library(Giotto)

# Ensure Giotto can access a python env
genv_exists <- suppressMessages(checkGiottoEnvironment())
print(genv_exists)

python_path <- file.path(Sys.getenv("HOME"), "miniconda3", "envs", "giotto_env", "bin", "python")
Sys.setenv(RETICULATE_PYTHON = python_path)

invisible(capture.output(suppressMessages(set_giotto_python_path(python_path = python_path))))

# library(scplotter)
devtools::load_all()

Loading required package: GiottoClass

Newer devel version of GiottoClass on GitHub: 0.4.8

Giotto Suite 4.2.1

[1] TRUE

ℹ Loading scplotter

library(Giotto)

## Set instructions
results_folder <- "data/Giotto_SlideSeq.results"

instructions <- createGiottoInstructions(
    save_dir = results_folder,
    save_plot = FALSE,
    show_plot = TRUE,
    return_plot = TRUE,
    python_path = python_path
)

## Provide the path to the visium folder
data_path <- "data/Giotto_SlideSeq"

expression_matrix <- get10Xmatrix(file.path(data_path, "2020-12-19_Puck_201112_26.matched.digital_expression"))
spatial_locs <- data.table::fread(file.path(data_path, "2020-12-19_Puck_201112_26.BeadLocationsForR.csv.tar"))

spatial_locs <- spatial_locs[spatial_locs$barcodes %in% colnames(expression_matrix),]

giotto_object <- createGiottoObject(
  expression = expression_matrix,
  spatial_locs = spatial_locs,
  instructions = instructions
)

force(giotto_object)

python already initialized in this session
 active environment : 'giotto_env'
 python version : 3.10

An object of class giotto 
>Active spat_unit:  cell 
>Active feat_type:  rna 
dimensions    : 21697, 20618 (features, cells)
[SUBCELLULAR INFO]
[AGGREGATE INFO]
expression -----------------------
  [cell][rna] raw
spatial locations ----------------
  [cell] raw


Use objHistory() to see steps and params used

options(repr.plot.width = 12, repr.plot.height = 6)
# devtools::load_all()

p1 <- SpatDimPlot(giotto_object, points_size = 2, points_color_by = "lightblue")
p2 <- SpatDimPlot(giotto_object, points_size = 2, points_color_by = "lightblue",
    points_shape = 21, points_border_color = "grey40")

p1 + p2

giotto_object <- filterGiotto(giotto_object,
                              min_det_feats_per_cell = 10,
                              feat_det_in_min_cells = 10)

giotto_object <- normalizeGiotto(giotto_object)
giotto_object <- addStatistics(giotto_object)
giotto_object <- runPCA(giotto_object)
giotto_object <- runUMAP(giotto_object,
                         dimensions_to_use = 1:10)
giotto_object <- createNearestNetwork(giotto_object)

giotto_object <- doLeidenCluster(giotto_object,
                                 resolution = 1)

completed 1: preparation

completed 2: subset expression data

completed 3: subset spatial locations

completed 4: subset cell metadata

completed 5: subset feature metadata

completed 6: subset spatial network(s)

completed 7: subsetted dimension reductions

completed 8: subsetted nearest network(s)

completed 9: subsetted spatial enrichment results

Feature type:  rna 
Number of cells removed:  0  out of  20343 
Number of feats removed:  0  out of  15493

first scale feats and then cells

Warning message in asMethod(object):
“sparse->dense coercion: allocating vector of size 2.3 GiB”
Setting expression [cell][rna] normalized

Setting expression [cell][rna] scaled

calculating statistics for "normalized" expression

"hvf" was not found in the gene metadata information.
 all genes will be used.

Setting dimension reduction [cell][rna] pca

Setting dimension reduction [cell][rna] umap

options(repr.plot.width = 7, repr.plot.height = 7)
# devtools::load_all()

SpatFeaturePlot(giotto_object, features = "nr_feats", points_shape = 21,
    points_palette = "Reds", points_border_color = "grey")

options(repr.plot.width = 7, repr.plot.height = 7)
# devtools::load_all()

giotto_object@cell_metadata$cell$rna$leiden_clus <- as.character(giotto_object@cell_metadata$cell$rna$leiden_clus)
SpatDimPlot(giotto_object, group_by = "leiden_clus")

x <- sessionInfo()
x <- capture.output(print(x))
# hide the BLAS/LAPACK paths
x <- x[!startsWith(x, "BLAS/LAPACK:")]
cat(paste(x, collapse = "\n"))

R version 4.4.3 (2025-02-28)
Platform: x86_64-conda-linux-gnu
Running under: Red Hat Enterprise Linux 8.10 (Ootpa)

Matrix products: default

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

time zone: America/Chicago
tzcode source: system (glibc)

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

other attached packages:
[1] scplotter_0.4.0   Giotto_4.2.1      GiottoClass_0.4.7

