===
API
===

.. automodule:: SNPmanifold

Import SNPmanifold as::

   import SNPmanifold

Main Object
-----------

Object of type :class:`~SNPmanifold.SNP_VAE` for clustering with binomial
mixture model

Parameters to initialize and load data into the main object SNP_VAE:
~~~~~~~~~~~~~~~~~~~~

**path** (string) - path of cellSNP-lite output folder which contains cellSNP.tag.AD.mtx, cellSNP.tag.DP.mtx, and cellSNP.base.vcf.gz

**SNP_mask** (list of string) - list of variant names to mask from VAE, please refer to the internal variant names: VCF['TEXT'], if you use VCF as input (default: [])

**AD** (string) - path of AD matrix in scipy.sparse.coo_matrix format with shape (SNP, cell)

**DP** (string) - path of DP matrix in scipy.sparse.coo_matrix format with shape (SNP, cell)
            
**VCF** (string) - path of VCF.gz file

**variant_name** (string) - path of variant_name.tsv file which is a list of custom variant name stored in pandas dataframe without header and index
            
**SNPread** (string) - optional observed-SNP normalization, 'normalized' or 'unnormalized' (default: 'normalized')
        
**missing_value** (float between 0 and 1) - impute value for missing allele frequency in AF matrix, i.e. DP = 0 (default: 0.5)
        
**cell_weight** (string) - optional cost normalization for each cell, 'normalized' or 'unnormalized' (default: 'unnormalized')

**prior** (string) - path of prior weights of mutation for each variant in csv format (default: None)

Functions
-----------

.. autoclass:: SNPmanifold.SNP_VAE
   :members: 

Attributes
-----------

After running SNP_VAE.filtering():
~~~~~~~~~~~~~~~~~~~~

**cell_filter** (np.array of booleans) - boolean filter for all input cells

**SNP_filter** (np.array of booleans) - boolean filter for all input SNPs

**cell_total** (integer) - total number of cells after filtering

**SNP_total** (integer) - total number of SNPs after filtering

**AD_filtered** (np.array with shape (cell_total, SNP_total)) - AD matrix after filtering

**DP_filtered** (np.array with shape (cell_total, SNP_total)) - DP matrix after filtering

**AF_filtered** (torch.tensor with shape (cell_total, SNP_total)) - AF matrix which is the input to VAE

**VCF_filtered** (pd.DataFrame) - VCF after filtering which contains variant names

After running SNP_VAE.training():
~~~~~~~~~~~~~~~~~~~~

**model** (VAE_normalized) - trained VAE model implemented in PyTorch

**latent** (np.array with shape (cell_total, z_dim)) - latent factors of all cells after filtering

**pc** (np.array with shape (cell_total, z_dim)) - principal components of PCA of the latent space

**embedding_2d** (np.array with shape (cell_total, 2)) - 2D UMAP embedding of the latent space

**embedding_3d** (np.array with shape (cell_total, 3)) - 3D UMAP embedding of the latent space

After running SNP_VAE.clustering() and SNP_VAE.phylogeny():
~~~~~~~~~~~~~~~~~~~~

**cluster_no** (integer) - total number of clusters 

**assigned_label** (np.array of integers with shape (cell_total)) - assigned cluster labels of all cells after filtering

**clusters** (list of np.arrays with length (cluster_no)) - np.where(assigned_label == r) for each cluster r

**colors** (np.array with shape (cluster_no, 4)) - colors of all clusters in figures

**edge** (list of tuples with length (cluster_no - 1)) - all connected edges in the phylogenetic tree

**f_stat** (np.array with shape (SNP_total)) - F-statistics of all SNPs after filtering

**p_value** (np.array with shape (SNP_total)) - P-values of all SNPs after filtering

**rank_SNP** (np.array with shape (SNP_total)) - ranking of SNPs from the lowest p-value