Figure 2
From: Neural network based integration of assays to assess pathogenic potential
Exploratory data analysis discovered that the SBRL dataset discriminate between different bacterial species. (A) 2D UMAP was performed on the SBRL assays followed by k-means clustering to provide the bacterial samples cluster labels. Every point in the plot is a bacterial sample. The points form groups in the UMAP, suggesting that the SBRL assays can aggregate similar bacteria together. The colors in the figure are the k-mean labels. (B) The neural network model pushes the samples from the same bacteria species closer together. An example output of two species, Vibrio parahaemolyticus and Yersinia enterocolitica, are shown in the UMAP before and after training to show clusters are refined by the model. We quantified how well the samples from the same species are clustered together before and after the training and found the normalized mutual information went from 0.65 to 0.74.
