Differential expression analyses are susceptible to false positives. permuteDE uses permutation testing to identify which comparisons have a higher number of significant differentially expressed features than would be expected by chance.

• Developed the permuteDE R package, overseeing maintenance, debugging, and feature development.
• Collected and integrated eight large-scale public snRNA-seq datasets from Synapse, performing rigorous data cleaning, metadata curation, and structuring data into Seurat objects and BPCells matrices.
• Applied the permuteDE pipeline across eight datasets to demonstrate its real-world utility.

Note: Currently we are finalizing the package and analyses; permuteDE will be released upon manuscript publication.

I joined the PsychENCODE project at Prof. Anshul Kundaje's lab. I led this project analyzing scRNA-seq and scATAC-seq multiome data from three brain regions to investigate cell type-specific chromatin accessibility and gene expression patterns underlying schizophrenia and bipolar disorder to better understand their pathogeneses.

• Preprocessed high-dimensional multiome data through quality control, dimensionality reduction, clustering, doublet detection, marker identification, and cell type annotation.
• Performed differential gene expression & peak accessibility analyses (pseudobulk: Wald test; single-cell: Wilcoxon + MAST) across multiple cell types.
• Trained cell type-specific ChromBPNet↗ models to identify causal genomic variants and their downstream effects on chromatin accessibility and gene regulation.

Black men face higher prostate cancer incidence and mortality than White men. This project leverages LC-MS metabolomics on a cohort of 34 men (17 Black, 17 White) from the IRONMAN Registry ↗ to characterize metabolites associated with such disparities. I presented this project as my Master's thesis.

• Applied t-test, PCA, PLS-DA, random forest, logistic & linear regression, chemical similarity enrichment analysis, pathway analysis, and WGCNA to LC-MS metabolomics data, characterizing metabolites associated with Black-White prostate cancer disparities.
• Interpreted complex statistical findings and communicated them through clear scientific writing and effective data visualizations; defended before Stanford Epidemiology faculty and students.

Website Link: https://dkristheltorres.github.io/CS-448B/ ↗

This project visualized the interplay between life expectancy (measured by central death rates) and economic development, healthcare access, energy production, and consumption patterns.

• Developed interactive data visualization dashboards (global heatmaps, cross-country correlation analyses, and time-evolving bubble charts) using JavaScript D3.

This project evaluated associations between multiple exposures (age [categorical and continuous], sex, race, and smoking) and assessed alcohol as an effect modifier of the smoking–SAH relationship.

• Cleaned and managed case-control datasets in SAS OnDemand: resolved duplicate records, standardized missing values, and created derived variables.
• Conducted descriptive analyses (chi-square tests and t-tests) and applied univariate and multivariable logistic regression to estimate crude and adjusted odds ratios for smoking.
• Performed stratified analysis using Mantel–Haenszel testing to adjust for alcohol use as a confounder.
• Generated summary tables and data visualizations for reporting.