I am currently the Head of Bioinformatics in the Department of Lymphoid Cancer Research, British Columbia Cancer Research Centre, Vancouver, Canada where I spearhead the overall bioinformatics strategic direction of the department.
I am also concurrently undertaking a PhD in Bioinformatics under the co-supervision of Dr. Sohrab Shah and Dr. Christian Steidl. My main research interest surrounds the implications of tumour diversity on disease progression in B-cell lymphomas. In particular, how tumour and microenvironment evolution plays a role in treatment resistance as understanding this process may aid in the determination of relevant and precise therapeutic approaches for each cancer patient.
I have a passion for applying statistical and machine learning approaches to big data, in particular genomics data (e.g. sequencing data). In terms of technical skills:
- I have expertise in dealing with big data in the R programming language (e.g. data.table, tidyr, dplyr) and a thorough understanding of the core principles around R
- I have extensive experience in doing reproducible research through interactive applications and D3 web reporting (e.g. Rmarkdown, knitr, pandoc, git, shiny)
- I have expertise in developing/managing big data processing pipelines through the Make engine with experience in using ruffus, bpipe, snakemake, and nextflow.
Selected Publications [Google Scholar]
Histological Transformation and Progression in Follicular Lymphoma: a Clonal Evolution Study
An RCOR1 loss-associated gene expression signature identifies a prognostically significant DLBCL subgroup
Recurrent somatic mutations of PTPN1 in primary mediastinal B cell lymphoma and Hodgkin lymphoma
Genomic rearrangements involving programmed death ligands are recurrent in primary mediastinal large B-cell lymphoma
Gene expression-based model using formalin-fixed paraffin-embedded biopsies predicts overall survival in advanced-stage classical Hodgkin lymphoma
An R package for generating cofeature (feature by sample) matrices. The package utilizies ggplot2::geom_tile to generate the matrix allowing for easy additions from the base matrix.
This package uses functional programming principles to iteratively run Cox regression and plot its results. The results are reported in tidy data frames. Additional utility functions are available for working with other aspects of survival analysis such as survival curves, C-statistics, etc.