Last updated: 2022-02-22
Checks: 7 0
Knit directory: MelanomaIMC/
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.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
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.
The command set.seed(20200728) 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.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
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 d00a53f. 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: .Rproj.user/
Ignored: Table_S4.csv
Ignored: code/.DS_Store
Ignored: code/._.DS_Store
Ignored: data/.DS_Store
Ignored: data/._.DS_Store
Ignored: data/data_for_analysis/
Ignored: data/full_data/
Unstaged changes:
Modified: .gitignore
Deleted: analysis/.smbdeleteAAA16a00f1
Modified: analysis/_site.yml
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/Supp-Figure_13.rmd) and HTML (docs/Supp-Figure_13.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 | 64e5fde | toobiwankenobi | 2022-02-16 | change order and naming of supp fig files |
| Rmd | 588dbb1 | toobiwankenobi | 2022-02-06 | Figure Order |
| Rmd | b20b6fb | toobiwankenobi | 2022-02-02 | update code for Supp Figures |
| Rmd | d6a945a | toobiwankenobi | 2021-12-06 | updated figures |
| Rmd | 3da15db | toobiwankenobi | 2021-11-24 | changes for revision |
This script generates plots for Supplementary Figure 13.
knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())sapply(list.files("code/helper_functions", full.names = TRUE), source) code/helper_functions/calculateSummary.R
value ?
visible FALSE
code/helper_functions/censor_dat.R
value ?
visible FALSE
code/helper_functions/detect_mRNA_expression.R
value ?
visible FALSE
code/helper_functions/DistanceToClusterCenter.R
value ?
visible FALSE
code/helper_functions/findMilieu.R code/helper_functions/findPatch.R
value ? ?
visible FALSE FALSE
code/helper_functions/getInfoFromString.R
value ?
visible FALSE
code/helper_functions/getSpotnumber.R
value ?
visible FALSE
code/helper_functions/plotCellCounts.R
value ?
visible FALSE
code/helper_functions/plotCellFractions.R
value ?
visible FALSE
code/helper_functions/plotDist.R code/helper_functions/read_Data.R
value ? ?
visible FALSE FALSE
code/helper_functions/scatter_function.R
value ?
visible FALSE
code/helper_functions/sceChecks.R
value ?
visible FALSE
code/helper_functions/validityChecks.R
value ?
visible FALSE library(ggridges)
library(SingleCellExperiment)
library(ggplot2)
library(dplyr)
library(tidyr)
library(ggbeeswarm)
library(ggrastr)
library(Hmisc)
library(data.table)
library(ggpubr)
library(corrplot)
library(gridExtra)
library(scater)
library(dittoSeq)
library(ComplexHeatmap)
library(colorRamps)
library(cowplot)
library(stringr)
library(circlize)sce_rna <- readRDS(file = "data/data_for_analysis/sce_RNA.rds")
sce_prot <- readRDS(file = "data/data_for_analysis/sce_protein.rds")
# remove LN margin samples and control samples
sce_rna$PunchLocation <- paste(sce_rna$MM_location, sce_rna$Location, sep = "_")
sce_prot$PunchLocation <- paste(sce_prot$MM_location, sce_prot$Location, sep = "_")
sce_rna <- sce_rna[,sce_rna$PunchLocation != "LN_M" & sce_rna$Location != "CTRL"]
