did not return "
"SampleIDs for all category combinations, as expected."
)
# Testing if the function accurately calculates for one category and condition
manual5 = {"Control_Never_Smoker": DataCategory({"PC.354", "PC.481", "PC.593"}, {}),
"Fast_Never_Smoker": DataCategory({"PC.607"}, {})}
self.assertDictEqual(
result5, manual5,
msg="With one category and one condition given, gather_categories() did not "
"return SampleIDs for all category-condition combinations, as expected."
)
# Testing if the function accurately calculates for one category and condition
manual6 = {"Control_Current_Smoker": DataCategory({"PC.355", "PC.356"}, {})}
self.assertDictEqual(
result6, manual6,
msg="With two specific conditions given, gather_categories() did not "
"return SampleIDs for all condition combinations, as expected."
)
# Testing if the function accurately calculates for one category and condition
manual7 = {"Current_Smoker": DataCategory({"PC.355", "PC.356", "PC.634",
"PC.635", "PC.636"}, {}),
"Never_Smoker": DataCategory({"PC.354", "PC.481", "PC.593", "PC.607"}, {})}
self.assertDictEqual(
result7, manual7,
msg="With two conditions from same category given, gather_categories() did "
"not return SampleIDs for all condition combinations, as expected."
)
# Testing if function accurately categorizes SampleIDs for multiple categories
manual8 = {"Control_Never_Smoker_Female": DataCategory({"PC.354", "PC.593"}, {})}
self.assertDictEqual(
result8, manual8,
msg="With two or more conditions/categories given, gather_categories() did "
"not return SampleIDs for all condition combinations, as expected."
)
# Testing invalid categories or conditions identified
manual9 = {"default": DataCategory({"PC.354", "PC.355", "PC.356", "PC.481",
"PC.593", "PC.607", "PC.634", "PC.635",
"PC.636"}, {})}
self.assertDictEqual(
result9, manual9,
msg="With invalid category or condition given, gather_categories() did not "
"return all SampleIDs as expected."
)
def main():
args = handle_program_options()
try:
with open(args.coord_fp):
pass
except IOError as ioe:
err_msg = "\nError in input principal coordinates filepath (-i): {}\n"
sys.exit(err_msg.format(ioe))
try:
with open(args.map_fp):
pass
except IOError as ioe:
err_msg = "\nError in input metadata mapping filepath (-m): {}\n"
sys.exit(err_msg.format(ioe))
with open(args.coord_fp) as F:
pcd = F.readlines()
pcd = [line.split("\t") for line in pcd]
map_header, imap = util.parse_map_file(args.map_fp)
data_gather = util.gather_categories(imap, map_header,
args.group_by.split(","))
categories = OrderedDict([(condition, {
"pc1": [],
"pc2": [],
"pc3": []
}) for condition in data_gather.keys()])
bcolors = itertools.cycle(Set3_12.hex_colors)
if not args.colors:
colors = [bcolors.next() for _ in categories]
else:
colors = util.color_mapping(imap, map_header, args.group_by,
args.colors)
colors = colors.values()
parsed_unifrac = util.parse_unifrac(args.coord_fp)
pco = args.pc_order
if args.dimensions == 3:
pco.append(3)
pc1v = parsed_unifrac["varexp"][pco[0] - 1]
pc2v = parsed_unifrac["varexp"][pco[1] - 1]
if args.dimensions == 3:
pc3v = parsed_unifrac["varexp"][pco[2] - 1]
for sid, points in parsed_unifrac["pcd"].items():
for condition, dc in data_gather.items():
if sid in dc.sids:
cat = condition
break
categories[cat]["pc1"].append((sid, points[pco[0] - 1]))
categories[cat]["pc2"].append((sid, points[pco[1] - 1]))
if args.dimensions == 3:
categories[cat]["pc3"].append((sid, points[pco[2] - 1]))
axis_str = "PC{} (Percent Explained Variance {:.3f}%)"
# initialize plot
fig = plt.figure(figsize=args.figsize)
if args.dimensions == 3:
ax = fig.add_subplot(111, projection="3d")
ax.view_init(elev=args.z_angles[1], azim=args.z_angles[0])
ax.set_zlabel(axis_str.format(3, pc3v), labelpad=args.label_padding)
if args.z_limits:
ax.set_zlim(args.z_limits)
else:
ax = fig.add_subplot(111)
# plot data
for i, cat in enumerate(categories):
if args.dimensions == 3:
ax.scatter(xs=[e[1] for e in categories[cat]["pc1"]],
ys=[e[1] for e in categories[cat]["pc2"]],
zs=[e[1] for e in categories[cat]["pc3"]],
zdir="z",
c=colors[i],
s=args.point_size,
label=cat,
edgecolors="k")
else:
ax.scatter([e[1] for e in categories[cat]["pc1"]],
[e[1] for e in categories[cat]["pc2"]],
c=colors[i],
s=args.point_size,
label=cat,
edgecolors="k")
