Merge samples

workflowr

• Summary
• Checks
• Past versions

Last updated: 2022-07-04

Checks: 6 1

Knit directory: humanCardiacFibroblasts/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: uncommitted changes

The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20210903)

The command set.seed(20210903) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 9fc92e5

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 9fc92e5. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/humanFibroblast/

Unstaged changes:
    Modified:   analysis/mergeHumanSamplesPlusGraz.Rmd
    Modified:   metadata2.txt

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/mergeHumanSamplesPlusGraz.Rmd) and HTML (docs/mergeHumanSamplesPlusGraz.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	9fc92e5	mluetge	2022-06-23	add samples from Graz
html	9fc92e5	mluetge	2022-06-23	add samples from Graz

---

load packages

suppressPackageStartupMessages({
  library(SingleCellExperiment)
  library(tidyverse)
  library(Seurat)
  library(magrittr)
  library(dplyr)
  library(purrr)
  library(ggplot2)
  library(here)
  library(runSeurat3)
  library(ggsci)
  library(ggpubr)
  library(pheatmap)
})

set dir

basedir <- here()
metaDat <- read_tsv(paste0(basedir, "/metadata2.txt"), col_names = T)

load and assign samples

assignSamples <- function(smpNam, basedirSmp, smpTec, smpID, smpCond, smpOri,
                          smpIso, smpProc){
  smpNamFull <- list.files(path = paste0(basedirSmp, "/data/humanFibroblast/"),
                 pattern = paste0(smpNam, ".*_seurat.rds"))
  seuratSmp <- readRDS(paste0(basedirSmp, "/data/humanFibroblast/", smpNamFull))
  seuratSmp$technique <- smpTec
  seuratSmp$ID <- smpID
  seuratSmp$cond <- smpCond
  seuratSmp$origin <- smpOri
  seuratSmp$isolation <- smpIso
  seuratSmp$processing <- smpProc
  return(seuratSmp)
}

####################################################################

for(i in 1:length(metaDat$Sample)){
  seuratX <- assignSamples(smpNam = metaDat$Sample[i],
                           basedirSmp = basedir,
                           smpTec = metaDat$technique[i],
                           smpID = metaDat$ID[i],
                           smpCond = metaDat$cond[i],
                           smpOri = metaDat$origin[i],
                           smpProc = metaDat$processing[i],
                           smpIso = metaDat$isolation[i])
  if(exists("seurat")){
    seurat <- merge(x = seurat, y = seuratX, project = "humanCardiacFibro")
  }else{
    seurat <- seuratX
  }
}

remove(seuratX)

run clustering and DR and remove contaminating cells

seurat <- rerunSeurat3(seurat)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 41081
Number of edges: 1416941

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9619
Number of communities: 16
Elapsed time: 9 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 41081
Number of edges: 1416941

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9283
Number of communities: 26
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 41081
Number of edges: 1416941

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9133
Number of communities: 26
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 41081
Number of edges: 1416941

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9455
Number of communities: 20
Elapsed time: 8 seconds

#seuratSub <- subset(seurat1, subset = `MGP-BALBcJ-G0026271.Grem1` >0) ## 5 cells
#seuratSub <- subset(seurat1, subset = `MGP-BALBcJ-G0026527.Bmp2` >0) ## 14104 cells

dat <- data.frame(table(seurat$dataset))
colnames(dat) <- c("dataset", "all")

knitr::kable(dat)