sce_prot <- sce_prot[,sce_prot$PunchLocation != "LN_M" & sce_prot$Location != "CTRL"]
# image
image_mat_prot <- read.csv("data/data_for_analysis/protein/Image.csv")
image_mat_rna <- read.csv("data/data_for_analysis/RNA/Image.csv")im_size_prot <- as.data.frame(cbind(image_mat_prot$Metadata_Description, (image_mat_prot$Height_cellmask * image_mat_prot$Width_cellmask)/1000000))
names(im_size_prot) <- c("Description", "mm2_prot")
im_size_prot$mm2_prot <- as.numeric(im_size_prot$mm2_prot)
im_size_prot[im_size_prot$Description %in% c("G1", "G1 - split"), ]$mm2_prot <- mean(im_size_prot[im_size_prot$Description %in% c("G1", "G1 - split"), ]$mm2_prot)
im_size_prot <- im_size_prot[im_size_prot != "G1 - split",]
im_size_prot <- im_size_prot[1:166,]im_size_rna <- as.data.frame(cbind(image_mat_rna$Metadata_Description, (image_mat_rna$Height_cellmask * image_mat_rna$Width_cellmask)/1000000))
names(im_size_rna) <- c("Description", "mm2_rna")
im_size_rna$mm2_rna <- as.numeric(im_size_rna$mm2_rna)
im_size_rna <- im_size_rna[1:166,]# loop through all patches
for(i in c("cxcl13only_clust")){
# subset sce object to only contain community cells
sce_sub <- sce_rna[,colData(sce_rna)[,i] > 0]
assay(sce_sub, "scaled_asinh") <- t(scale(t(assay(sce_sub, "asinh"))))
# create UAMP
set.seed(12345)
sce_sub <- runDiffusionMap(sce_sub,
exprs_values = "asinh",
subset_row = rowData(sce_sub)$good_marker,
ncomponents = 2)
# add patch size to sce
cur_df <- data.frame(colData(sce_sub))
clust_size <- cur_df %>%
group_by(cur_df[,i]) %>%
summarise(clust_size = n())
names(clust_size)[1] <- i
cur_df <- left_join(cur_df, clust_size)
sce_sub$clust_size = as.numeric(log10(cur_df$clust_size))
# col by clust size
a <- dittoDimPlot(sce_sub,
reduction.use = "DiffusionMap",
var = "clust_size",
size = 1,
legend.show = TRUE,
opacity = 1,
max.color = "red", min.color = "blue",
main = NULL,
legend.title = "Patch Size (log10)") +
xlim(quantile(reducedDim(sce_sub, "DiffusionMap")[,1], 0.05)[[1]],
quantile(reducedDim(sce_sub, "DiffusionMap")[,1], 0.95)[[1]]) +
ylim(quantile(reducedDim(sce_sub, "DiffusionMap")[,2], 0.05)[[1]],
quantile(reducedDim(sce_sub, "DiffusionMap")[,2], 0.95)[[1]]) +
theme_bw() +
theme(text = element_text(size=18))
# col by celltype
b <- dittoDimPlot(sce_sub,
reduction.use = "DiffusionMap",
var = "celltype",
opacity = 1,
color.panel = metadata(sce_sub)$colour_vector$celltype,
size = 1,
legend.show = TRUE,
main = NULL,
legend.title = "Cell Type") +
theme_bw() +
xlim(quantile(reducedDim(sce_sub, "DiffusionMap")[,1], 0.05)[[1]],
quantile(reducedDim(sce_sub, "DiffusionMap")[,1], 0.95)[[1]]) +
ylim(quantile(reducedDim(sce_sub, "DiffusionMap")[,2], 0.05)[[1]],
quantile(reducedDim(sce_sub, "DiffusionMap")[,2], 0.95)[[1]]) +
theme(text = element_text(size=18)) +
guides(colour = guide_legend(override.aes = list(alpha = 1, size=3)))
leg_a <- cowplot::get_legend(a)
leg_b <- cowplot::get_legend(b)
}Warning: 'runDiffusionMap' is deprecated.
See help("Deprecated")Warning: 'calculateDiffusionMap' is deprecated.
See help("Deprecated")Warning: Removed 1063 rows containing missing values (geom_point).
