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Fig. 4 | Molecular Medicine

Fig. 4

From: Use of machine learning-based integration to develop a monocyte differentiation-related signature for improving prognosis in patients with sepsis

Fig. 4

Characterization of high- and low-risk patients. A Differential expression of 17 signature gene between healthy individuals and sepsis patients. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by two-tailed t-test; B Expression of 17 signature gene in monocyte differentiation trajectory. Each row is a gene, each column is a sample, and the differentiation time is from left to right. The color changes from blue to red indicating increasing levels of gene expression; C Identification of cell subpopulations highly correlated with the phenotypes of high-risk and low-risk patients. Blue are identified low-risk cells, red are identified high-risk cells, and gray are unclassified cells; D Proportional analysis of phenotype-associated cell subpopulations in three differentiation states. The horizontal axis is the percentage of cells and the vertical axis is the different states of monocytes. Blue are identified low-risk cells, red are identified high-risk cells, and gray are unclassified cells; E Cellular communication landscape. Different colors represent that the signal originates from different cells, the thicker the line represents the higher weight of the signal action, and the arrows represent the direction of signal sending and receiving; F Analysis of ligand-receptor communication between different immune cells. The horizontal axis is the ligand cell and the corresponding receptor cell, and the vertical axis represents the different signal pairs. The color of the dots from blue to red represents the stronger the strength of the signal; G Cellular communication analysis of CD99 signaling; H Analysis of immune cell content. Low-risk patients in blue and high-risk patients in red; I Analysis of immune function. Low-risk patients in blue and high-risk patients in red. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by two-tailed t-test

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