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style: format with ruff
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@Jhsmit
Jhsmit committed Jun 14, 2024
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Showing 1 changed file with 122 additions and 81 deletions.
203 changes: 122 additions & 81 deletions pyhdx/convert_data.py
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''' convert_data.py 12june2024 LMT
""" convert_data.py 12june2024 LMT
Function to convert data tables exported from HDExaminer to the processed DynamX format pyHDX expects
this will leave any extra columns, but will chop out the MAX time points after processing
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use_data = pepdata.copy()[(pepdata["state"]==mutant) & (pepdata["quality"]!="Low")]
hdxm[mutant] = HDXMeasurement(use_data, temperature=303.15, pH=8., sequence=sequence)
'''
"""


import pandas as pd
import numpy as np
import pandas as pd


def hdexa_to_pyhdx(data,d_percentage=0.85,protein='protein'):
drop_first=2

def _time_to_sec(tp,tpunit):
return tp * np.power(60.0,'smh'.find(tpunit[0]))
if '# Deut' in data.columns:
data = data.rename(columns={"# Deut":"#D"})
data['#D'] = data['#D'].fillna(0.0)
data['#D'] = data['#D'].astype(float)
if 'Deut %' in data.columns:
data = data.rename(columns={"Deut %":"%D"})
data['%D'] = data['%D'].fillna(0.0)
data['%D'] = data['%D'].astype(float)
if 'Deut Time' in data.columns:
data.loc[data['Deut Time'] == 'FD','Deut Time'] = '1e6s'
data['time unit'] = data['Deut Time'].str[-1]
data['Deut Time (sec)'] = data['Deut Time'].str[:-1].astype(float)
data['Deut Time (sec)'] = data.apply(lambda x: _time_to_sec(tp=x['Deut Time (sec)'],tpunit=x['time unit']),axis=1)
data.loc[data['Deut Time (sec)'] == 1e6,'Deut Time (sec)'] = 'MAX'
if 'Protein' not in data.columns:
data['Protein'] = protein


pyhdx_cols = ['start', 'end' ,'stop' ,'sequence', 'state', 'exposure' ,'uptake' ,'maxuptake',
'fd_uptake' ,'fd_uptake_sd' ,'nd_uptake' ,'nd_uptake_sd' ,'rfu', 'protein',
'modification', 'fragment', 'mhp' ,'center' ,'center_sd' ,'uptake_sd' ,'rt',
'rt_sd' ,'rfu_sd' ,'_sequence' ,'_start' ,'_stop' ,'ex_residues',
'uptake_corrected']
data = data.rename(columns={
"Protein State":"state",
"Protein":"protein",
"Start":"start",
"End":"end",
"Sequence":"_sequence",
"Peptide Mass":"mhp",
"RT (min)":"rt",
"Deut Time (sec)":"exposure",
"maxD":"maxuptake",
"Theor Uptake #D":"uptake_corrected",
"#D":"uptake",
"%D":"rfu",
"Conf Interval (#D)":"rfu_sd",
"#Rep":"rep",
"Confidence":"quality",
"Stddev":"center_sd",
#"p"
})

missing = list(set(pyhdx_cols)-set(data.columns))
def hdexa_to_pyhdx(data, d_percentage=0.85, protein="protein"):
drop_first = 2

def _time_to_sec(tp, tpunit):
return tp * np.power(60.0, "smh".find(tpunit[0]))

if "# Deut" in data.columns:
data = data.rename(columns={"# Deut": "#D"})
data["#D"] = data["#D"].fillna(0.0)
data["#D"] = data["#D"].astype(float)
if "Deut %" in data.columns:
data = data.rename(columns={"Deut %": "%D"})
data["%D"] = data["%D"].fillna(0.0)
data["%D"] = data["%D"].astype(float)
if "Deut Time" in data.columns:
data.loc[data["Deut Time"] == "FD", "Deut Time"] = "1e6s"
data["time unit"] = data["Deut Time"].str[-1]
data["Deut Time (sec)"] = data["Deut Time"].str[:-1].astype(float)
data["Deut Time (sec)"] = data.apply(
lambda x: _time_to_sec(tp=x["Deut Time (sec)"], tpunit=x["time unit"]), axis=1
)
data.loc[data["Deut Time (sec)"] == 1e6, "Deut Time (sec)"] = "MAX"
if "Protein" not in data.columns:
data["Protein"] = protein

pyhdx_cols = [
"start",
"end",
"stop",
"sequence",
"state",
"exposure",
"uptake",
"maxuptake",
"fd_uptake",
"fd_uptake_sd",
"nd_uptake",
"nd_uptake_sd",
"rfu",
"protein",
"modification",
"fragment",
"mhp",
"center",
"center_sd",
"uptake_sd",
"rt",
"rt_sd",
"rfu_sd",
"_sequence",
"_start",
"_stop",
"ex_residues",
"uptake_corrected",
]
data = data.rename(
columns={
"Protein State": "state",
"Protein": "protein",
"Start": "start",
"End": "end",
"Sequence": "_sequence",
"Peptide Mass": "mhp",
"RT (min)": "rt",
"Deut Time (sec)": "exposure",
"maxD": "maxuptake",
"Theor Uptake #D": "uptake_corrected",
"#D": "uptake",
"%D": "rfu",
"Conf Interval (#D)": "rfu_sd",
"#Rep": "rep",
"Confidence": "quality",
"Stddev": "center_sd",
# "p"
}
)

