Hi, I'm in Seurat tutorial - Interoperability between single-cell object formats https://satijalab.org/seurat/articles/conversion_vignette.html

but it has error. I don't know what's wrong....Please help. Thanks

library(scater)
library(Seurat)
library(SeuratDisk)
library(SeuratData)
library(patchwork)

pbmc <- LoadData(ds = "pbmc3k", type = "pbmc3k.final")
pbmc.sce <- as.SingleCellExperiment(pbmc)
p1 <- plotExpression(pbmc.sce, features = "MS4A1", x = "ident") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
p2 <- plotPCA(pbmc.sce, colour_by = "ident")
p1 + p2

manno <- readRDS(file = "~/seurat/data/manno_human.rds")
manno <- runPCA(manno)

Error in runSVD(x, k = rank, nu = ifelse(get.pcs, rank, 0), nv = ifelse(get.rotation,  : 
  argument "rank" is missing, with no default

```r
sessionInfo( )

R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=ko_KR.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=ko_KR.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=ko_KR.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=ko_KR.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets 
[7] methods   base     

other attached packages:
 [1] patchwork_1.1.1               thp1.eccite.SeuratData_3.1.5 
 [3] stxBrain.SeuratData_0.1.1     ssHippo.SeuratData_3.1.4     
 [5] pbmcMultiome.SeuratData_0.1.2 pbmc3k.SeuratData_3.1.4      
 [7] panc8.SeuratData_3.0.2        ifnb.SeuratData_3.0.0        
 [9] hcabm40k.SeuratData_3.0.0     bmcite.SeuratData_0.3.0      
[11] SeuratData_0.2.1              SeuratDisk_0.0.0.9019        
[13] SeuratObject_4.0.4            Seurat_4.1.0                 
[15] scater_1.22.0                 ggplot2_3.3.5                
[17] scuttle_1.4.0                 SingleCellExperiment_1.16.0  
[19] SummarizedExperiment_1.24.0   Biobase_2.54.0               
[21] GenomicRanges_1.46.1          GenomeInfoDb_1.30.0          
[23] IRanges_2.28.0                S4Vectors_0.32.3             
[25] BiocGenerics_0.40.0           MatrixGenerics_1.6.0         
[27] matrixStats_0.61.0           

loaded via a namespace (and not attached):
  [1] plyr_1.8.6                igraph_1.2.11            
  [3] lazyeval_0.2.2            splines_4.1.2            
  [5] BiocParallel_1.28.3       listenv_0.8.0            
  [7] scattermore_0.7           digest_0.6.29            
  [9] htmltools_0.5.2           viridis_0.6.2            
 [11] fansi_1.0.2               magrittr_2.0.1           
 [13] ScaledMatrix_1.2.0        tensor_1.5               
 [15] cluster_2.1.2             ROCR_1.0-11              
 [17] remotes_2.4.2             globals_0.14.0           
 [19] spatstat.sparse_2.1-0     prettyunits_1.1.1        
 [21] colorspace_2.0-2          rappdirs_0.3.3           
 [23] ggrepel_0.9.1             dplyr_1.0.7              
 [25] callr_3.7.0               crayon_1.4.2             
 [27] RCurl_1.98-1.5            jsonlite_1.7.3           
 [29] spatstat.data_2.1-2       survival_3.2-13          
 [31] zoo_1.8-9                 glue_1.6.1               
 [33] polyclip_1.10-0           gtable_0.3.0             
 [35] zlibbioc_1.40.0           XVector_0.34.0           
 [37] leiden_0.3.9              DelayedArray_0.20.0      
 [39] pkgbuild_1.3.1            BiocSingular_1.10.0      
 [41] future.apply_1.8.1        abind_1.4-5              
 [43] scales_1.1.1              DBI_1.1.2                
 [45] miniUI_0.1.1.1            Rcpp_1.0.8               
 [47] viridisLite_0.4.0         xtable_1.8-4             
 [49] reticulate_1.23           spatstat.core_2.3-2      
 [51] bit_4.0.4                 rsvd_1.0.5               
 [53] htmlwidgets_1.5.4         httr_1.4.2               
 [55] RColorBrewer_1.1-2        ellipsis_0.3.2           
 [57] ica_1.0-2                 farver_2.1.0             
 [59] pkgconfig_2.0.3           uwot_0.1.11              
 [61] deldir_1.0-6              utf8_1.2.2               
 [63] labeling_0.4.2            tidyselect_1.1.1         
 [65] rlang_0.4.12              reshape2_1.4.4           
 [67] later_1.3.0               munsell_0.5.0            
 [69] tools_4.1.2               cli_3.1.1                
 [71] generics_0.1.1            ggridges_0.5.3           
 [73] stringr_1.4.0             fastmap_1.1.0            
 [75] goftest_1.2-3             processx_3.5.2           
 [77] bit64_4.0.5               fitdistrplus_1.1-6       
 [79] purrr_0.3.4               RANN_2.6.1               
 [81] pbapply_1.5-0             future_1.23.0            
 [83] nlme_3.1-155              sparseMatrixStats_1.6.0  
 [85] mime_0.12                 rstudioapi_0.13          
 [87] hdf5r_1.3.5               compiler_4.1.2           
 [89] curl_4.3.2                beeswarm_0.4.0           
 [91] plotly_4.10.0             png_0.1-7                
 [93] spatstat.utils_2.3-0      tibble_3.1.6             
 [95] stringi_1.7.6             ps_1.6.0                 
 [97] lattice_0.20-45           Matrix_1.4-0             
 [99] vctrs_0.3.8               pillar_1.6.5             
[101] lifecycle_1.0.1           BiocManager_1.30.16      
[103] spatstat.geom_2.3-1       lmtest_0.9-39            
[105] RcppAnnoy_0.0.19          BiocNeighbors_1.12.0     
[107] data.table_1.14.2         cowplot_1.1.1            
[109] bitops_1.0-7              irlba_2.3.5              
[111] httpuv_1.6.5              R6_2.5.1                 
[113] promises_1.2.0.1          KernSmooth_2.23-20       
[115] gridExtra_2.3             vipor_0.4.5              
[117] parallelly_1.30.0         codetools_0.2-18         
[119] MASS_7.3-55               assertthat_0.2.1         
[121] rprojroot_2.0.2           withr_2.4.3              
[123] sctransform_0.3.3         GenomeInfoDbData_1.2.7   
[125] mgcv_1.8-38               parallel_4.1.2           
[127] grid_4.1.2                rpart_4.1-15             
[129] beachmat_2.10.0           tidyr_1.1.4              
[131] DelayedMatrixStats_1.16.0 Rtsne_0.15               
[133] shiny_1.7.1               ggbeeswarm_0.6.0

You probably want to runPCA on a SingleCellExperiment object, not a list with fields data and labels. The example you posted is using BiocSingular::runPCA, which expects rank to be specified, rather than scater::runPCA.

Something like this:

library("scater")
library("SingleCellExperiment")

manno <- readRDS(file = "manno_human.rds")
sce_manno <- SingleCellExperiment(assay = list(counts = manno$data))
sce_manno <- logNormCounts(sce_manno)
sce_manno <- runPCA(sce_manno)

I'd like to emphasise once more that this would have been much easier to resolve if you had provided a reproducible example.