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XClone
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XClone is implementated on Python 3. 
XClone integrates expression levels and allelic balance to detect haplotype-aware CNAs and reconstruct tumour clonal substructure from scRNA-seq data.

XClone algorithm
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.. image:: ./image/XClone_overview_150dpi.png
   :width: 800px
   :align: center


XClone has two modules of information: the read depth ratio (RDR) module and the B-allele frequency (BAF) module,
where each of the modules has its own CNA states and noise models for likelihood function. 


XClone RDR module
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In the RDR module, XClone considers three CNA states about the absolute copy numbers: copy loss, copy neutral and copy gain. 
It takes the raw read or UMI counts (raw counts defaultly used for 10x scRNA-seq, log transformed counts for smart-seq data) as input and models the noise via a negative-binomial distribution.

XClone BAF module
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In the BAF module, we introduce a three-step phasing strategy to aggregate allelic features: 

- from one SNP to multiple SNPs on a gene
- from a single gene to a mega gene 
- from a mega gene to a whole chromosome arm

XClone takes the phased mega gene as the feature and defines three allele-based CNA states: allele A drop, allele balance 
and allele B drop. 
By taking the allelic count matrices of both alleles, it models the read or UMI counts of the B allele for each feature 
in each cell via a beta-binomial distribution.