This function calculates gene-set scores from the specified database (db) for each lineage using the specified scoring method (score_method).
It then treats these scores as expression values and uses them as input to the RunDynamicFeatures function to identify dynamically enriched terms along the lineage.
Usage
RunDynamicEnrichment(
srt,
lineages,
score_method = "AUCell",
layer = "data",
assay = NULL,
min_expcells = 20,
r.sq = 0.2,
dev.expl = 0.2,
padjust = 0.05,
IDtype = "symbol",
species = "Homo_sapiens",
db = "GO_BP",
db_update = FALSE,
db_version = "latest",
convert_species = TRUE,
Ensembl_version = NULL,
mirror = NULL,
TERM2GENE = NULL,
TERM2NAME = NULL,
minGSSize = 10,
maxGSSize = 500,
cores = 1,
verbose = TRUE,
seed = 11
)Arguments
- srt
A Seurat object containing the results of differential expression analysis (RunDEtest). If specified, the genes and groups will be extracted from the Seurat object automatically. If not specified, the
geneIDandgeneID_groupsarguments must be provided.- lineages
A character vector specifying the lineages to plot.
- score_method
The method to use for scoring. Can be
"Seurat","AUCell", or"UCell". Defaults to"Seurat".- layer
A character vector specifying the layer in the Seurat object to use. Default is
"counts".- assay
A character vector specifying the assay in the Seurat object to use. Default is
NULL.- min_expcells
The minimum number of expected cells. Default is
20.- r.sq
The R-squared threshold. Default is
0.2.- dev.expl
The deviance explained threshold. Default is
0.2.- padjust
The p-value adjustment threshold. Default is
0.05.- IDtype
A character vector specifying the type of gene IDs in the
srtobject orgeneIDargument. This argument is used to convert the gene IDs to a different type ifIDtypeis different fromresult_IDtype.- species
A character vector specifying the species for which the analysis is performed.
- db
A character vector specifying the name of the database to be used for enrichment analysis.
- db_update
Whether the gene annotation databases should be forcefully updated. If set to FALSE, the function will attempt to load the cached databases instead. Default is FALSE.
- db_version
A character vector specifying the version of the database to be used. This argument is ignored if
db_updateisTRUE. Default is "latest".- convert_species
Whether to use a species-converted database when the annotation is missing for the specified species. The default value is TRUE.
- Ensembl_version
Ensembl database version. If NULL, use the current release version.
- mirror
Specify an Ensembl mirror to connect to. The valid options here are
"www","uswest","useast","asia".- TERM2GENE
A data frame specifying the gene-term mapping for a custom database. The first column should contain the term IDs, and the second column should contain the gene IDs.
- TERM2NAME
A data frame specifying the term-name mapping for a custom database. The first column should contain the term IDs, and the second column should contain the corresponding term names.
- minGSSize
The minimum size of a gene set to be considered in the enrichment analysis.
- maxGSSize
The maximum size of a gene set to be considered in the enrichment analysis.
- cores
The number of cores to use for parallelization with foreach::foreach. Default is
1.- verbose
Whether to print the message. Default is
TRUE.- seed
An integer specifying the random seed. Default is
11.
Examples
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-11-13 12:16:02] Start standard scop workflow...
#> ℹ [2025-11-13 12:16:03] Checking a list of <Seurat> object...
