Studying the interactions between cancer and the immune system has led to the development of immunotherapeutics that can cure patients with historically untreatable tumors. However, the mechanisms of action and predictive biomarkers of these therapeutics -- immune checkpoint inhibitors, or ICIs -- are yet to be fully characterized. Discovering these mechanisms requires measuring and contrasting various immune celltypes’ responses to ICIs in different cancer contexts across many potentially relevant pathways, a task for which single cell transcriptomics sequencing (scRNAseq) is particularly well suited. In Chapter 1 we compared the immune response to ICIs of melanoma and basal cell carcinoma (BCC) measured via scRNAseq and found that the ratio of macrophages to memory B cells can predict response to ICIs. In Chapter 2 we compared resistance mechanisms of melanoma and BCC to pancreatic ductal adenocarcinoma (PDAC) which does not respond to ICIs. We determined that PDAC’s resistance to ICIs is explained by low expression of the target proteins. In Chapter 3, we studied why BCC doesn’t induce an immune response. We discovered that most BCCs downregulate antigen presentation genes and therefore avoid immune detection entirely. In Chapter 4, we pivoted to studying feature selection (a step in standard machine learning pipelines whereby uninformative gene expression is removed) in scRNAseq data analyses. We found that feature selection, despite being a step that has not been studied in great depth in the context of scRNAseq analyses, can be a crucial step in correctly stratifying different groups of cells. We then derived an analytical feature selection method that correctly ranks features in terms of their utility in separating different cell states and removes false positive features that would lead to spurious cell states. The findings of this thesis will lead to advances in both immuno-oncology and scRNAseq analyses.