Bioinformatics Analyst | Cancer Genomics | Multi-Omics | Computational Biology
Bioinformatics scientist developing reproducible computational workflows for precision oncology across cancer genomics, transcriptomics, single-cell biology, epigenomics, population genomics, and clinical outcomes.
My work focuses on translating high-dimensional cancer datasets into biologically interpretable and statistically defensible results.
- Cancer genomics and precision oncology
- Multi-omics integration
- Epigenomics and DNA methylation
- Bulk and single-cell transcriptomics
- Tumor immune microenvironment
- Population genomics and genetic ancestry
- Statistical genomics and survival analysis
Integrated somatic DNA, bulk RNA-seq, clinical phenotypes, molecular drivers, and disease-status information to characterize molecular heterogeneity in thyroid cancer.
Methods: GATK Mutect2, MAF harmonization, somatic-driver reconciliation, DESeq2, PCA, transcriptional program scoring, inflammasome analysis, pathway-level interpretation, multivariable modeling, and DNA–RNA integration.
Single-cell analysis of ancestry-associated immune-state heterogeneity within CMS4 colorectal cancer.
Methods: Seurat, compartment-specific QC, Harmony batch integration, Azimuth reference annotation, patient-level pseudobulk aggregation, DESeq2, GO Biological Process GSEA, MSigDB Hallmark GSEA.
Immune programs: CD8 cytotoxic/exhaustion states, CD4/Treg/Tfh states, B-cell/plasma differentiation, M2/TAM programs, mast-cell signaling, and progenitor-like lineage states.
Longitudinal Illumina EPIC methylation analysis investigating epigenetic alterations associated with progression from oral epithelial dysplasia to oral squamous cell carcinoma.
Methods: EPIC methylation arrays, beta/M-value modeling, limma, duplicateCorrelation, CpG- and gene-level analysis, baseline-definition sensitivity analyses, delta-beta effect sizes, BH-FDR, candidate-gene analysis, and spatial-transcriptomic integration.
Evaluated reciprocal metabolic-gene expression states and survival outcomes in HPV-negative head and neck squamous cell carcinoma.
Methods: Kaplan–Meier estimation, log-rank testing, Cox proportional hazards regression, continuous-expression models, interaction testing, subsite-specific sensitivity analysis, and nonlinear modeling.
Integrated methylation-derived immune composition with LINE-1, CIMP, ATM methylation, clinical variables and environmental context.
Methods: EpiDISH RPC, EPIC methylation, immune deconvolution, Spearman correlation, nonparametric testing, multivariable models.
Evaluated ancestry-stratified chromosomal instability and clonal heterogeneity in CMS4 colorectal tumors using expression-derived single-cell copy-number profiles.
Methods: scRNA-seq, inferCNV, chromosome- and gene-level CNV summarization, clonal diversity, ancestry-stratified comparisons, BH-FDR, and leave-one-sample-out sensitivity analysis.
Developed a population-genomics workflow for genetic ancestry estimation from RNA-derived germline variation in colorectal cancer.
Methods: nf-core/rnavar, GATK HaplotypeCaller, bcftools, PLINK, 1000 Genomes/HGDP reference integration, PCA, supervised and unsupervised ADMIXTURE, cross-validation, missingness sensitivity analysis, and Linux/Slurm HPC.
CRC Immunoepigenomics — EPIC methylation, EpiDISH immune deconvolution, CD8/TIL analysis, LINE-1, CIMP, ATM methylation, environmental and neighborhood-level variables.
CRC Ancestry Transcriptomics & Immunomics — CMS1–4-stratified DESeq2, ancestry-associated transcriptional programs, continuous AFR gradients, xCell and CIBERSORT immune deconvolution.
CRC Single-Cell Cellular Composition — patient-level immune/stromal composition, genetic ancestry, self-reported race, CMS composition, nonparametric inference and FDR control.
DNA Damage & Repair Transcriptomics — DESeq2, ancestry/disease contrasts, DNA-repair and metabolic programs, ashr shrinkage, GO/KEGG/Reactome enrichment.
Cancer Genomics / Exposure Analyses — somatic variation, mutational signatures, copy-number analysis, methylation-associated environmental exposure signatures, and translational cancer genomics.
Languages: R · Python · Bash · Linux
Workflow / HPC: Nextflow · nf-core · Slurm · Singularity · Conda · Git · GitHub Actions
Cancer Genomics: GATK · Mutect2 · HaplotypeCaller · Funcotator · bcftools · samtools · VEP · ANNOVAR · maftools · GISTIC2
Bulk Transcriptomics: Salmon · DESeq2 · limma · ashr · clusterProfiler · ReactomePA · MSigDB/GSEA
Single-Cell: Seurat · Harmony · Azimuth · SingleR · Monocle3 · inferCNV · pseudobulk differential expression
Epigenomics: Illumina EPIC · sesame · limma · EpiDISH · methylation deconvolution · beta/M-value modeling
Population Genomics: PLINK · ADMIXTURE · SNPRelate · 1000 Genomes · HGDP · PCA
Tumor Immunology: xCell · CIBERSORT · EpiDISH · immune-state scoring · tumor microenvironment profiling
Statistical Genomics: GLM · Cox proportional hazards · Kaplan–Meier · likelihood-ratio tests · interaction models · nonparametric inference · multiple-testing correction