Last updated: 2021-02-11
Checks: 6 1
Knit directory: melanoma_publication_old_data/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
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.
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 2e443a5. 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: ._.DS_Store
Ignored: analysis/figure/
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/
Ignored: output/.DS_Store
Ignored: output/._.DS_Store
Ignored: output/._protein_neutrophil.png
Ignored: output/._rna_neutrophil.png
Ignored: output/PSOCKclusterOut/
Ignored: output/bcell_grouping.png
Ignored: output/dysfunction_correlation.pdf
Untracked files:
Untracked: code/helper_functions/findMilieu.R
Untracked: code/helper_functions/findPatch.R
Unstaged changes:
Modified: .gitignore
Modified: analysis/01_Protein_read_data.rmd
Modified: analysis/01_RNA_read_data.rmd
Modified: analysis/02_Protein_annotations.rmd
Modified: analysis/02_RNA_annotations.rmd
Modified: analysis/03_Protein_quality_control.rmd
Modified: analysis/03_RNA_quality_control.rmd
Modified: analysis/04_1_Protein_celltype_classification.rmd
Modified: analysis/04_1_RNA_celltype_classification.rmd
Modified: analysis/04_2_RNA_classification_subclustering.rmd
Modified: analysis/04_2_protein_classification_subclustering.rmd
Modified: analysis/05_RNA_chemokine_expressing_cells.rmd
Modified: analysis/06_RNA_chemokine_patch_detection.rmd
Modified: analysis/07_TCF7_PD1_gating.rmd
Modified: analysis/08_color_vectors.rmd
Modified: analysis/09_Tcell_Score.Rmd
Modified: analysis/10_Dysfunction_Score.rmd
Modified: analysis/11_Bcell_Score.Rmd
Modified: analysis/Figure_1.rmd
Modified: analysis/Figure_2.rmd
Modified: analysis/Figure_3.rmd
Modified: analysis/Figure_4.rmd
Modified: analysis/Figure_5.rmd
Modified: analysis/Summary_Statistics.rmd
Modified: analysis/Supp-Figure_1.rmd
Modified: analysis/Supp-Figure_2.rmd
Modified: analysis/Supp-Figure_3.rmd
Modified: analysis/Supp-Figure_4.rmd
Modified: analysis/Supp-Figure_5.rmd
Modified: analysis/XX_hazard_ratio.rmd
Deleted: code/findPackages.R
Deleted: code/helper_functions/findClusters.R
Deleted: code/helper_functions/findCommunity.R
Deleted: code/helper_functions/getCellCount.R
Deleted: code/helper_functions/plotBarFracCluster.R
Deleted: code/helper_functions/plotCellFrac.R
Deleted: code/helper_functions/plotCellFracGroups.R
Deleted: code/helper_functions/plotCellFracGroupsSubset.R
Deleted: code/helper_functions/scatter_function.R
Modified: code/helper_functions/validityChecks.R
Deleted: data/mask_comparison/20190809_ZTMA256.1_slide2_TH_s1_p1_r15_a15_ac_full.tiff
Deleted: data/mask_comparison/20190809_ZTMA256.1_slide2_TH_s1_p1_r15_a15_ac_ilastik_s2_Probabilities_equalized_cellmask.tiff
Deleted: data/mask_comparison/20191023_ZTMA256.1_slide3_TH_s0_p10_r4_a4_ac_full.tiff
Deleted: data/mask_comparison/20191023_ZTMA256.1_slide3_TH_s0_p10_r4_a4_ac_ilastik_s2_Probabilities_equalized_cellmask.tiff
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/Figure_1.rmd) and HTML (docs/Figure_1.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 |
|---|---|---|---|---|
| html | 3f5af3f | toobiwankenobi | 2021-02-09 | add .html files |
| Rmd | 20a1458 | toobiwankenobi | 2021-02-04 | adapt figure order |
| Rmd | f9bb33a | toobiwankenobi | 2021-02-04 | new Figure 5 and minor changes in figure order |
| Rmd | 9442cb9 | toobiwankenobi | 2020-12-22 | add all new files |
| Rmd | d8c7699 | Tobias Hoch | 2020-10-23 | adapt figure 1 and supp fig 6 |
| Rmd | 1af3353 | toobiwankenobi | 2020-10-16 | add stuff |
| Rmd | d8819f2 | toobiwankenobi | 2020-10-08 | read new data (nuclei expansion) and adapt scripts |
| Rmd | 2c11d5c | toobiwankenobi | 2020-08-05 | add new scripts |
This script generates plots for Figure 1.
knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())library(SingleCellExperiment)
library(data.table)
library(scater)
library(ggplot2)
library(dittoSeq)
library(cba)
library(ComplexHeatmap)
library(corrplot)
library(dplyr)
library(reshape2)
library(tidyverse)
library(rms)
library(cytomapper)
library(ggrepel)
library(circlize)
library(ggbeeswarm)
library(dendextend)sce_rna <- readRDS(file = "data/data_for_analysis/sce_RNA.rds")
sce_prot <- readRDS(file = "data/data_for_analysis/sce_protein.rds")# add marker expression to cells
marker_expression <- data.frame(t(assay(sce_rna[rowData(sce_rna)$good_marker,], "asinh")))
marker_expression$cellID <- rownames(marker_expression)
dat <- data.frame(colData(sce_rna))[,c("cellID", "celltype")]
dat <- left_join(dat, marker_expression)
dat$cellID <- NULL
# aggregate the groups
dat_aggr <- dat %>%
group_by(celltype) %>%
summarise_all(funs(mean))Warning: `funs()` is deprecated as of dplyr 0.8.0.
