Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples
Fig 3
Comparison of PROB with other existing pseudotime inference methods and GRN inference methods using a real dataset.
We employed a set of scRNA-seq data of dendritic cells (DCs) for benchmarking since the gold standard in this situation is available. The cells were sequenced at 1, 2, 4 and 6h after stimulation of LPS. (a) The estimated latent-temporal progression of cells recapitulated the real progression with R2 = 0.851 to the capture times. (b) Benchmarking PROB with other pseudotime inference methods (Slice, Slicer, PhenoPath, Wishbone, PAGA, Monocole2, DPT, Tscan) evaluated by Kendall Tau and R2 (S4 Fig). (c) a TF network inferred by PROB. (d) Benchmarking PROB with eight existing GRN inference methods (PCOR, LASSO, GENIE3, ARACNe, CLR, MRNET, SCODE and LEAP) based on an experimentally-defined TF network [37] evaluated by AUC of ROC. (e) PROB correctly revealed the ordering of the outgoing causality scores (on a log10 scale) for the known regulators and targets [38] on the DC scRNA-seq dataset. (f) Comparing properties of different methods in their capabilities of predicting network links, regulatory directions and signs as well as gene expression dynamics.