Annotate single cells using SingleR

Annotate single cells using SingleR

Usage

RunSingleR(
  srt_query,
  srt_ref,
  query_group = NULL,
  ref_group = NULL,
  query_assay = "RNA",
  ref_assay = "RNA",
  genes = "de",
  de.method = "wilcox",
  sd.thresh = 1,
  de.n = NULL,
  aggr.ref = FALSE,
  aggr.args = list(),
  quantile = 0.8,
  fine.tune = TRUE,
  tune.thresh = 0.05,
  prune = TRUE,
  cores = 1,
  verbose = TRUE
)

Arguments

srt_query

An object of class Seurat to be annotated with cell types.

srt_ref

An object of class Seurat storing the reference cells.

query_group

A character vector specifying the column name in the `srt_query` metadata that represents the cell grouping.

ref_group

A character vector specifying the column name in the `srt_ref` metadata that represents the cell grouping.

query_assay

A character vector specifying the assay to be used for the query data. Defaults to the default assay of the `srt_query` object.

ref_assay

A character vector specifying the assay to be used for the reference data. Defaults to the default assay of the `srt_ref` object.

genes

"genes" parameter in SingleR::SingleR function.

de.method

"de.method" parameter in SingleR::SingleR function.

sd.thresh

Deprecated and ignored.

de.n

An integer scalar specifying the number of DE genes to use when genes="de". If de.method="classic", defaults to 500 * (2/3) ^ log2(N) where N is the number of unique labels. Otherwise, defaults to 10. Ignored if genes is a list of markers/DE genes.

aggr.ref, aggr.args

Arguments controlling the aggregation of the references prior to annotation, see trainSingleR.

quantile

"quantile" parameter in SingleR::SingleR function.

fine.tune

"fine.tune" parameter in SingleR::SingleR function.

tune.thresh

"tune.thresh" parameter in SingleR::SingleR function.

prune

"prune" parameter in SingleR::SingleR function.

cores

The number of cores to use for parallelization with foreach::foreach. Default is 1.

verbose

Whether to print the message. Default is TRUE.

Examples

data(panc8_sub)
# Simply convert genes from human to mouse and preprocess the data
genenames <- make.unique(
  thisutils::capitalize(
    rownames(panc8_sub),
    force_tolower = TRUE
  )
)
names(genenames) <- rownames(panc8_sub)
panc8_sub <- RenameFeatures(
  panc8_sub,
  newnames = genenames
)
#> ℹ [2025-11-13 12:32:35] Rename features for the assay: RNA
panc8_sub <- CheckDataMerge(
  panc8_sub,
  batch = "tech"
)[["srt_merge"]]
#> ℹ [2025-11-13 12:32:35] Spliting `srt_merge` into `srt_list` by column "tech"...
#> ℹ [2025-11-13 12:32:36] Checking a list of <Seurat> object...
#> ! [2025-11-13 12:32:36] Data 1/5 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 12:32:36] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/5 of the `srt_list`...
#> ℹ [2025-11-13 12:32:38] Perform `Seurat::FindVariableFeatures()` on the data 1/5 of the `srt_list`...
#> ! [2025-11-13 12:32:38] Data 2/5 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 12:32:38] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 2/5 of the `srt_list`...
#> ℹ [2025-11-13 12:32:40] Perform `Seurat::FindVariableFeatures()` on the data 2/5 of the `srt_list`...
#> ! [2025-11-13 12:32:40] Data 3/5 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 12:32:40] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 3/5 of the `srt_list`...
#> ℹ [2025-11-13 12:32:42] Perform `Seurat::FindVariableFeatures()` on the data 3/5 of the `srt_list`...
#> ! [2025-11-13 12:32:43] Data 4/5 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 12:32:43] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 4/5 of the `srt_list`...
#> ℹ [2025-11-13 12:32:45] Perform `Seurat::FindVariableFeatures()` on the data 4/5 of the `srt_list`...
#> ! [2025-11-13 12:32:45] Data 5/5 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 12:32:45] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 5/5 of the `srt_list`...
#> ℹ [2025-11-13 12:32:47] Perform `Seurat::FindVariableFeatures()` on the data 5/5 of the `srt_list`...
#> ℹ [2025-11-13 12:32:47] Use the separate HVF from srt_list
#> ℹ [2025-11-13 12:32:47] Number of available HVF: 2000
#> ℹ [2025-11-13 12:32:48] Finished check

