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10adavis/README.md

science

Hi ๐Ÿ‘‹, I'm Andrew!

๐Ÿ”ฌ I'm a bioinformatician with a passion for uncovering the intricate genetic and molecular mechanisms that underpin life itself. I'm enthusiastic about the remarkable advances in biomolecular technology, computational resources, algorithm development, and machine learning, all of which promise to revolutionize our understanding of biology.

๐Ÿงฌ My journey in bioinformatics has taken me across diverse research landscapes. I've explored the intricate world of molecular biology, delved into the complexities of cancer biology, ventured into the fascinating realm of dermatology and skin care technology, and examined the science of aging and longevity. This wide-ranging experience has equipped me with a unique perspective on life sciences and technology.

๐Ÿ’ก Currently, I'm deeply immersed in the world of cutting-edge bioinformatics. My work involves analyzing single-cell RNA-seq and spatial transcriptomics data, unlocking the secrets held within individual cells. Additionally, I'm engaged in multiomic analysis methods, including Weighted Gene Co-Expression Network Analysis (WGCNA). These methodologies provide powerful insights into complex biological systems and help us decode the intricate orchestra of life at a molecular level.

๐Ÿค– Beyond my work in bioinformatics, I hold a profound interest in science and technology in general. Artificial Intelligence (AI) has always fascinated me, and, I must admit, I find it both incredibly useful and somewhat terrifying in its implications for our future as a society. In fact, I find myself using AI tools quite frequently now, all while contemplating the exciting and challenging paths it presents to us as a civilization.

single cell

๐Ÿ“ซ How to reach me: ...

linkedin: andrewjdavis-utmda

ResearchGate: Andrew_Davis6

visitors

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  1. Git_Training Git_Training Public

    Tutorial for Git and GitHub

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  2. DESeq2_fGSEA_Flow DESeq2_fGSEA_Flow Public template

    This repo is a template for running differential gene expression analysis of RNA-seq count data followed by gene set enrichment analysis. This workflow is run in R using Rmarkdown. It is based arouโ€ฆ

    R 1

  3. Rmarkdown_Template Rmarkdown_Template Public template

    Rmarkdown Analysis Template

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  4. scRNAseq_Seurat_Integration_Tutorial scRNAseq_Seurat_Integration_Tutorial Public

    Use of Seurat's integration methods to perform a comparative analysis of human immune cells (PBMC) in either a resting or interferon-stimulated state

    HTML