Single cell sequencing offers tremendous insight in understanding the composition of individual cells and identifying the features or combination of features that lead from one state to another. However, the increased dimensionality of studying 1000s of cells over 20,000+ genes necessitates new approaches to identify the potentially small fraction of cells whose state are truly in transition. This is particularly relevant in cancer where the development of drug resistant states is endemic. Transition Topological Data Analysis (T2DA) enables the identification of single cell sub-populations, i.e. states, along with the features that characterize this transition between states. T2DA performs a transformation of the data that preserves the original feature space while finding a backbone connecting cell states. Transition features extracted from the backbone may then be used to understand the mechanisms of cell state change. In a melanoma single cell RNA sequencing study of drug resistance, T2DA was able to identify biologically relevant transition genes describing cells that change from a drug responsive to resistive state. Such putative resistance genes can guide patient treatment planning or drug development. T2DA offers a novel approach for extracting greater value from single cell sequencing data and capturing the mechanisms that underlie cell state transitions.