def test_validate_fails_missing():
df = pd.DataFrame([{
"filepath":
Path("tests/test_files/full_scan_nat_rep1.mzml"),
}])
inp = InputSpec(df)
assert inp.validate() == False
def test_validate_fails_missing():
df = pd.DataFrame([{
"filepath": Path("missing"),
"organism": "org1",
"type": "f",
"element": "c",
"isotope": 13,
"condition": "cond1",
"replicate": 1,
}])
inp = InputSpec(df)
assert inp.validate() == False
def generate_featurelist(
input_spec: InputSpec,
source_dir: Path,
exp_name: str,
config: Dict,
n_jobs: int = -1,
blank_remove: bool = True,
):
"""
Create an mz ground truth list of all features detected in experiment and basketed
to align between conditions and replicates. Optional blank subtraction.
"""
print("Collecting feature list files")
# define here so input_spec and config in scope and not needed as params
def do_munge_featurelist(cond: str):
out_dir = source_dir.joinpath(cond)
out_dir.mkdir(parents=True, exist_ok=True)
print(f"Working on {cond}")
all_collapsed = utils.munge_featurelist(inp_spec=input_spec,
cond=cond,
out_dir=out_dir,
config=config)
return all_collapsed
# peaks in blanks to subtract later
# This should not break in the event that there are no blanks
# but the printing may be misleading
if input_spec.get_feature_filepaths("blank"):
blanks = do_munge_featurelist("blank")
else:
print("No blanks found")
blank_remove = False
# Run pre-processing on conditions in separate processes
conditions = input_spec.get_conditions()
cond_dfs = joblib.Parallel(n_jobs=n_jobs)(
joblib.delayed(do_munge_featurelist)(c) for c in conditions)
all_cond_df = utils.combine_dfs(cond_dfs)
if blank_remove:
print("Substracting blanks")
utils.blank_subtract(blanks, all_cond_df, config=config)
else:
print("Not subtracting blanks")
print("Grouping all features")
all_cond_df.reset_index(inplace=True, drop=True)
dereplicator.group_features(
all_cond_df, exp_name, "exp_id",
config=config) # final grouping - exp_id used for scan munging
all_cond_df.to_csv(source_dir.joinpath(f"{exp_name}_all_features.csv"),
index=False)
return all_cond_df
def run_scrape(
name: str,
input_specification: Path,
jobs: int,
minscans: int,
scanwindow: int,
mztol: float,
minintensity: int,
minrt: float,
configfile: Optional[Path] = None,
):
"""Collects relevant scan data for all members of the ground truth feature list"""
click.echo(f"Running scrape for {name} using {input_specification}")
spec = InputSpec.from_csv(input_specification)
config = get_config(configfile, mztol=mztol)
source_dir = Path(name.replace(" ", "_"))
core.isotope_scraper(
input_spec=spec,
source_dir=source_dir,
exp_name=name,
n_jobs=jobs,
min_scans=minscans,
scanwindow=scanwindow,
min_intensity=minintensity,
min_rt=minrt,
config=config,
)
def validate_input(input_spec: InputSpec):
"""Make sure required input directories exist
Args:
input_spec (InputSpec): input file specification
"""
assert input_spec.validate()
def isotope_label_detector(
input_spec: InputSpec,
source_dir: Path,
exp_name: str,
min_scans: int = 5,
num_cond: int = 1,
n_jobs: int = -1,
):
scan_dir = source_dir.joinpath("all_scan_data")
slope_dir = source_dir.joinpath("all_slope_data")
slope_dir.mkdir(parents=True, exist_ok=True)
out_dir = source_dir.joinpath("all_isotope_analysis")
out_dir.mkdir(parents=True, exist_ok=True)
features = utils.get_featurelist(source_dir=source_dir, exp_name=exp_name)
conditions = input_spec.get_conditions()
def run_label_detector(cond):
out_file = slope_dir.joinpath(f"all_slope_data_{cond}.csv")
print(f"Detecting labels in {cond}")
#load both unlabeled and labeled scan data for cond
scan_files = list(scan_dir.glob(f"all_ions_*{cond}.csv"))
