Prepare a Seurat object for downstream analysis

Data transformation (via RunSeuratTransformation()) and sample integration (via RunSeuratIntegration()) are run in one go.

Usage

RunSeuratPreparation(
  object,
  use_sct = FALSE,
  SCTransformArgs = list(),
  NormalizeDataArgs = list(),
  FindVariableFeaturesArgs = list(),
  ScaleDataArgs = list(),
  RunPCAArgs = list(),
  no_integration = FALSE,
  IntegrateLayersArgs = list(),
  log = NULL,
  cache = NULL
)

Arguments

object

Seurat object

use_sct

Whether to use Seurat::SCTransform

SCTransformArgs

Arguments to pass to Seurat::SCTransform

NormalizeDataArgs

Arguments to pass to Seurat::NormalizeData

FindVariableFeaturesArgs

Arguments to pass to Seurat::FindVariableFeatures

ScaleDataArgs

Arguments to pass to Seurat::ScaleData You can use features = "__all__" to scale all features, which will be expanded to all features in the object.

RunPCAArgs

Arguments to pass to Seurat::RunPCA

no_integration

Whether to skip integration, just join layers

IntegrateLayersArgs

Arguments to pass to Seurat::IntegrateLayers

log

Logger

cache

Directory to cache the results. Set to FALSE to disable caching

Value

The transformed and integrated Seurat object

Examples

# \donttest{
# `pbmc_small` ships with a v3 assay, which integration doesn't support
obj <- SeuratObject::pbmc_small
obj[["RNA"]] <- as(obj[["RNA"]], "Assay5")
#> Warning: Assay RNA changing from Assay to Assay5
obj <- RunSeuratPreparation(obj, no_integration = TRUE)
#> INFO    [2026-11-04 17:42:23] Performing data transformation and scaling ...
#> INFO    [2026-11-04 17:42:23] - Running NormalizeData ...
#> Normalizing layer: counts
#> INFO    [2026-11-04 17:42:24] - Running FindVariableFeatures ...
#> Finding variable features for layer counts
#> INFO    [2026-11-04 17:42:26] - Running ScaleData ...
#> Centering and scaling data matrix
#> Warning: Different features in new layer data than already exists for scale.data
#> INFO    [2026-11-04 17:42:27] - Running RunPCA (npcs=50) ...
#> Warning: You're computing too large a percentage of total singular values, use a standard svd instead.
#> PC_ 1 
#> Positive:  TYMP, LST1, AIF1, CST3, IFITM3, LYZ, FCGRT, GRN, BID, SERPINA1 
#> 	   S100A11, IFI30, CFP, S100A8, S100A9, HLA-DPA1, HLA-DPB1, HLA-DRB1, FCER1G, HCK 
#> 	   CTSS, FCN1, CFD, RNF130, TYROBP, HLA-DRB5, LINC00936, SMCO4, HLA-DRA, LGALS1 
#> Negative:  CCL5, LCK, CTSW, GZMA, GZMM, CST7, CD7, PRF1, CD3D, RARRES3 
#> 	   IL32, CD3E, CD247, FGFBP2, GNLY, LAMP1, GP9, NKG7, GZMH, NGFRAP1 
#> 	   TUBB1, GZMB, PF4, GNG11, KLRG1, HIST1H2AC, XBP1, CLU, PGRMC1, GYPC 
#> PC_ 2 
#> Positive:  GNG11, CLU, SDPR, SPARC, GP9, PF4, HIST1H2AC, PPBP, CD9, ITGA2B 
#> 	   CA2, NRGN, TUBB1, TREML1, TMEM40, NGFRAP1, MYL9, PTCRA, PGRMC1, NCOA4 
#> 	   RUFY1, ODC1, TSC22D1, ACRBP, GPX1, TPM4, PARVB, FERMT3, TALDO1, SAT1 
#> Negative:  IFITM2, NKG7, CST7, CTSW, GZMA, GZMM, LCK, CD7, GNLY, FGFBP2 
#> 	   SSR2, XBP1, GZMB, IL32, PRF1, CD247, EIF4A2, HNRNPA3, SRSF7, SPON2 
#> 	   CD3D, GZMH, HNRNPF, KLRD1, CCL4, LAMP1, YWHAB, GIMAP1, EIF3G, GYPC 
#> PC_ 3 
#> Positive:  MS4A1, CD79A, TCL1A, CD79B, LTB, FCER2, HLA-DQB1, HVCN1, PPAPDC1B, CYB561A3 
#> 	   CD180, HLA-DMB, FCRLA, LINC00926, KIAA0125, RP11-693J15.5, CXCR4, CD19, LY86, CD200 
#> 	   IGLL5, NCF1, SP100, HLA-DRB5, SNHG7, HLA-DQA1, NT5C, HLA-DRB1, HLA-DRA, BANK1 
#> Negative:  LGALS1, FCER1G, TYROBP, GZMB, PRF1, GNLY, CCL4, CCL5, FCGR3A, AKR1C3 
#> 	   GZMA, FGFBP2, KLRD1, PSAP, GZMH, TTC38, IGFBP7, NKG7, C12orf75, CST7 
#> 	   SPON2, RHOC, GZMM, CD247, PCMT1, LAMP1, CTSW, PTGDR, SAT1, GSTP1 
#> PC_ 4 
#> Positive:  FGFBP2, AKR1C3, GZMB, CCL4, MS4A1, CLIC3, HLA-DRB1, TTC38, CD79A, NT5C 
#> 	   TCL1A, IL2RB, GNLY, KLRD1, GZMH, HLA-DMB, PRF1, CTSW, HLA-DPA1, NKG7 
#> 	   HLA-DPB1, ARHGDIA, HLA-DQB1, IGFBP7, CST7, C12orf75, XCL2, LY86, CD79B, PTGDR 
#> Negative:  CD3D, MAL, SAFB2, IL7R, MPHOSPH6, THYN1, ASNSD1, TAGAP, LDHB, CD2 
#> 	   NOSIP, TMEM204, DNAJB1, CCR7, CCDC104, ACAP1, TAF7, TMUB1, LTB, CD3E 
#> 	   TMEM123, PIK3IP1, IL32, ACSM3, SRSF7, EPC1, EIF4A2, GYPC, TNFAIP8, BLOC1S4 
#> PC_ 5 
#> Positive:  FCGR3A, LILRA3, C5AR1, RP11-290F20.3, CD79B, TNFRSF1B, SMCO4, CFD, CD68, CTSS 
#> 	   ADAR, VSTM1, EPC1, HVCN1, RHOC, FCN1, SERPINA1, MPHOSPH6, MS4A7, NCF1 
#> 	   CLIC3, RBP7, WARS, FAM96A, STX10, FPR1, SCO2, AKR1C3, CD200, BLOC1S4 
#> Negative:  FCER1A, CLEC10A, RGS1, CD1C, LGALS2, IL1B, HLA-DMA, HLA-DQA2, FUOM, ZNF330 
#> 	   HLA-DQA1, MMADHC, RNF130, MS4A6A, POP7, HNRNPH1, RPL7L1, GPX1, ZFP36L1, LINC00936 
#> 	   HLA-DMB, LY86, SRSF7, HSP90AA1, GSTP1, SSR2, SATB1, GZMK, CCL5, HLA-DQB1 
#> Warning: Number of dimensions changing from 19 to 50
#> INFO    [2026-11-04 17:42:29] Performing data integration ...
#> INFO    [2026-11-04 17:42:29] - Joining layers ...
# }