phylovelo.pseudo_time
=====================

.. py:module:: phylovelo.pseudo_time


Functions
---------

.. autoapisummary::

   phylovelo.pseudo_time.get_nearest_neighbor
   phylovelo.pseudo_time._as_float_array
   phylovelo.pseudo_time._normalize_01
   phylovelo.pseudo_time._normalize_with_bounds
   phylovelo.pseudo_time._time_intervals
   phylovelo.pseudo_time.time_interval
   phylovelo.pseudo_time._build_knn_adjacency
   phylovelo.pseudo_time._connected_components
   phylovelo.pseudo_time._bridge_components
   phylovelo.pseudo_time._prim_edges
   phylovelo.pseudo_time._graph_pseudotime
   phylovelo.pseudo_time.graph_dict
   phylovelo.pseudo_time.prim
   phylovelo.pseudo_time._sample_cells
   phylovelo.pseudo_time._positions_for_names
   phylovelo.pseudo_time._get_expression_data
   phylovelo.pseudo_time._meg_pseudotime_scores
   phylovelo.pseudo_time.calc_phylo_pseudotime
   phylovelo.pseudo_time.calc_meg_pseudotime


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

.. py:function:: get_nearest_neighbor(data: numpy.ndarray, target: int, n_neighbors: int = 10)

   Get nearest neighbors of the target

   Args:
       data:
           Data to train knn
       target:
           Target point to get nearest neighbors
       n_neighbors:
           How many nearest neighbors to return

   Returns:
       list:
           Euclidean distance from target to neighbors
       list:
           Neighbors' indices



.. py:function:: _as_float_array(x)

.. py:function:: _normalize_01(x, robust_quantiles=None)

.. py:function:: _normalize_with_bounds(x, lo, hi)

.. py:function:: _time_intervals(pt1, pt2, v1, v2)

.. py:function:: time_interval(pt1: numpy.ndarry, pt2: numpy.ndarry, v1: numpy.ndarry, v2: numpy.ndarry)

   Given two points' coordinate and velocity, calculate the time interval

   Args:
       pt1: 
           Coordinate of one point
       pt2: 
           Coordinate of the other point
       v1: 
           Velocity of one point
       v2: 
           Velocity of the other point

   Return:
       float:
           Time interval


.. py:function:: _build_knn_adjacency(pts, v, n_neighbors=30)

.. py:function:: _connected_components(adjacency)

.. py:function:: _bridge_components(adjacency, pts, v, root=0)

.. py:function:: _prim_edges(adjacency, root=0)

.. py:function:: _graph_pseudotime(pts, v, n_neighbors=30, root=0, robust_quantiles=(0.01, 0.99))

.. py:function:: graph_dict(pts: numpy.ndarry, v: numpy.ndarry, n_neighbors: int = 30)

   Build graph to construct MST

   Args:
       pts: 
           All cells' coordinate in embedding
       v:
           Phylo velocity
       n_neighbors:
           N nearest neighbors to build MST

   Return:
       dict:
           Graph to build MSt


.. py:function:: prim(graph, root)

   Prim algorithm to build MST from graph


.. py:function:: _sample_cells(index, r_sample, random_state=None)

.. py:function:: _positions_for_names(index, names)

.. py:function:: _get_expression_data(obj, target)

.. py:function:: _meg_pseudotime_scores(X, lo, spread, signs, weights, aggregation)

.. py:function:: calc_phylo_pseudotime(sd: scData, n_neighbors: int = 30, r_sample: float = 1, method: str = 'graph', target: str = 'x_normed', random_state: int = None)

   Calculate the phyloVelo pseudotime

   Args:
       sd:
           sc data
       n_neighbors:
           N nearest neighbors to build MST. The smaller the number, the faster the calculation, but there is a chance of error
       r_sample:
           [0-1], random sample a subset calculate pseudotime.
       method:
           'graph' uses embedding velocities and a kNN MST; 'meg' uses robust MEG expression.
       target:
           Expression matrix used when method='meg'.
       random_state:
           Seed for subsampling.
           
   Return:
       scData.phylo_pseudotime


.. py:function:: calc_meg_pseudotime(sd: scData, target: str = 'x_normed', genes: list = None, robust_quantiles: tuple = (0.05, 0.95), aggregation: str = 'median', min_genes: int = 3, query_data=None, query_sd: scData = None, query_target: str = None)

   Calculate pseudotime directly from robustly oriented MEG expression.

   Args:
       sd:
           sc data
       target:
           Expression matrix to use, usually 'x_normed' or 'count'.
       genes:
           MEGs to use. Default uses sd.megs.
       robust_quantiles:
           Lower and upper quantiles used to clip per-gene expression.
       aggregation:
           'median' for robust L1 aggregation or 'weighted_mean'.
       min_genes:
           Minimum number of usable MEGs.
       query_data:
           Independent expression matrix (cells x genes) to score with the
           reference dataset's MEGs, velocity directions, and robust scaling.
       query_sd:
           Independent scData object. Uses query_target or target as expression
           matrix and writes query pseudotime to query_sd.phylo_pseudotime.
       query_target:
           Expression matrix name for query_sd. Default: same as target.

   Return:
       sd if no query is provided; query pseudotime array if query_data is
       provided; query_sd if query_sd is provided.