Annotate single cells using scmap.

Usage

RunScmap(
  srt_query,
  srt_ref,
  ref_group = NULL,
  query_assay = "RNA",
  ref_assay = "RNA",
  method = "scmapCluster",
  nfeatures = 500,
  threshold = 0.5,
  k = 10
)

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.
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.
method: The method to be used for scmap analysis. Can be any of "scmapCluster" or "scmapCell". The default value is "scmapCluster".
nfeatures: The number of top features to be selected. The default value is 500.
threshold: The threshold value on similarity to determine if a cell is assigned to a cluster. This should be a value between 0 and 1. The default value is 0.5.
k: Number of clusters per group for k-means clustering when method is "scmapCell".

Examples

data(panc8_sub)

genenames <- make.unique(
  thisutils::capitalize(
    rownames(panc8_sub),
    force_tolower = TRUE
  )
)
names(genenames) <- rownames(panc8_sub)
panc8_sub <- RenameFeatures(
  panc8_sub,
  newnames = genenames
)
#> ℹ [2025-09-20 13:48:12] Rename features for the assay: RNA
panc8_sub <- CheckDataMerge(
  panc8_sub,
  batch = "tech"
)[["srt_merge"]]
#> ℹ [2025-09-20 13:48:12] Spliting `srt_merge` into `srt_list` by column "tech"...
#> ℹ [2025-09-20 13:48:12] Checking a list of <Seurat> object...
#> ! [2025-09-20 13:48:12] Data 1/5 of the `srt_list` is "unknown"
#> ℹ [2025-09-20 13:48:12] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/5 of the `srt_list`...
#> ℹ [2025-09-20 13:48:14] Perform `Seurat::FindVariableFeatures()` on the data 1/5 of the `srt_list`...
#> ! [2025-09-20 13:48:14] Data 2/5 of the `srt_list` is "unknown"
#> ℹ [2025-09-20 13:48:14] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 2/5 of the `srt_list`...
#> ℹ [2025-09-20 13:48:16] Perform `Seurat::FindVariableFeatures()` on the data 2/5 of the `srt_list`...
#> ! [2025-09-20 13:48:16] Data 3/5 of the `srt_list` is "unknown"
#> ℹ [2025-09-20 13:48:16] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 3/5 of the `srt_list`...
#> ℹ [2025-09-20 13:48:18] Perform `Seurat::FindVariableFeatures()` on the data 3/5 of the `srt_list`...
#> ! [2025-09-20 13:48:18] Data 4/5 of the `srt_list` is "unknown"
#> ℹ [2025-09-20 13:48:18] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 4/5 of the `srt_list`...
#> ℹ [2025-09-20 13:48:20] Perform `Seurat::FindVariableFeatures()` on the data 4/5 of the `srt_list`...
#> ! [2025-09-20 13:48:20] Data 5/5 of the `srt_list` is "unknown"
#> ℹ [2025-09-20 13:48:21] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 5/5 of the `srt_list`...
#> ℹ [2025-09-20 13:48:22] Perform `Seurat::FindVariableFeatures()` on the data 5/5 of the `srt_list`...
#> ℹ [2025-09-20 13:48:23] Use the separate HVF from srt_list
#> ℹ [2025-09-20 13:48:23] Number of available HVF: 2000
#> ℹ [2025-09-20 13:48:23] Finished check

