Computational biologist focused on cancer transcriptomics, tumor microenvironment biology, and biomarker discovery using bulk and single-cell RNA-seq data.

Research interests include tumor immune evasion, survival modeling, and development of reproducible genomics workflows for clinically relevant insights.

Genomic data → RNA-seq processing → Differential expression → Functional pathway analysis → Survival modeling → Prognostic biomarker discovery → Clinical interpretation

https://github.com/ag48665/lusc-transcriptomic-prognostic-signature

Objective: Develop and validate a robust survival prediction model for LUSC patients.

Data: TCGA (training), GEO (external validation: GSE30219, GSE37745)

Methods:

• Cox proportional hazards modeling
• Elastic-net feature selection
• Kaplan–Meier survival analysis
• Time-dependent ROC analysis
• Multivariable Cox regression
• Calibration and decision curve analysis

https://github.com/ag48665/tcga-lusc-biomarker-analysis

Objective: Identify survival-associated gene expression programs in LUSC.

Methods:

• TCGA data acquisition (TCGAbiolinks)
• Differential expression analysis (DESeq2)
• Functional enrichment (GO / KEGG)
• Survival analysis

https://github.com/ag48665/lusc-immune-escape-analysis

Objective: Transcriptomic analysis of immune heterogeneity in lung squamous cell carcinoma (LUSC), focusing on immune activation, checkpoint signaling, and exhaustion-associated tumor states using TCGA and GEO datasets.

• Immune gene signature scoring
• T-cell exhaustion profiling
• Checkpoint signaling analysis
• UMAP visualization
• Survival analysis
• External cohort validation

https://github.com/ag48665/tcga-lung-immune-evasion-scRNAseq

Objective: Explore immune cell populations and functional states in the tumor microenvironment at single-cell resolution.

Methods:

• Scanpy preprocessing and normalization
• PCA / UMAP embedding
• Clustering and cell-type annotation

https://github.com/ag48665/scrna-pbmc-cell-atlas

Objective: Reconstruct immune cell populations from human PBMC single-cell RNA-seq data using an unsupervised Scanpy workflow.

Data: Public PBMC3K dataset from Scanpy (~2,700 human peripheral blood mononuclear cells)

Methods:

• Single-cell RNA-seq quality control and filtering
• Normalization and highly variable gene selection
• PCA / UMAP dimensionality reduction
• Leiden clustering
• Marker gene identification
• Cell-type annotation using canonical immune markers

https://github.com/ag48665/Pilot-Hypoxia-Detection-using-Physiological-Signals

Objective: Develop a machine learning–based system for early detection of hypoxia in pilots using physiological signals.

Data: Multimodal physiological signals (e.g., heart rate, oxygen saturation, respiration)

Methods:

• Signal preprocessing and feature extraction
• Time-series analysis of physiological data
• Machine learning classification models
• Model evaluation (accuracy, ROC, confusion matrix)
• Data visualization and pattern detection

Bioinformatics: RNA-seq analysis • differential expression (DESeq2) • survival modeling (Cox, Kaplan–Meier) • functional enrichment (GO / KEGG) • prognostic modeling • single-cell RNA-seq (Scanpy) • TCGA / GEO data analysis • biomarker discovery

Machine Learning & Data Analysis: Supervised learning • classification models • feature selection • model evaluation (ROC, AUC, confusion matrix) • time-series analysis • physiological signal processing

Programming: R (tidyverse, survival, DESeq2) • Python (pandas, numpy, scikit-learn, scanpy)

Tools & Methods: Linux • Git • reproducible workflows • statistical modeling • data visualization (ggplot2, matplotlib, seaborn) • data preprocessing • pipeline development

Email: agatagabara@gmail.com LinkedIn: https://www.linkedin.com/in/agatha-gabara-06494a37/