#> ! [2025-11-13 12:16:03] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 12:16:03] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 12:16:05] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 12:16:06] Use the separate HVF from srt_list
#> ℹ [2025-11-13 12:16:06] Number of available HVF: 2000
#> ℹ [2025-11-13 12:16:06] Finished check
#> ℹ [2025-11-13 12:16:06] Perform `Seurat::ScaleData()`
#> ℹ [2025-11-13 12:16:07] Perform pca linear dimension reduction
#> StandardPC_ 1
#> Positive: Aplp1, Cpe, Gnas, Fam183b, Map1b, Hmgn3, Pcsk1n, Chga, Tuba1a, Bex2
#> Syt13, Isl1, 1700086L19Rik, Pax6, Chgb, Scgn, Rbp4, Scg3, Gch1, Camk2n1
#> Cryba2, Pcsk2, Pyy, Tspan7, Mafb, Hist3h2ba, Dbpht2, Abcc8, Rap1b, Slc38a5
#> Negative: Spp1, Anxa2, Sparc, Dbi, 1700011H14Rik, Wfdc2, Gsta3, Adamts1, Clu, Mgst1
#> Bicc1, Ldha, Vim, Cldn3, Cyr61, Rps2, Mt1, Ptn, Phgdh, Nudt19
#> Smtnl2, Smco4, Habp2, Mt2, Col18a1, Rpl12, Galk1, Cldn10, Acot1, Ccnd1
#> StandardPC_ 2
#> Positive: Rbp4, Tagln2, Tuba1b, Fkbp2, Pyy, Pcsk2, Iapp, Tmem27, Meis2, Tubb4b
#> Pcsk1n, Dbpht2, Rap1b, Dynll1, Tubb2a, Sdf2l1, Scgn, 1700086L19Rik, Scg2, Abcc8
#> Atp1b1, Hspa5, Fam183b, Papss2, Slc38a5, Scg3, Mageh1, Tspan7, Ppp1r1a, Ociad2
#> Negative: Neurog3, Btbd17, Gadd45a, Ppp1r14a, Neurod2, Sox4, Smarcd2, Mdk, Pax4, Btg2
#> Sult2b1, Hes6, Grasp, Igfbpl1, Gpx2, Cbfa2t3, Foxa3, Shf, Mfng, Tmsb4x
#> Amotl2, Gdpd1, Cdc14b, Epb42, Rcor2, Cotl1, Upk3bl, Rbfox3, Cldn6, Cer1
#> StandardPC_ 3
#> Positive: Nusap1, Top2a, Birc5, Aurkb, Cdca8, Pbk, Mki67, Tpx2, Plk1, Ccnb1
#> 2810417H13Rik, Incenp, Cenpf, Ccna2, Prc1, Racgap1, Cdk1, Aurka, Cdca3, Hmmr
#> Spc24, Kif23, Sgol1, Cenpe, Cdc20, Hist1h1b, Cdca2, Mxd3, Kif22, Ska1
#> Negative: Anxa5, Pdzk1ip1, Acot1, Tpm1, Anxa2, Dcdc2a, Capg, Sparc, Ttr, Pamr1
#> Clu, Cxcl12, Ndrg2, Hnf1aos1, Gas6, Gsta3, Krt18, Ces1d, Atp1b1, Muc1
#> Hhex, Acadm, Spp1, Enpp2, Bcl2l14, Sat1, Smtnl2, 1700011H14Rik, Tgm2, Fam159a
#> StandardPC_ 4
#> Positive: Glud1, Tm4sf4, Akr1c19, Cldn4, Runx1t1, Fev, Pou3f4, Gm43861, Pgrmc1, Arx
#> Cd200, Lrpprc, Hmgn3, Ppp1r14c, Pam, Etv1, Tsc22d1, Slc25a5, Akap17b, Pgf
#> Fam43a, Emb, Jun, Krt8, Dnajc12, Mid1ip1, Ids, Rgs17, Uchl1, Alcam
#> Negative: Ins2, Ins1, Ppp1r1a, Nnat, Calr, Sytl4, Sdf2l1, Iapp, Pdia6, Mapt
#> G6pc2, C2cd4b, Npy, Gng12, P2ry1, Ero1lb, Adra2a, Papss2, Arhgap36, Fam151a
#> Dlk1, Creld2, Gip, Tmem215, Gm27033, Cntfr, Prss53, C2cd4a, Lyve1, Ociad2
#> StandardPC_ 5
#> Positive: Pdx1, Nkx6-1, Npepl1, Cldn4, Cryba2, Fev, Jun, Chgb, Gng12, Adra2a
#> Mnx1, Sytl4, Pdk3, Gm27033, Nnat, Chga, Ins2, 1110012L19Rik, Enho, Krt7
#> Mlxipl, Tmsb10, Flrt1, Pax4, Tubb3, Prrg2, Gars, Frzb, BC023829, Gm2694
#> Negative: Irx2, Irx1, Gcg, Ctxn2, Tmem27, Ctsz, Tmsb15l, Nap1l5, Pou6f2, Gria2
#> Ghrl, Peg10, Smarca1, Arx, Lrpap1, Rgs4, Ttr, Gast, Tmsb15b2, Serpina1b
#> Slc16a10, Wnk3, Ly6e, Auts2, Sct, Arg1, Dusp10, Sphkap, Dock11, Edn3
#> ℹ [2025-11-13 12:16:08] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-11-13 12:16:08] Reorder clusters...