scan_dfs = []
for s in scan_files:
assert s.exists()
s_df = pd.read_csv(s)
scan_dfs.append(s_df)
df = utils.combine_dfs(scan_dfs)
#fil = scan_dir.joinpath(f"all_ions_{cond}.csv")
#assert fil.exists()
#df = pd.read_csv(fil)
grouped = df.groupby(["exp_id", "isotope", "condition"])
data = []
for (e_id, iso, c), g in grouped:
res = utils.calc_rep_stats(g, e_id, iso, c, min_scans=min_scans)
# print(data)
if len(res) < 1:
continue
data.extend(res)
if data:
agg_df = pd.DataFrame(data)
res_df = utils.aggregate_results(agg_df)
res_df.to_csv(out_file, index=False)
utils.run_label_analysis(res_df, cond, out_dir)
else:
print(f"No labels to detect for {cond}")
# Run processing of each condition in separate process
joblib.Parallel(n_jobs=n_jobs)(joblib.delayed(run_label_detector)(c)
for c in conditions)
# [run_label_detector(c) for c in conditions]
sum_df = utils.summarize_labels(out_dir, features, conditions)
sum_df.to_csv(source_dir.joinpath(f"{exp_name}_data_summary.csv"))
filtered_df = utils.filter_summary(sum_df, num_cond)
filtered_df.to_csv(
source_dir.joinpath(f"{exp_name}_data_summary_filtered.csv"))
def test_get_filepath_info():
inp = InputSpec.from_csv(TESTFILE)
data = inp.get_filepath_info(
Path("tests/test_files/full_scan_labelled_rep3.mzml"))
assert data["organism"] == "ORG1"
assert data["type"] == "s"
assert data["isotope"] == 13
assert data["element"] == "C"
assert data["condition"] == "COND1"
def test_get_feature_filepaths_blanks():
inp = InputSpec.from_csv(TESTFILE)
expected = list(
map(
Path,
[
"tests/test_files/blanks_rep1.csv",
"tests/test_files/blanks_rep2.csv"
],
))
assert expected == inp.get_feature_filepaths("blank")
def test_get_scan_filepaths():
inp = InputSpec.from_csv(TESTFILE)
expected = list(
map(
Path,
[
"tests/test_files/full_scan_nat_rep1.mzml",
"tests/test_files/full_scan_nat_rep2.mzml",
"tests/test_files/full_scan_nat_rep3.mzml",
],
))
assert expected == inp.get_scan_filepaths("12cond1")
def test_get_feature_filepaths_condition():
inp = InputSpec.from_csv(TESTFILE)
expected = list(
map(
Path,
[
"tests/test_files/feature_list_rep1.csv",
"tests/test_files/feature_list_rep2.csv",
"tests/test_files/feature_list_rep3.csv",
],
))
assert expected == inp.get_feature_filepaths("cond1")
def test_validate_fails_duplicated():
df = pd.DataFrame([
{
"filepath": Path("tests/test_files/full_scan_nat_rep1.mzml"),
"organism": "org1",
"type": "f",
"element": "c",
"isotope": 13,
"condition": "cond1",
"replicate": 1,
},
{
"filepath": Path("tests/test_files/full_scan_nat_rep1.mzml"),
"organism": "org1",
"type": "f",
"element": "c",
"isotope": 13,
"condition": "cond1",
"replicate": 2,
},
])
inp = InputSpec(df)
assert inp.validate() == False
def run_validate(
name: str, input_specification: Path, configfile: Optional[Path] = None
):
"""
Performs some simple checks on your input specification file
"""
click.echo(f"Running validation for {name} on {input_specification}")
spec = InputSpec.from_csv(input_specification)
try:
core.validate_input(spec)
click.echo(click.style("Validation successful! ✅", fg="green"))
except AssertionError as e:
click.echo(click.style("Validation failed... ❌", fg="red"), err=True)
sys.exit(1)
def run_analyze(
name: str,
input_specification: Path,
jobs: int,
minscans: int,
minconditions: int,
configfile: Optional[Path] = None,
):
"""Performs Stable Isotope Labelling detecting and analysis"""
click.echo(f"Running analysis for {name} using {input_specification}")
spec = InputSpec.from_csv(input_specification)
source_dir = Path(name.replace(" ", "_"))
core.isotope_label_detector(
input_spec=spec,
source_dir=source_dir,
exp_name=name,
