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preprocessing.py
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preprocessing.py
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# Copyright by Paul Rudolph
# Research Group Applied Systems Biology - Head: Prof. Dr. Marc Thilo Figge
# https://www.leibniz-hki.de/en/applied-systems-biology.html
# HKI-Center for Systems Biology of Infection
# Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Insitute (HKI)
# Adolf-Reichwein-Straße 23, 07745 Jena, Germany
# This code is licensed under BSD 2-Clause
# See the LICENSE file provided with this code for the full license.
import pandas as pd
import numpy as np
def read_data(file_name):
'''This functions converts the data into panda dataframes'''
df_ex5 = (pd.read_excel(file_name,sheet_name=2,header=None)
.iloc[-3:,:]
.rename(columns = dict(zip(np.arange(5),np.array([1,3,24,48,72]))))
.melt(var_name="Time",value_name="LDH")
.assign(LDH = lambda df_:df_.LDH)
)
df_ex1 =(pd.read_excel(file_name,sheet_name=0,header=None)
.pipe(lambda df_:pd.concat((df_.iloc[:,:6].T, df_.iloc[:,6:].T)).T)
.dropna(axis=1,how='all')
.dropna(axis=0,how='all')
.iloc[3:,:]
.reset_index(drop=True)
.pipe(lambda df_:pd.concat((
pd.concat((df_.iloc[2:5,:3].set_axis(["Ab","CaL","Ab+CaL"],axis=1).assign(Interval= 24),
df_.iloc[2:5,3:6].set_axis(["Ab","CaL","Ab+CaL"],axis=1).assign(Interval= 48),
df_.iloc[2:5,6:9].set_axis(["Ab","CaL","Ab+CaL"],axis=1).assign(Interval= 72))).assign(QVQ= 0),
pd.concat((df_.iloc[6:9,:3].set_axis(["Ab","CaL","Ab+CaL"],axis=1).assign(Interval= 24),
df_.iloc[6:9,3:6].set_axis(["Ab","CaL","Ab+CaL"],axis=1).assign(Interval= 48),
df_.iloc[6:9,6:9].set_axis(["Ab","CaL","Ab+CaL"],axis=1).assign(Interval= 72))).assign(QVQ= 1),
pd.concat((df_.iloc[10:13,:3].set_axis(["Ab","CaL","Ab+CaL"],axis=1).assign(Interval= 24),
df_.iloc[10:13,3:6].set_axis(["Ab","CaL","Ab+CaL"],axis=1).assign(Interval= 48),
df_.iloc[10:13,6:9].set_axis(["Ab","CaL","Ab+CaL"],axis=1).assign(Interval= 72))).assign(QVQ= 2),
pd.concat((df_.iloc[14:17,:3].set_axis(["Ab","CaL","Ab+CaL"],axis=1).assign(Interval= 24),
df_.iloc[14:17,3:6].set_axis(["Ab","CaL","Ab+CaL"],axis=1).assign(Interval= 48),
df_.iloc[14:17,6:9].set_axis(["Ab","CaL","Ab+CaL"],axis=1).assign(Interval= 72))).assign(QVQ= 4)))
)
.melt(id_vars=["Interval","QVQ"],var_name="Condition",value_name="LDH")
.dropna()
.rename(columns={"QVQ":"Ab"})
.assign(CaL = lambda df_: pd.Series(66.67, index =df_.index).where(df_.Condition != "Ab",0.0),
Nb = lambda df_:df_.Ab.where(df_.Condition != "CaL",0.0),
Time = 24,
Condition = lambda df_:df_.Interval,
LDH = lambda df_:df_.LDH)
.drop(columns=["Interval"])
)
df_ex3 =(pd.read_excel(file_name,sheet_name=1,header=None)
