Fig. 2: Classification accuracy of scDHA, XGBoost (XGB), Random Forest (RF), Deep Learning (DL), Gradient Boosted Machine (GBM) using five human pancreatic data sets. | Nature Communications

Fig. 2: Classification accuracy of scDHA, XGBoost (XGB), Random Forest (RF), Deep Learning (DL), Gradient Boosted Machine (GBM) using five human pancreatic data sets.

From: Fast and precise single-cell data analysis using a hierarchical autoencoder

Fig. 2

In each scenario (row), we use one data set as training and the rest as testing, resulting in 20 train-predict pairs. The overall panel shows the average accuracy values and their variance (vertical segment). The accuracy values of scDHA are significantly higher than those of other methods (p = 2.1 × 10−8 using Wilcoxon one-tailed test).

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