Determining the types and amounts of cells present in bulk proteome samples can be of clinical relevance, for example by aiding in stratification of cancer treatment groups. Traditionally, gene expression data has been used for this purpose, but the potential of protein data has been largely unexplored. This study investigates computational methods for estimating immune cell composition from bulk sample protein data. By collecting proteome data of immune cell populations with DIA mass spectrometry, as well as simulating mixtures of collected sample data, we evaluate the feasibility of cell type deconvolution algorithms for estimation of cell type content. We furthermore explore the use of either peptide level data or protein level data for this purpose. Our findings demonstrate how high accuracy in estimation of mixed cell populations can be achieved, with some estimates closely matching the expected cell levels depending on sample complexity. Moreover, we provide an R package to streamline the preprocessing of proteomics data and facilitate the interpretation of results. This study emphasizes the potential of utilizing proteome data for deciphering cell-type composition in complex biological samples.