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Annotate single cells using SingleR

Source: R/RunSingleR.R
RunSingleR.Rd

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. Default is the default assay of the srt_query object.

ref_assay

A character vector specifying the assay to be used for the reference data. Default is 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.

See also

RunKNNPredict, RunKNNMap

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
)
panc8_sub <- CheckDataMerge(
  panc8_sub,
  batch = "tech"
)[["srt_merge"]]

# Annotation
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> 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 
pancreas_sub <- RunSingleR(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  query_group = "Standardpca_SNN_res.0.6",
  ref_group = "celltype"
)
#>  
#> → Will install 8 packages.
#> → All 8 packages (0 B) are cached.
#> + DelayedMatrixStats   1.32.0 [bld]
#> + Rigraphlib           1.2.0  [bld][cmp]
#> + SingleR              2.12.0 [bld][cmp]
#> + biocmake             1.2.0  [bld]
#> + celldex              1.20.0 [bld]
#> + dir.expiry           1.18.0 [bld]
#> + scrapper             1.4.0  [bld][cmp]
#> + sparseMatrixStats    1.22.0 [bld][cmp]
#> ✔ All system requirements are already installed.
#>   
#> ℹ No downloads are needed, 8 pkgs are cached
#> ✔ Got biocmake 1.2.0 (source) (229.12 kB)
#> ✔ Got dir.expiry 1.18.0 (source) (308.96 kB)
#> ✔ Got DelayedMatrixStats 1.32.0 (source) (274.35 kB)
#> ✔ Got celldex 1.20.0 (source) (412.25 kB)
#> ✔ Got SingleR 2.12.0 (source) (695.24 kB)
#> ✔ Got scrapper 1.4.0 (source) (958.15 kB)
#> ✔ Got sparseMatrixStats 1.22.0 (source) (706.40 kB)
#> ✔ Got Rigraphlib 1.2.0 (source) (4.53 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:6 https://packages.microsoft.com/repos/azure-cli 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:7 https://packages.microsoft.com/ubuntu/24.04/prod noble InRelease
#> Reading package lists...
#> ℹ Executing `sudo sh -c apt-get -y install make libcurl4-openssl-dev libnode-dev libxml2-dev pandoc libssl-dev libpng-dev libicu-dev`
#> Reading package lists...
#> Building dependency tree...
#> Reading state information...
#> make is already the newest version (4.3-4.1build2).
#> libcurl4-openssl-dev is already the newest version (8.5.0-2ubuntu10.6).
#> libnode-dev is already the newest version (18.19.1+dfsg-6ubuntu5).
#> libxml2-dev is already the newest version (2.9.14+dfsg-1.3ubuntu3.6).
#> pandoc is already the newest version (3.1.3+ds-2).
#> libssl-dev is already the newest version (3.0.13-0ubuntu3.6).
#> libpng-dev is already the newest version (1.6.43-5build1).
#> libicu-dev is already the newest version (74.2-1ubuntu3.1).
#> 0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.
#> ℹ Building dir.expiry 1.18.0
#> ℹ Building sparseMatrixStats 1.22.0
#> ✔ Built dir.expiry 1.18.0 (1.1s)
#> ✔ Installed dir.expiry 1.18.0  (27ms)
#> ℹ Building biocmake 1.2.0
#> ✔ Built biocmake 1.2.0 (1.2s)
#> ✔ Installed biocmake 1.2.0  (1s)
#> ℹ Building Rigraphlib 1.2.0
#> ✔ Built sparseMatrixStats 1.22.0 (18.6s)
#> ✔ Installed sparseMatrixStats 1.22.0  (1.1s)
#> ℹ Building DelayedMatrixStats 1.32.0
#> ✔ Built DelayedMatrixStats 1.32.0 (11.2s)
#> ✔ Installed DelayedMatrixStats 1.32.0  (30ms)
#> ℹ Building SingleR 2.12.0
#> ℹ Building celldex 1.20.0
#> ✔ Built celldex 1.20.0 (23.3s)
#> ✔ Installed celldex 1.20.0  (42ms)
#> ✔ Built SingleR 2.12.0 (47.2s)
#> ✔ Installed SingleR 2.12.0  (95ms)
#> ✔ Built Rigraphlib 1.2.0 (2m 18.9s)
#> ✔ Installed Rigraphlib 1.2.0  (249ms)
#> ℹ Building scrapper 1.4.0
#> ✔ Built scrapper 1.4.0 (4m 10.4s)
#> ✔ Installed scrapper 1.4.0  (697ms)
#> ✔ 1 pkg + 158 deps: kept 150, added 8, dld 8 (8.12 MB) [6m 40.7s]
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"
)
CellDimPlot(
  pancreas_sub,
  group.by = c("singler_annotation", "CellType")
)