dataset	all
1_20210930_Hu_nucseq_HTrans_Heart22_24_GEM	2381
1_20211018_Hu_nucseq_Myocar_Heart27_GEM	238
10_20210907_Hu_nucseq_iDCM_Heart01_GEM	826
2_20210930_Hu_nucseq_HTrans_Heart23_25_GEM	3131
9_20210817_Hu_nucleoi_seq_cardiac_transplant_Heart17_biopsy_NG	1321
9_20210907_Hu_nucseq_HTrans_Heart21_GEM	1536
o27533_1_09-9_20220203_Hu_nucseq_EMB28_GEM	1545
o27533_1_10-10_20220203_Hu_nucseq_EMB29_GEM	1254
o27533_1_11-11_20220203_Hu_nucseq_EMB30_GEM	1242
o27533_1_12-12_20220203_Hu_nucseq_EMB31_GEM	236
o27936_1_7-7_20220309_Hu_nucseq_EMB32_GEM	1192
o28576_1_01-1_20220525_Hu_nucseq_Graz_1_EMB_GEM	2740
o28576_1_02-2_20220525_Hu_nucseq_Graz_2_EMB_GEM	1684
o28576_1_03-3_20220525_Hu_nucseq_Graz_3_EMB_GEM	2396
o28576_1_04-4_20220525_Hu_nucseq_Graz_4_EMB_GEM	545
o28576_1_05-5_20220525_Hu_nucseq_Graz_5_EMB_GEM	781
o28576_1_06-6_20220525_Hu_nucseq_Graz_6_EMB_GEM	491
o28576_1_07-7_20220525_Hu_nucseq_Graz_7_EMB_GEM	653
o28576_1_08-8_20220525_Hu_nucseq_Graz_8_HH_GEM	3921
o28576_1_10-10_20220525_Hu_nucseq_Graz_10_HH_GEM	3731
o28576_1_11-11_20220525_Hu_nucseq_Graz_11_HH_GEM	3991
o28576_1_12-12_20220525_Hu_nucseq_Graz_12_HH_GEM	3818
o28576_1_13-13_20220525_Hu_nucseq_EMB32_GEM	1428

color vectors

colPal <- pal_igv()(length(levels(seurat)))
colTec <- pal_jama()(length(unique(seurat$technique)))
colSmp <- c(pal_uchicago()(8), pal_npg()(8), pal_aaas()(10))[1:length(unique(seurat$dataset))]
colCond <- pal_npg()(length(unique(seurat$cond)))
colID <- c(pal_jco()(10), pal_npg()(10))[1:length(unique(seurat$ID))]
colOrig <- pal_aaas()(length(unique(seurat$origin)))
colIso <- pal_nejm()(length(unique(seurat$isolation)))
colProc <- pal_aaas()(length(unique(seurat$processing)))

names(colPal) <- levels(seurat)
names(colTec) <- unique(seurat$technique)
names(colSmp) <- unique(seurat$dataset)
names(colCond) <- unique(seurat$cond)
names(colID) <- unique(seurat$ID)
names(colOrig) <- unique(seurat$origin)
names(colIso) <- unique(seurat$isolation)
names(colProc) <- unique(seurat$processing)

vis data

clusters

DimPlot(seurat, reduction = "umap", cols=colPal)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of "vis cluster-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

technique

DimPlot(seurat, reduction = "umap", group.by = "technique", cols=colTec)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of "vis tecnique-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

Sample

DimPlot(seurat, reduction = "umap", group.by = "dataset", cols=colSmp)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of "vis sample-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

ID

DimPlot(seurat, reduction = "umap", group.by = "ID", cols=colID)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of "vis ID-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

Origin

DimPlot(seurat, reduction = "umap", group.by = "origin", cols=colOrig)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of "vis origin-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

isolation

DimPlot(seurat, reduction = "umap", group.by = "isolation", cols=colIso)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of "vis isolation-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

cond

DimPlot(seurat, reduction = "umap", group.by = "cond", cols=colCond)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of "vis cond-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

processing

DimPlot(seurat, reduction = "umap", group.by = "processing", cols=colProc)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Past versions of "vis proc-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

marker genes

seurat_markers_all <- FindAllMarkers(object = seurat, assay ="RNA",
                                     only.pos = TRUE, min.pct = 0.25,
                                     logfc.threshold = 0.25,
                                     test.use = "wilcox")

top 15 marker genes per cluster

cluster <- levels(seurat)
selGenesAll <- seurat_markers_all %>% group_by(cluster) %>% 
  top_n(-15, p_val_adj) %>% 
  top_n(15, avg_log2FC)
selGenesAll <- selGenesAll %>% mutate(geneIDval=gsub("^.*\\.", "", gene)) %>% filter(nchar(geneIDval)>1)

template_hm <- c(
    "#### {{cl}}\n",
    "```{r top marker {{cl}}, fig.height=8, fig.width=6, echo = FALSE}\n",
    "selGenes <- selGenesAll %>% filter(cluster=='{{cl}}')",
    "pOut <- avgHeatmap(seurat = seurat, selGenes = selGenes,
                  colVecIdent = colPal, 
                  ordVec=levels(seurat),
                  gapVecR=NULL, gapVecC=NULL,cc=FALSE,
                  cr=T, condCol=F)\n",
    "```\n",
    "\n"
  )

plots_gp <- lapply(cluster, 
  function(cl) knitr::knit_expand(text = template_hm)
)