# Script to annotate PCoA sample points.
if args.annotate_points:
for x, y in zip(categories[cat]["pc1"], categories[cat]["pc2"]):
ax.annotate(
x[0],
xy=(x[1], y[1]),
xytext=(-10, -15),
textcoords="offset points",
ha="center",
va="center",
)
# customize plot options
if args.x_limits:
ax.set_xlim(args.x_limits)
if args.y_limits:
ax.set_ylim(args.y_limits)
ax.set_xlabel(axis_str.format(pco[0], float(pc1v)),
labelpad=args.label_padding)
ax.set_ylabel(axis_str.format(pco[1], float(pc2v)),
labelpad=args.label_padding)
leg = plt.legend(loc="best", scatterpoints=3, frameon=True, framealpha=1)
leg.get_frame().set_edgecolor('k')
# Set the font characteristics
font = {"family": "normal", "weight": "bold", "size": args.font_size}
mpl.rc("font", **font)
if args.title:
ax.set_title(args.title)
if args.ggplot2_style and not args.dimensions == 3:
gu.ggplot2_style(ax)
# save or display result
if args.out_fp:
fig.savefig(args.out_fp,
facecolor="white",
edgecolor="none",
bbox_inches="tight",
pad_inches=0.2)
else:
plt.show()
def test_gather_categories(self):
"""
Testing gather_category function from iTol.py. If successful, the
function will be moved to util.py.
:return: Returns OK if test goals were achieved, otherwise raises
error.
"""
DataCategory = namedtuple("DataCategory", "sids results")
result = ut.gather_categories(self.map_data, self.map_header)
result1 = ut.gather_categories(self.map_data, self.map_header,
["Treatment"]) # one category given
result2 = ut.gather_categories(
self.map_data, self.map_header,
["Smoking=Control"]) # incorrect condition given
result3 = ut.gather_categories(
self.map_data, self.map_header,
["Treatment=Fast"]) # correct condition given
result4 = ut.gather_categories(
self.map_data, self.map_header,
["Treatment", "Smoking"]) # 2 categories given
result5 = ut.gather_categories(
self.map_data, self.map_header,
["Treatment", "Smoking=Never_Smoker"]) # 1 category 1 condition
result6 = ut.gather_categories(
self.map_data, self.map_header,
["Treatment=Control", "Smoking=Current_Smoker"
]) # 1 category 1 condition
result7 = ut.gather_categories(
self.map_data, self.map_header,
["Smoking=Current_Smoker", "Smoking=Never_Smoker"
]) # 2 conditions given
result8 = ut.gather_categories(
self.map_data, self.map_header,
["Smoking=Never_Smoker", "Treatment", "Gender=Female"
]) # more than 2 categories - mix
result9 = ut.gather_categories(self.map_data,
self.map_header,
categories="Nationality:Peru")
# Testing if the function calculates without any categories.
manual = {
"default":
DataCategory(
{
"PC.354", "PC.355", "PC.356", "PC.481", "PC.593", "PC.607",
"PC.634", "PC.635", "PC.636"
}, {})
}
self.assertDictEqual(
result,
manual,
msg=
"With no category or condition given, gather_categories() did not return "
"all SampleIDs as expected.")
