scPhyloX.estimation
===================

.. py:module:: scPhyloX.estimation


Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/scPhyloX/estimation/haematopoiesis/index
   /autoapi/scPhyloX/estimation/mutation_rate/index
   /autoapi/scPhyloX/estimation/tissue/index
   /autoapi/scPhyloX/estimation/tumor/index


Classes
-------

.. autoapisummary::

   scPhyloX.estimation.LogLike
   scPhyloX.estimation.LogLike
   scPhyloX.estimation.LogLike


Functions
---------

.. autoapisummary::

   scPhyloX.estimation.cellnumber
   scPhyloX.estimation.cellnumber_nc
   scPhyloX.estimation.nc_sol
   scPhyloX.estimation.cellnumber_ac
   scPhyloX.estimation.p_xi
   scPhyloX.estimation.my_loglike
   scPhyloX.estimation.para_inference_DE
   scPhyloX.estimation.mcmc_inference
   scPhyloX.estimation.bt
   scPhyloX.estimation.cellnumber
   scPhyloX.estimation.ncyc
   scPhyloX.estimation.nnc
   scPhyloX.estimation.p_xi
   scPhyloX.estimation.my_loglike
   scPhyloX.estimation.para_inference_DE
   scPhyloX.estimation.mcmc_inference
   scPhyloX.estimation.cellnumber
   scPhyloX.estimation.stem_num
   scPhyloX.estimation.nstem_num
   scPhyloX.estimation.p_xi
   scPhyloX.estimation.my_loglike
   scPhyloX.estimation.para_inference_DE
   scPhyloX.estimation.mcmc_inference


Package Contents
----------------

.. py:function:: cellnumber(t, xx, r, a, s, u)

   ODE of cell number changes over time

   Args:
       t: 
           time
       xx: 
           [n_stemcell, n_nonstemcell]


.. py:function:: 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.


.. py:function:: 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.


.. py:function:: 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.


.. py:function:: 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.


.. py:function:: 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


.. py:function:: 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)


.. py:class:: LogLike(loglike, data, args)

   Bases: :py:obj:`pytensor.tensor.Op`


   .. py:attribute:: itypes


   .. py:attribute:: otypes


   .. py:attribute:: likelihood


   .. py:attribute:: data


   .. py:attribute:: args


   .. py:method:: perform(node, inputs, outputs)


.. py:function:: 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


.. py:function:: bt(t: float, a: float, b: float, k: float, t0: float)

   Stem cell growth rate eta(t)

   Args:
       t: 
           time
       a,b,k,t0: 
           parameters


.. py:function:: cellnumber(t, xx, a, b, k, t0, p, r, d)

   ODE of cell number changes over time

   Args:
       t: 
           time
       xx: 
           [n_stemcell, n_nonstemcell]


.. py:function:: ncyc(i, t, c0, ax, bx, r, d, k, t0)

   Stem cell number calculator

   Args:
       i:
           generation
       t:
           time
       c0:
           initial cell number
       ax,bx,r,d,k,t0:
           parameters
   return:
       float:
           Stem cell number in generation i at time t.


.. py:function:: nnc(i, t, c0, ax, bx, r, d, k, t0)

   non-stem cell number calculator

   Args:
       i:
           generation
       t:
           time
       c0:
           initial cell number
       ax,bx,r,d,k,t0:
           parameters
   Return:
       float:
           non-stem cell number in generation i at time t.


.. py:function:: p_xi(n_gen, T, c0, ax, bx, r, d, k, t0)

   Probability density function of LR distance
   Args:
       n_gen:
           generation
       T: 
           time
       c0:
           initial cell number
       ax, bx, r, d, k, t0: 
           parameters
   Return:
       np.array:
           Probability density of LR distance at time T.


.. py:function:: my_loglike(theta, data, args)

   Likelihood of lr-dist

   Args:
       theta:
           parameters, (ax, bx, r, d, k ,t0)
       data:
           Observed lr dist
       args:
           paramteres, (time, initial_cell_number, prior_sigma)
   Return:
       float:
           Sum of log-likelihood of given lr dist parameters


.. py:function:: 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)


.. py:class:: LogLike(loglike, data, args)

   Bases: :py:obj:`pytensor.tensor.Op`


   .. py:attribute:: itypes


   .. py:attribute:: otypes


   .. py:attribute:: likelihood


   .. py:attribute:: data


   .. py:attribute:: args


   .. py:method:: perform(node, inputs, outputs)


.. py:function:: mcmc_inference(data, para_prior, T, c0, sigma, draw=1000, tune=1000, chains=8)

   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


.. py:function:: cellnumber(t, xx, r, a, b, k, t0, p, r1, b1)

   ODE of cell number changes over time

   Args:
       t: 
           time
       xx: 
           [n_stemcell, n_nonstemcell]


.. py:function:: stem_num(i, t, c0, ax, bx, r, k, t0, r1, b1)

   Stem cell number calculator

   Args:
       i:
           generation
       t:
           time
       c0:
           initial cell number
       ax,bx,r,d,k,t0:
           parameters
   return:
       float:
           Stem cell number in generation i at time t.


.. py:function:: nstem_num(t, x, c0, ax, bx, r, k, t0, r1, b1)

.. py:function:: p_xi(gen, T, c0, ax, bx, r, k, t0, r1, b1)

   Probability density function of LR distance
   Args:
       n_gen:
           generation
       T: 
           time
       c0:
           initial cell number
       ax, bx, r, k, t0, r1, b1: 
           parameters
   Return:
       np.array:
           Probability density of LR distance at time T.


.. py:function:: my_loglike(theta, data, args)

   Likelihood of lr-dist

   Args:
       theta:
           parameters, (ax, bx, r, k, t0, r1, b1)
       data:
           Observed lr dist
       args:
           paramteres, (time, initial_cell_number, prior_sigma)
   Return:
       float:
           Sum of log-likelihood of given lr dist parameters


.. py:function:: 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)


.. py:class:: LogLike(loglike, data, args)

   Bases: :py:obj:`pytensor.tensor.Op`


   .. py:attribute:: itypes


   .. py:attribute:: otypes


   .. py:attribute:: likelihood


   .. py:attribute:: data


   .. py:attribute:: args


   .. py:method:: perform(node, inputs, outputs)


.. py:function:: mcmc_inference(data, para_prior, T, c0, sigma, draw=1000, tune=1000, chains=9, est_bx=False)

   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