0

Past versions of "top marker 0-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

1

Past versions of "top marker 1-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

2

Past versions of "top marker 2-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

3

Past versions of "top marker 3-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

4

Past versions of "top marker 4-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

5

Past versions of "top marker 5-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

6

Past versions of "top marker 6-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

7

Past versions of "top marker 7-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

8

Past versions of "top marker 8-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

9

Past versions of "top marker 9-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

10

Past versions of "top marker 10-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

11

Past versions of "top marker 11-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

12

Past versions of "top marker 12-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

13

Past versions of "top marker 13-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

14

Past versions of "top marker 14-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

15

Past versions of "top marker 15-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

16

Past versions of "top marker 16-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

17

Past versions of "top marker 17-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

18

Past versions of "top marker 18-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

19

Past versions of "top marker 19-1.png"

Version	Author	Date
9fc92e5	mluetge	2022-06-23

save objects

Idents(seurat) <- seurat$seurat_clusters
saveRDS(seurat, file = paste0(basedir, 
                              "/data/humanHeartsPlusGraz_merged_seurat.rds"))

write.table(seurat_markers_all,
            file=paste0(basedir, "/data/humanHeartsPlusGraz_merged_markerGenes.txt"),
            row.names = FALSE, col.names = TRUE, quote = FALSE, sep = "\t")

session info

sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] pheatmap_1.0.12             ggpubr_0.4.0               
 [3] ggsci_2.9                   runSeurat3_0.1.0           
 [5] here_1.0.1                  magrittr_2.0.3             
 [7] sp_1.4-7                    SeuratObject_4.1.0         
 [9] Seurat_4.1.1                forcats_0.5.1              
[11] stringr_1.4.0               dplyr_1.0.9                
[13] purrr_0.3.4                 readr_2.1.2                
[15] tidyr_1.2.0                 tibble_3.1.7               
[17] ggplot2_3.3.6               tidyverse_1.3.1            
[19] SingleCellExperiment_1.14.1 SummarizedExperiment_1.22.0
[21] Biobase_2.52.0              GenomicRanges_1.44.0       
[23] GenomeInfoDb_1.28.4         IRanges_2.26.0             
[25] S4Vectors_0.30.2            BiocGenerics_0.38.0        
[27] MatrixGenerics_1.4.3        matrixStats_0.62.0         