min_scans=minscans,
num_cond=minconditions,
n_jobs=jobs,
)
def run_prep(
name: str,
input_specification: Path,
jobs: int,
blank_remove: bool,
minreps: int,
mztol: int,
rttol: int,
configfile: Optional[Path] = None,
):
"""Prepares the ground truth feature list"""
click.echo(f"Running prep for {name} using {input_specification}")
spec = InputSpec.from_csv(input_specification)
config = get_config(configfile, minreps=minreps, mztol=mztol, rttol=rttol)
source_dir = Path(name.replace(" ", "_"))
core.generate_featurelist(
input_spec=spec,
source_dir=source_dir,
exp_name=name,
n_jobs=jobs,
config=config,
blank_remove=blank_remove,
)
def test_get_conditions():
inp = InputSpec.from_csv(TESTFILE)
expected = ["COND1"]
assert expected == inp.get_conditions()
def isotope_scraper(
input_spec: InputSpec,
source_dir: Path,
exp_name: str,
config: Dict,
min_scans: int,
scanwindow: int,
min_intensity: int,
min_rt: float,
n_jobs: int,
):
"""Scrape all the isotope data for all ions in the all scan data"""
conditions = input_spec.get_isoconditions()
print("Running isotope scraper")
features = utils.get_featurelist(source_dir=source_dir, exp_name=exp_name)
# dir for all output files containing scan data for each cond
out_dir = source_dir.joinpath("all_scan_data")
out_dir.mkdir(parents=True, exist_ok=True)
def run_isoslicer(cond, features=features):
outfile = out_dir.joinpath(f"all_ions_{cond}.csv")
# add replicate scan files for current condition
c_scans = input_spec.get_scan_filepaths(cond)
scanfile = out_dir.joinpath(f"all_scans_{cond}.csv")
if scanfile.exists():
print(f"Loading all scan file - {scanfile}")
scan_df = pd.read_csv(scanfile)
else:
c_dfs = (utils.prep_scan(
Path(f),
input_spec,
min_intensity=min_intensity,
min_rt=min_rt,
) for f in c_scans)
# merged data frame of all scans for the given condition
print(f"Combining files for {cond}")
scan_df = utils.combine_dfs(c_dfs)
# Save checkpoint DF
print(f"Saving all scan file - {scanfile}")
scan_df.to_csv(scanfile, index=False)
# If no scan ranges in feature_df, we need to add them
if not "lowscan" in features.columns:
# Make a copy for parallel safety
print(f"Assigning scan range window to feature list for {cond}")
features = utils.add_scan_window(features,
scan_df,
scanwindow=scanwindow)
len_uni = len(features.exp_id.unique())
exps = features.groupby("exp_id")
print(f"There are {len_uni} ions to look at for {cond}")
# Parallelized solution
def do_slice(i, idx, g):
# nonlocal seen
if i % 20 == 0 and i > 0:
print(f"Working on {i}/{len_uni} for {cond}")
mz, low_scan, high_scan = utils.calc_exp(g)
# function 'iso_slicer' slices relevant isotope data for a given mz and all its isotopomers
# within given scan range in given scan file
return (
idx,
utils.isotope_slicer(
scan_df,
mz,
low_scan,
high_scan,
min_scans=min_scans,
mz_tol=get_mz_tol(config),
),
)
to_mark = joblib.Parallel(n_jobs=-1, prefer="threads")(
joblib.delayed(do_slice)(i, idx, g)
for i, (idx, g) in enumerate(exps))
print(f"Finished collecting ions for {cond}")
res_df = utils.mark_scans(scan_df, to_mark)
# write iso_scan_data to a file for that condition
res_df.to_csv(outfile)
# Run processing of each condition in separate process
joblib.Parallel(n_jobs=n_jobs)(joblib.delayed(run_isoslicer)(c)
for c in conditions)
def test_get_filepath_info_missing():
inp = InputSpec.from_csv(TESTFILE)
data = inp.get_filepath_info("MISSING")
assert len(data) == 0
def test_validate_success():
inp = InputSpec.from_csv(TESTFILE)
print(inp.df.head())
assert inp.validate()
def test_csv_factory():
inp = InputSpec.from_csv(TESTFILE)
assert isinstance(inp, InputSpec)
assert isinstance(inp.df, pd.DataFrame)
