Standard workflow for scop — standard

This function performs a standard single-cell analysis workflow.

Usage

standard_scop(
  srt,
  prefix = "Standard",
  assay = NULL,
  do_normalization = NULL,
  normalization_method = "LogNormalize",
  do_HVF_finding = TRUE,
  HVF_method = "vst",
  nHVF = 2000,
  HVF = NULL,
  do_scaling = TRUE,
  vars_to_regress = NULL,
  regression_model = "linear",
  linear_reduction = "pca",
  linear_reduction_dims = 50,
  linear_reduction_dims_use = NULL,
  linear_reduction_params = list(),
  force_linear_reduction = FALSE,
  nonlinear_reduction = "umap",
  nonlinear_reduction_dims = c(2, 3),
  nonlinear_reduction_params = list(),
  force_nonlinear_reduction = TRUE,
  neighbor_metric = "euclidean",
  neighbor_k = 20L,
  cluster_algorithm = "louvain",
  cluster_resolution = 0.6,
  verbose = TRUE,
  seed = 11
)

Arguments

srt: A Seurat object.
prefix: A prefix to add to the names of intermediate objects created by the function. Default is "Standard".
assay: Which assay to use. If NULL, the default assay of the Seurat object will be used.
do_normalization: Whether to perform normalization. If NULL, normalization will be performed if the specified assay does not have scaled data.
normalization_method: The method to use for normalization. Options are "LogNormalize", "SCT", or "TFIDF". Default is "LogNormalize".
do_HVF_finding: Whether to perform high variable feature finding. If TRUE, the function will force to find the highly variable features (HVF) using the specified HVF method.
HVF_method: The method to use for finding highly variable features. Options are "vst", "mvp", or "disp". Default is "vst".
nHVF: The number of highly variable features to select. If NULL, all highly variable features will be used. Default is 2000.
HVF: A vector of feature names to use as highly variable features. If NULL, the function will use the highly variable features identified by the HVF method.
do_scaling: Whether to perform scaling. If TRUE, the function will force to scale the data using the Seurat::ScaleData function.
vars_to_regress: A vector of feature names to use as regressors in the scaling step. If NULL, no regressors will be used.
regression_model: The regression model to use for scaling. Options are "linear", "poisson", or "negativebinomial". Default is "linear".
linear_reduction: The linear dimensionality reduction method to use. Options are "pca", "svd", "ica", "nmf", "mds", or "glmpca". Default is "pca".
linear_reduction_dims: The number of dimensions to keep after linear dimensionality reduction. Default is 50.
linear_reduction_dims_use: The dimensions to use for downstream analysis. If NULL, all dimensions will be used.
linear_reduction_params: A list of parameters to pass to the linear dimensionality reduction method.
force_linear_reduction: Whether to force linear dimensionality reduction even if the specified reduction is already present in the Seurat object.
nonlinear_reduction: The nonlinear dimensionality reduction method to use. Options are "umap", "umap-naive", "tsne", "dm", "phate", "pacmap", "trimap", "largevis", or "fr". Default is "umap".
nonlinear_reduction_dims: The number of dimensions to keep after nonlinear dimensionality reduction. If a vector is provided, different numbers of dimensions can be specified for each method. Default is c(2, 3).
nonlinear_reduction_params: A list of parameters to pass to the nonlinear dimensionality reduction method.
force_nonlinear_reduction: Whether to force nonlinear dimensionality reduction even if the specified reduction is already present in the Seurat object. Default is TRUE.
neighbor_metric: The distance metric to use for finding neighbors. Options are "euclidean", "cosine", "manhattan", or "hamming". Default is "euclidean".
neighbor_k: The number of nearest neighbors to use for finding neighbors. Default is 20.
cluster_algorithm: The clustering algorithm to use. Options are "louvain", "slm", or "leiden". Default is "louvain".
cluster_resolution: The resolution parameter to use for clustering. Larger values result in fewer clusters. Default is 0.6.
verbose: Whether to print the message. Default is TRUE.
seed: Random seed for reproducibility. Default is 11.

Value

A Seurat object.

Examples

library(Matrix)
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-01-22 04:27:41] Start standard scop workflow...
#> ℹ [2026-01-22 04:27:41] Checking a list of <Seurat>...
#> ! [2026-01-22 04:27:41] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-01-22 04:27:41] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-22 04:27:44] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-22 04:27:44] Use the separate HVF from srt_list
#> ℹ [2026-01-22 04:27:44] Number of available HVF: 2000
#> ℹ [2026-01-22 04:27:44] Finished check
#> ℹ [2026-01-22 04:27:45] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-22 04:27:45] Perform pca linear dimension reduction
#> ℹ [2026-01-22 04:27:46] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-22 04:27:46] Reorder clusters...
#> ℹ [2026-01-22 04:27:46] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-22 04:27:46] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-22 04:27:51] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-01-22 04:27:56] Run scop standard workflow completed
CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType"
)