loaded via a namespace (and not attached):
  [1] utf8_1.2.2             reticulate_1.24        tidyselect_1.1.2      
  [4] htmlwidgets_1.5.4      grid_4.1.0             Rtsne_0.16            
  [7] munsell_0.5.0          codetools_0.2-18       ica_1.0-2             
 [10] future_1.25.0          miniUI_0.1.1.1         withr_2.5.0           
 [13] spatstat.random_2.2-0  colorspace_2.0-3       progressr_0.10.0      
 [16] highr_0.9              knitr_1.39             rstudioapi_0.13       
 [19] ROCR_1.0-11            ggsignif_0.6.3         tensor_1.5            
 [22] listenv_0.8.0          labeling_0.4.2         git2r_0.30.1          
 [25] GenomeInfoDbData_1.2.6 polyclip_1.10-0        farver_2.1.0          
 [28] bit64_4.0.5            rprojroot_2.0.3        parallelly_1.31.1     
 [31] vctrs_0.4.1            generics_0.1.2         xfun_0.30             
 [34] R6_2.5.1               bitops_1.0-7           spatstat.utils_2.3-1  
 [37] DelayedArray_0.18.0    assertthat_0.2.1       promises_1.2.0.1      
 [40] scales_1.2.0           vroom_1.5.7            rgeos_0.5-9           
 [43] gtable_0.3.0           globals_0.15.0         goftest_1.2-3         
 [46] workflowr_1.7.0        rlang_1.0.2            splines_4.1.0         
 [49] rstatix_0.7.0          lazyeval_0.2.2         spatstat.geom_2.4-0   
 [52] broom_0.8.0            yaml_2.3.5             reshape2_1.4.4        
 [55] abind_1.4-5            modelr_0.1.8           backports_1.4.1       
 [58] httpuv_1.6.5           tools_4.1.0            ellipsis_0.3.2        
 [61] spatstat.core_2.4-2    jquerylib_0.1.4        RColorBrewer_1.1-3    
 [64] ggridges_0.5.3         Rcpp_1.0.8.3           plyr_1.8.7            
 [67] zlibbioc_1.38.0        RCurl_1.98-1.6         rpart_4.1.16          
 [70] deldir_1.0-6           pbapply_1.5-0          cowplot_1.1.1         
 [73] zoo_1.8-10             haven_2.5.0            ggrepel_0.9.1         
 [76] cluster_2.1.3          fs_1.5.2               RSpectra_0.16-1       
 [79] data.table_1.14.2      scattermore_0.8        lmtest_0.9-40         
 [82] reprex_2.0.1           RANN_2.6.1             whisker_0.4           
 [85] fitdistrplus_1.1-8     hms_1.1.1              patchwork_1.1.1       
 [88] mime_0.12              evaluate_0.15          xtable_1.8-4          
 [91] readxl_1.4.0           gridExtra_2.3          compiler_4.1.0        
 [94] KernSmooth_2.23-20     crayon_1.5.1           htmltools_0.5.2       
 [97] mgcv_1.8-40            later_1.3.0            tzdb_0.3.0            
[100] lubridate_1.8.0        DBI_1.1.2              dbplyr_2.1.1          
[103] MASS_7.3-57            Matrix_1.4-1           car_3.0-13            
[106] cli_3.3.0              igraph_1.3.1           pkgconfig_2.0.3       
[109] plotly_4.10.0          spatstat.sparse_2.1-1  xml2_1.3.3            
[112] bslib_0.3.1            XVector_0.32.0         rvest_1.0.2           
[115] digest_0.6.29          sctransform_0.3.3      RcppAnnoy_0.0.19      
[118] spatstat.data_2.2-0    rmarkdown_2.14         cellranger_1.1.0      
[121] leiden_0.3.10          uwot_0.1.11            shiny_1.7.1           
[124] lifecycle_1.0.1        nlme_3.1-157           jsonlite_1.8.0        
[127] carData_3.0-5          limma_3.48.3           viridisLite_0.4.0     
[130] fansi_1.0.3            pillar_1.7.0           lattice_0.20-45       
[133] fastmap_1.1.0          httr_1.4.3             survival_3.3-1        
[136] glue_1.6.2             png_0.1-7              bit_4.0.4             
[139] stringi_1.7.6          sass_0.4.1             irlba_2.3.5           
[142] future.apply_1.9.0    

date()

[1] "Mon Jul  4 16:53:00 2022"

Session information

sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] pheatmap_1.0.12             ggpubr_0.4.0               
 [3] ggsci_2.9                   runSeurat3_0.1.0           
 [5] here_1.0.1                  magrittr_2.0.3             
 [7] sp_1.4-7                    SeuratObject_4.1.0         
 [9] Seurat_4.1.1                forcats_0.5.1              
[11] stringr_1.4.0               dplyr_1.0.9                
[13] purrr_0.3.4                 readr_2.1.2                
[15] tidyr_1.2.0                 tibble_3.1.7               
[17] ggplot2_3.3.6               tidyverse_1.3.1            
[19] SingleCellExperiment_1.14.1 SummarizedExperiment_1.22.0
[21] Biobase_2.52.0              GenomicRanges_1.44.0       
[23] GenomeInfoDb_1.28.4         IRanges_2.26.0             
[25] S4Vectors_0.30.2            BiocGenerics_0.38.0        
[27] MatrixGenerics_1.4.3        matrixStats_0.62.0         

