Last updated: 2025-05-23

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Knit directory: CosMx_pipeline_LGA/

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Ignored files:
    Ignored:    .Rproj.user/
    Ignored:    NBClust-Plots/
    Ignored:    analysis/figure/
    Ignored:    data/flatFiles/CoronalHemisphere/Run1000_S1_Half_exprMat_file.csv
    Ignored:    data/flatFiles/CoronalHemisphere/Run1000_S1_Half_fov_positions_file.csv
    Ignored:    data/flatFiles/CoronalHemisphere/Run1000_S1_Half_metadata_file.csv
    Ignored:    output/processed_data/Log/
    Ignored:    output/processed_data/RC/
    Ignored:    output/processed_data/SCT/
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    Ignored:    output/processed_data/fov_positions_unfiltered.RDS
    Ignored:    output/processed_data/metadata_unfiltered.RDS
    Ignored:    output/processed_data/negMat_unfiltered.RDS
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    Modified:   output/performance_reports/2.0_normalization_PR.csv
    Modified:   output/performance_reports/3.0_dimensional_reduction_PR.csv
    Modified:   output/performance_reports/4.0_insitutype_cell_typing_PR.csv
    Modified:   output/performance_reports/4.1_insitutype_unsup_clustering_PR.csv
    Modified:   output/performance_reports/4.2_seurat_unsup_clustering_PR.csv
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    Modified:   output/performance_reports/6.0_Log_normalization_PR.csv
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    Modified:   output/performance_reports/pipeline_PR.csv

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Dependencies

library(data.table) # Efficient data management
library(here) # Enhanced file referencing in project-oriented workflows
library(dplyr) # For the use of pipes %>%
library(kableExtra) # For table formatting
library(Seurat) # Seurat object
library(ggplot2) # Graphics
library(patchwork) # Layout graphics

Load the data

First of all, data needs to be loaded into the session. For this script, only the filtered Seurat object is needed.

# Load Seurat object
seu <- readRDS(here("output","processed_data","seu_filtered.RDS"))

Normalization

According to Nanostring recommendations (see CosMx Scratch Space blog), CosMx SMI data should be normalize using total counts. Further transformations, such as log1p, square root or “Pearson residuals”, are only recommended for UMAP or distance-based clustering methods. Instead, they suggest using linear-scale data for the rest of the analysis.

In the CosMx Scratch Space vignette, for example, normalization is made by total counts. Afterwards, square root transformation is implemented to create a UMAP plot. Finally, downstream analysis are performed: raw counts are used for cell typing (with UMAP visualization) and normalize (non-transformed) counts are used for neighborhood expression analysis.

However, the CosMxLite vignette proposes using “SCTransform”, reporting a better clustering performance when using this method in comparison to the classic log-normalization. This method creates a different assay for the normalize/transformed data, leaving the original assay intact in case its needed. Additionally, “SCTransform” also calculates highly variable genes and scales data, which the other two methods do not include and has to be done afterwards.

For simplicity, this pipeline will execute normalization according to CosMxLite, using the “SCTransform” method, however, the code is parameterized so that the user can select which option to follow, between Total counts (“RC”), Total counts + Log1p transformation (“LogNormalize”) or SCTransform (“SCT”).

# Select normalization method between "RC", "LogNormalize" or "SCT"
norm_method <- "SCT"

# Apply normalization
seu <- normalize_CosMx(seu, method = norm_method)

## normalize_CosMx() is a self created function that performs either RC, 
## LogNormalize or SCT in a Seurat object of CosMx data using Seurat built-in
## functions. Available code is in "code/aux_functions.R"
# Calculate top10 genes
top10 <- head(VariableFeatures(seu), 10)

# Plot variable genes
p1 <- VariableFeaturePlot(seu)
p2 <- LabelPoints(plot = p1, points = top10, repel = TRUE) + NoLegend()

p2

Version Author Date
8a9f079 lidiaga 2025-05-20
  • Top 10 variable genes: Ttr, Ptgds, Plp1, Penk, Sst, Npy, Vip, Mbp, Apod, Vtn.

Note that, by default, the functions “FindVariableFeatures” or “SCTransform” return the 2000 and 3000 most variable genes, respectively. However, in this case, as the panel covers only 1K genes, all the genes are reported as variable genes, but ranked from most to less variable.

This parameter could be changed to a lower number, if desired, but for such a small panel, all genes will be used for the dimensional reduction.