# Use a combination of different linear
# or non-linear dimension reduction methods
linear_reductions <- c(
  "pca", "nmf", "mds"
)
pancreas_sub <- standard_scop(
  pancreas_sub,
  linear_reduction = linear_reductions,
  nonlinear_reduction = "umap"
)
#> ℹ [2026-01-22 04:27:56] Start standard scop workflow...
#> ℹ [2026-01-22 04:27:56] Checking a list of <Seurat>...
#> ℹ [2026-01-22 04:27:56] Data 1/1 of the `srt_list` has been log-normalized
#> ℹ [2026-01-22 04:27:56] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-22 04:27:57] Use the separate HVF from srt_list
#> ℹ [2026-01-22 04:27:57] Number of available HVF: 2000
#> ℹ [2026-01-22 04:27:57] Finished check
#> ℹ [2026-01-22 04:27:57] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-22 04:27:57] Perform pca linear dimension reduction
#> ℹ [2026-01-22 04:27:58] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-22 04:27:58] Reorder clusters...
#> ℹ [2026-01-22 04:27:59] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-22 04:27:59] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-22 04:28:03] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-22 04:28:08] Perform nmf linear dimension reduction
#> ℹ [2026-01-22 04:28:08] Running NMF...
#> ℹ StandardBE_ 1 
#> ℹ Positive:  Ccnd1, Spp1, Mdk, Rps2, Ldha, Pebp1, Cd24a, Dlk1, Krt8, Mgst1 
#> ℹ 	   Clu, Gapdh, Eno1, Prdx1, Cldn10, Mif, Cldn7, Npm1, Dbi, Vim 
#> ℹ 	   Sox9, Rpl12, Aldh1b1, Rplp1, Wfdc2, Krt18, Tkt, Aldoa, Hspe1, Ptma 
#> ℹ Negative:  Tmem108, Poc1a, Epn3, Wipi1, Tmcc3, Nhsl1, Fgf12, Plekho1, Tecpr2, Zbtb4 
#> ℹ 	   Gm10941, Trf, Man1c1, Hmgcs1, Nipal1, Jam3, Pgap1, Alpl, Kcnip3, Tnr 
#> ℹ 	   Gm15915, Rbp2, Cbfa2t2, Sh2d4a, Bbc3, Megf6, Naaladl2, Fam46d, Hist2h2ac, Tox2 
#> ℹ StandardBE_ 2 
#> ℹ Positive:  Spp1, Gsta3, Sparc, Vim, Atp1b1, Mt1, Dbi, Anxa2, Rps2, Id2 
#> ℹ 	   Rpl22l1, Rplp1, Mgst1, Clu, Sox9, Cldn6, Mdk, Pdzk1ip1, Bicc1, 1700011H14Rik 
#> ℹ 	   Rps12, S100a10, Cldn3, Rpl36a, Ppp1r1b, Adamts1, Serpinh1, Mt2, Ifitm2, Rpl39 
#> ℹ Negative:  Rpa3, Aacs, Tmem108, Poc1a, Epn3, Wipi1, B830012L14Rik, Tmcc3, Wsb1, Plekho1 
#> ℹ 	   Ppp2r2b, Tecpr2, Zbtb4, Haus8, Trf, Gm5420, Man1c1, Hmgcs1, Nipal1, Jam3 
#> ℹ 	   Tcerg1, Pgap1, Snrpa1, Alpl, Larp1b, Kcnip3, Tnr, Lsm12, Ptbp3, Gm15915 
#> ℹ StandardBE_ 3 
#> ℹ Positive:  Cck, Mdk, Gadd45a, Neurog3, Selm, Sox4, Btbd17, Tmsb4x, Btg2, Cldn6 
#> ℹ 	   Cotl1, Ptma, Jun, Ppp1r14a, Rps2, Ifitm2, Neurod2, Igfbpl1, Gnas, Krt7 
#> ℹ 	   Nkx6-1, Aplp1, Ppp3ca, Lrpap1, Rplp1, Hn1, Rps12, Mfng, BC023829, Smarcd2 
#> ℹ Negative:  Elovl6, Tmem108, Poc1a, Epn3, Nop56, Wipi1, B830012L14Rik, Rrp15, Rfc1, Fgf12 
#> ℹ 	   Slc20a1, Ppp2r2b, Lama1, Tecpr2, Zbtb4, Eif1ax, Fam162a, P4ha3, Gm10941, Tenm4 
#> ℹ 	   Pde4b, Gm5420, Man1c1, Hmgcs1, Pgap1, Mgst2, Larp1b, Kcnip3, Tnr, Lsm12 
#> ℹ StandardBE_ 4 
#> ℹ Positive:  Spp1, Cyr61, Krt18, Tpm1, Krt8, Myl12a, Vim, Jun, Anxa5, Tnfrsf12a 
#> ℹ 	   Csrp1, Sparc, Cldn7, Nudt19, Anxa2, Clu, Myl9, Atp1b1, Cldn3, Tagln2 
#> ℹ 	   S100a10, 1700011H14Rik, Cd24a, Rps2, Dbi, Id2, Lurap1l, Rplp1, Myl12b, Klf6 
#> ℹ Negative:  Rpa3, Elovl6, Aacs, Tmem108, Poc1a, Tmcc3, Rfc1, Plekho1, Slc20a1, Ppp2r2b 
#> ℹ 	   Lama1, Tecpr2, Gm10941, Tenm4, Pde4b, Man1c1, Nipal1, Jam3, Pgap1, Alpl 
#> ℹ 	   Mgst2, Kcnip3, Tnr, Ptbp3, Gm15915, Cntln, Ocln, Fras1, Rbp2, Cbfa2t2 
#> ℹ StandardBE_ 5 
#> ℹ Positive:  2810417H13Rik, Rrm2, Hmgb2, Dut, Pcna, Lig1, H2afz, Tipin, Tuba1b, Tk1 
#> ℹ 	   Mcm5, Dek, Tyms, Gmnn, Ran, Tubb5, Rfc2, Srsf2, Ranbp1, Orc6 
#> ℹ 	   Mcm3, Uhrf1, Gins2, Dnajc9, Mcm6, Siva1, Rfc3, Mcm7, Rpa2, Ptma 
#> ℹ Negative:  1110002L01Rik, Aacs, Wipi1, B830012L14Rik, Tmcc3, Trib1, Fgf12, Plekho1, Ppp2r2b, Lama1 
#> ℹ 	   Tenm4, Trf, Gm5420, Man1c1, Jam3, Mgst2, Kcnip3, Tnr, Gm15915, Cbfa2t2 
#> ℹ 	   Sh2d4a, Bbc3, Fkbp9, Ano6, Prkcb, Megf6, Fam46d, Slc52a3, Ankrd2, Tox2 
#> ✔ [2026-01-22 04:28:13] NMF compute completed
#> ℹ [2026-01-22 04:28:13] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-22 04:28:13] Reorder clusters...
#> ℹ [2026-01-22 04:28:13] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-22 04:28:13] Non-linear dimensionality reduction (umap) using (Standardnmf) dims (1-50) as input
#> ℹ [2026-01-22 04:28:18] Non-linear dimensionality reduction (umap) using (Standardnmf) dims (1-50) as input
#> ℹ [2026-01-22 04:28:23] Perform mds linear dimension reduction
#> ℹ [2026-01-22 04:28:24] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-22 04:28:24] Reorder clusters...
#> ℹ [2026-01-22 04:28:24] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-22 04:28:24] Non-linear dimensionality reduction (umap) using (Standardmds) dims (1-50) as input
#> ℹ [2026-01-22 04:28:29] Non-linear dimensionality reduction (umap) using (Standardmds) dims (1-50) as input
#> ✔ [2026-01-22 04:28:34] Run scop standard workflow completed
plist1 <- lapply(
  linear_reductions, function(lr) {
    CellDimPlot(
      pancreas_sub,
      group.by = "SubCellType",
      reduction = paste0(
        "Standard", lr, "UMAP2D"
      ),
      xlab = "", ylab = "", title = lr,
      legend.position = "none",
      theme_use = "theme_blank"
    )
  }
)
patchwork::wrap_plots(plist1)