.pipe(lambda df_:
pd.concat((
df_.iloc[4:7,:].assign(Time = 1),
df_.iloc[8:11,:].assign(Time = 3),
df_.iloc[12:15,:].assign(Time = 24),
df_.iloc[16:19,:].assign(Time = 48),
df_.iloc[20:23,:].assign(Time = 72),))
)
.rename(columns = dict(zip(np.arange(5),["Candida",1,10,35,70])))
.melt(id_vars=["Time"],var_name="CaL",value_name="LDH")
.query("CaL != 'Candida'")
.assign(CaL = lambda df_:df_.CaL,
Nb = 0.0,
LDH = lambda df_:df_.LDH)
)
df_ex3_candida =(pd.read_excel(file_name,sheet_name=1,header=None)
.pipe(lambda df_:
pd.concat((
df_.iloc[4:7,:].assign(Time = 1),
df_.iloc[8:11,:].assign(Time = 3),
df_.iloc[12:15,:].assign(Time = 24),
df_.iloc[16:19,:].assign(Time = 48),
df_.iloc[20:23,:].assign(Time = 72),))
)
.rename(columns = dict(zip(np.arange(5),["Candida",1,10,35,70])))
.melt(id_vars=["Time"],var_name="CaL",value_name="LDH")
.query("CaL == 'Candida'")
.assign(Nb = 0.0,
LDH = lambda df_:df_.LDH)
.drop(columns= ["CaL"])
)
df_fig_cal = (pd.read_excel(file_name,sheet_name=4,header=None)
.pipe(lambda df_:pd.concat((
df_.iloc[6:10,:].set_axis(["Ab","Ab+CaL","CaL"],axis=1).assign(QVQ= 4,Condition= "CaL_Pre"),
df_.iloc[11:15,:].set_axis(["Ab","Ab+CaL","CaL"],axis=1).assign(QVQ= 8,Condition= "CaL_Pre"),
df_.iloc[16:20,:].set_axis(["Ab","Ab+CaL","CaL"],axis=1).assign(QVQ= 16,Condition= "CaL_Pre"),
df_.iloc[25:29,:].set_axis(["Ab","Ab+CaL","CaL"],axis=1).assign(QVQ= 4,Condition= "CaL_Sim"),
df_.iloc[30:34,:].set_axis(["Ab","Ab+CaL","CaL"],axis=1).assign(QVQ= 8,Condition= "CaL_Sim"),
df_.iloc[35:39,:].set_axis(["Ab","Ab+CaL","CaL"],axis=1).assign(QVQ= 16,Condition= "CaL_Sim"),
df_.iloc[43:47,:].set_axis(["Ab","Ab+CaL","CaL"],axis=1).assign(QVQ= 4,Condition= "CaL_Post"),
df_.iloc[48:52,:].set_axis(["Ab","Ab+CaL","CaL"],axis=1).assign(QVQ= 8,Condition= "CaL_Post"),
df_.iloc[53:57,:].set_axis(["Ab","Ab+CaL","CaL"],axis=1).assign(QVQ= 16,Condition= "CaL_Post"),))
)
.melt(id_vars=["QVQ","Condition"],var_name="Condition2",value_name="LDH")
.dropna()
.rename(columns={"QVQ":"Ab"})
.assign(CaL = lambda df_:pd.Series(70, index =df_.index).where(df_.Condition2 != "Ab",0.0),
Nb = lambda df_:df_.Ab.where(df_.Condition2 != "CaL",0.0),
LDH = lambda df_:df_.LDH,
Time = 24)
.drop(columns = ["Condition2"])
)
df_fig_candida = (pd.read_excel(file_name,sheet_name=3,header=None)
.pipe(lambda df_:pd.concat((
df_.iloc[6:10,:].set_axis(["Ab","Ab+Ca","Ca"],axis=1).assign(QVQ= 4,Condition= "Ca_Pre"),
df_.iloc[11:15,:].set_axis(["Ab","Ab+Ca","Ca"],axis=1).assign(QVQ= 8,Condition= "Ca_Pre"),
df_.iloc[16:20,:].set_axis(["Ab","Ab+Ca","Ca"],axis=1).assign(QVQ= 16,Condition= "Ca_Pre"),
df_.iloc[25:29,:].set_axis(["Ab","Ab+Ca","Ca"],axis=1).assign(QVQ= 4,Condition= "Ca_Sim"),
