Last updated: 2022-02-22
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Knit directory: MelanomaIMC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 1affd7b | toobiwankenobi | 2022-02-22 | update .html |
| 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), ]$maxClustSize <- 0
# only when not 0 - CHANGE?
c <- ggplot(Bcell_patch[rowSums(Bcell_patch[,-1]) > 0,], aes(x = maxClustSize, y = maxPatchSize, label=Description)) +
geom_point() +
geom_smooth(method="lm") +
stat_cor(method = "pearson",
aes(label = paste0("atop(", ..r.label.., ",", ..p.label.. ,")")),
size = 6, cor.coef.name = "R", label.sep="\n", label.y = 0.5, label.x = 2) +
ylab("Max Size of B cell\nPatches (log10)") +
xlab("Max Size of CXCL13 Patches (log10)") +
theme_bw() +
theme(text = element_text(size=18))
d <- ggplot(Bcell, aes(x = maxClustSize, y = `B cell`, label=Description)) +
geom_point() +
geom_smooth(method="lm") +
stat_cor(method = "pearson",
aes(label = paste0("atop(", ..r.label.., ",", ..p.label.. ,")")),
size = 6, cor.coef.name = "R", label.sep="\n", label.y = 0.05, label.x = 2) +
ylab("B Cell Fraction") +
xlab("Max Size of CXCL13 Patches (log10)") +
theme_bw() +
theme(text = element_text(size=18))
plot_grid(grid.arrange(a + theme(legend.position = "none"),
b + theme(legend.position = "none"),
ncol = 2),
grid.arrange(d,c, ncol = 2),
ncol = 1,
rel_heights = c(0.65,0.35))Warning: Removed 1063 rows containing missing values (geom_point).
Removed 1063 rows containing missing values (geom_point).
| Version | Author | Date |
|---|---|---|
| 235386f | toobiwankenobi | 2022-02-22 |
| Version | Author | Date |
|---|---|---|
| 235386f | toobiwankenobi | 2022-02-22 |
| Version | Author | Date |
|---|---|---|
| 235386f | toobiwankenobi | 2022-02-22 |
sce_rna$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)
| Version | Author | Date |
|---|---|---|
| 235386f | toobiwankenobi | 2022-02-22 |
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$celltype[c("CD4+ T cell", "CD8+ T cell")]),
show_legend = FALSE)
h <- Heatmap(cor,
name = "Pearson\nCorrelation",
cluster_rows = FALSE,
cluster_columns = FALSE,
show_column_names = FALSE,
show_row_names = TRUE,
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.2f", cor[i, j]), x, y, gp = gpar(fontsize = 15, col = "black"))
},
col = colorRamp2(c(-1, 0, 1), c("red", "white", "blue")),
row_title = "Expressor",
row_names_side = "left",
top_annotation = ha1,
width = unit(18, "cm"),
height = unit(10, "cm"),
show_heatmap_legend = FALSE)
# draw heatmap
draw(h)
| Version | Author | Date |
|---|---|---|
| 235386f | toobiwankenobi | 2022-02-22 |
lgd1 = color_mapping_legend(h@matrix_color_mapping, plot = FALSE, legend_direction = "vertical", legend_width=unit(3,"cm"), at = c(-1:1))
lgd2 = color_mapping_legend(ha1@anno_list$`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)
| Version | Author | Date |
|---|---|---|
| 235386f | toobiwankenobi | 2022-02-22 |
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