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scPred: accurate supervised method for cell type classification from single cell RNA seq data

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Figure

Fig. 1 Summary of the scPred method.step. The gene expression values of the cells from an independent test or validation dataset are projected onto the principal component basisfrom the training model, and the informative PCs are used to predict the class
Fig. 2 Classification performance of tumor cells from gastric adenocarcinoma. scPred shows high prediction accuracy to classify tumor cells (0.979(95% bootstrap CI 0.973–0.984) and non-tumor cells 0.974 (95% bootstrap CI 0.960–0.989)
Fig. 3 Principal component alignment of pancreatic cells.epsilon, acinar, stellate, ductal, endothelial, Schwann, and T cells (bright green cells)
Table 1 Prediction of pancreatic cells. The training panel corresponds to the Muraro, Segerstolpe, and Xin datasets used as areference to train the prediction models for each cell type from the islets of Langerhans
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