Dataset: IBD cosMX GarridoTrigo2023

Data is from paper Macrophage and neutrophil heterogeneity at single-cell spatial resolution in human inflammatory bowel disease from Garrido-Trigo et al 2023, (Garrido-Trigo et al. 2023)

The study included 9 cosmx slides of colonic biopsies

• 3x HC - Healthy controls
• 3x UC - Ulcerative colitis
• 3x CD - Chrones’s disease

Fastantically - not only have they made their raw and annotated data available, but have also shared their analysis code; https://github.com/HelenaLC/CosMx-SMI-IBD

They have also shared browseable interface here: https://servidor2-ciberehd.upc.es/external/garrido/app/

Libraries

library(Seurat)

Loading required package: SeuratObject

Loading required package: sp

'SeuratObject' was built with package 'Matrix' 1.6.5 but the current
version is 1.7.1; it is recomended that you reinstall 'SeuratObject' as
the ABI for 'Matrix' may have changed

Attaching package: 'SeuratObject'

The following objects are masked from 'package:base':

    intersect, t

library(SeuratObject)
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.2     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Data download

From the project description:

Raw data on GEO here; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE234713

Inflammatory bowel diseases (IBDs) including ulcerative colitis (UC) and Crohn’s disease (CD) are chronic inflammatory diseases with increasing worldwide prevalence that show a perplexing heterogeneity in manifestations and response to treatment. We applied spatial transcriptomics at single-cell resolution (CosMx Spatial Molecular Imaging) to human inflamed and uninflamed intestine.

The following files were download from GEO

• GSE234713_CosMx_annotation.csv.gz
• GSE234713_CosMx_normalized_matrix.txt.gz
• GSE234713_RAW.tar: RAW data was downloaded via custom downlod in 3 batches, one per group
  • GSE234713_RAW_CD.tar
  • GSE234713_RAW_HC.tar
  • GSE234713_RAW_UC.tar
• GSE234713_ReadMe_SMI_Data_File.html

The polygon files defining the cell outlines were provided separately. Typically these would be included with the other files.

• CD_a.csv : A .csv file which defines the outline of every cell. One file per slide.

cd raw_data
tar -xf GSE234713_RAW_CD.tar
tar -xf GSE234713_RAW_HC.tar
tar -xf GSE234713_RAW_UC.tar

Data load

dataset_dir      <- '~/projects/spatialsnippets/datasets/'
project_data_dir <- file.path(dataset_dir,'GSE234713_IBDcosmx_GarridoTrigo2023')
sample_dir            <- file.path(project_data_dir, "raw_data_for_sfe/") 
annotation_file       <- file.path(project_data_dir,"GSE234713_CosMx_annotation.csv.gz")
data_dir              <- file.path(project_data_dir, "processed_data/") 


seurat_file_00_raw    <- file.path(data_dir, "GSE234713_CosMx_IBD_seurat_00_raw.RDS")
seurat_file_01_loaded <- file.path(data_dir, "GSE234713_CosMx_IBD_seurat_01_loaded.RDS")

# config
min_count_per_cell <- 100
max_pc_negs        <- 1.5
max_avg_neg        <- 0.5

sample_codes <- c(HC="Healthy controls",UC="Ulcerative colitis",CD="Crohn's disease")

Raw data

Load data

# Load LoadNanostring.X for keeping metadata e.t.c
source('/home/s.williams/projects/loadnanostring/code/LoadNanostring.R')


load_sample_into_seurat <- function(the_sample){
  
  # Using a modified version of the LoadNanostring function, which keeps metadata, and loads a litte more effiently for large datasets (which is not this one.)
  # ..../datasets//GSE234713_IBDcosmx_GarridoTrigo2023/raw_data_for_sfe//GSM7473682_HC_a
  so <- LoadNanostring.X(file.path(sample_dir, the_sample), 
                         project = the_sample,
                         assay='RNA',
                         fov=the_sample, tempdir= '~/tmp')
  
  # sample info
  so$individual_code <- factor(substr(so$orig.ident,12,16))
  so$tissue_sample   <- factor(substr(so$orig.ident,12,16))
  so$group     <- factor(substr(the_sample, 12, 13), levels=names(sample_codes))
  so$condition <- factor(as.character(sample_codes[so$group]), levels=sample_codes)
  
  so$fov_name        <- paste0(so$individual_code,"_", str_pad(so$fov, 3, 'left',pad='0'))