loaded via a namespace (and not attached):
  [1] utf8_1.2.2             reticulate_1.24        tidyselect_1.1.2      
  [4] htmlwidgets_1.5.4      grid_4.1.0             Rtsne_0.16            
  [7] munsell_0.5.0          codetools_0.2-18       ica_1.0-2             
 [10] future_1.25.0          miniUI_0.1.1.1         withr_2.5.0           
 [13] spatstat.random_2.2-0  colorspace_2.0-3       progressr_0.10.0      
 [16] highr_0.9              knitr_1.39             rstudioapi_0.13       
 [19] ROCR_1.0-11            ggsignif_0.6.3         tensor_1.5            
 [22] listenv_0.8.0          labeling_0.4.2         git2r_0.30.1          
 [25] GenomeInfoDbData_1.2.6 polyclip_1.10-0        farver_2.1.0          
 [28] bit64_4.0.5            rprojroot_2.0.3        parallelly_1.31.1     
 [31] vctrs_0.4.1            generics_0.1.2         xfun_0.30             
 [34] R6_2.5.1               bitops_1.0-7           spatstat.utils_2.3-1  
 [37] DelayedArray_0.18.0    assertthat_0.2.1       promises_1.2.0.1      
 [40] scales_1.2.0           vroom_1.5.7            rgeos_0.5-9           
 [43] gtable_0.3.0           globals_0.15.0         goftest_1.2-3         
 [46] workflowr_1.7.0        rlang_1.0.2            splines_4.1.0         
 [49] rstatix_0.7.0          lazyeval_0.2.2         spatstat.geom_2.4-0   
 [52] broom_0.8.0            yaml_2.3.5             reshape2_1.4.4        
 [55] abind_1.4-5            modelr_0.1.8           backports_1.4.1       
 [58] httpuv_1.6.5           tools_4.1.0            ellipsis_0.3.2        
 [61] spatstat.core_2.4-2    jquerylib_0.1.4        RColorBrewer_1.1-3    
 [64] ggridges_0.5.3         Rcpp_1.0.8.3           plyr_1.8.7            
 [67] zlibbioc_1.38.0        RCurl_1.98-1.6         rpart_4.1.16          
 [70] deldir_1.0-6           pbapply_1.5-0          cowplot_1.1.1         
 [73] zoo_1.8-10             haven_2.5.0            ggrepel_0.9.1         
 [76] cluster_2.1.3          fs_1.5.2               RSpectra_0.16-1       
 [79] data.table_1.14.2      scattermore_0.8        lmtest_0.9-40         
 [82] reprex_2.0.1           RANN_2.6.1             whisker_0.4           
 [85] fitdistrplus_1.1-8     hms_1.1.1              patchwork_1.1.1       
 [88] mime_0.12              evaluate_0.15          xtable_1.8-4          
 [91] readxl_1.4.0           gridExtra_2.3          compiler_4.1.0        
 [94] KernSmooth_2.23-20     crayon_1.5.1           htmltools_0.5.2       
 [97] mgcv_1.8-40            later_1.3.0            tzdb_0.3.0            
[100] lubridate_1.8.0        DBI_1.1.2              dbplyr_2.1.1          
[103] MASS_7.3-57            Matrix_1.4-1           car_3.0-13            
[106] cli_3.3.0              igraph_1.3.1           pkgconfig_2.0.3       
[109] plotly_4.10.0          spatstat.sparse_2.1-1  xml2_1.3.3            
[112] bslib_0.3.1            XVector_0.32.0         rvest_1.0.2           
[115] digest_0.6.29          sctransform_0.3.3      RcppAnnoy_0.0.19      
[118] spatstat.data_2.2-0    rmarkdown_2.14         cellranger_1.1.0      
[121] leiden_0.3.10          uwot_0.1.11            shiny_1.7.1           
[124] lifecycle_1.0.1        nlme_3.1-157           jsonlite_1.8.0        
[127] carData_3.0-5          limma_3.48.3           viridisLite_0.4.0     
[130] fansi_1.0.3            pillar_1.7.0           lattice_0.20-45       
[133] fastmap_1.1.0          httr_1.4.3             survival_3.3-1        
[136] glue_1.6.2             png_0.1-7              bit_4.0.4             
[139] stringi_1.7.6          sass_0.4.1             irlba_2.3.5           
[142] future.apply_1.9.0