International Journal of Molecular Medical Science, 2024, Vol.14, No.4, 239-251 http://medscipublisher.com/index.php/ijmms 245 heterogeneous immune landscape and pivotal cell subpopulations associated with prognosis (Zhang et al., 2023). These methods facilitate the visualization and interpretation of high-dimensional data, enabling the identification of distinct cellular populations and their relationships. 5.3 Clustering and cell type identification Clustering algorithms, such as k-means, hierarchical clustering, and graph-based methods, are employed to group cells with similar expression profiles. This step is crucial for identifying distinct cell types and subpopulations within the tumor microenvironment. In the study of advanced non-small cell lung cancer, single-cell RNA sequencing was used to map the cell type-specific transcriptome landscape, identifying rare cell types and revealing large heterogeneity in cellular composition and phenotype dominance (Wu et al., 2021). Similarly, in colorectal cancer, single-cell sequencing has identified diverse genetic subclones within CD133 positive cancer stem cells, highlighting the complexity of tumor heterogeneity (Min et al., 2020). 5.4 Trajectory inference and lineage tracing Trajectory inference and lineage tracing are advanced computational techniques used to reconstruct the developmental pathways and evolutionary trajectories of cells. These methods can elucidate the dynamic processes of cell differentiation and tumor progression. For instance, the study by Bian et al. (2018) demonstrated the feasibility of reconstructing genetic lineages and tracing their epigenomic and transcriptomic dynamics using single-cell multiomics sequencing. Additionally, trajectory models have been applied to understand the evolutionary trajectory from ulcerative colitis to colitis-associated cancer, providing insights into disease progression at the single-cell level (Wang et al., 2020). These approaches are instrumental in uncovering the mechanisms underlying cancer development and metastasis. In summary, the integration of preprocessing, quality control, data integration, dimensionality reduction, clustering, cell type identification, trajectory inference, and lineage tracing in single-cell sequencing studies provides a comprehensive framework for unveiling cellular heterogeneity in colon cancer. These methodologies collectively enhance our understanding of tumor biology and hold promise for improving cancer diagnosis, prognosis, and treatment strategies. Figure 2 summarized the main analysis process of single cell sequencing. Figure 2 The main analysis process of single cell sequencing.
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