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Fig. 1 | Genome Biology

Fig. 1

From: A comparison of automatic cell identification methods for single-cell RNA sequencing data

Fig. 1

Performance comparison of supervised classifiers for cell identification using different scRNA-seq datasets. Heatmap of the a median F1-scores and b percentage of unlabeled cells across all cell populations per classifier (rows) per dataset (columns). Gray boxes indicate that the corresponding method could not be tested on the corresponding dataset. Classifiers are ordered based on the mean of the median F1-scores. Asterisk (*) indicates that the prior-knowledge classifiers, SCINA, DigitalCellSorter, GarnettCV, Garnettpretrained, and Moana, could not be tested on all cell populations of the PBMC datasets. SCINADE, GarnettDE, and DigitalCellSorterDE are versions of SCINA, GarnettCV, and DigitalCellSorter; the marker genes are defined using differential expression from the training data. Different numbers of marker genes, 5, 10, 15, and 20, were tested, and the best result is shown here. SCINA, Garnett, and DigitalCellSorter produced the best result for the Zheng sorted dataset using 20, 15, and 5 markers, and for the Zheng 68K dataset using 10, 5, and 5 markers, respectively

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