  # Put neg probes into their own assay.
  neg_probes <- rownames(so)[grepl(x=rownames(so), pattern="NegPrb")]
  neg_matrix         <- GetAssayData(so,assay = 'RNA', layer = 'counts')[neg_probes,]
  #so[["negprobes"]] <- CreateAssayObject(counts = neg_matrix)
  so[["negprobes"]] <- CreateAssay5Object(counts = neg_matrix)
    
  ## and remove from the main one
  rna_probes  <- rownames(so)[(! rownames(so) %in% neg_probes)]
  so[['RNA']] <- subset( so[['RNA']],features = rna_probes)
  

  return(so)
  
}

samples <- c('GSM7473682_HC_a','GSM7473683_HC_b','GSM7473684_HC_c',
             'GSM7473685_UC_a','GSM7473686_UC_b','GSM7473687_UC_c',
             'GSM7473688_CD_a','GSM7473689_CD_b','GSM7473690_CD_c')
sample_prefix <- paste0(substr(samples, 12,15))

so.list <- lapply(FUN=load_sample_into_seurat, X=samples)

#NB: merge is in SeuratObject packages, but must be called without :: 
options(future.globals.maxSize= 10*1024^3) # 10G.
so.raw <- merge(so.list[[1]], y=so.list[2:length(so.list)], add.cell.ids=sample_prefix)
#Error in getGlobalsAndPackages(expr, envir = envir, globals = globals) : 
#  The total size of the 8 globals exported for future expression (‘FUN()’) is 5.90 GiB.. This exceeds the maximum #allowed size of 500.00 MiB (option 'future.globals.maxSize'). The three largest globals are ‘FUN’ (5.90 GiB of #class ‘function’), ‘p’ (25.22 KiB of class ‘function’) and ‘slot<-’ (1.36 KiB of class ‘function’)
rm(so.list)

# save
saveRDS(so.raw, seurat_file_00_raw)

# load previously saved.
so.raw <- readRDS(seurat_file_00_raw)

Negative probe handling

so.raw$pc_neg <-  ( so.raw$nCount_negprobes / so.raw$nCount_RNA ) * 100

so.raw[["negprobes"]] <- JoinLayers(so.raw[["negprobes"]]) # For caluclating these, need to have the negprobes merged
so.raw$avg_neg <-  colMeans(so.raw[["negprobes"]])   # only defined firsts sample.

Warning: data layer is not found and counts layer is used

Pull in annotation

Most cell annotations.

anno_table <- read_csv(annotation_file)

Rows: 463967 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): id, subset, SingleR2

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

anno_table <- as.data.frame(anno_table)
rownames(anno_table) <- anno_table$id

head(so.raw@meta.data)

