Outline

Programmatically clean and preprocess raw intensity data from microarray experiments and plate readers using the limma and affy packages. • Map experimental laboratory metadata structures to standardized R tidy data frames to ensure reproducible links to downstream molecular assays. • Develop quality control pipelines using R to identify and filter out technical artifacts, outliers, and batch effects in PCR and sequencing datasets.

Outline

Perform differential gene expression analysis on high-throughput RNA-Seq count matrices using statistical frameworks in DESeq2 and EdgeR. • Build phylogenetic trees and conduct sequence alignment analysis utilizing Biostrings, msa, and ape packages in R. • Execute cluster analysis and principal component analysis (PCA) on high-dimensional genomic datasets to identify molecular subtypes.

Outline

Design robust statistical power analysis models in R using the pwr package to determine optimal sample sizes for clinical and genomic studies. • Implement randomized block design and multi-factor ANOVA frameworks in R to control for confounding variables in biological experiments. • Formulate statistical hypothesis testing pipelines, applying false discovery rate (FDR) corrections like Benjamini-Hochberg to large-scale biological screens.

Outline

Develop predictive machine learning models for clinical classification of genomic profiles using the caret and randomForest R libraries. • Construct interactive biological network visualizations and pathway enrichment maps using igraph, RCy3, and clusterProfiler. • Process single-cell RNA-sequencing (scRNA-seq) datasets, executing cell-clustering and marker gene identification via the Seurat framework.

Outline

Implement data de-identification and anonymization protocols on clinical datasets in R to comply with HIPAA and GDPR regulations. • Generate automated, reproducible audit trails and compliance reports for computational workflows using R Markdown and knitr. • Program data verification scripts to validate genomic database integrity against international standard reference databases like NCBI and Ensembl.

Outline

Analyze real-world pharmaceutical screening datasets to identify lead drug candidates using quantitative structure-activity relationship models in R. • Build scalable pipeline architectures integrating R scripts with command-line bioinformatic tools for industrial pipeline integration. • Create dynamic, production-grade Shiny dashboards to present molecular assay findings to cross-functional R&D and clinical stakeholders.