#> ℹ [2025-11-13 12:16:08] Perform umap nonlinear dimension reduction
#> ℹ [2025-11-13 12:16:08] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 12:16:08] UMAP will return its model
#> ℹ [2025-11-13 12:16:13] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 12:16:13] UMAP will return its model
#> ✔ [2025-11-13 12:16:18] Run scop standard workflow done
pancreas_sub <- RunSlingshot(
pancreas_sub,
group.by = "SubCellType",
reduction = "UMAP"
)
pancreas_sub <- RunDynamicFeatures(
pancreas_sub,
lineages = "Lineage1",
n_candidates = 200
)
#> ℹ [2025-11-13 12:16:20] Start find dynamic features
#> ✔ [2025-11-13 12:16:20] mgcv installed successfully
#> ℹ [2025-11-13 12:16:21] Data type is raw counts
#> ℹ [2025-11-13 12:16:22] Number of candidate features (union): 200
#> ℹ [2025-11-13 12:16:22] Data type is raw counts
#> ℹ [2025-11-13 12:16:22] Calculating dynamic features for "Lineage1"...
#> ℹ [2025-11-13 12:16:22] Using 1 core
#> ⠙ [2025-11-13 12:16:22] Running [1/200] ETA: 8s
#> ⠹ [2025-11-13 12:16:22] Running [59/200] ETA: 5s
#> ⠸ [2025-11-13 12:16:22] Running [147/200] ETA: 2s
#> ✔ [2025-11-13 12:16:22] Completed 200 tasks in 7.1s
#>
#> ℹ [2025-11-13 12:16:22] Building results
#> ✔ [2025-11-13 12:16:29] Find dynamic features done
ht1 <- DynamicHeatmap(
pancreas_sub,
lineages = "Lineage1",
cell_annotation = "SubCellType",
n_split = 4
)
#> ℹ [2025-11-13 12:16:30] 148 features from Lineage1 passed the threshold (exp_ncells>20 & r.sq>0.2 & dev.expl>0.2 & padjust<0.05):
#> ℹ Iapp,Pyy,Rbp4,Chgb,Slc38a5,Lrpprc,Cck,Chga,2810417H13Rik,Cdc20...
#> ✔ [2025-11-13 12:16:30] e1071 installed successfully
#> 'magick' package is suggested to install to give better rasterization.
#>
#> Set `ht_opt$message = FALSE` to turn off this message.
#> ℹ [2025-11-13 12:16:31]
#> ℹ The size of the heatmap is fixed because certain elements are not scalable.
#> ℹ The width and height of the heatmap are determined by the size of the current viewport.
#> ℹ If you want to have more control over the size, you can manually set the parameters 'width' and 'height'.
ht1$plot
pancreas_sub <- RunDynamicEnrichment(
pancreas_sub,
lineages = "Lineage1",
score_method = "UCell",
db = "GO_BP",
species = "Mus_musculus"
)
#> ℹ [2025-11-13 12:16:32] Species: "Mus_musculus"
#> ℹ [2025-11-13 12:16:32] Loading cached: GO_BP version: 3.22.0 nterm:15169 created: 2025-11-13 11:51:23
#> ℹ [2025-11-13 12:16:35] Start cell scoring
#> ℹ [2025-11-13 12:16:35] Data type is log-normalized
#> ℹ [2025-11-13 12:16:36] Number of feature lists to be scored: 2794
#> ◌ [2025-11-13 12:16:36] Installing: UCell...