# Testing if the function accurately calculates for one category
manual1 = {
"Control":
DataCategory({"PC.355", "PC.356", "PC.354", "PC.481", "PC.593"},
{}),
"Fast":
DataCategory({"PC.634", "PC.635", "PC.636", "PC.607"}, {})
}
self.assertDictEqual(
result1,
manual1,
msg=
"With one category given, gather_categories() did not return per "
"category SampleIDs as expected.")
# Testing if the function accurately calculates for incorrect condition given
self.assertDictEqual(
result2,
manual,
msg=
"With incorrect condition given, gather_categories() did not return "
"all SampleIDs by default as expected.")
# Testing if the function accurately calculates for correct one condition given
manual3 = {
"Fast": DataCategory({"PC.634", "PC.635", "PC.636", "PC.607"}, {})
}
self.assertDictEqual(
result3,
manual3,
msg=
"With one correct condition given, gather_categories() did not return "
"SampleIDs for the condition given, as expected.")
# Testing if the function accurately calculates for correct one condition given
manual4 = {
"Control_Current_Smoker":
DataCategory({"PC.355", "PC.356"}, {}),
"Control_Never_Smoker":
DataCategory({"PC.354", "PC.481", "PC.593"}, {}),
"Fast_Current_Smoker":
DataCategory({"PC.634", "PC.635", "PC.636"}, {}),
"Fast_Never_Smoker":
DataCategory({"PC.607"}, {})
}
self.assertDictEqual(
result4,
manual4,
msg=
"With multiple categories given, gather_categories() did not return "
"SampleIDs for all category combinations, as expected.")
# Testing if the function accurately calculates for one category and condition
manual5 = {
"Control_Never_Smoker":
DataCategory({"PC.354", "PC.481", "PC.593"}, {}),
"Fast_Never_Smoker":
DataCategory({"PC.607"}, {})
}
self.assertDictEqual(
result5,
manual5,
msg=
"With one category and one condition given, gather_categories() did not "
"return SampleIDs for all category-condition combinations, as expected."
)
# Testing if the function accurately calculates for one category and condition
manual6 = {
"Control_Current_Smoker": DataCategory({"PC.355", "PC.356"}, {})
}
self.assertDictEqual(
result6,
manual6,
msg=
"With two specific conditions given, gather_categories() did not "
"return SampleIDs for all condition combinations, as expected.")
# Testing if the function accurately calculates for one category and condition
manual7 = {
"Current_Smoker":
DataCategory({"PC.355", "PC.356", "PC.634", "PC.635", "PC.636"},
{}),
"Never_Smoker":
DataCategory({"PC.354", "PC.481", "PC.593", "PC.607"}, {})
}
self.assertDictEqual(
result7,
manual7,
msg=
"With two conditions from same category given, gather_categories() did "
"not return SampleIDs for all condition combinations, as expected."
)
# Testing if function accurately categorizes SampleIDs for multiple categories
manual8 = {
"Control_Never_Smoker_Female": DataCategory({"PC.354", "PC.593"},
{})
}
self.assertDictEqual(
result8,
manual8,
msg=
"With two or more conditions/categories given, gather_categories() did "
"not return SampleIDs for all condition combinations, as expected."
