Metadata-Version: 2.1
Name: scvoting-vjbaskar
Version: 0.0.2
Summary: Projection of on scRNAseq on another and voting. Sam Watcham's thesis chapter 3
Home-page: https://github.com/pypa/scvoting
Author: Vijay
Author-email: vjbaskar@gmail.com
License: UNKNOWN
Description: # scvoting
        This is a voting method to obtain the density (smoothed votes) of a 10 sc-RNASeq dataset on a common reference sc-RNASeq data's landscape which can be viewed using umap, fdg etc
        
        The method works with scanpy annData objects only and contains two parts.
        
        ## Projecting onto reference landscape
        This package follows the Xiaonan Wang's method of projecting a 10x sc-RNASeq dataset onto a reference sc-RNASeq data. 
        After projection
        
        ## Voting and smoothing
        * For every cell in the dataset its NN in the reference dataset gets 1 vote.
        * Smooth out the votes for each cell in the reference dataset by sharing them equally among 100 NN in it. So if its vote is 40 you share 40/100 among each of them.
        * Compute this for WT and mutant datasets. 
        * Plot their difference or logFC etc on the reference landscape (UMAP)
        
        ## Example run
        
        ```python
        from scvoting import Projector
        # Initantiation
        votp = Projector(adata = adata, ref_adata=adata_niki, ref_hvg=niki_hvg, npcs=25)
        # Proj onto ref
        votp.project_on_ref()
        # Plot PCAs (works only with X11, use jupyter on remote servers)
        votp.plotPCA()
        # Compute distances between cells in two datasets in PCA space
        votp.pwdist()
        # Voting starts
        votp.voting(tot_votes = 500000)
        # Compute distances between cells in reference datasets
        votp.pwdist(rtype = 'ref')
        # Smooth the voting for better visualisation
        votp.vote_smoothing()
        # Smoothed votes
        results = votp.votes_smooth
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
