My research is centered at the intersection of population health, data science, and disease prevention. I investigate the epidemiology of major health conditions with a goal to translate findings into actionable public health strategies.

What I Use

Cancer Bioinformatics

Data Sources

• The Cancer Genome Atlas (TCGA): TCGA is a comprehensive collection of multi-dimensional cancer genomics data covering multiple cancer types.
• Gene Expression Omnibus (GEO): GEO is a public repository hosted by the National Center for Biotechnology Information (NCBI) containing a vast collection of gene expression data, including cancer datasets.
• National Cancer Institute (NCI) Genomic Data Commons (GDC): Description: GDC is an open-access data portal providing access to a wide range of cancer genomics datasets.
• cellxgene.cziscience.com - Download and visually explore reference-quality data to understand the functionality of human tissues at the cellular level with Chan Zuckerberg CELL by GENE Discover (CZ CELLxGENE Discover).
• 10XGenomics - High-performance in situ from the single cell leader

Analysis Tools

• UCSC Xena: An online exploration tool for public and private, multi-omic and clinical/phenotype data
• GEO2R: GEO2R is an interactive web tool that allows users to compare two or more groups of Samples in a GEO Series in order to identify genes that are differentially expressed across experimental conditions. Results are presented as a table of genes ordered by P-value, and as a collection of graphic plots to help visualize differentially expressed genes and assess data set quality. GEO2R uses a variety of R packages from the Bioconductor project. Bioconductor is an open-source software project based on the R programming language that provides tools for the analysis of high-throughput genomic data.
• GEPIA2: GEPIA2 is a web-based tool for analyzing gene expression data in cancer. It stands for Gene Expression Profiling Interactive Analysis 2 and is an updated version of the original GEPIA tool. GEPIA2 allows users to explore gene expression patterns, perform survival analyses, and visualize gene expression data across various cancer types.
• TIMER2.0: TIMER is a comprehensive resource for systematical analysis of immune infiltrates across diverse cancer types. This version of webserver provides immune infiltrates' abundances estimated by multiple immune deconvolution methods, and allows users to generate high-quality figures dynamically to explore tumor immunological, clinical and genomic features comprehensively.
• UALCAN: UALCAN is a web-based platform that provides interactive and comprehensive analysis of cancer transcriptome data. It enables users to explore gene expression patterns, perform survival analyses, and compare gene expression between tumor and normal samples across different cancer types. UALCAN utilizes data from The Cancer Genome Atlas (TCGA) to facilitate cancer research and provide insights into tumor biology.
• cBioPortal for Cancer Genomics:: cBioPortal hosts a large collection of cancer genomics datasets, allowing users to explore and visualize the data.
• GREIN : GEO RNA-seq Experiments Interactive Navigator: GREIN is an interactive web platform that provides user-friendly options to explore and analyze GEO RNA-seq data. GREIN is powered by the back-end computational pipeline for uniform processing of RNA-seq data and the large number (>6,000) of already processed datasets. These datasets were retrieved from GEO and reprocessed consistently by the back-end GEO RNA-seq experiments processing pipeline (GREP2).
• UCSC Cancer Genomics Browser: The UCSC Cancer Genomics Browser offers a comprehensive collection of cancer genomics data integrated with genomic annotations.

R Packages

• TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data. TCGAbiolinks is able to access The National Cancer Institute (NCI) Genomic Data Commons (GDC) thorough its GDC Application Programming Interface (API) to search, download and prepare relevant data for analysis in R.
• maftools: Summarize, Analyze and Visualize MAF Files. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner from either TCGA sources or any in-house studies as long as the data is in MAF format.
• SummarizedExperiment: The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples.
• MutationalPatterns: Comprehensive genome-wide analysis of mutational processes. he package covers a wide range of patterns including: mutational signatures, transcriptional and replicative strand bias, lesion segregation, genomic distribution and association with genomic features, which are collectively meaningful for studying the activity of mutational processes.
• GenVisR : Short for "Genomic Visualizations in R," this tool provides visualization capabilities tailored to a variety of genomic data types, including data common in cancer research such as somatic mutations, copy number variations, and more.

Data Wrangling

• readxl for importing data into R
• dplyr, tidyr and others from the tidyverse for data preparation.

Data Visualization

• ggplot2 for the vast majority of the graphics, together with the hrbrtheme for styling.
• patchwork to put graphics together.
• ggraph and igraph for most of the network related graphics
• plotly and other html widgets for interactive graphics.
• RColorBrewer and viridis and colormap to control color in charts.
• Ggrepel and other ggplot2 extension that make your life simpler.
• Heatmaply for most of the heatmaps

Publication-ready Tables

• gtsummary for creating publication-ready descriptives and analytical tables.
• gt to customize tables and export as docs or tex.

Reproducible Research

• R Markdown to produce statistical reports.
• Quarto to build 95% of the website for my courses and others.

Statistical Modeling

• easystats for easy statistical modeling, visualization, and reporting

Data Science, Machine Learning and Deep Learning

• NumPy for scientific computing.
• Pandas for data wrangling and analysis
• Matplotlib for data visulization
• Seaborn for advance statistical visualizations
• Plotly for interative data visualization
• researchpy to summarize data and perform statistical tests.
• Dask for big data analysis
• scikit-learn for machine learning
• scikit-image for life science image manipulation