Removed 1063 rows containing missing values (geom_point).sce_sub <- sce_rna[,colData(sce_rna)[,"cxcl13only_clust"] > 0]
# add patch size to sce
cur_df <- data.frame(colData(sce_sub))
clust_size <- cur_df %>%
group_by(cxcl13only_clust) %>%
summarise(clust_size = n())
names(clust_size)[1] <- "cxcl13only_clust"
cur_df <- left_join(cur_df, clust_size)
sce_sub$clust_size = as.numeric(log10(cur_df$clust_size))
# add clust size correlation plot
clust_size <- data.frame(colData(sce_sub)) %>%
distinct(Description, cxcl13only_clust, .keep_all = T) %>%
group_by(Description) %>%
summarise(maxClustSize = max(clust_size))
Bcell_patch <- data.frame(colData(sce_prot)) %>%
group_by(Description, bcell_patch) %>%
summarise(n=n()) %>%
mutate(n = ifelse(bcell_patch == 0, 0, n)) %>%
mutate(maxPatchSize = log10(max(n+1))) %>%
distinct(Description, .keep_all = T) %>%
dplyr::select(Description, maxPatchSize)
Bcell_patch <- left_join(Bcell_patch, clust_size)
Bcell_patch[is.na(Bcell_patch$maxClustSize), ]$maxClustSize <- 0
Bcell <- data.frame(colData(sce_prot)) %>%
group_by(Description, celltype) %>%
summarise(n=n()) %>%
mutate(fraction = n / sum(n)) %>%
reshape2::dcast(Description ~ celltype, value.var = "fraction", fill = 0) %>%
dplyr::select(Description, `B cell`)
Bcell <- left_join(Bcell, clust_size)
Bcell[is.na(Bcell$maxClustSize), ]$maxClustSize <- 0
# only when not 0 - CHANGE?
c <- ggplot(Bcell_patch[rowSums(Bcell_patch[,-1]) > 0,], aes(x = maxClustSize, y = maxPatchSize, label=Description)) +
geom_point() +
geom_smooth(method="lm") +
stat_cor(method = "pearson",
aes(label = paste0("atop(", ..r.label.., ",", ..p.label.. ,")")),
size = 6, cor.coef.name = "R", label.sep="\n", label.y = 0.5, label.x = 2) +
ylab("Max Size of B cell\nPatches (log10)") +
xlab("Max Size of CXCL13 Patches (log10)") +
theme_bw() +
theme(text = element_text(size=18))
d <- ggplot(Bcell, aes(x = maxClustSize, y = `B cell`, label=Description)) +
geom_point() +
geom_smooth(method="lm") +
stat_cor(method = "pearson",
aes(label = paste0("atop(", ..r.label.., ",", ..p.label.. ,")")),
size = 6, cor.coef.name = "R", label.sep="\n", label.y = 0.05, label.x = 2) +
ylab("B Cell Fraction") +
xlab("Max Size of CXCL13 Patches (log10)") +
theme_bw() +
theme(text = element_text(size=18))
plot_grid(grid.arrange(a + theme(legend.position = "none"),
b + theme(legend.position = "none"),
ncol = 2),
grid.arrange(d,c, ncol = 2),
ncol = 1,
rel_heights = c(0.65,0.35))Warning: Removed 1063 rows containing missing values (geom_point).
Removed 1063 rows containing missing values (geom_point).grid.arrange(leg_a)grid.arrange(leg_b)sce_rna$CD8pos_CXCL13 <- 0
sce_rna[,sce_rna$celltype %in% c("CD8+ T cell") & sce_rna$CXCL13 == 1]$CD8pos_CXCL13 <- 1
sce_rna$CD8neg_CXCL13 <- 0
sce_rna[,sce_rna$celltype %in% c("CD8- T cell") & sce_rna$CXCL13 == 1]$CD8neg_CXCL13 <- 1perc_cd8_cxcl13 <- as.data.frame(colData(sce_rna)) %>%
group_by(Description, CD8pos_CXCL13) %>%
