Candy P

Bioinformatics analyst and computational biologist with an M.S. in Bioinformatics developing reproducible workflows for translational cancer research and precision oncology.

Experience spans somatic genomics, bulk and single-cell transcriptomics, Illumina EPIC methylation, population genomics, tumor immunology, and clinical outcome modeling. Integrates molecular and clinical data to investigate genomic drivers, immune states, epigenomic dysregulation, genetic ancestry, tumor heterogeneity, and cancer outcomes, with emphasis on rigorous QC, sensitivity analysis, and statistically defensible inference. Experienced in R, Python, Bash, Linux/HPC, Nextflow/nf-core, GATK, Bioconductor, Seurat, PLINK/ADMIXTURE, and publication-ready scientific communication.

Bioinformatics Analyst - Lombardi Comprehensive Cancer Center, Georgetown University

(2024-08)

Bioinformatics Intern: May 2024 – August 2024

• Develop and execute reproducible computational workflows across DNA-seq, bulk RNA-seq, scRNA-seq, and Illumina EPIC methylation studies in colorectal, thyroid, head and neck, and oral cancers, integrating molecular data with clinical and demographic metadata.
• Analyze somatic variation using GATK Mutect2, Funcotator, bcftools, maftools, GISTIC2.0, MutationalPatterns, and COSMIC signatures, including QC, MAF harmonization, SNV/indel profiling, curated driver events, TMB, Ti/Tv, mutational signatures, and copy-number alterations.
• Build integrated thyroid cancer DNA/RNA analyses evaluating somatic drivers, MAPK molecular groups, disease status, demographic covariates, transcriptional programs, inflammasome biology, pathway activity, and DNA-RNA concordance using DESeq2 and multivariable models.
• Design lineage-specific single-cell workflows using Seurat, Harmony, and Azimuth to characterize CD8/CD4 T-cell, B/plasma-cell, macrophage, mast-cell, and progenitor-like states; implement patient-level pseudobulk DESeq2 and GO/MSigDB Hallmark GSEA to avoid cell-level pseudoreplication.
• Develop longitudinal EPIC methylation analyses for OED-to-OSCC progression using probe/sample QC, beta/M-value modeling, limma, duplicateCorrelation, grade-adjusted models, CpG/gene-level effect estimation, alternative baseline definitions, and leave-one-out/sensitivity analyses.
• Characterize tumor immune microenvironments using EpiDISH, xCell, and CIBERSORT and integrate inferred immune composition with molecular subtype, methylation, genetic ancestry, clinical variables, and socio-environmental measures.
• Develop population-genomics workflows from RNA-derived germline variation using nf-core/rnavar/GATK, bcftools, PLINK, ADMIXTURE, SNPRelate/SeqArray, 1000 Genomes, and HGDP; perform reference harmonization, LD pruning, PCA projection, supervised/unsupervised ancestry inference, and missingness sensitivity.
• Perform clinical/statistical genomics using GLMs, Cox proportional-hazards models, Kaplan-Meier/log-rank tests, interaction models, nonparametric inference, BH-FDR, outlier assessment, and leave-one-sample-out sensitivity analyses; communicate results through figures, reports, presentations, abstracts, and manuscript-ready outputs.

Undergraduate Research Assistant - Venkitanarayanan Microbiology and Food Safety Laboratory, UConn

(2022 - 2023)

Supported microbiology and food-safety studies involving bacterial culture, sample processing, quantitative data collection, and antimicrobial intervention studies.

Undergraduate Research Assistant - Rehabilitation Innovations & Emerging Technologies in PT Laboratory, UConn

(2021 - 2023)

Supported quantitative research on powered-mobility interventions for children with cerebral palsy.

M.S. - Bioinformatics - Georgetown University (2024-08)

B.S. - Biological Sciences - University of Connecticut (2023-05)