Elena’s paper

Elena’s paper

Started to play with Elena’s paper: Link to the paper

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import pandas as pd
import plotnine as p9
import numpy as np
from scipy.integrate import odeint
from plotnine import ggplot, aes, geom_point, geom_line, facet_wrap
p9.theme_set(p9.theme_bw())

def dict_to_tuple(x):
    return tuple(x.values())

def get_p_and_y0(df, d = 1, i = 1):
    pp = df.iloc[i].to_dict()
    p = {key: pp[key] for key in ['Pz', 'Pyx', 'Pxy', 'TKI', 'Kz', 'Ky', 'Py', 'm', 'rz', 'a']}
    p['d'] = d
    y_0 = {key: pp[key] for key in ['X(0)', 'Y(0)', 'Z(0)']}
    return p, y_0


params_df = pd.read_csv('ParameterSets.csv')

p, y0 = get_p_and_y0(params_df, 1)
params_df

FileNotFoundError: [Errno 2] No such file or directory: 'ParameterSets.csv'

def cml_model(y, t, Pz, Pyx, Pxy, TKI, Kz, Ky, Py, m, rz, a, d):
    X, Y, Z = y
    dX = Pyx * Y - Pxy * X
    dY = Pxy * X - Pyx * Y + Py * (1 - Y / Ky) * Y - m * Z * Y - d * TKI * Y
    dZ = rz + Z * Pz * (Y / (Kz ** 2 + Y ** 2)) - a * Z
    return (dX, dY, dZ)

ii = tuple(range(0, len(params_df) - 1))
ii = tuple(range(0, 9))

t = np.arange(0.0, 100, 0.01)
result_list = []

for i in ii:
    p, y0 = get_p_and_y0(params_df, i = i)
    result = odeint(cml_model, 
                    dict_to_tuple(y0), 
                    t,
                    args=dict_to_tuple(p))
    df = pd.DataFrame(result, columns = ['X', 'Y', 'Z'])
    df = df.assign(t = t)
    df['PatID'] = i
    result_list.append(df)

result = pd.concat(result_list, ignore_index=True)
result['PatID'] = result['PatID'].astype('category')

result_long = pd.melt(result, 
                      id_vars = ['t', 'PatID'], 
                      value_vars = ['X', 'Y', 'Z'],
                      value_name = 'cell_count')
(
    ggplot(result_long, aes('t', 'cell_count', color = 'variable')) + 
    geom_line() + 
    facet_wrap('PatID')+ p9.scale_y_continuous(trans='log10') +
    p9.theme(figure_size=(12,9)) +
    p9.labs(y='Cell count', color = 'Compartment')
)

(
    ggplot(result, aes('t', 'Y', color = 'PatID')) + 
    geom_line() + p9.scale_y_continuous(trans='log10')
)

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def LRATIO(Y, KY=1e6):
    return Y / (Y + 2 * (KY - Y))

result['LRATIO'] = LRATIO(result["Y"])

(
    ggplot(result, aes('t', 'LRATIO', color='PatID')) + 
    geom_line() + p9.scale_y_continuous(trans='log10', 
                                        breaks=(1, 1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6), 
                                        labels=('MR0 (100%)', 'MR1 (10%)', 'MR2 (1%)', 'MR3 (0.1%)', 'MR4 (0.01%)', 'MR5 (0.001%)', 'MR6 (0.0001)')) +
    p9.theme(figure_size=(10, 7))
)