phylovelo.inference
===================

.. py:module:: phylovelo.inference


Classes
-------

.. autoapisummary::

   phylovelo.inference.InfMu


Functions
---------

.. autoapisummary::

   phylovelo.inference.mle_zinb
   phylovelo.inference.mle_nb
   phylovelo.inference.mle_norm
   phylovelo.inference.mle_zinorm
   phylovelo.inference.lrtest
   phylovelo.inference.est_z
   phylovelo.inference.latenct_z_inference
   phylovelo.inference.is_meg
   phylovelo.inference.velocity_inference


Module Contents
---------------

.. py:function:: mle_zinb(data: list)

   Maximum likelihood estimation of ZINB distribution

   Args:
       data:
           Data
   Returns:
       tuple:
           (-loglikelihood, MLE)


.. py:function:: mle_nb(data)

   Maximum likelihood estimation of NB distribution

   Args:
       data:
           Data
   Returns:
       tuple:
           (-loglikelihood, MLE)


.. py:function:: mle_norm(data)

   Maximum likelihood estimation of Normal distribution

   Args:
       data:
           Data
   Returns:
       tuple:
           (-loglikelihood, MLE)


.. py:function:: mle_zinorm(data)

   Maximum likelihood estimation of ZI-Normal distribution

   Args:
       data:
           Data
   Returns:
       tuple:
           (-loglikelihood, MLE)


.. py:function:: lrtest(lh1: float, lh0: float, n: int, alpha: float)

   Likelihood ratio test

   Args:
       lh1: 
           Likelihood of alternative hypothesis
       lh0:
           Likelihood of null hypothesis
       n:
           Degrees of freedom
       alpha:
           Significance level

   Returns:
       tuple:
           (Likelihood ratio statistics, is reject)


.. py:function:: est_z(x: ndarray, alpha: float, mu0: float, sigma2: float, model: str)

   Estimation of latent expression z


.. py:class:: InfMu(time, paras)

   Inference mean of z at time t


   .. py:attribute:: slope


   .. py:attribute:: inte


   .. py:attribute:: sigma2


   .. py:method:: get_mu(t)


.. py:function:: latenct_z_inference(data: list, time: int, model: str)

   Inference latent expression z at time t in given model


.. py:function:: is_meg(x, y, trend=0)

   Determine whether a gene is meg

   Args:
       x: 
           Gene expression
       y:
           Cell generation
       trend:
           positive for incresing megs, negative for decreasing megs

   Return:
       bool:
           If a gene is MEG


.. py:function:: velocity_inference(sd: scData, time: list = None, cutoff: float = 0.97, alpha: float = 0.05, target: str = 'x_normed', exact: bool = False)

   Inference phylogenetic velocity

   Args:
       sd:
           scData
       time:
           if None, cell generation will be automatically calculated from phylo tree 
       cutoff:
           Only calculate genes with top 'cutoff' correlation
       alpha:
           Significance level
       target:
           which data to inference, 'count' for nb model or 'x_normed' for normal model
       exact:
           True to use 'is_meg' function; False do not use

   Return:
       sd.velocity