Fig 1.
Simulation following the SPARK-X manuscript.
A Four spatial expression patterns that the genes were assumed to follow. B Statistical power plots of the three methods, SMASH, SPARK-X, and SpaGene under varying values of N and fold-size, for K = 500 genes at a level of α = 0.05. The results were averaged over five replications.
Fig 2.
Simulation using Gaussian process-based regression model with the Gaussian covariance.
A) Four spatial expression patterns that were generated using Gaussian covariance matrices with four different values of the lengthscale l. B) Statistical power plots of the three methods under varying values of N and effect-size (h) for K = 1000 genes at a level of α = 0.05. The results were averaged over five replications.
Fig 3.
Simulation using Gaussian process-based regression model with the cosine covariance.
A) Four spatial expression patterns that were generated using cosine covariance matrices with four different values of the period p. B) Statistical power plots of the three methods under varying values of N and effect-size (h) for K = 1000 genes at a level of α = 0.05. The results were averaged over five replications.
Table 1.
Computational complexity and run-time comparison.
The table lists the theoretical complexity and run-time (in seconds) of the four methods, SMASH, SPARK-X, SpaGene, and SpatialDE in a simulation setup with K = 1000 genes and varying number of cells N. The number of spatial coordinates d was equal to 2. *SpaGene constructs multiple kNN graphs and performs permutation tests. We are only listing the complexity of the KNN algorithm.
Fig 4.
Analysis of mouse cerebellum data.
A) Location of the major cell types corresponding to the four spatial layers of the mouse cerebellum. B) Overlap between the detected SVGs by the three methods. C) Enrichment scores of the methods in the four spatial layers.
Fig 5.
Expression patterns in mouse cerebellum data.
Three representative genes from the detected pathways for the four sets of genes: a) the common genes identified by all three methods, b) the genes identified by SMASH and SpaGene but not by SPARK-X, c) the genes identified by SMASH and SPARK-X but not by SpaGene, and d) the genes identified only by SMASH.
Fig 6.
A) Manually labeled cortical layers (layers 1–6) and white matter layer (WML). B) Overlap between the detected SVGs by the three methods. C) Expression of three representative genes identified only by SMASH and SPARK-X. D) Enrichment scores of the methods in different layers.
Fig 7.
A) Pre-identified clusters of cells using Seurat. B) Overlap between the detected SVGs by the three methods. C) Enrichment scores of the methods in different clusters.
Fig 8.
Expression patterns in SCCOHT data.
Three representative genes from the four sets of SVGs: a) the common genes identified by all three methods, b) the genes identified by SMASH and SpaGene but not by SPARK-X, c) the genes identified by SMASH and SPARK-X but not by SpaGene, and d) the genes identified only by SMASH.
Fig 9.
Analysis of mouse hypothalamus data.
A) Overlap between the detected SVGs by the three methods. B) Spatial organization of a few major cell types. C) Expression of two representative genes from each of the three sets, a) the genes identified only by SMASH and SpaGene, b) the genes identified only by SMASH and SPARK-X, and c) the genes identified only by SMASH.
Table 2.
Kernel choices in different methods.
The table shows (yes/no) if a particular kernel covariance or Gram matrix is considered in different methods.