scPhyloX.estimation.tumor
Classes
Functions
|
ODE of cell number changes over time |
|
Neutral cell number calculator |
|
Analytical solution of neutral cell number |
|
Neutral cell number calculator |
|
Probability density function of LR distance |
|
Likelihood of lr-dist |
|
Mutation rate estimation using DE |
|
Mutation rate estimation using DE-MCMC |
Module Contents
- cellnumber(t, xx, r, a, s, u)
ODE of cell number changes over time
- Args:
- t:
time
- xx:
[n_stemcell, n_nonstemcell]
- cellnumber_nc(t, x, r, a)
Neutral cell number calculator
- Args:
- t:
time
- x:
neutral cell number
- r, a:
parameters
- return:
- np.array:
neutral cell number at time t.
- nc_sol(t, c0, k, r, a)
Analytical solution of neutral cell number
- Args:
- t:
time
- c0:
initial neutral cell number
- k:
generation
- r, a:
parameters
- return:
- float:
neutral cell number in generation k at time t.
- cellnumber_ac(t, x, c0, r, a, s, u)
Neutral cell number calculator
- Args:
- t:
time
- x:
advantageous cell number
- c0:
initial ac number
- r, a, s, u:
parameters
- return:
- np.array:
advantageous cell number at time t.
- p_xi(gen, T, x0, r, a, s, u)
Probability density function of LR distance Args:
- gen:
generation
- T:
time
- x0:
initial cell number
- r, a, s, u:
parameters
- Return:
- np.array:
Probability density of LR distance at time T.
- my_loglike(theta, data, args)
Likelihood of lr-dist
- Args:
- theta:
parameters, (r, a, s, u)
- data:
Observed lr dist
- args:
paramteres, (time, initial_cell_number, prior_sigma)
- Return:
- float:
Sum of log-likelihood of given lr dist parameters
- para_inference_DE(data, T=20, c0=None, sigma=1, n_iter=100, bootstrape=0, verbose='text')
Mutation rate estimation using DE
- Args:
- data:
lp-dist
- n_iter:
Iterations of de estimation
- bootstrape:
Weather using bootstrape to accuratly estimate mutation rate, 0 to turn off.
- Return:
- tuple:
(accepted parameters, loss, de-estimator)
- class LogLike(loglike, data, args)
Bases:
pytensor.tensor.Op- itypes
- otypes
- likelihood
- data
- args
- perform(node, inputs, outputs)
- mcmc_inference(data, para_prior, T, c0, sigma, draw=1000, tune=1000, chains=5)
Mutation rate estimation using DE-MCMC
- Args:
- data:
Observed lp-dist
- data_prior:
mean of prior distributions of all parameters
- T:
time of phylodynamics eqns
- c0:
initial cell numbers
- sigma:
variation of loss function
- draw:
Number of smaples to draw
- tune:
Number of iterations to tune
- chain:
number of chains to sample