              orig.ident nCount_RNA nFeature_RNA fov cell_ID  Area AspectRatio
HC_a_1_1 GSM7473682_HC_a        366          187   1       1  6153        0.67
HC_a_2_1 GSM7473682_HC_a        805          283   1       2 16178        0.85
HC_a_3_1 GSM7473682_HC_a        118           73   1       3  3119        1.52
HC_a_4_1 GSM7473682_HC_a        106           59   1       4  3988        1.52
HC_a_5_1 GSM7473682_HC_a        462          207   1       5  4773        0.53
HC_a_6_1 GSM7473682_HC_a       1314          436   1       6 11588        0.87
         CenterX_local_px CenterY_local_px CenterX_global_px CenterY_global_px
HC_a_1_1             2119             3443          19274.56          173198.6
HC_a_2_1             1847             3404          19002.56          173159.6
HC_a_3_1             1986             3458          19141.56          173213.6
HC_a_4_1             2362             3454          19517.56          173209.6
HC_a_5_1             2159             3415          19314.56          173170.6
HC_a_6_1             1664             3402          18819.56          173157.6
         Width Height Mean.MembraneStain Max.MembraneStain Mean.PanCK Max.PanCK
HC_a_1_1    79    118                680              2218      17170     29735
HC_a_2_1   137    161                615              4453      16775     29702
HC_a_3_1    79     52                989              2827      24033     29662
HC_a_4_1    91     60                449              2099      13575     29651
HC_a_5_1    63    118               1002              2425      24946     29670
HC_a_6_1   116    134               1024              2320      25700     29712
         Mean.CD45 Max.CD45 Mean.CD3 Max.CD3 Mean.DAPI Max.DAPI cell
HC_a_1_1       198     9639      234    1237        20      131  1_1
HC_a_2_1       222    21371      234    1520        12      122  2_1
HC_a_3_1       171     6858      298    1721        13       58  3_1
HC_a_4_1       146      935      206    1402         6       56  4_1
HC_a_5_1       299     6387      330    1443        22      116  5_1
HC_a_6_1       354    23243      328    1610        23      113  6_1
         individual_code tissue_sample group        condition fov_name
HC_a_1_1            HC_a          HC_a    HC Healthy controls HC_a_001
HC_a_2_1            HC_a          HC_a    HC Healthy controls HC_a_001
HC_a_3_1            HC_a          HC_a    HC Healthy controls HC_a_001
HC_a_4_1            HC_a          HC_a    HC Healthy controls HC_a_001
HC_a_5_1            HC_a          HC_a    HC Healthy controls HC_a_001
HC_a_6_1            HC_a          HC_a    HC Healthy controls HC_a_001
         nCount_negprobes nFeature_negprobes    pc_neg    avg_neg
HC_a_1_1                2                  2 0.5464481 0.10526316
HC_a_2_1                5                  3 0.6211180 0.26315789
HC_a_3_1                1                  1 0.8474576 0.05263158
HC_a_4_1                0                  0 0.0000000 0.00000000
HC_a_5_1                3                  2 0.6493506 0.15789474
HC_a_6_1                8                  5 0.6088280 0.42105263

head(anno_table)

               id   subset             SingleR2
HC_c_2_1 HC_c_2_1   stroma            Pericytes
HC_c_3_1 HC_c_3_1   stroma          Endothelium
HC_c_4_1 HC_c_4_1   stroma            Pericytes
HC_c_5_1 HC_c_5_1      epi Secretory progenitor
HC_c_7_1 HC_c_7_1 myeloids                   M2
HC_c_8_1 HC_c_8_1      epi           Tuft cells

so.raw$full_cell_id      <- as.character(rownames(so.raw@meta.data))
so.raw$celltype_subset   <- factor(anno_table[so.raw$full_cell_id,]$subset)
so.raw$celltype_SingleR2 <- factor(anno_table[so.raw$full_cell_id,]$SingleR2)
so.raw$fov_name          <- factor(so.raw$fov_name)
so.raw$group          <- factor(so.raw$group, levels=c("CD","UC","HC"))
so.raw$condition      <- factor(so.raw$condition, levels=c("Crohn's disease",  'Ulcerative colitis', 'Healthy controls'))
table(is.na(so.raw$celltype_subset))

 FALSE   TRUE 
459145  21240 

Basic QC filter

Min count per cell

ggplot(so.raw@meta.data, aes(x=nCount_RNA, col=orig.ident)) +
  geom_density() + 
  geom_vline(xintercept = min_count_per_cell, lty=3) +
  scale_x_log10() +
  theme_bw() +
  ggtitle("Counts per cell")

Warning in scale_x_log10(): log-10 transformation introduced infinite values.

Warning: Removed 17 rows containing non-finite outside the scale range
(`stat_density()`).

Past versions of unnamed-chunk-10-1.png

Version	Author	Date
a722f36	swbioinf	2024-04-18
cf8a2ee	Sarah Williams	2024-03-08

Percent Negative probes

ggplot(so.raw@meta.data, aes(x=pc_neg, col=orig.ident)) +
  geom_density() + 
  geom_vline(xintercept = max_pc_negs, lty=3) +
  scale_x_log10() +
  theme_bw() +
  ggtitle("Negative probe composition")

Warning in scale_x_log10(): log-10 transformation introduced infinite values.

Warning: Removed 227722 rows containing non-finite outside the scale range
(`stat_density()`).

