spateo.tools.glm
================

.. py:module:: spateo.tools.glm


Functions
---------

.. autoapisummary::

   spateo.tools.glm.glm_degs
   spateo.tools.glm.glm_test
   spateo.tools.glm.lrt


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

.. py:function:: glm_degs(adata: anndata.AnnData, X_data: Optional[numpy.ndarray] = None, genes: Optional[list] = None, layer: Optional[str] = None, key_added: str = 'glm_degs', fullModelFormulaStr: str = '~cr(time, df=3)', reducedModelFormulaStr: str = '~1', qval_threshold: Optional[float] = 0.05, llf_threshold: Optional[float] = -2000, ci_alpha: float = 0.05, inplace: bool = True) -> Optional[anndata.AnnData]

   Differential genes expression tests using generalized linear regressions. Here only size factor normalized gene
   expression matrix can be used, and SCT/pearson residuals transformed gene expression can not be used.

   Tests each gene for differential expression as a function of integral time (the time estimated via the reconstructed
   vector field function) or pseudo-time using generalized additive models with natural spline basis. This function can
   also use other co-variates as specified in the full (i.e `~clusters`) and reduced model formula to identify differentially
   expression genes across different categories, group, etc.
   glm_degs relies on statsmodels package and is adapted from the `differentialGeneTest` function in Monocle. Note that
   glm_degs supports performing deg analysis for any layer or normalized data in your adata object. That is you can either
   use the total, new, unspliced or velocity, etc. for the differential expression analysis.

   :param adata: An Anndata object. The anndata object must contain a size factor normalized gene expression matrix.
   :param X_data: The user supplied data that will be used for differential expression analysis directly.
   :param genes: The list of genes that will be used to subset the data for differential expression analysis. If ``genes = None``, all genes will be used.
   :param layer: The layer that will be used to retrieve data for dimension reduction and clustering. If ``layer = None``, ``.X`` is used.
   :param key_added: The key that will be used for the glm_degs key in ``.uns``.
   :param fullModelFormulaStr: A formula string specifying the full model in differential expression tests (i.e. likelihood ratio tests) for each gene/feature.
   :param reducedModelFormulaStr: A formula string specifying the reduced model in differential expression tests (i.e. likelihood ratio tests) for each gene/feature.
   :param qval_threshold: Only keep the glm test results whose qval is less than the ``qval_threshold``.
   :param llf_threshold: Only keep the glm test results whose log-likelihood is less than the ``llf_threshold``.
   :param ci_alpha: The significance level for the confidence interval. The default ``ci_alpha = .05`` returns a 95% confidence interval.
   :param inplace: Whether to copy adata or modify it inplace.

   :returns: An ``AnnData`` object is updated/copied with the ``key_added`` dictionary in the ``.uns`` attribute, storing the differential
             expression test results after the GLM test.


.. py:function:: glm_test(data, fullModelFormulaStr='~cr(time, df=3)', reducedModelFormulaStr='~1')

.. py:function:: lrt(full, restr)