Extended Data Fig. 4: Performance of cell-cycle state inference by deconvoluting melanoma pseudo samples with monocytes as reference. | Nature Machine Intelligence

Extended Data Fig. 4: Performance of cell-cycle state inference by deconvoluting melanoma pseudo samples with monocytes as reference.

From: Deep domain adversarial neural network for the deconvolution of cell type mixtures in tissue proteome profiling

Extended Data Fig. 4: Performance of cell-cycle state inference by deconvoluting melanoma pseudo samples with monocytes as reference.The alternative text for this image may have been generated using AI.

a, Scatter plot of true (y axis) and predicted cell-cycle states proportions (x axis) of all stages, G1 stage, S stage, and G2 stage for melanoma pseudo samples (from left to right) by scpDeconv with monocytes samples as reference. b, Scatter plot of true (y axis) and predicted cell-cycle states proportions (x axis) of all stages, G1 stage, S stage, and G2 stage for melanoma pseudo samples (from left to right) by Scaden with monocytes samples as reference. c, Scatter plot of true (y axis) and predicted cell-cycle states proportions (x axis) of all stages, G1 stage, S stage, and G2 stage for melanoma pseudo samples (from left to right) by MuSiC with monocytes samples as reference. d, Scatter plot of true (y axis) and predicted cell-cycle states proportions (x axis) of all stages, G1 stage, S stage, and G2 stage for melanoma pseudo samples (from left to right) by BayesPrism with monocytes samples as reference. e, Scatter plot of true (y axis) and predicted cell-cycle states proportions (x axis) of all stages, G1 stage, S stage, and G2 stage for melanoma pseudo samples (from left to right) by DestVI with monocytes samples as reference.

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