Figures
There is an error in Fig 3. The authors have provided a corrected version here.
(A) The label of each cell in target dataset (left), the label each cell was given by the classifiers, black for unclassified (center), and the correctness of classification (right). (B) Heatmap of the scores given to each cell by each classifier, from zero (blue) to one (red). Horizontal black line separates the labels of those classifiers from labels for which classifiers were not trained. (a) Classification of target dataset GSE81904 by multiple binary classifiers built on source dataset GSE63473. Top bar shows which classifiers classified for labels that are in the target dataset. Note that many unknown cells were classified as bipolar or muller, which may be correct. (b) Classification of target dataset GSE81608 by multiple binary classifiers built on source dataset EMTAB5061. Top bar shows which classifiers classified for labels that are in the target dataset. Note that the labels that some of the seemingly incorrect results are very likely correct—the cells labeled as 'alpha contaminated' are classified as alpha or ductal, and same for beta, gamma and delta contaminated. (c) Classification of target dataset EMTAB5061 by multiple binary classifiers built on source dataset GSE81608. Note that many of the 'novel' cell types (acinar, ductal) were not classified, thus 'identified as novel'. Many of the incorrect cells are labeled 'coexpression' or 'not applicable' or 'unclassified', meaning that their classification may be correct. (d) Classification of target dataset GSE63473 by multiple binary classifiers built on source dataset GSE81904.
Reference
- 1. Lieberman Y, Rokach L, Shay T (2018) CaSTLe–Classification of single cells by transfer learning: Harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments. PLoS ONE 13(10): e0205499. https://doi.org/10.1371/journal.pone.0205499 pmid:30304022
Citation: Lieberman Y, Rokach L, Shay T (2018) Correction: CaSTLe - Classification of single cells by transfer learning: Harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments. PLoS ONE 13(11): e0208349. https://doi.org/10.1371/journal.pone.0208349
Published: November 27, 2018
Copyright: © 2018 Lieberman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.