#>
#> → Will install 1 package.
#> → The package (0 B) is cached.
#> + UCell 2.14.0 [bld]
#>
#> ℹ No downloads are needed, 1 pkg is cached
#> ℹ Building UCell 2.14.0
#> ✔ Built UCell 2.14.0 (11.6s)
#> ✔ Installed UCell 2.14.0 (53ms)
#> ✔ 1 pkg + 28 deps: kept 28, added 1 [12.7s]
#> ✔ [2025-11-13 12:16:49] UCell installed successfully
#> ✔ [2025-11-13 12:19:31] Cell scoring completed
#> ℹ [2025-11-13 12:19:31] Start find dynamic features
#> ✔ [2025-11-13 12:19:31] mgcv installed successfully
#> ℹ [2025-11-13 12:19:32] Data type is log-normalized
#> ℹ [2025-11-13 12:19:32] Number of candidate features (union): 2794
#> ℹ [2025-11-13 12:19:32] Data type is log-normalized
#> ℹ [2025-11-13 12:19:32] Calculating dynamic features for "Lineage1"...
#> ℹ [2025-11-13 12:19:32] Using 1 core
#> ⠙ [2025-11-13 12:19:32] Running [1/2794] ETA: 1m
#> ⠹ [2025-11-13 12:19:32] Running [129/2794] ETA: 27s
#> ⠸ [2025-11-13 12:19:32] Running [439/2794] ETA: 23s
#> ⠼ [2025-11-13 12:19:32] Running [746/2794] ETA: 20s
#> ⠴ [2025-11-13 12:19:32] Running [1057/2794] ETA: 17s
#> ⠦ [2025-11-13 12:19:32] Running [1367/2794] ETA: 14s
#> ⠧ [2025-11-13 12:19:32] Running [1666/2794] ETA: 11s
#> ⠇ [2025-11-13 12:19:32] Running [1969/2794] ETA: 8s
#> ⠏ [2025-11-13 12:19:32] Running [2276/2794] ETA: 5s
#> ⠋ [2025-11-13 12:19:32] Running [2584/2794] ETA: 2s
#> ✔ [2025-11-13 12:19:32] Completed 2794 tasks in 27.4s
#>
#> ℹ [2025-11-13 12:19:32] Building results
#> ✔ [2025-11-13 12:20:00] Find dynamic features done
#> ✔ [2025-11-13 12:20:00] Dynamic enrichment analysis completed
ht2 <- DynamicHeatmap(
pancreas_sub,
assay = "GO_BP",
lineages = "Lineage1_GO_BP",
cell_annotation = "SubCellType",
n_split = 4,
split_method = "kmeans-peaktime"
)
#> ℹ [2025-11-13 12:20:00] 1825 features from Lineage1_GO_BP passed the threshold (exp_ncells>20 & r.sq>0.2 & dev.expl>0.2 & padjust<0.05):
#> ℹ GO-BP-2..deoxyribonucleotide.biosynthetic.process,GO-BP-2..deoxyribonucleotide.metabolic.process,GO-BP-ATP.biosynthetic.process,GO-BP-ATP.metabolic.process,GO-BP-ATP.synthesis.coupled.electron.transport,GO-BP-B.cell.activation,GO-BP-B.cell.apoptotic.process,GO-BP-B.cell.differentiation,GO-BP-B.cell.proliferation,GO-BP-BMP.signaling.pathway...
#> ! [2025-11-13 12:20:00] The values in the 'counts' layer are non-integer. Set the library size to 1.
#> 'magick' package is suggested to install to give better rasterization.
#>
#> Set `ht_opt$message = FALSE` to turn off this message.
#> ℹ [2025-11-13 12:20:01]
#> ℹ The size of the heatmap is fixed because certain elements are not scalable.
#> ℹ The width and height of the heatmap are determined by the size of the current viewport.
#> ℹ If you want to have more control over the size, you can manually set the parameters 'width' and 'height'.
ht2$plot