)
# Testing invalid categories or conditions identified
manual9 = {
"default":
DataCategory(
{
"PC.354", "PC.355", "PC.356", "PC.481", "PC.593", "PC.607",
"PC.634", "PC.635", "PC.636"
}, {})
}
self.assertDictEqual(
result9,
manual9,
msg=
"With invalid category or condition given, gather_categories() did not "
"return all SampleIDs as expected.")
def main():
args = handle_program_options()
try:
with open(args.otu_table):
pass
except IOError as ioe:
sys.exit(
"\nError with OTU_Sample abundance data file:{}\n".format(ioe))
try:
with open(args.mapping):
pass
except IOError as ioe:
sys.exit("\nError with mapping file:{}\n".format(ioe))
# input data
biomf = biom.load_table(args.otu_table)
map_header, imap = util.parse_map_file(args.mapping)
# rewrite tree file with otu names, skip if keep_otuids specified
if args.input_tree and not args.keep_otuids:
with open(args.input_tree) as treF, open(args.output_tre, "w") as outF:
tree = treF.readline()
if "'" in tree:
tree = tree.replace("'", '')
outF.write(newick_replace_otuids(tree, biomf))
if not args.keep_otuids:
oid_rows = {
id_: md["taxonomy"]
for val, id_, md in biomf.iter(axis="observation")
}
# calculate analysis results
categories = None
if args.map_categories is not None and args.analysis_metric != "raw":
categories = args.map_categories.split(",")
# set transform if --stabilize_variance is specfied
tform = bc.arcsine_sqrt_transform if args.stabilize_variance else None
groups = util.gather_categories(imap, map_header, categories)
for group in groups.values():
if args.analysis_metric in ["MRA", "NMRA"]:
results = bc.MRA(biomf, group.sids, transform=tform)
elif args.analysis_metric == "raw":
results = bc.transform_raw_abundance(biomf,
sampleIDs=group.sids,
sample_abd=False)
if args.keep_otuids:
group.results.update({oid: results[oid] for oid in results})
else:
group.results.update(
{oc.otu_name(oid_rows[oid]): results[oid]
for oid in results})
# write iTol data set file
with open(args.output_itol_table, "w") as itolF:
if args.analysis_metric == "raw":
itolF.write("DATASET_GRADIENT\nSEPARATOR TAB\n")
itolF.write("DATASET_LABEL\tLog Total Abundance\n")
itolF.write("COLOR\t#000000\n")
itolF.write("LEGEND_TITLE\tLog Total Abundance\n")
itolF.write("LEGEND_SHAPES\t1\n")
itolF.write("LEGEND_COLORS\t#000000\n")
itolF.write("LEGEND_LABELS\tLog Total Abundance\n")
itolF.write("COLOR_MIN\t#FFFFFF\n")
itolF.write("COLOR_MAX\t#000000\n")
else:
itolF.write("DATASET_MULTIBAR\nSEPARATOR TAB\n")
itolF.write("DATASET_LABEL\t{}\n".format(args.analysis_metric))
itolF.write("FIELD_COLORS\t{}\n".format("\t".join(
["#ff0000" for _ in range(len(groups))])))
itolF.write("FIELD_LABELS\t" + "\t".join(groups.keys()) + "\n")
itolF.write("LEGEND_TITLE\t{}\n".format(args.analysis_metric))
itolF.write("LEGEND_SHAPES\t{}\n".format("\t".join(
["1" for _ in range(len(groups))])))
itolF.write("LEGEND_COLORS\t{}\n".format("\t".join(
["#ff0000" for _ in range(len(groups))])))
itolF.write("LEGEND_LABELS\t" + "\t".join(groups.keys()) + "\n")