nonlinear_reductions <- c(
  "umap", "tsne", "fr"
)
pancreas_sub <- standard_scop(
  pancreas_sub,
  linear_reduction = "pca",
  nonlinear_reduction = nonlinear_reductions
)
#> ℹ [2026-01-22 04:28:34] Start standard scop workflow...
#> ℹ [2026-01-22 04:28:34] Checking a list of <Seurat>...
#> ℹ [2026-01-22 04:28:35] Data 1/1 of the `srt_list` has been log-normalized
#> ℹ [2026-01-22 04:28:35] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-22 04:28:35] Use the separate HVF from srt_list
#> ℹ [2026-01-22 04:28:36] Number of available HVF: 2000
#> ℹ [2026-01-22 04:28:36] Finished check
#> ℹ [2026-01-22 04:28:36] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-22 04:28:36] Perform pca linear dimension reduction
#> ℹ [2026-01-22 04:28:37] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-22 04:28:37] Reorder clusters...
#> ℹ [2026-01-22 04:28:37] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-22 04:28:37] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-22 04:28:42] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-22 04:28:47] Perform tsne nonlinear dimension reduction
#> ℹ [2026-01-22 04:28:47] Non-linear dimensionality reduction (tsne) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-22 04:28:49] Non-linear dimensionality reduction (tsne) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-22 04:28:53] Perform fr nonlinear dimension reduction
#> ℹ [2026-01-22 04:28:53] Non-linear dimensionality reduction (fr) using (Standardpca_SNN) as input
#> ℹ [2026-01-22 04:28:55] Non-linear dimensionality reduction (fr) using (Standardpca_SNN) as input
#> ✔ [2026-01-22 04:28:56] Run scop standard workflow completed
plist2 <- lapply(
  nonlinear_reductions, function(nr) {
    CellDimPlot(
      pancreas_sub,
      group.by = "SubCellType",
      reduction = paste0(
        "Standardpca", nr, "2D"
      ),
      xlab = "", ylab = "", title = nr,
      legend.position = "none",
      theme_use = "theme_blank"
    )
  }
)
patchwork::wrap_plots(plist2)