Figure 1: | Scientific Reports

Figure 1

From: Machine learning based classification of cells into chronological stages using single-cell transcriptomics

Figure 1

A Chronological age classifier for zebrafish beta-cells. (a) Schematic of the machine learning framework for classifying the chronological stage of zebrafish beta-cells based on single-cell transcriptome (see Online Methods for details). (b) Barplot showing the accuracy of GERAS for classifying the ages of beta-cells that were excluded during the training of the model. The classification of the excluded beta-cells displayed greater than 91% accuracy. Error bars indicate standard error. The F1-score for each stage is displayed at the bottom. The F1-score is a metric evaluating the precision and the sensitivity of the classifier, with the highest being 1, and the lowest being 0. (c) Balloonplots showing the age-classification of de-novo sequenced beta-cells. GERAS classified the age of the cells from independent sources with greater than or equal to 92% accuracy, showcasing the robustness of the model in handling biological and technical noise. (d) Balloonplots showing the age-classification of beta-cells from 3 mpf animals sequenced using the Fluidigm C1 platform. GERAS classified the age of the cells from the cohort with 92.3% accuracy, demonstrating the robustness of the model in handling alternative sequencing pipelines. (e) The capacity of GERAS to perform interpolation was tested using cells with ages in-between the chronological stages used to train GERAS. More than 97% of the cells from the intermediate time-points classify in the nearest-neighbor stages. Number of cells for each condition is denoted by ‘n’.