I am a research scholar and provide biology data analysis and visualization in R using already processed transcriptomics and genomics datasets (for example, count matrices, normalized expression tables, or variant/feature tables). I focus on cleaning and organizing your data, performing exploratory and statistical analyses (including differential expression or differential abundance), and assessing issues such as batch effects or sample outliers. I can apply dimensionality‑reduction and clustering methods (PCA, t‑SNE, UMAP, and related approaches) to reveal structure in your data, and carry out functional enrichment, pathway, and gene‑set analyses to help interpret biological meaning. I also specialize in creating clear, publication‑ready visualizations such as heatmaps, volcano plots, MA plots, and pathway diagrams, tailored to your manuscript, presentation, or report. All work is delivered with reproducible R code and concise written summaries of methods and key findings, so that your results are transparent, easy to understand, and straightforward to reuse in future projects.