summarise(n_cd8pos=n()) %>%
ungroup() %>%
complete(Description, CD8pos_CXCL13, fill=list(n_cd8pos=0)) %>%
filter(CD8pos_CXCL13 == 1)
perc_cd20 <- as.data.frame(colData(sce_prot)) %>%
group_by(Description, celltype) %>%
summarise(n_cd20=n()) %>%
ungroup() %>%
complete(Description, celltype, fill=list(n_cd20=0)) %>%
filter(celltype == "B cell")
info <- as.data.frame(colData(sce_prot)) %>%
distinct(Description, .keep_all = T) %>%
select(Description, bcell_patch_score)
perc_cd20 <- left_join(info, perc_cd20)
data <- left_join(perc_cd20, perc_cd8_cxcl13)
data_cd8pos <- left_join(data, im_size_prot)
data_cd8pos <- left_join(data_cd8pos, im_size_rna)
data_cd8pos$n_cd20 <- data_cd8pos$n_cd20 / data_cd8pos$mm2_prot
data_cd8pos$n_cd8pos <- data_cd8pos$n_cd8pos / data_cd8pos$mm2_rnaperc_cd4_cxcl13 <- as.data.frame(colData(sce_rna)) %>%
group_by(Description, CD8neg_CXCL13) %>%
summarise(n_cd8neg=n()) %>%
ungroup() %>%
complete(Description, CD8neg_CXCL13, fill=list(n_cd8neg=0)) %>%
filter(CD8neg_CXCL13 == 1)
perc_cd20 <- as.data.frame(colData(sce_prot)) %>%
group_by(Description, celltype) %>%
summarise(n_cd20=n()) %>%
ungroup() %>%
complete(Description, celltype, fill=list(n_cd20=0)) %>%
filter(celltype == "B cell")
info <- as.data.frame(colData(sce_prot)) %>%
distinct(Description, .keep_all = T) %>%
select(Description, bcell_patch_score)
perc_cd20 <- left_join(info, perc_cd20)
data <- left_join(perc_cd20, perc_cd4_cxcl13)
data_cd8neg <- left_join(data, im_size_prot)
data_cd8neg <- left_join(data_cd8neg, im_size_rna)
data_cd8neg$n_cd20 <- data_cd8neg$n_cd20 / data_cd8neg$mm2_prot
data_cd8neg$n_cd8neg <- data_cd8neg$n_cd8neg / data_cd8neg$mm2_rnadata_complete <- left_join(data_cd8neg, data_cd8pos[,c("Description","n_cd8pos")])
# remove images with follicles
data_complete <- data_complete[data_complete$bcell_patch_score != "B cell Follicles",]
a <- ggplot(data_complete, aes(x=n_cd8neg, y=n_cd20)) +
geom_point() +
geom_smooth(method="lm") +
stat_cor(method = "pearson",
aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
size = 4, cor.coef.name = "R", label.sep="\n", label.y.npc = "top", label.x.npc = "left") +
xlab("CXCL13+CD8- T cell / mm2") +
ylab("B cells / mm2") +
theme_bw() +
theme(text=element_text(size=15))
b <- ggplot(data_complete, aes(x=n_cd8pos, y=n_cd20)) +
geom_point() +
geom_smooth(method="lm") +
stat_cor(method = "pearson",
aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
size = 4, cor.coef.name = "R", label.sep="\n", label.y.npc = "top", label.x.npc = "left") +
xlab("CXCL13+CD8+ T cell / mm2") +
ylab("B cells / mm2") +
theme_bw() +
theme(text=element_text(size=15))
grid.arrange(a,b, ncol=2)targets <- metadata(sce_rna)$chemokines_morethan600_withcontrol
# top abundant chemokines
cur_rna <- data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL")
# protein data
cur_prot <- data.frame(colData(sce_prot)) %>%
filter(Location != "CTRL")
# sum
rna_sum <- cur_rna %>%
group_by(Description) %>%