loaded via a namespace (and not attached):
  [1] fs_1.6.6                    matrixStats_1.5.0          
  [3] spatstat.sparse_3.1-0       bitops_1.0-9               
  [5] devtools_2.4.5              httr_1.4.7                 
  [7] RColorBrewer_1.1-3          repr_1.1.7                 
  [9] profvis_0.4.0               tools_4.4.3                
 [11] sctransform_0.4.2           backports_1.5.0            
 [13] R6_2.6.1                    uwot_0.2.3                 
 [15] lazyeval_0.2.2              urlchecker_1.0.1           
 [17] withr_3.0.2                 sp_2.2-0                   
 [19] gridExtra_2.3               GiottoUtils_0.2.5          
 [21] progressr_0.15.1            quantreg_6.00              
 [23] cli_3.6.5                   Biobase_2.62.0             
 [25] spatstat.explore_3.4-3      fastDummies_1.7.5          
 [27] iNEXT_3.0.1                 Seurat_5.3.0               
 [29] spatstat.data_3.1-6         ggridges_0.5.6             
 [31] pbapply_1.7-2               pbdZMQ_0.3-14              
 [33] stringdist_0.9.15           parallelly_1.45.0          
 [35] sessioninfo_1.2.3           VGAM_1.1-13                
 [37] rstudioapi_0.17.1           generics_0.1.4             
 [39] shape_1.4.6.1               gtools_3.9.5               
 [41] ica_1.0-3                   spatstat.random_3.4-1      
 [43] dplyr_1.1.4                 Matrix_1.7-3               
 [45] S4Vectors_0.40.2            abind_1.4-5                
 [47] terra_1.8-42                lifecycle_1.0.4            
 [49] SummarizedExperiment_1.32.0 SparseArray_1.2.4          
 [51] Rtsne_0.17                  grid_4.4.3                 
 [53] promises_1.3.2              crayon_1.5.3               
 [55] miniUI_0.1.2                lattice_0.22-7             
 [57] cowplot_1.1.3               pillar_1.10.2              
 [59] GenomicRanges_1.54.1        rjson_0.2.23               
 [61] future.apply_1.20.0         codetools_0.2-20           
 [63] glue_1.8.0                  spatstat.univar_3.1-3      
 [65] data.table_1.17.4           remotes_2.5.0              
 [67] vctrs_0.6.5                 png_0.1-8                  
 [69] spam_2.11-1                 gtable_0.3.6               
 [71] assertthat_0.2.1            cachem_1.1.0               
 [73] S4Arrays_1.2.1              mime_0.13                  
 [75] tidygraph_1.3.0             survival_3.8-3             
 [77] SingleCellExperiment_1.24.0 ellipsis_0.3.2             
 [79] scRepertoire_2.2.1          fitdistrplus_1.2-2         
 [81] ROCR_1.0-11                 nlme_3.1-168               
 [83] usethis_3.1.0               RcppAnnoy_0.0.22           
 [85] evd_2.3-7.1                 GenomeInfoDb_1.38.8        
 [87] rprojroot_2.0.4             irlba_2.3.5.1              
 [89] KernSmooth_2.23-26          plotthis_0.7.0             
 [91] colorspace_2.1-1            BiocGenerics_0.48.1        
 [93] tidyselect_1.2.1            compiler_4.4.3             
 [95] SparseM_1.84-2              xml2_1.3.8                 
 [97] desc_1.4.3                  ggdendro_0.2.0             
 [99] DelayedArray_0.28.0         plotly_4.10.4              
[101] checkmate_2.3.2             scales_1.4.0               
[103] lmtest_0.9-40               rappdirs_0.3.3             
[105] stringr_1.5.1               digest_0.6.37              
[107] goftest_1.2-3               spatstat.utils_3.1-4       
[109] XVector_0.42.0              htmltools_0.5.8.1          
[111] GiottoVisuals_0.2.12        pkgconfig_2.0.3            
[113] base64enc_0.1-3             MatrixGenerics_1.14.0      
[115] fastmap_1.2.0               rlang_1.1.6                
[117] GlobalOptions_0.1.2         htmlwidgets_1.6.4          
[119] shiny_1.10.0                farver_2.1.2               
[121] zoo_1.8-14                  jsonlite_2.0.0             
[123] RCurl_1.98-1.17             magrittr_2.0.3             
[125] GenomeInfoDbData_1.2.11     dotCall64_1.2              
[127] patchwork_1.3.0             IRkernel_1.3.2             
[129] Rcpp_1.0.14                 evmix_2.12                 
[131] ggnewscale_0.5.1            viridis_0.6.5              
[133] reticulate_1.42.0           truncdist_1.0-2            
[135] stringi_1.8.7               ggalluvial_0.12.5          
[137] ggraph_2.2.1                zlibbioc_1.48.2            
[139] MASS_7.3-64                 plyr_1.8.9                 
[141] pkgbuild_1.4.8              parallel_4.4.3             
[143] listenv_0.9.1               ggrepel_0.9.6              
[145] forcats_1.0.0               deldir_2.0-4               
[147] graphlayouts_1.2.2          IRdisplay_1.1              
[149] splines_4.4.3               gridtext_0.1.5             
[151] tensor_1.5                  circlize_0.4.16            
[153] colorRamp2_0.1.0            igraph_2.0.3               
[155] uuid_1.2-1                  spatstat.geom_3.4-1        
[157] cubature_2.1.4              RcppHNSW_0.6.0             
[159] reshape2_1.4.4              stats4_4.4.3               
[161] pkgload_1.4.0               evaluate_1.0.3             
[163] SeuratObject_5.1.0          tweenr_2.0.3               
[165] httpuv_1.6.15               MatrixModels_0.5-4         
[167] RANN_2.6.2                  tidyr_1.3.1                
[169] purrr_1.0.4                 polyclip_1.10-7            
[171] future_1.58.0               scattermore_1.2            
[173] ggplot2_3.5.2               ggforce_0.4.2              
[175] xtable_1.8-4                RSpectra_0.16-2            
[177] later_1.4.2                 viridisLite_0.4.2          
[179] gsl_2.1-8                   tibble_3.2.1               
[181] memoise_2.0.1               IRanges_2.36.0             
[183] cluster_2.1.8.1             globals_0.18.0

scplotter to work with SlideSeq data prepared by Giotto¶