itolF.write("WIDTH\t300\n")
itolF.write("DATA\n")
if args.keep_otuids:
all_otus = frozenset(
{id_
for id_ in biomf.ids(axis="observation")})
else:
all_otus = frozenset({
oc.otu_name(md["taxonomy"])
for val, id_, md in biomf.iter(axis="observation")
})
for oname in all_otus:
row = ["{name}"] # \t{s:.2f}\t{ns:.2f}\n"
row_data = {"name": oname}
msum = 0
for name, group in groups.iteritems():
row.append("{{{}:.5f}}".format(name))
if oname in group.results:
row_data[name] = group.results[oname]
else:
row_data[name] = 0.0
msum += row_data[name]
# normalize avg relative abundance data
if args.analysis_metric == "NMRA" and msum > 0:
row_data.update({
key: data / msum
for key, data in row_data.items() if key != "name"
})
itolF.write("\t".join(row).format(**row_data) + "\n")
def main():
args = handle_program_options()
try:
with open(args.coord_fp):
pass
except IOError as ioe:
err_msg = '\nError in input principal coordinates filepath (-i): {}\n'
sys.exit(err_msg.format(ioe))
try:
with open(args.map_fp):
pass
except IOError as ioe:
err_msg = '\nError in input metadata mapping filepath (-m): {}\n'
sys.exit(err_msg.format(ioe))
with open(args.coord_fp) as F:
pcd = F.readlines()
pcd = [line.split('\t') for line in pcd]
map_header, imap = util.parse_map_file(args.map_fp)
data_gather = util.gather_categories(imap, map_header,
args.colorby.split(','))
categories = OrderedDict([(condition, {'pc1': [], 'pc2': [], 'pc3': []})
for condition in data_gather.keys()])
bmap = qualitative.Paired[12]
bcolors = itertools.cycle(bmap.hex_colors)
if not args.colors:
colors = [bcolors.next() for _ in categories]
else:
colors = parse_colors(args.colors, categories)
parsed_unifrac = util.parse_unifrac(args.coord_fp)
pco = args.pc_order if args.dimensions == 2 else [1, 2, 3]
pc1v = parsed_unifrac['varexp'][pco[0]]
pc2v = parsed_unifrac['varexp'][pco[1]]
if args.dimensions == 3:
pc3v = parsed_unifrac['varexp'][pco[2]]
for sid, points in parsed_unifrac['pcd'].iteritems():
for condition, dc in data_gather.iteritems():
if sid in dc.sids:
cat = condition
break
categories[cat]['pc1'].append((sid, float(points[pco[0] - 1])))
categories[cat]['pc2'].append((sid, float(points[pco[1] - 1])))
if args.dimensions == 3:
categories[cat]['pc3'].append((sid, float(points[pco[2] - 1])))
axis_str = "PC{} - Percent variation explained {:.2f}%"
# initialize plot
fig = plt.figure(figsize=(14,8))
if args.dimensions == 3:
ax = fig.add_subplot(111, projection='3d')
ax.view_init(elev=23., azim=-134.5)
ax.set_zlabel(axis_str.format(pco[2], float(pc3v)))
if args.z_limits:
ax.set_zlim(args.z_limits)
else:
ax = fig.add_subplot(111)
# plot data
for i, cat in enumerate(categories):
if args.dimensions == 3:
ax.scatter(xs=[e[1] for e in categories[cat]['pc1']],
ys=[e[1] for e in categories[cat]['pc2']],
zs=[e[1] for e in categories[cat]['pc3']], zdir='z',
c=colors[i],
s=args.point_size)
else:
ax.scatter([e[1] for e in categories[cat]['pc1']],
[e[1] for e in categories[cat]['pc2']],
c=colors[i], s=args.point_size)