# Annotation
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-11-13 12:32:50] Start standard scop workflow...
#> ℹ [2025-11-13 12:32:51] Checking a list of <Seurat> object...
#> ! [2025-11-13 12:32:51] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 12:32:51] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 12:32:53] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 12:32:54] Use the separate HVF from srt_list
#> ℹ [2025-11-13 12:32:54] Number of available HVF: 2000
#> ℹ [2025-11-13 12:32:54] Finished check
#> ℹ [2025-11-13 12:32:54] Perform `Seurat::ScaleData()`
#> ℹ [2025-11-13 12:32:55] 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 12:32:56] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-11-13 12:32:56] Reorder clusters...
#> ℹ [2025-11-13 12:32:56] Perform umap nonlinear dimension reduction
#> ℹ [2025-11-13 12:32:56] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 12:32:56] UMAP will return its model
#> ℹ [2025-11-13 12:33:01] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 12:33:01] UMAP will return its model
#> ✔ [2025-11-13 12:33:05] Run scop standard workflow done
pancreas_sub <- RunSingleR(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  query_group = "Standardpca_SNN_res.0.6",
  ref_group = "celltype"
)
#> ℹ [2025-11-13 12:33:05] Start SingleR annotation
#> ◌ [2025-11-13 12:33:05] Installing: SingleR...
#>  
#> → Will install 3 packages.
#> → All 3 packages (0 B) are cached.
#> + DelayedMatrixStats   1.32.0 [bld]
#> + SingleR              2.12.0 [bld][cmp]
#> + sparseMatrixStats    1.22.0 [bld][cmp]
#>   
#> ℹ No downloads are needed, 3 pkgs are cached
#> ℹ Building sparseMatrixStats 1.22.0
#> ✔ Built sparseMatrixStats 1.22.0 (17.7s)
#> ✔ Installed sparseMatrixStats 1.22.0  (56ms)
#> ℹ Building DelayedMatrixStats 1.32.0
#> ✔ Built DelayedMatrixStats 1.32.0 (10.9s)
#> ✔ Installed DelayedMatrixStats 1.32.0  (29ms)
#> ℹ Building SingleR 2.12.0
#> ✔ Built SingleR 2.12.0 (42.8s)
#> ✔ Installed SingleR 2.12.0  (87ms)
#> ✔ 1 pkg + 30 deps: kept 28, added 3 [1m 13s]
#> ◌ [2025-11-13 12:34:19] Installing: scrapper...
#>  
#> → Will install 4 packages.
#> → All 4 packages (0 B) are cached.
#> + Rigraphlib   1.2.0  [bld][cmp]
#> + biocmake     1.2.0  [bld]
#> + dir.expiry   1.18.0 [bld]
#> + scrapper     1.4.0  [bld][cmp]
#>   
#> ℹ No downloads are needed, 4 pkgs are cached
#> ✔ Got biocmake 1.2.0 (source) (229.12 kB)
#> ✔ Got dir.expiry 1.18.0 (source) (308.96 kB)
#> ✔ Got scrapper 1.4.0 (source) (958.15 kB)
#> ✔ Got Rigraphlib 1.2.0 (source) (4.53 MB)
#> ℹ Building dir.expiry 1.18.0
#> ✔ Built dir.expiry 1.18.0 (1s)
#> ✔ Installed dir.expiry 1.18.0  (1.1s)
#> ℹ Building biocmake 1.2.0
#> ✔ Built biocmake 1.2.0 (1.1s)
#> ✔ Installed biocmake 1.2.0  (1s)
#> ℹ Building Rigraphlib 1.2.0
#> ✔ Built Rigraphlib 1.2.0 (2m 15s)
#> ✔ Installed Rigraphlib 1.2.0  (292ms)
#> ℹ Building scrapper 1.4.0
#> ✔ Built scrapper 1.4.0 (4m 21.6s)
#> ✔ Installed scrapper 1.4.0  (696ms)
#> ✔ 1 pkg + 21 deps: kept 18, added 4, dld 4 (6.03 MB) [6m 44.1s]
#> ✔ [2025-11-13 12:41:03] SingleR and scrapper installed successfully
#> ℹ [2025-11-13 12:41:03] Data type is log-normalized
#> ℹ [2025-11-13 12:41:03] Detected srt_query data type: "log_normalized_counts"
#> ℹ [2025-11-13 12:41:05] Data type is log-normalized
#> ℹ [2025-11-13 12:41:05] Detected srt_ref data type: "log_normalized_counts"
#> ℹ [2025-11-13 12:41:09] Perform "SingleRCluster"
#> ✔ [2025-11-13 12:41:10] SingleR annotation completed
CellDimPlot(
  pancreas_sub,
  group.by = c("singler_annotation", "CellType")
)


pancreas_sub <- RunSingleR(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  query_group = NULL,
  ref_group = "celltype"
)
#> ℹ [2025-11-13 12:41:10] Start SingleR annotation
#> ✔ [2025-11-13 12:41:10] SingleR and scrapper installed successfully
#> ℹ [2025-11-13 12:41:11] Data type is log-normalized
#> ℹ [2025-11-13 12:41:11] Detected srt_query data type: "log_normalized_counts"
#> ℹ [2025-11-13 12:41:14] Data type is log-normalized
#> ℹ [2025-11-13 12:41:14] Detected srt_ref data type: "log_normalized_counts"
#> ℹ [2025-11-13 12:41:18] Perform "SingleRCell"
#> ✔ [2025-11-13 12:41:22] SingleR annotation completed
CellDimPlot(
  pancreas_sub,
  group.by = c("singler_annotation", "CellType")
)