The popularity of single-cell analysis has risen due to recent advancements in single-cell transcriptomics, especially in sequencing technologies. Single-cell analysis provides us with a new perspective of cellular data and helps us study cellular heterogeneity. Expression profiles of individual cells can be derived with single-cell RNA sequencing (scRNA data) and corresponding gene expressions. In the Thesis we propose an approach for cell type discovery in such data. Input to the proposed approach is a gene expression matrix, along with a set of marker genes, typical for a specific cell type. Our method starts with visualizing data in 2D and then finds regions of chosen cell type by measuring functional enrichments across local neighborhoods and estimating their significances. We applied the proposed approach on three datasets from recent single-cell sequencing studies. We got encouraging results and showed the approach to be robust to dimensionality reduction and neighborhood size. There is still room for improvement and in order to further illustrate full functionality of the approach, more testing on larger datasets would be required.