Past versions of unnamed-chunk-11-1.png

Version	Author	Date
a722f36	swbioinf	2024-04-18
cf8a2ee	Sarah Williams	2024-03-08

ggplot(so.raw@meta.data, aes(x=avg_neg, col=orig.ident)) +
  geom_density() + 
  geom_vline(xintercept = max_avg_neg, lty=3) +
  theme_bw() +
  ggtitle("Negative probe average")

Use bottom right corner;

ggplot(so.raw@meta.data, aes(y=avg_neg, x=nCount_RNA)) +
  geom_point(pch=3, alpha=0.1) + 
  geom_hline(yintercept = max_avg_neg, lty=3) +
  geom_vline(xintercept = min_count_per_cell, lty=3) +
  scale_x_log10() + 
  theme_bw() +
  ggtitle("Negative probes vs counts")

Warning in scale_x_log10(): log-10 transformation introduced infinite values.

Apply filteres

From paper: “Cells with an average negative control count greater than 0.5 and less than 20 detected features were filtered out.”

Also remove cell with no cell type annotations.

###so.raw <- JoinLayers(so.raw)

so <- so.raw[ ,so.raw$nCount_RNA >= min_count_per_cell &
               so.raw$avg_neg <= max_avg_neg &
               !(is.na(so.raw$celltype_subset) )]

Warning: Not validating FOV objects

Warning: Not validating Centroids objects
Not validating Centroids objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects

Warning: Not validating FOV objects
Not validating FOV objects
Not validating FOV objects

Warning: Not validating Seurat objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects
Not validating Centroids objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects

Warning: Not validating FOV objects
Not validating FOV objects
Not validating FOV objects

Warning: Not validating Seurat objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects
Not validating Centroids objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects

Warning: Not validating FOV objects
Not validating FOV objects
Not validating FOV objects

Warning: Not validating Seurat objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects
Not validating Centroids objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects

Warning: Not validating FOV objects
Not validating FOV objects
Not validating FOV objects

Warning: Not validating Seurat objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects
Not validating Centroids objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects

Warning: Not validating FOV objects
Not validating FOV objects
Not validating FOV objects

Warning: Not validating Seurat objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects
Not validating Centroids objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects

Warning: Not validating FOV objects
Not validating FOV objects
Not validating FOV objects

Warning: Not validating Seurat objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects
Not validating Centroids objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects

Warning: Not validating FOV objects
Not validating FOV objects
Not validating FOV objects

Warning: Not validating Seurat objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects
Not validating Centroids objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects

Warning: Not validating FOV objects
Not validating FOV objects
Not validating FOV objects

Warning: Not validating Seurat objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects
Not validating Centroids objects

Warning: Not validating FOV objects

Warning: Not validating Centroids objects

Warning: Not validating FOV objects
Not validating FOV objects
Not validating FOV objects

Warning: Not validating Seurat objects

ncol(so.raw)

[1] 480385

ncol(so)

[1] 353239

so <- readRDS(seurat_file_01_loaded)

How many cells per sample?

table(so@meta.data$orig.ident)

GSM7473682_HC_a GSM7473683_HC_b GSM7473684_HC_c GSM7473685_UC_a GSM7473686_UC_b 
          30356           46142           15420           44028           59901 
GSM7473687_UC_c GSM7473688_CD_a GSM7473689_CD_b GSM7473690_CD_c 
          38310           14469           69114           35499 

Basic preprocessing

num_dims <- 15
# Run through preprocessing
so <- NormalizeData(so)

## Do per sample to mimic paper approach somewhat.
so <- FindVariableFeatures(so, nfeatures = 200) 


so <- ScaleData(so) # Just 2k variable features
so <- RunPCA(so, features = VariableFeatures(so))
so <- RunUMAP(so, dims=1:num_dims)
so <- FindNeighbors(so, dims = 1:num_dims)
so <- FindClusters(so)

Basic plots

UMAP

Sample

DimPlot(so, group.by = 'orig.ident')

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Past versions of unnamed-chunk-18-1.png

Version	Author	Date
6978154	swbioinf	2024-05-30
a722f36	swbioinf	2024-04-18
cf8a2ee	Sarah Williams	2024-03-08

