Cell deconvolution methods have emerged in recent years as relevant bioinformatics approaches for predicting the proportions of cell types present in biological samples profiled by bulk RNA-seq. Within the framework of the European KATY project (https://katy-project.eu/), we are interested in the heterogeneity of the tumor microenvironment of the clear cell renal cell carcinoma (ccRCC) and its influence on the ability to predict a patient's response to a treatment. To meet this objective, we have optimized a bioinformatics protocol for the analysis of single-cell RNA-seq data and the prediction of cell fractions by deconvolution methods. We evaluated the cell deconvolution methods CIBERSORTx and MuSiC. The single cell RNA-seq matrix used in our work to perform cell deconvolution was optimized from the one obtained from 11 adult patients with ccRCC. We generated pseudo-Bulk RNA-seq matrices from the single cell RNA- seq matrix to assess the performance of cell deconvolution methods. Then, we performed deconvolution on a cohort of bulk RNA-seq data consisting of 311 ccRCC tumor samples and assessed the quality of our predictions by comparison with tumor purity scores