Machine learning algorithm integrates bulk and single-cell transcriptome sequencing to reveal immune-related personalized therapy prediction features for pancreatic cancer

Longjun Zang; Baoming Zhang; Yanling Zhou; Fusheng Zhang; Xiaodong Tian; Zhongming Tian; Dongjie Chen; Qingwang Miao

doi:doi:10.18632/aging.205293

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Research Paper Volume 15, Issue 23 pp 14109—14140

Machine learning algorithm integrates bulk and single-cell transcriptome sequencing to reveal immune-related personalized therapy prediction features for pancreatic cancer

Figure 6. The underlying mechanism of IRS via scRNA-seq analsysis. (A) The distribution of cells after quality check. (B) UMAP plot showing the cells were clustered into 9 types. (C) The expression of marker genes in 9 types of cells. (D) The distribution of IRS score among 9 types of cells. (E) The expression of 8 IRGs in 9 types of cells. Cell-cell communication analysis were implemented via “CellChat” (F) and “iTALK” (G–I) algorithms.