Save the Seurat objects

# Chop name for LogNormalize
if (norm_method == "LogNormalize") {
  norm_method <- "Log"
}

# Create specific folder
dir.create(here("output","processed_data",norm_method))

# Save the Seurat object
seu_name <- paste0("seu_",norm_method,".RDS")
saveRDS(seu, here("output","processed_data",norm_method,seu_name))

Performance and Session Info

Performance Report
Chunk Time_sec Memory_Mb
Libraries 2.06 146.8
LoadData 1.94 425.2
Norm 41.02 774.5
VizVar 0.85 11.4
SavingSeuObjs 41.72 0.0
Total 87.59 1357.9 

sessionInfo()
R version 4.4.3 (2025-02-28 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22000)

Matrix products: default


locale:
[1] LC_COLLATE=Spanish_Spain.utf8  LC_CTYPE=Spanish_Spain.utf8   
[3] LC_MONETARY=Spanish_Spain.utf8 LC_NUMERIC=C                  
[5] LC_TIME=Spanish_Spain.utf8    

time zone: Europe/Madrid
tzcode source: internal

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

other attached packages:
[1] patchwork_1.3.0    ggplot2_3.5.1      SeuratObject_4.1.4 Seurat_4.4.0      
[5] kableExtra_1.4.0   dplyr_1.1.4        here_1.0.1         data.table_1.17.0 
[9] workflowr_1.7.1   

loaded via a namespace (and not attached):
  [1] deldir_2.0-4           pbapply_1.7-2          gridExtra_2.3         
  [4] rlang_1.1.6            magrittr_2.0.3         git2r_0.35.0          
  [7] RcppAnnoy_0.0.22       spatstat.geom_3.3-5    matrixStats_1.5.0     
 [10] ggridges_0.5.6         compiler_4.4.3         getPass_0.2-4         
 [13] reshape2_1.4.4         png_0.1-8              systemfonts_1.2.1     
 [16] callr_3.7.6            vctrs_0.6.5            stringr_1.5.1         
 [19] pkgconfig_2.0.3        fastmap_1.2.0          labeling_0.4.3        
 [22] promises_1.3.2         rmarkdown_2.29         ps_1.9.0              
 [25] purrr_1.0.4            xfun_0.51              cachem_1.1.0          
 [28] jsonlite_1.9.1         goftest_1.2-3          later_1.4.1           
 [31] spatstat.utils_3.1-2   irlba_2.3.5.1          parallel_4.4.3        
 [34] cluster_2.1.8          R6_2.6.1               ica_1.0-3             
 [37] spatstat.data_3.1-6    bslib_0.9.0            stringi_1.8.4         
 [40] RColorBrewer_1.1-3     reticulate_1.41.0.1    spatstat.univar_3.1-2 
 [43] parallelly_1.43.0      scattermore_1.2        lmtest_0.9-40         
 [46] jquerylib_0.1.4        Rcpp_1.0.14            knitr_1.50            
 [49] tensor_1.5             future.apply_1.11.3    zoo_1.8-13            
 [52] sctransform_0.4.1      httpuv_1.6.15          Matrix_1.7-2          
 [55] splines_4.4.3          igraph_2.1.4           tidyselect_1.2.1      
 [58] abind_1.4-8            rstudioapi_0.17.1      yaml_2.3.10           
 [61] spatstat.random_3.3-2  spatstat.explore_3.3-4 codetools_0.2-20      
 [64] miniUI_0.1.1.1         processx_3.8.6         listenv_0.9.1         
 [67] plyr_1.8.9             lattice_0.22-6         tibble_3.2.1          
 [70] withr_3.0.2            shiny_1.10.0           ROCR_1.0-11           
 [73] evaluate_1.0.3         Rtsne_0.17             future_1.34.0         
 [76] survival_3.8-3         polyclip_1.10-7        xml2_1.3.7            
 [79] fitdistrplus_1.2-2     pillar_1.10.1          whisker_0.4.1         
 [82] KernSmooth_2.23-26     plotly_4.10.4          generics_0.1.3        
 [85] rprojroot_2.0.4        sp_2.2-0               munsell_0.5.1         
 [88] scales_1.3.0           globals_0.16.3         xtable_1.8-4          
 [91] glue_1.8.0             lazyeval_0.2.2         tools_4.4.3           
 [94] RANN_2.6.2             fs_1.6.5               leiden_0.4.3.1        
 [97] cowplot_1.1.3          grid_4.4.3             tidyr_1.3.1           
[100] colorspace_2.1-1       nlme_3.1-167           cli_3.6.4             
[103] spatstat.sparse_3.1-0  viridisLite_0.4.2      svglite_2.1.3         
[106] uwot_0.2.3             gtable_0.3.6           sass_0.4.9            
[109] digest_0.6.37          progressr_0.15.1       ggrepel_0.9.6         
[112] htmlwidgets_1.6.4      farver_2.1.2           htmltools_0.5.8.1     
[115] lifecycle_1.0.4        httr_1.4.7             mime_0.12             
[118] MASS_7.3-65