Plots for metabolism pathway scoring
Usage
MetabolismPlot(
object = NULL,
res = NULL,
group.by = NULL,
assay_name = "METABOLISM",
...,
verbose = TRUE,
srt = NULL
)Arguments
- object
A Seurat object containing the results of RunMetabolism.
- res
GSVA results generated by RunGSVA function. If provided, 'srt' and 'group.by' are ignored.
- group.by
Grouping variable used in RunMetabolism.
- assay_name
Assay or tools slot containing metabolism results.
- ...
Additional arguments passed to GSVAPlot.
- verbose
Whether to print messages.
- srt
Deprecated alias for
object; supply exactly one of the two. It will be removed in scop 1.0.0.
Examples
data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-10-11 23:01:17] Start standard processing workflow...
#> ℹ [2026-10-11 23:01:17] Checking a list of <Seurat>...
#> ! [2026-10-11 23:01:17] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 23:01:17] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:01:17] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:01:17] Use the separate HVF from `srt_list`
#> ℹ [2026-10-11 23:01:17] Number of available HVF: 2000
#> ℹ [2026-10-11 23:01:17] Finished check
#> ℹ [2026-10-11 23:01:17] Perform `ScaleData()`
#> ℹ [2026-10-11 23:01:17] Perform pca linear dimension reduction
#> ℹ [2026-10-11 23:01:18] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-10-11 23:01:18] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-11 23:01:18] Reorder clusters...
#> ℹ [2026-10-11 23:01:18] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 23:01:18] Perform umap nonlinear dimension reduction
#> ✔ [2026-10-11 23:01:24] Standard processing workflow completed
pancreas_sub <- RunMetabolism(
pancreas_sub,
db = c("KEGG", "REACTOME"),
group.by = "CellType",
species = "Mus_musculus",
method = "AUCell",
use_preparedb = FALSE
)
#> ℹ [2026-10-11 23:01:24] Start metabolism pathway scoring
#> ℹ [2026-10-11 23:01:25] Data type is raw counts
#> ℹ [2026-10-11 23:01:25] Averaging expression by "CellType" ...
#> ℹ [2026-10-11 23:01:25] Aggregated expression: 15998 genes x 5 groups
#> ℹ [2026-10-11 23:01:25] Using raw scMetabolism gene sets with species conversion to "Mus_musculus"
#> ℹ [2026-10-11 23:01:25] Converting 3297 human gene symbols to "Mus_musculus" via biomaRt ...
#> ! [2026-10-11 23:01:25] `GeneConvert()` failed: "variable names are limited to 10000 bytes". Falling back to direct symbol matching.
#> ℹ [2026-10-11 23:01:25] Total metabolism gene sets to score: 127
#> ✔ [2026-10-11 23:01:25] Metabolism scores stored in tools slot "Metabolism_CellType_AUCell"
ht1 <- MetabolismPlot(
pancreas_sub,
group.by = "CellType",
plot_type = "heatmap",
topTerm = 10,
show_row_names = TRUE,
width = 1,
height = 2
)
ht1$plot
ht2 <- MetabolismPlot(
pancreas_sub,
group.by = "CellType",
plot_type = "heatmap",
n_split = 3,
topTerm = 100,
use_raster = TRUE,
width = 1,
height = 2
)
ht2$plot
MetabolismPlot(
pancreas_sub,
group.by = "CellType",
db = "GO_BP",
plot_type = "comparison",
topTerm = 5
)
MetabolismPlot(
pancreas_sub,
group.by = "CellType",
db = "GO_BP",
group_use = "Ductal",
plot_type = "bar",
topTerm = 5
)
MetabolismPlot(
pancreas_sub,
group.by = "CellType",
group_use = "Ductal",
db = "GO_BP",
plot_type = "network",
topTerm = 3
)
#> ✔ [2026-10-11 23:01:28] shadowtext installed successfully
MetabolismPlot(
pancreas_sub,
group.by = "CellType",
group_use = "Ductal",
db = "GO_BP",
plot_type = "enrichmap"
)
MetabolismPlot(
pancreas_sub,
group.by = "CellType",
group_use = "Ductal",
plot_type = "wordcloud",
word_type = "feature"
)
pancreas_sub <- RunMetabolism(
pancreas_sub,
assay_name = "METABOLISM",
db = c("KEGG", "REACTOME"),
species = "Mus_musculus",
use_preparedb = FALSE
)
#> ℹ [2026-10-11 23:01:33] Start metabolism pathway scoring
#> ℹ [2026-10-11 23:01:33] Data type is raw counts
#> ℹ [2026-10-11 23:01:33] Using raw scMetabolism gene sets with species conversion to "Mus_musculus"
#> ℹ [2026-10-11 23:01:33] Converting 3297 human gene symbols to "Mus_musculus" via biomaRt ...
#> ! [2026-10-11 23:01:33] `GeneConvert()` failed: "variable names are limited to 10000 bytes". Falling back to direct symbol matching.
#> ℹ [2026-10-11 23:01:33] Total metabolism gene sets to score: 127
#> ✔ [2026-10-11 23:01:34] Metabolism scores stored in tools slot "Metabolism_AUCell"
#> ℹ [2026-10-11 23:01:34] Metabolism scores also stored in assay "METABOLISM"
FeatureDimPlot(
pancreas_sub,
assay = "METABOLISM",
features = rownames(pancreas_sub[["METABOLISM"]])[1:2],
reduction = "umap"
)
FeatureStatPlot(
pancreas_sub,
stat.by = rownames(pancreas_sub[["METABOLISM"]])[1:2],
group.by = "CellType",
assay = "METABOLISM"
)
ht <- GroupHeatmap(
pancreas_sub,
exp_legend_title = "Z-score",
features = rownames(pancreas_sub[["METABOLISM"]])[1:10],
group.by = "CellType",
assay = "METABOLISM",
width = 1,
height = 2
)
#> ! [2026-10-11 23:01:35] The values in the "counts" layer are non-integer. Set the library size to "1"