This project aims to determine the cellular identity of the immune cells composing the tumor microenvironment of a melanoma mouse model using single-cell RNA-sequencing (scRNA-seq). The recent development in the scRNA-seq technology has now made the full transcriptome of individual cells within reach and therefore enables us to characterize the identity of cells according to their full gene expression. However the substantial stochasticity and high dropout rates of scRNA-seq data challenge the process of cell type identification. To overcome these limitations I apply here a computational approach that takes advantages of immune transcriptomic repositories to define cell type specific gene markers in order to quantify the cellular identity of immune cells in melanoma. The performance of the identification algorithm has been first tested with cross-validation analysis of sorted cell bulk gene expression profiles. Then, the cell type identification algorithm has been applied on scRNA-seq data and revealed that the melanoma immune microenvironment from our mouse model is composed of a large proportion of T and NK cells.