df_.iloc[30:34,:].set_axis(["Ab","Ab+Ca","Ca"],axis=1).assign(QVQ= 8,Condition= "Ca_Sim"),
df_.iloc[35:39,:].set_axis(["Ab","Ab+Ca","Ca"],axis=1).assign(QVQ= 16,Condition= "Ca_Sim"),
df_.iloc[43:47,:].set_axis(["Ab","Ab+Ca","Ca"],axis=1).assign(QVQ= 4,Condition= "Ca_Post"),
df_.iloc[48:52,:].set_axis(["Ab","Ab+Ca","Ca"],axis=1).assign(QVQ= 8,Condition= "Ca_Post"),
df_.iloc[53:57,:].set_axis(["Ab","Ab+Ca","Ca"],axis=1).assign(QVQ= 16,Condition= "Ca_Post"),))
)
.melt(id_vars=["QVQ","Condition"],var_name="Condition2",value_name="LDH")
.dropna()
.rename(columns={"QVQ":"Ab"})
.assign(CaL = lambda df_:pd.Series(70, index =df_.index).where(df_.Condition2 != "Ab",0.0),
Nb = lambda df_:df_.Ab.where(df_.Condition2 != "Ca",0.0),
LDH = lambda df_:df_.LDH,
Time = 24)
.drop(columns = ["Condition2"])
)
return df_ex5,df_ex3,df_ex3_candida,df_ex1,df_fig_cal,df_fig_candida
def preprocess_data(file_name,time_scaling, adjust_CaL=8.0, adjust_LDH=True, system_size = 1.0):
'''Data is preprocessed into the right units and scaled to account for different LDH concentrations due to technical replicates'''
df_ex5,df_ex3,df_ex3_candida,df_ex1,df_fig_cal,df_fig_candida = read_data(file_name)
LDH_scale = 1.0
# Reference scale is taken from the simultaneous experiments from 70 CaL
# If we assume an aggregate size we have to divide by the it in order to get a linearization for the tranistion. This division only applies to the intial CaL concentration
if adjust_LDH:
cal_ref=70
LDH_scale = 1/(df_ex3.query(f"Nb ==0.0 and Time == 24 and CaL=={cal_ref}").LDH.mean()/df_fig_cal.query("Nb ==0.0 and Condition == 'CaL_Sim'").LDH.mean())
# LDH : µg/ml -> 1e-3*g/ml (LDH_scale)
# CaL : µM = µmol/l -> 1e-3µmol/ml * system_size/adjust_CaL
# Nb : µM = µmol/l -> 1e-3µmol/ml * system_size
df_data_LDH = (df_ex5
.assign(Time = lambda df_:df_.Time*time_scaling,
Readout = lambda df_:df_.LDH*1e-3)
.drop(columns=["LDH"])
)
df_data_sim = (
pd.concat(((df_fig_cal
.assign(Time = lambda df_: df_.Time * time_scaling,
Nb = lambda df_: df_.Nb*system_size*1e-3,
CaL = lambda df_: df_.CaL*system_size/adjust_CaL*1e-3,
Readout = lambda df_:df_.LDH.where(df_.CaL != 0,0.0)*1e-3*1/LDH_scale)
.query("Condition == 'CaL_Sim'")
[["CaL","Nb","Time","Readout"]]),
(df_ex3
.assign(Nb = lambda df_: df_.Nb*system_size*1e-3,
CaL = lambda df_: df_.CaL*system_size/adjust_CaL*1e-3,
Time = lambda df_:df_.Time*time_scaling,
Readout = lambda df_:df_.LDH.where(df_.CaL != 0,0.0)*1e-3)
[["CaL","Nb","Time","Readout"]]
)
))
.sort_values("Time").reset_index(drop=True)
)
df_data_pre = (df_ex1.assign(Time = lambda df_:df_.Time*time_scaling,
Condition = lambda df_: df_.Condition*time_scaling)