# Script to annotate PCoA points.
# for x, y in zip(categories[cat]['pc1'], categories[cat]['pc2']):
# ax.annotate(
# x[0], xy=(x[1], y[1]), xytext=(-10, -15),
# textcoords='offset points', ha='center', va='center',
# )
# customize plot options
if args.x_limits:
ax.set_xlim(args.x_limits)
if args.y_limits:
ax.set_ylim(args.y_limits)
ax.set_xlabel(axis_str.format(pco[0], float(pc1v)))
ax.set_ylabel(axis_str.format(pco[1], float(pc2v)))
ax.legend([Rectangle((0, 0), 1, 1, fc=colors[i])
for i in range(len(categories))], categories.keys(), loc='best')
if args.title:
title(args.title)
# save or display result
if args.out_fp:
fig.savefig(args.out_fp, facecolor='white',
edgecolor='none', dpi=args.dpi,
bbox_inches='tight', pad_inches=0.2)
else:
plt.show()
def main():
args = handle_program_options()
try:
with open(args.otu_table):
pass
except IOError as ioe:
sys.exit(
'\nError with OTU_Sample abundance data file:{}\n'
.format(ioe)
)
try:
with open(args.mapping):
pass
except IOError as ioe:
sys.exit(
'\nError with mapping file:{}\n'
.format(ioe)
)
# input data
with open(args.otu_table) as bF:
biom = json.loads(bF.readline())
map_header, imap = util.parse_map_file(args.mapping)
# rewrite tree file with otu names
if args.input_tree:
with open(args.input_tree) as treF, open(args.output_tre, 'w') as outF:
tree = treF.readline()
if "'" in tree:
tree = tree.replace("'", '')
outF.write(newick_replace_otuids(tree, biom))
oid_rows = {row['id']: row for row in biom['rows']}
# calculate analysis results
categories = None
if args.map_categories is not None:
categories = args.map_categories.split(',')
# set transform if --stabilize_variance is specfied
tform = bc.arcsine_sqrt_transform if args.stabilize_variance else None
groups = util.gather_categories(imap, map_header, categories)
for group in groups.values():
if args.analysis_metric in ['MRA', 'NMRA']:
results = bc.MRA(biom, group.sids, transform=tform)
elif args.analysis_metric == 'raw':
results = bc.transform_raw_abundance(biom, sampleIDs=group.sids,
sample_abd=False)
group.results.update({oc.otu_name_biom(oid_rows[oid]): results[oid]
for oid in results})
# write iTol data set file
with open(args.output_itol_table, 'w') as itolF:
itolF.write('LABELS\t' + '\t'.join(groups.keys())+'\n')
itolF.write('COLORS\t{}\n'.format('\t'.join(['#ff0000'
for _ in range(len(groups))])))
all_otus = frozenset({oc.otu_name_biom(row) for row in biom['rows']})
for oname in all_otus:
row = ['{name}'] # \t{s:.2f}\t{ns:.2f}\n'
row_data = {'name': oname}
msum = 0
for name, group in groups.iteritems():
row.append('{{{}:.5f}}'.format(name))
if oname in group.results:
row_data[name] = group.results[oname]
else:
row_data[name] = 0.0
msum += row_data[name]
# normalize avg relative abundance data
if args.analysis_metric == 'NMRA' and msum > 0:
row_data.update({key: data/msum
for key, data in row_data.items()
if key != 'name'})
itolF.write('\t'.join(row).format(**row_data) + '\n')
def main():
args = handle_program_options()
try:
with open(args.otu_table):
pass
except IOError as ioe:
sys.exit("\nError with BIOM format file:{}\n".format(ioe))
try:
with open(args.pcoa_fp):
pass
except IOError as ioe:
sys.exit("\nError with principal coordinates file:{}\n".format(ioe))
try:
with open(args.mapping):
pass
except IOError as ioe:
sys.exit("\nError with mapping file:{}\n".format(ioe))
# check that the output dir exists, create it if not
util.ensure_dir(args.output_dir)
# load the BIOM table
biomtbl = biom.load_table(args.otu_table)
# Read unifrac principal coordinates file
unifrac = util.parse_unifrac(args.pcoa_fp)
# Read otu data file
otus = set()
with open(args.otu_ids_fp, "rU") as nciF:
for line in nciF.readlines():
line = line.strip()
otus.add(line)
# Gather categories from mapping file
header, imap = util.parse_map_file(args.mapping)
try:
category_idx = header.index(args.group_by)
except ValueError:
msg = "Error: Specified mapping category '{}' not found."