mutate(total_cells=n()) %>%
ungroup() %>%
group_by(Description, total_cells, expressor) %>%
summarise(n=n()) %>%
mutate(fraction=n/total_cells) %>%
reshape2::dcast(Description ~ expressor, value.var = "fraction", fill = 0)
# only keep highly abundant chemokines
rna_sum <- rna_sum[,c("Description", targets)]
prot_sum <- cur_prot %>%
mutate(celltype2 = ifelse(celltype %in% c("CD4+ T cell", "CD8+ T cell"),
paste(paste(celltype, TCF7, sep="_"), PD1, sep = "_"), celltype)) %>%
group_by(Description, celltype2) %>%
summarise(n = n()) %>%
group_by(Description) %>%
mutate(fraction = n/sum(n)) %>%
#filter(!celltype2 %in% c("CD4+ T cell_TCF7+_PD1+", "CD4+ T cell_TCF7-_PD1+", "CD8+ T cell_TCF7+_PD1+")) %>%
reshape2::dcast(Description ~ celltype2, value.var = "fraction", fill = 0) %>%
select(Description,contains(c("CD8+ T cell", "CD4+ T cell")))
# equal images
all(rna_sum$Description == prot_sum$Description)[1] TRUE# correlation
cor <- cor(rna_sum[,-1], prot_sum[,-1], method = "pearson")
ha <- t(str_split_fixed(colnames(cor), "_", n=3))
dat_sum <- cur_prot %>%
filter(celltype %in% c("CD4+ T cell", "CD8+ T cell")) %>%
mutate(celltype2 = ifelse(celltype %in% c("CD4+ T cell", "CD8+ T cell"),
paste(paste(celltype, TCF7, sep="_"), PD1, sep = "_"), celltype)) %>%
group_by(celltype2) %>%
summarise(n = n())
ha1 <- HeatmapAnnotation("TCF7_PD1" = anno_text(paste(ha[2,], ha[3,], sep = " ")),
"Cell Type" = ha[1,],
"Number of Cells" = anno_barplot(dat_sum$n,
height = unit(2,"cm"),
axis_param = list(gp = gpar(fontsize=14))),
"Numbers" = anno_text(dat_sum$n,
which = "column",
rot = 0,
height = unit(0.5,"cm"),
just = "center",
location = 0.5),
col = list("Cell Type" = metadata(sce_prot)$colour_vectors$celltype[c("CD4+ T cell", "CD8+ T cell")]),
show_legend = FALSE)
h <- Heatmap(cor,
name = "Pearson\nCorrelation",
cluster_rows = FALSE,
cluster_columns = FALSE,
show_column_names = FALSE,
show_row_names = TRUE,
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.2f", cor[i, j]), x, y, gp = gpar(fontsize = 15, col = "black"))
},
col = colorRamp2(c(-1, 0, 1), c("red", "white", "blue")),
row_title = "Expressor",
row_names_side = "left",
top_annotation = ha1,
width = unit(18, "cm"),
height = unit(10, "cm"),
show_heatmap_legend = FALSE)
# draw heatmap
draw(h)lgd1 = color_mapping_legend(h@matrix_color_mapping, plot = FALSE, legend_direction = "vertical", legend_width=unit(3,"cm"), at = c(-1:1))
lgd2 = color_mapping_legend(ha1@anno_list$`Cell Type`@color_mapping, plot = FALSE, legend_direction = "vertical", nrow = 4)
lgd_list = packLegend(lgd1,lgd2,direction = "vertical", gap = unit(1,"cm"))
draw(lgd_list)
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/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[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
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] circlize_0.4.13 stringr_1.4.0
[3] cowplot_1.1.1 colorRamps_2.3
[5] ComplexHeatmap_2.10.0 dittoSeq_1.6.0