.groupby("Condition",group_keys=False).apply(lambda grp_:grp_.assign(Readout = lambda df_:df_.LDH.where(df_.CaL != 0,0.0)*66.66/70*1e-3*(df_ex3.query(f"Nb ==0.0 and Time == 24 and CaL=={cal_ref}").LDH.mean()/grp_.query("CaL == 66.67 and Nb == 0").LDH.mean()),
Nb = lambda df_: df_.Nb*system_size*1e-3,
CaL = lambda df_: df_.CaL*system_size/adjust_CaL*1e-3)).reset_index()
[["Nb","Condition","CaL","Time","Readout"]]
.sort_values("Time").reset_index(drop=True)
)
df_data_pre_co = (df_fig_cal
.query("Condition == 'CaL_Pre'")
.assign(Time = lambda df_: df_.Time * time_scaling,
Nb = lambda df_: df_.Nb*system_size*1e-3,
CaL = lambda df_: df_.CaL*system_size/adjust_CaL*1e-3,
Readout = lambda df_:df_.LDH.where(df_.CaL != 0,0.0)*1e-3*1/LDH_scale,
Condition = 1*time_scaling)
[["Nb","Condition","CaL","Time","Readout"]]
.sort_values("Time").reset_index(drop=True))
df_data_post = (df_fig_cal
.query("Condition == 'CaL_Post'")
.assign(Time=21*time_scaling,
Nb = lambda df_: df_.Nb*system_size*1e-3,
CaL = lambda df_: df_.CaL*system_size/adjust_CaL*1e-3,
Readout = lambda df_:df_.LDH.where(df_.CaL != 0,0.0)*1e-3*1/LDH_scale,
Condition = 3*time_scaling)
[["Nb","Condition","CaL","Time","Readout"]]
.sort_values("Time").reset_index(drop=True)
)
LDH_scale_candida = 1#/(df_ex3_candida.query(f"Nb ==0.0 and Time == 24").LDH.mean()/df_fig_candida.query("Nb ==0.0 and Condition == 'Ca_Sim'").LDH.mean())
df_data_candida_sim = (
pd.concat(((df_fig_candida
.assign(Time = lambda df_: df_.Time * time_scaling,
Nb = lambda df_: df_.Nb*system_size*1e-3,
Readout = lambda df_:df_.LDH.where(df_.CaL != 0,0.0)*1e-3*1/LDH_scale_candida)
.query("Condition == 'Ca_Sim'")
[["Nb","Time","Readout"]]),
(df_ex3_candida
.assign(Nb = lambda df_: df_.Nb*system_size*1e-3,
Time = lambda df_:df_.Time*time_scaling,
Readout = lambda df_:df_.LDH*1e-3)
[["Nb","Time","Readout"]]
)
))
.sort_values("Time").reset_index(drop=True)
)
df_data_candida_pre = (df_fig_candida
.query("Condition == 'Ca_Pre'")
.assign(Time = lambda df_: df_.Time * time_scaling,
Nb = lambda df_: df_.Nb*system_size*1e-3,
Readout = lambda df_:df_.LDH.where(df_.CaL != 0,0.0)*1e-3*1/LDH_scale_candida,
Condition = 1*time_scaling)
[["Nb","Condition","Time","Readout"]]
.sort_values("Time").reset_index(drop=True))
df_data_candida_post = (df_fig_candida
.query("Condition == 'Ca_Post'")
.assign(Time=lambda df_:(df_.Time-3)*time_scaling,
Nb = lambda df_: df_.Nb*system_size*1e-3,
Readout = lambda df_:df_.LDH.where(df_.CaL != 0,0.0)*1e-3*1/LDH_scale_candida,
Condition = 3*time_scaling)
[["Nb","Condition","Time","Readout"]]
.sort_values("Time").reset_index(drop=True)
)
return df_data_LDH,df_data_sim,df_data_pre,df_data_pre_co,df_data_post,df_data_candida_sim,df_data_candida_pre,df_data_candida_post