spateo.external.CAST.utils
==========================

.. py:module:: spateo.external.CAST.utils


Functions
---------

.. autoapisummary::

   spateo.external.CAST.utils.coords2adjacentmat
   spateo.external.CAST.utils.hv_cutoff
   spateo.external.CAST.utils.detect_highly_variable_genes
   spateo.external.CAST.utils.extract_coords_exp
   spateo.external.CAST.utils.Harmony_integration
   spateo.external.CAST.utils.random_sample
   spateo.external.CAST.utils.sub_node_sum
   spateo.external.CAST.utils.nearest_neighbors_idx
   spateo.external.CAST.utils.non_zero_center_scale
   spateo.external.CAST.utils.sub_data_extract
   spateo.external.CAST.utils.preprocess_fast
   spateo.external.CAST.utils.cell_select
   spateo.external.CAST.utils.get_neighborhood_rad
   spateo.external.CAST.utils.delta_cell_cal
   spateo.external.CAST.utils.delta_exp_cal
   spateo.external.CAST.utils.delta_exp_sigplot
   spateo.external.CAST.utils.delta_exp_statistics


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

.. py:function:: coords2adjacentmat(coords, output_mode='adjacent', strategy_t='convex')

.. py:function:: hv_cutoff(max_col, threshold=2000)

.. py:function:: detect_highly_variable_genes(sdata, batch_key='batch', n_top_genes=4000, count_layer='count')

.. py:function:: extract_coords_exp(sdata, batch_key='batch', cols='spatial', count_layer='count', data_format='norm1e4', ifcombat=False, if_inte=False)

.. py:function:: Harmony_integration(sdata_inte, scaled_layer, use_highly_variable_t, batch_key, umap_n_neighbors, umap_n_pcs, min_dist, spread_t, source_sample_ctype_col, output_path, n_components=50, ifplot=True, ifcombat=False)

.. py:function:: random_sample(coords_t, nodenum, seed_t=2)

.. py:function:: sub_node_sum(coords_t, exp_t, nodenum=1000, vis=True, seed_t=2)

.. py:function:: nearest_neighbors_idx(coord1, coord2, mode_t='knn')

.. py:function:: non_zero_center_scale(sdata_t_X)

.. py:function:: sub_data_extract(sample_list, coords_raw, exps, nodenum_t=20000, if_non_zero_center_scale=True)

.. py:function:: preprocess_fast(sdata1, mode='customized', target_sum=10000.0, base=2, zero_center=True, regressout=False)

.. py:function:: cell_select(coords_t, s=0.5, c=None, output_path_t=None)

   Select cells by drawing a polygon on the plot.
   Click the "Finish Polygon" button to finish drawing the polygon.
   Click the "Clear Polygon" button to clear the polygon.


.. py:function:: get_neighborhood_rad(coords_centroids, coords_candidate, radius_px, dist=None)

.. py:function:: delta_cell_cal(coords_tgt, coords_ref, ctype_tgt, ctype_ref, radius_px)

   coords_tgt: coordinates of niche centroids (target cells).
   coords_ref: coordinates of reference cells.
   ctype_tgt: cell type of niche centroids.
   ctype_ref: cell type of reference cells.
   radius_px: radius of neighborhood.

   Output:
   return: delta_cell_tgt, delta_cell_ref, delta_cell.

   e.g.
   coords_tgt = coords_final['injured']
   coords_ref = coords_final['normal']
   ctype_tgt = sdata.obs['Annotation'][right_idx]
   ctype_ref = sdata.obs['Annotation'][left_idx]
   radius_px = 1000
   df_delta_cell_tgt,df_delta_cell_ref,df_delta_cell = delta_cell(coords_tgt,coords_ref,ctype_tgt,ctype_ref,radius_px)


.. py:function:: delta_exp_cal(coords_tgt, coords_ref, exp_tgt, exp_ref, radius_px, valid_tgt_idx=None, valid_ref_idx=None)

   coords_tgt: coordinates of niche centroids (target cells).
   coords_ref: coordinates of reference cells.
   exp_tgt: gene expression of target cells.
   exp_ref: gene expression of reference cells.
   radius_px: radius of neighborhood.

   Output:
   return: delta_exp_tgt, delta_exp_ref, delta_exp.

   e.g.


.. py:function:: delta_exp_sigplot(p_values, avg_differences, abs_10logp_cutoff=None, abs_avg_diff_cutoff=None, sig=True)

.. py:function:: delta_exp_statistics(delta_exp_tgt, delta_exp_ref)