sys.exit(msg.format(args.group_by))
category_ids = util.gather_categories(imap, header, [args.group_by])
color_map = util.color_mapping(imap, header, args.group_by, args.colors)
rel_abd = bc.relative_abundance(biomtbl)
rel_abd = bc.arcsine_sqrt_transform(rel_abd)
# plot samples based on relative abundance of some OTU ID
for otuid in otus:
otuname = oc.otu_name(
biomtbl.metadata(otuid, axis="observation")["taxonomy"])
cat_data = {
cat: {
"pc1": [],
"pc2": [],
"size": []
}
for cat in category_ids
}
for sid in unifrac["pcd"]:
category = cat_data[imap[sid][category_idx]]
try:
size = rel_abd[sid][otuid] * args.scale_by
except KeyError as ke:
print("{} not found in {} sample.".format(ke, sid))
continue
category["pc1"].append(float(unifrac["pcd"][sid][0]))
category["pc2"].append(float(unifrac["pcd"][sid][1]))
category["size"].append(size)
if args.verbose:
print("Saving chart for {}".format(" ".join(otuname.split("_"))))
xr, yr = calculate_xy_range(cat_data)
plot_PCoA(cat_data, otuname, unifrac, color_map.keys(), color_map, xr,
yr, args.output_dir, args.save_as, args.ggplot2_style)
def main():
args = program_options()
try:
biomf = biom.load_table(args.in_biomf)
except IOError as ioe:
sys.exit("Error with input BIOM format file: {}".format(ioe))
else:
rel_abd = relative_abundance(biomf)
ast_rel_abd = ast(rel_abd)
# Get pairwise combinations of OTUs
otu_combos = list(combinations(biomf.ids("observation"), 2))
try:
mheader, mdata = parse_map_file(args.map_fnh)
except IOError as ioe:
sys.exit("Error with input mapping file: {}".format(ioe))
else:
# Gather sampleID categories
sid_cat = gather_categories(mdata, mheader, [args.category_column])
# Create arguments for helper function to be supplied to multiprocessing pool.map()
chunksize = 10000
jobs = [(
otu_combos[x:x + chunksize],
sid_cat,
ast_rel_abd,
) for x in xrange(0, len(otu_combos), chunksize)]
print("{0} jobs created.".format(len(jobs)))
# Start multiprocessing jobs
try:
print("Starting map_async()...")
pool = Pool()
res = pool.map_async(calc_corr_helper, jobs)
pool.close()
pool.join()
except Exception:
sys.exit("Error while calculating correlations\n{}".format(
format_exc()))
else:
s_rho_calc = []
k_tau_calc = []
for r in res.get():
for s in r:
if s[0] == "Spearman":
s_rho_calc.append(s)
else:
k_tau_calc.append(s)
# Get FDR corrected correlation results
print("Running FDR correction on {} Spearman's Rho.".format(
len(s_rho_calc)))
fdr_corr_s_rho = run_fdr(s_rho_calc)
print("Running FDR correction on {} Kendall Tau.".format(len(k_tau_calc)))
fdr_corr_k_tau = run_fdr(k_tau_calc)
# Consolidate correlation results
k_kos = {(
e[2],
e[3],
)
for e in fdr_corr_k_tau}
s_kos = {(
f[2],
f[3],
)
for f in fdr_corr_s_rho}
final_kos = s_kos & k_kos
print(
"{0} elements from KendallTau\n{1} elements from SpearmanRho\n{2} elements are "
"common to both.".format(len(k_kos), len(s_kos), len(final_kos)))
final_fdr_corr_results = [
cdata[1:] for cdata in fdr_corr_s_rho if (
cdata[2],
cdata[3],
) in final_kos
]
# Write our results to file
with open(args.out_fnh, "w") as outf:
outf.write("Category\tVariable\tby Variable\tCorrelation\tp value\n")
for k in final_fdr_corr_results:
outf.write("{0}\t{1}\t{2}\t{3}\t{4}\n".format(
k[0], k[1], k[2], k[3], k[4]))
