This project initiates an exploratory data analysis (EDA) of the rich dataset presented in the research paper 'Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing' by Guo et al., Nature Medicine (2018). The initial phase focuses on dissecting and visualizing the key findings related to T cell composition, lineage, and functional states within the context of non-small-cell lung cancer (NSCLC) as detailed in the study.

Beyond this initial analysis, the project aims to develop an interactive dashboard. This dashboard will be designed to make the complex single-cell RNA sequencing data from this NSCLC GEO dataset more accessible and interpretable, particularly for users who may not have extensive coding experience. The goal is to provide an intuitive interface for exploring T cell heterogeneity, clonal expansion, and the distinct characteristics of T cell clusters identified in the paper, such as 'pre-exhausted' and exhausted T cells, and regulatory T cells (Tregs). Ultimately, this tool will facilitate easier a deeper understanding of T cell dynamics in lung cancer as presented in this significant public dataset."