Fig 1.
Workflow representation of the randomized version of RESET as defined by Algorithm 3.
Fig 2.
Classification performance of RESET.det, RESET.ran, VAM, GSVA, ssGSEA, and PLAGE on scRNA-seq data simulated according to the block design for a single gene set as detailed in the Methods section in S1 Text.
Each panel illustrates the relationship between the area under the receiver operating characteristic curve (AUC) and one of the simulation parameters. The vertical dotted lines mark the default parameter value used in the other panels. Error bars represent the standard error of the mean.
Fig 3.
Classification performance of RESET.det, RESET.ran, VAM, GSVA, ssGSEA, and PLAGE on scRNA-seq data simulated according to the complex design for a single gene set as detailed in the Methods section of S1 Text.
Each panel illustrates the relationship between the area under the receiver operating characteristic curve (AUC) and one of the simulation parameters. The vertical dotted lines mark the default parameter value used in the other panels. Error bars represent the standard error of the mean.
Fig 4.
Classification performance of RESET.det, RESET.ran, VAM, GSVA, ssGSEA, and PLAGE on scRNA-seq data simulated according to the pure self-contained design for a single gene set as detailed in the Methods section in S1 Text.
Each panel illustrates the relationship between the area under the receiver operating characteristic curve (AUC) and one of the simulation parameters. The vertical dotted lines mark the default parameter value used in the other panels. Error bars represent the standard error of the mean.
Fig 5.
Classification performance of RESET.det, RESET.ran, RESET.pervar, VAM, GSVA, ssGSEA, and PLAGE on scRNA-seq data simulated according to the pure competitive design for disjoint and equally sized genes as detailed in the Methods section in S1 Text.
Note that RESET.det and RESET.pervar have identical performance in this case since the gene sets all have the same size. Each panel illustrates the relationship between the area under the receiver operating characteristic curve (AUC) and one of the simulation parameters. The vertical dotted lines mark the default parameter value used in the other panels. Error bars represent the standard error of the mean.
Fig 6.
Overall classification performance of RESET.det, RESET.ran and RESET.pervar on scRNA-seq data simulated according to the block design with disjoint and equal sized gene sets as detailed in the Methods section in S1 Text.
Each panel illustrates the relationship between the area under the receiver operating characteristic curve (AUC) and one of the simulation parameters. The vertical dotted lines mark the default parameter value used in the other panels. Error bars represent the standard error of the mean.
Table 1.
Ratio of execution time for the GSVA, ssGSEA, VAM, and PLAGE methods to the execution time for RESET on simulated scRNA-seq data, the PBMC scRNA-seq data set for the MSigDB C2.CP.BIOCARTA collection, the cord blood scRNA-seq data set for the MSigDB C2.CP.BIOCARTA collection, the mouse brain scRNA-seq data set for the MSigDB C5.BP collection, and the Mouse Cell Atlas for a single synthetic gene set.
For the real scRNA-seq data, RESET was executed using the parameters specified in the Methods section in S1 Text. For the simulated data, execution times are relative to the fully randomized version (i.e., “RESET.ran”) as detailed in the Methods section in S1 Text.
Table 2.
Top 20 BioCarta pathways according to overall RESET score for the PBMC data set.
Fig 7.
Visualization of cell type BioCarta pathway enrichment as computed using either VAM or RESET scores on the PBMC scRNA-seq data.