This project presents a comprehensive analysis of squamous cell lung carcinoma (SqCLC) by integrating single-cell RNA sequencing and spatial transcriptomics to uncover the molecular and cellular heterogeneity within tumor tissues. Using public datasets, we performed detailed quality control, clustering, and trajectory inference on both normal and cancerous lung cells, revealing altered lineage relationships and key differentially expressed genes such as AGER and CAV1 in Type I pneumocytes. To preserve spatial context, we mapped single-cell-derived cell type labels onto tissue sections through a custom cosine-distance-based label transfer pipeline. This enabled the localization of distinct immune and epithelial populations within the tumor microenvironment and highlighted disrupted intercellular communication networks using LIANA-based ligand-receptor analysis. Together, this integrative approach captures both transcriptional diversity and spatial architecture, offering insights into disease progression and biomarker localization in SqCLC.