Condition

DimPlot(so, group.by = 'condition')

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Past versions of unnamed-chunk-19-1.png

Version	Author	Date
a722f36	swbioinf	2024-04-18
cf8a2ee	Sarah Williams	2024-03-08

Condition X Sample

DimPlot(so, group.by = 'orig.ident',split.by = "condition")

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Past versions of unnamed-chunk-20-1.png

Version	Author	Date
a722f36	swbioinf	2024-04-18
cf8a2ee	Sarah Williams	2024-03-08

Total counts

FeaturePlot(so, 'nCount_RNA') + scale_colour_viridis_c(option="magma", direction = -1)

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Past versions of unnamed-chunk-21-1.png

Version	Author	Date
a722f36	swbioinf	2024-04-18
cf8a2ee	Sarah Williams	2024-03-08

Clustered

DimPlot(so, group.by = 'seurat_clusters')

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Past versions of unnamed-chunk-22-1.png

Version	Author	Date
a722f36	swbioinf	2024-04-18
cf8a2ee	Sarah Williams	2024-03-08

celltype_subset

Classifications from Garrido-Trigo et al 2023.

DimPlot(so, group.by = 'celltype_subset')

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

celltype_SingleR2

Classifications from Garrido-Trigo et al 2023.

DimPlot(so, group.by = 'celltype_SingleR2')

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Past versions of unnamed-chunk-24-1.png

Version	Author	Date
a722f36	swbioinf	2024-04-18
cf8a2ee	Sarah Williams	2024-03-08

Clustering

Double checking the clusters line up with annotaiaon

table(so$celltype_subset)

     epi myeloids  plasmas   stroma   tcells 
   82558    43916   130386    84654    11725 

table(so$celltype_SingleR2)

                      B cell                  BEST4 OTOP2 
                        5304                        10122 
                         CD4                          CD8 
                        2729                         2879 
                 Colonocytes                Cycling cells 
                       17651                         2105 
             Cycling myeloid              Cycling T cells 
                        1286                          322 
                  Cycling TA                          DCs 
                        1836                         1468 
                          DN                  Endothelium 
                         607                        13773 
             Enteroendocrine                  Eosinophils 
                         847                          191 
            Epithelium Ribhi                  Fibroblasts 
                        4357                         1665 
                        FRCs                    GC B cell 
                       10001                         3554 
                      gd IEL                         Glia 
                         366                         4337 
                      Goblet                         ILC4 
                        8837                          115 
    Inflammatory fibroblasts       Inflammatory monocytes 
                       12571                         1730 
                          M0                           M1 
                        2989                          934 
                          M2              Macrophage NRG1 
                       21269                         9575 
                        MAIT                         Mast 
                        1230                         2929 
               Memory B cell                   MT T cells 
                        7022                          175 
              Myofibroblasts                           N1 
                       16628                          544 
                          N2                           N3 
                         425                          576 
                Naïve B cell                           NK 
                        8894                          240 
                 Paneth-like PC  immediate early response 
                        1810                         9797 
                      PC IgA            PC IgA heat shock 
                        1294                         5077 
                  PC IgA IgM                       PC IgG 
                       12181                        75156 
                   Pericytes                Ribhi T cells 
                        9262                           86 
                          S1                          S2a 
                        5206                         3550 
                         S2b                           S3 
                        4596                         3065 
        Secretory progenitor                T cells CCL20 
                       34636                          816 
                       Tregs                   Tuft cells 
                        2160                         2462 

Composition

heatmap(table(so$celltype_subset,so$celltype_SingleR2))

Save data

saveRDS(so, seurat_file_01_loaded)

Garrido-Trigo, Alba, Ana M. Corraliza, Marisol Veny, Isabella Dotti, Elisa Melón-Ardanaz, Aina Rill, Helena L. Crowell, et al. 2023. “Macrophage and Neutrophil Heterogeneity at Single-Cell Spatial Resolution in Human Inflammatory Bowel Disease.” Nature Communications 14 (1): 4506. https://doi.org/10.1038/s41467-023-40156-6.

