Model-based tumour subclonal deconvolution using population genetics

Analysis pipleine to model tumour clonal evolution from WGS data (driver annotation, quality control of copy number calls, subclonal and mutational signature deconvolution)

Maximum-likelihood inference for subclonal deconvolution with univariate Binomial and Beta-Binomial mixtures

Tumour clone trees for subclonal deconvolution from bulk sequencing data

Variational inference for subclonal deconvolution with multivariate Binomial mixtures

Tumour mutation trees for subclonal deconvolution from bulk sequencing data

Supplementary Data released with Caravagna et al. Subclonal reconstruction of tumors by using machine learning and population genetics. Nature Genetics volume 52, 898–907(2020).

Process and setup for determining clonal populations from WGS data of multiple chorangiomas tissue

Tumour evolution bioinformatics analysis pipeline from bulk sequencing