As a bioinformatics scientist, I have revised the paragraph to improve its clarity and readability:
The tumor microenvironment (TME) is a complex system of cell populations that includes tumor cells, stromal cells, and immune cells. These components play crucial roles in cancer development, metastasis, and response to therapy. To better understand cancer and identify effective targets for treatment, it is essential to reveal the heterogeneity of cell phenotypes within TME. The recent advancements in single-cell transcriptome sequencing provide unprecedented opportunities to identify specific cell types and their functions during tumorigenesis.
However, manual annotation of single-cell transcriptome data is inefficient and subjective. Automated methods for cell annotation still need improvement when annotating highly heterogeneous TME cells. Therefore, there is an urgent need for more accurate and automatic TME cell annotation methods.
To address this challenge, we propose two novel methods based on unsupervised and supervised strategies for cell annotation using single-cell transcriptome data. These approaches aim to improve the efficiency and accuracy of TME cell identification and characterization.
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