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-09-20 13:48:26] Start standard scop workflow...
#> ℹ [2025-09-20 13:48:26] Checking a list of <Seurat> object...
#> ! [2025-09-20 13:48:26] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2025-09-20 13:48:26] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2025-09-20 13:48:29] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-09-20 13:48:29] Use the separate HVF from srt_list
#> ℹ [2025-09-20 13:48:29] Number of available HVF: 2000
#> ℹ [2025-09-20 13:48:29] Finished check
#> ℹ [2025-09-20 13:48:30] Perform `Seurat::ScaleData()`
#> Warning: Different features in new layer data than already exists for scale.data
#> ℹ [2025-09-20 13:48:30] 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 [2025-09-20 13:48:31] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-09-20 13:48:31] Reorder clusters...
#> ! [2025-09-20 13:48:31] Using `Seurat::AggregateExpression()` to calculate pseudo-bulk data for <Assay5>
#> ℹ [2025-09-20 13:48:31] Perform umap nonlinear dimension reduction
#> ℹ [2025-09-20 13:48:31] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 13:48:31] UMAP will return its model
#> ℹ [2025-09-20 13:48:35] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 13:48:35] UMAP will return its model
#> ✔ [2025-09-20 13:48:40] Run scop standard workflow done
pancreas_sub <- RunScmap(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  ref_group = "celltype",
  method = "scmapCluster"
)
#> ℹ [2025-09-20 13:48:40] Installing: scmap...
#>  
#> → Will install 3 packages.
#> → All 3 packages (0 B) are cached.
#> + googleVis      0.7.3   
#> + randomForest   4.7-1.2 
#> + scmap          1.30.0  [bld][cmp]
#> ✔ All system requirements are already installed.
#>   
#> ℹ No downloads are needed, 3 pkgs are cached
#> ✔ Got randomForest 4.7-1.2 (x86_64-pc-linux-gnu-ubuntu-24.04) (218.82 kB)
#> ✔ Got googleVis 0.7.3 (x86_64-pc-linux-gnu-ubuntu-24.04) (498.39 kB)
#> ✔ Got scmap 1.30.0 (source) (2.34 MB)
#> ℹ Installing system requirements
#> ℹ Executing `sudo sh -c apt-get -y update`
#> Get:1 file:/etc/apt/apt-mirrors.txt Mirrorlist [144 B]
#> Hit:2 http://azure.archive.ubuntu.com/ubuntu noble InRelease
#> Hit:3 http://azure.archive.ubuntu.com/ubuntu noble-updates InRelease
#> Hit:4 http://azure.archive.ubuntu.com/ubuntu noble-backports InRelease
#> Hit:5 http://azure.archive.ubuntu.com/ubuntu noble-security InRelease
#> Hit:6 https://packages.microsoft.com/repos/azure-cli noble InRelease
#> Hit:7 https://packages.microsoft.com/ubuntu/24.04/prod noble InRelease
#> Reading package lists...
#> ℹ Executing `sudo sh -c apt-get -y install libcurl4-openssl-dev libssl-dev libicu-dev`
#> Reading package lists...
#> Building dependency tree...
#> Reading state information...
#> libcurl4-openssl-dev is already the newest version (8.5.0-2ubuntu10.6).
#> libssl-dev is already the newest version (3.0.13-0ubuntu3.5).
#> libicu-dev is already the newest version (74.2-1ubuntu3.1).
#> 0 upgraded, 0 newly installed, 0 to remove and 41 not upgraded.
#> ✔ Installed googleVis 0.7.3  (32ms)
#> ✔ Installed randomForest 4.7-1.2  (47ms)
#> ℹ Building scmap 1.30.0
#> ✔ Built scmap 1.30.0 (21.6s)
#> ✔ Installed scmap 1.30.0  (1.1s)
#> ✔ 1 pkg + 63 deps: kept 60, added 3, dld 3 (3.06 MB) [26.9s]
#> ℹ [2025-09-20 13:49:07] scmap installed successfully
#> Error in CheckDataType(data = GetAssayData5(srt_query, layer = "data",     assay = query_assay, verbose = FALSE)): argument "object" is missing, with no default
CellDimPlot(
  pancreas_sub,
  group.by = "scmap_annotation"
)
#> Error in CellDimPlot(pancreas_sub, group.by = "scmap_annotation"): Scmap_annotation is not in the meta.data of srt object.

pancreas_sub <- RunScmap(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  ref_group = "celltype",
  method = "scmapCell"
)
#> ℹ [2025-09-20 13:49:07] scmap installed successfully
#> Error in CheckDataType(data = GetAssayData5(srt_query, layer = "data",     assay = query_assay, verbose = FALSE)): argument "object" is missing, with no default
CellDimPlot(
  pancreas_sub,
  group.by = "scmap_annotation"
)
#> Error in CellDimPlot(pancreas_sub, group.by = "scmap_annotation"): Scmap_annotation is not in the meta.data of srt object.