This article investigates the use of a decision tree machine learning algorithm to accurately reconstruct the tumor microenvironment (TME). To build this algorithm, the researchers first curated a collection of more than 18,000 bulk RNA-seq within cancerous and noncancerous tissues using databases such as GEO and ArrayExpress. Then, the raw RNA-seq datasets were annotated into different cell types, and RNA from different categories were mixed to create millions of artificial tumor transcriptomes that were then used to train the decision tree algorithm. A certain number of these artificial tumor transcriptomes and blood from different donors were set aside to test the decision tree algorithm in terms of its ability to detect the correct cell type. The researchers determined that their algorithm was able to successfully detect 11 novel cell types and accurately predict CD4+ T cells, T regulatory cells, and plasma and non- plasma B cells even with overlapping gene signatures. Furthermore, their algorithm was able to predict the presence of low-abundance cell types when supplied with a dataset consisting of primary early-stage non-small cell lung carcinoma (NSCLC) and clear cell renal cell carcinoma (ccRCC) tumors. Researchers found that this algorithm accurately detects cell types even when faced with a mixture of phenotypically similar cell types and can accurately predict cell percentages in different tissues. Ultimately, their decision tree algorithm was able to identify 18 TME subpopulations and a total of 51 unique cell types from bulk RNA-seq data. Some of the main and important points from the discussion include the comparison of their algorithm to other platforms. According to the results presented in this paper, accurate recognition of these many unique cell types is not found in other platforms, such as CIBERSORT, ABIS, and EPIC. The approach explained in this paper can be applied to any cell type based on its unique RNA profile thus making it a tool that can be widely used.