missing = list(set(pyhdx_cols) - set(data.columns))
for mcol in missing:
data[mcol] = np.nan
if mcol == "rfu_sd": data[mcol] = 0.05 #set 5% error as dummy value

data['rfu']=data['rfu']/100.
data.loc[data['exposure']=="0",'rfu_sd']=0.0
data['stop']=data['end']+1
data['sequence']=data["_sequence"].copy()
data['sequence']=[s.replace("P", "p") for s in data["sequence"]]
if mcol == "rfu_sd":
data[mcol] = 0.05 # set 5% error as dummy value

data["rfu"] = data["rfu"] / 100.0
data.loc[data["exposure"] == "0", "rfu_sd"] = 0.0
data["stop"] = data["end"] + 1
data["sequence"] = data["_sequence"].copy()
data["sequence"] = [s.replace("P", "p") for s in data["sequence"]]
# Find the total number of n terminal / c_terminal residues to remove from pyhdx/process.py
n_term = np.array([len(seq) - len(seq[drop_first:].lstrip("p")) for seq in data["sequence"]])
c_term = np.array([len(seq) - len(seq.rstrip("p")) for seq in data["sequence"]])
data["sequence"] = ["x" * nt + s[nt:] for nt, s in zip(n_term, data["sequence"])]
data["_start"] = data["start"] + n_term
data["_stop"] = data["stop"] - c_term
ex_residues = (np.array([len(s) - s.count("x") - s.count("p") for s in data["sequence"]])* d_percentage)
ex_residues = (
np.array([len(s) - s.count("x") - s.count("p") for s in data["sequence"]]) * d_percentage
)
data["ex_residues"] = ex_residues
data["uptake_sd"]=data["center_sd"]
data["nd_uptake"]=0.0
data["nd_uptake_sd"]=0.0
data["modification"]=float("nan")
data["fragment"]=float("nan")
data["uptake_sd"] = data["center_sd"]
data["nd_uptake"] = 0.0
data["nd_uptake_sd"] = 0.0
data["modification"] = float("nan")
data["fragment"] = float("nan")
# upeps = data[data["exposure"]=="0"]["_sequence"].unique()
# fpeps = data[data["exposure"]=="MAX"]["_sequence"].unique()
# good_peps = np.array(list(set(upeps) & set(fpeps)))
#peps = data["_sequence"].unique()
# peps = data["_sequence"].unique()
states = data["state"].unique()
data["fd_uptake"]="novalue"
data["fd_uptake_sd"]="novalue"
data["fd_uptake"] = "novalue"
data["fd_uptake_sd"] = "novalue"

for state in states:
peps = data[data["state"]==state]["_sequence"].unique()
peps = data[data["state"] == state]["_sequence"].unique()
for pep in peps:
fd_up = data[(data["_sequence"]==pep) & (data["exposure"]=="MAX")& (data["state"]==state)]['uptake'].iat[0]
fd_up_sd = data[(data["_sequence"]==pep) & (data["exposure"]=="MAX")& (data["state"]==state)]['center_sd'].iat[0]
data.loc[data["_sequence"]==pep, "fd_uptake"]=fd_up
data.loc[data["_sequence"]==pep, "fd_uptake_sd"]=fd_up_sd
data["center"]=data["mhp"]+data["uptake"]
data["rt_sd"]=0.05 #dummy value

data['uptake_corrected_orig'] = data['uptake_corrected'] #sometimes the HDExaminer output value is incorrect
data['uptake_corrected'] = data["rfu"]*data['maxuptake'] # so revert to conversion from the rfu
fd_up = data[
(data["_sequence"] == pep) & (data["exposure"] == "MAX") & (data["state"] == state)
]["uptake"].iat[0]
fd_up_sd = data[
(data["_sequence"] == pep) & (data["exposure"] == "MAX") & (data["state"] == state)
]["center_sd"].iat[0]
data.loc[data["_sequence"] == pep, "fd_uptake"] = fd_up
data.loc[data["_sequence"] == pep, "fd_uptake_sd"] = fd_up_sd
data["center"] = data["mhp"] + data["uptake"]
data["rt_sd"] = 0.05 # dummy value

data["uptake_corrected_orig"] = data[
"uptake_corrected"
] # sometimes the HDExaminer output value is incorrect
data["uptake_corrected"] = (
data["rfu"] * data["maxuptake"]
) # so revert to conversion from the rfu


data = data[data["exposure"] != "MAX"]
data = data[data["fd_uptake"] != 0]
data = data[~data["uptake"].isna()]
data["exposure"]=data["exposure"].astype(float)
data["exposure"] = data["exposure"].astype(float)

new_columns = [col for col in pyhdx_cols if col in data.columns] + [col for col in data.columns if col not in pyhdx_cols]
new_columns = [col for col in pyhdx_cols if col in data.columns] + [
col for col in data.columns if col not in pyhdx_cols
]
return data[new_columns]

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