Session information

sessionInfo()

R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

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

time zone: Etc/UTC
tzcode source: system (glibc)

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

other attached packages:
 [1] lubridate_1.9.4    forcats_1.0.0      stringr_1.5.1      dplyr_1.1.4       
 [5] purrr_1.0.2        readr_2.1.5        tidyr_1.3.1        tibble_3.2.1      
 [9] ggplot2_3.5.1      tidyverse_2.0.0    Seurat_5.2.1       SeuratObject_5.0.2
[13] sp_2.2-0           workflowr_1.7.1   

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3     rstudioapi_0.17.1      jsonlite_1.8.9        
  [4] magrittr_2.0.3         spatstat.utils_3.1-2   farver_2.1.2          
  [7] rmarkdown_2.29         fs_1.6.5               vctrs_0.6.5           
 [10] ROCR_1.0-11            spatstat.explore_3.3-4 htmltools_0.5.8.1     
 [13] sass_0.4.9             sctransform_0.4.1      parallelly_1.42.0     
 [16] KernSmooth_2.23-26     bslib_0.9.0            htmlwidgets_1.6.4     
 [19] ica_1.0-3              plyr_1.8.9             plotly_4.10.4         
 [22] zoo_1.8-12             cachem_1.1.0           whisker_0.4.1         
 [25] igraph_2.1.4           mime_0.12              lifecycle_1.0.4       
 [28] pkgconfig_2.0.3        Matrix_1.7-1           R6_2.5.1              
 [31] fastmap_1.2.0          fitdistrplus_1.2-2     future_1.34.0         
 [34] shiny_1.10.0           digest_0.6.37          colorspace_2.1-1      
 [37] patchwork_1.3.0        ps_1.8.1               rprojroot_2.0.4       
 [40] tensor_1.5             RSpectra_0.16-2        irlba_2.3.5.1         
 [43] labeling_0.4.3         progressr_0.15.1       timechange_0.3.0      
 [46] spatstat.sparse_3.1-0  httr_1.4.7             polyclip_1.10-7       
 [49] abind_1.4-8            compiler_4.4.0         bit64_4.6.0-1         
 [52] withr_3.0.2            fastDummies_1.7.5      MASS_7.3-64           
 [55] tools_4.4.0            lmtest_0.9-40          httpuv_1.6.15         
 [58] future.apply_1.11.3    goftest_1.2-3          glue_1.8.0            
 [61] callr_3.7.6            nlme_3.1-166           promises_1.3.2        
 [64] grid_4.4.0             Rtsne_0.17             getPass_0.2-4         
 [67] cluster_2.1.8          reshape2_1.4.4         generics_0.1.3        
 [70] gtable_0.3.6           spatstat.data_3.1-4    tzdb_0.4.0            
 [73] hms_1.1.3              data.table_1.16.4      spatstat.geom_3.3-5   
 [76] RcppAnnoy_0.0.22       ggrepel_0.9.6          RANN_2.6.2            
 [79] pillar_1.10.1          vroom_1.6.5            spam_2.11-1           
 [82] RcppHNSW_0.6.0         later_1.4.1            splines_4.4.0         
 [85] lattice_0.22-6         bit_4.5.0.1            renv_1.0.5            
 [88] survival_3.8-3         deldir_2.0-4           tidyselect_1.2.1      
 [91] miniUI_0.1.1.1         pbapply_1.7-2          knitr_1.49            
 [94] git2r_0.33.0           gridExtra_2.3          scattermore_1.2       
 [97] xfun_0.50              matrixStats_1.5.0      stringi_1.8.4         
[100] lazyeval_0.2.2         yaml_2.3.10            evaluate_1.0.3        
[103] codetools_0.2-20       BiocManager_1.30.25    cli_3.6.3             
[106] uwot_0.2.2             xtable_1.8-4           reticulate_1.40.0     
[109] munsell_0.5.1          processx_3.8.5         jquerylib_0.1.4       
[112] Rcpp_1.0.14            globals_0.16.3         spatstat.random_3.3-2 
[115] png_0.1-8              spatstat.univar_3.1-1  parallel_4.4.0        
[118] dotCall64_1.2          listenv_0.9.1          viridisLite_0.4.2     
[121] scales_1.3.0           ggridges_0.5.6         crayon_1.5.3          
[124] rlang_1.1.5            cowplot_1.1.3