[7] scater_1.22.0 scuttle_1.4.0
[9] gridExtra_2.3 corrplot_0.92
[11] ggpubr_0.4.0 data.table_1.14.2
[13] Hmisc_4.6-0 Formula_1.2-4
[15] survival_3.2-13 lattice_0.20-45
[17] ggrastr_1.0.1 ggbeeswarm_0.6.0
[19] tidyr_1.2.0 ggplot2_3.3.5
[21] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[23] Biobase_2.54.0 GenomicRanges_1.46.1
[25] GenomeInfoDb_1.30.1 IRanges_2.28.0
[27] S4Vectors_0.32.3 BiocGenerics_0.40.0
[29] MatrixGenerics_1.6.0 matrixStats_0.61.0
[31] ggridges_0.5.3 dplyr_1.0.7
[33] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 tidyselect_1.1.1
[3] htmlwidgets_1.5.4 ranger_0.13.1
[5] BiocParallel_1.28.3 munsell_0.5.0
[7] ScaledMatrix_1.2.0 destiny_3.8.1
[9] codetools_0.2-18 withr_2.4.3
[11] colorspace_2.0-2 highr_0.9
[13] knitr_1.37 rstudioapi_0.13
[15] robustbase_0.93-9 ggsignif_0.6.3
[17] vcd_1.4-9 VIM_6.1.1
[19] TTR_0.24.3 labeling_0.4.2
[21] git2r_0.29.0 GenomeInfoDbData_1.2.7
[23] farver_2.1.0 pheatmap_1.0.12
[25] rprojroot_2.0.2 vctrs_0.3.8
[27] generics_0.1.2 xfun_0.29
[29] ggthemes_4.2.4 R6_2.5.1
[31] doParallel_1.0.16 clue_0.3-60
[33] rsvd_1.0.5 RcppEigen_0.3.3.9.1
[35] bitops_1.0-7 DelayedArray_0.20.0
[37] assertthat_0.2.1 promises_1.2.0.1
[39] scales_1.1.1 nnet_7.3-17
[41] beeswarm_0.4.0 gtable_0.3.0
[43] beachmat_2.10.0 processx_3.5.2
[45] rlang_1.0.0 scatterplot3d_0.3-41
[47] GlobalOptions_0.1.2 splines_4.1.2
[49] rstatix_0.7.0 hexbin_1.28.2
[51] broom_0.7.12 checkmate_2.0.0
[53] reshape2_1.4.4 yaml_2.2.2
[55] abind_1.4-5 backports_1.4.1
[57] httpuv_1.6.5 tools_4.1.2
[59] ellipsis_0.3.2 jquerylib_0.1.4
[61] RColorBrewer_1.1-2 proxy_0.4-26
[63] Rcpp_1.0.8 plyr_1.8.6
[65] base64enc_0.1-3 sparseMatrixStats_1.6.0
[67] zlibbioc_1.40.0 purrr_0.3.4
[69] RCurl_1.98-1.5 ps_1.6.0
[71] rpart_4.1.16 GetoptLong_1.0.5
[73] viridis_0.6.2 zoo_1.8-9
[75] ggrepel_0.9.1 cluster_2.1.2
[77] fs_1.5.2 magrittr_2.0.2
[79] magick_2.7.3 RSpectra_0.16-0
[81] lmtest_0.9-39 pcaMethods_1.86.0
[83] whisker_0.4 evaluate_0.14
[85] smoother_1.1 jpeg_0.1-9
[87] shape_1.4.6 compiler_4.1.2
[89] tibble_3.1.6 crayon_1.4.2
[91] htmltools_0.5.2 mgcv_1.8-38
[93] later_1.3.0 DBI_1.1.2
[95] MASS_7.3-55 boot_1.3-28
[97] Matrix_1.4-0 car_3.0-12
[99] cli_3.1.1 parallel_4.1.2
[101] pkgconfig_2.0.3 getPass_0.2-2
[103] foreign_0.8-82 laeken_0.5.2
[105] sp_1.4-6 foreach_1.5.2
[107] vipor_0.4.5 bslib_0.3.1
[109] XVector_0.34.0 callr_3.7.0
[111] digest_0.6.29 rmarkdown_2.11
[113] htmlTable_2.4.0 DelayedMatrixStats_1.16.0
[115] curl_4.3.2 ggplot.multistats_1.0.0
[117] rjson_0.2.21 nlme_3.1-155
[119] lifecycle_1.0.1 jsonlite_1.7.3
[121] carData_3.0-5 BiocNeighbors_1.12.0
[123] viridisLite_0.4.0 fansi_1.0.2
[125] pillar_1.7.0 fastmap_1.1.0
[127] httr_1.4.2 DEoptimR_1.0-10
[129] glue_1.6.1 xts_0.12.1
[131] png_0.1-7 iterators_1.0.13
[133] class_7.3-20 stringi_1.7.6
[135] sass_0.4.0 RcppHNSW_0.3.0
[137] BiocSingular_1.10.0 latticeExtra_0.6-29
[139] knn.covertree_1.0 irlba_2.3.5
[141] e1071_1.7-9