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.# number of cells per group
dat_sum <- dat %>%
group_by(celltype) %>%
summarise(n=n())
dat_sum <- data.frame(t(dat_sum))
# scale and center expression
dat_aggr[,-c(1)] <- scale(dat_aggr[,-c(1)])
# create matrix
m <- as.matrix(t(dat_aggr[,-c(1)]))
colnames(m) <- dat_aggr$celltype
# top annotation with number of cells
ha <- HeatmapAnnotation("Number of Cells" = anno_barplot(ifelse(as.numeric(dat_sum[2,])>100000, 101000, as.numeric(dat_sum[2,])),
height = unit(3,"cm"),
ylim = range(0,100000),
gp = gpar(fill = ifelse(as.numeric(dat_sum[2,]) > 100000, "white", "black"),
col="white"),
axis_param = list(gp = gpar(fontsize=14))),
"Numbers" = anno_text(round(as.numeric(dat_sum[2,])),
which = "column",
rot = 0,
just = "center",
location = 0.5,
gp = gpar(fontsize=10,col = ifelse(as.numeric(dat_sum[2,]) > 100000, "red", "black"))),
annotation_name_gp = gpar(fontsize = 16))
# row_split for markers
rowData(sce_rna)$heatmap_relevance <- ""
rowData(sce_rna[rowData(sce_rna)$good_marker,])$heatmap_relevance <- "lineage"
rowData(sce_rna[grepl("CXCL|CCL|DapB", rownames(sce_rna)),])$heatmap_relevance <- "chemokine"
rowData(sce_rna[grepl("B2M|GLUT1|CD134|Lag3|CD163|cleavedPARP|pRB", rownames(sce_rna)),])$heatmap_relevance <- "other"
# plot heatmap
h <- Heatmap(m, name = "Scaled Expression",
row_split = rowData(sce_rna[rowData(sce_rna)$good_marker,])$heatmap_relevance,
cluster_columns = TRUE,
show_column_dend = FALSE,
column_names_gp = gpar(fontsize=18),
column_names_rot = 45,
column_names_centered = TRUE,
show_column_names = TRUE,
top_annotation = ha,
row_names_gp = gpar(fontsize = 16),
row_title_gp = gpar(fontsize = 23),
col = colorRamp2(c(-2, 0, 2), c("blue", "white", "red")),
heatmap_legend_param = list(at = c(-2:2), legend_width = unit(6,"cm"),
direction="horizontal", title_gp = gpar(fontsize=16),
labels_gp = gpar(fontsize=12)),
column_names_side = "top",
height = unit(20, "cm"),
width = unit(17,"cm"))
draw(h, heatmap_legend_side = "bottom")# add marker expression to cells
marker_expression <- data.frame(t(assay(sce_prot[rowData(sce_prot)$good_marker,], "asinh")))
marker_expression$cellID <- rownames(marker_expression)
dat <- data.frame(colData(sce_prot))[,c("cellID", "celltype")]
dat <- left_join(dat, marker_expression)
dat$cellID <- NULL
# aggregate the groups
dat_aggr <- dat %>%
group_by(celltype) %>%
summarise_all(funs(mean))
# number of cells per group
dat_sum <- dat %>%
group_by(celltype) %>%
summarise(n=n())
dat_sum <- data.frame(t(dat_sum))
# scale and center expression
dat_aggr[,-c(1)] <- scale(dat_aggr[,-c(1)])
# create matrix
m <- as.matrix(t(dat_aggr[,-c(1)]))
colnames(m) <- dat_aggr$celltype
# top annotation with number of cells
ha <- HeatmapAnnotation("Number of Cells" = anno_barplot(ifelse(as.numeric(dat_sum[2,])>70000, 70000, as.numeric(dat_sum[2,])),
height = unit(3,"cm"),
ylim = range(0,70000),
gp = gpar(fill = ifelse(as.numeric(dat_sum[2,]) > 70000, "white", "black"),
col = "white"),
axis_param = list(gp = gpar(fontsize=14))),
"Numbers" = anno_text(round(as.numeric(dat_sum[2,])),
which = "column",
rot = 0,
just = "center",
location = 0.5,
gp = gpar(fontsize=10,col = ifelse(as.numeric(dat_sum[2,]) > 70000, "red", "black"))),
annotation_name_gp = gpar(fontsize = 16))
# row_split for markers
rowData(sce_prot)$heatmap_relevance <- ""
rowData(sce_prot[rowData(sce_prot)$good_marker,])$heatmap_relevance <- "lineage"
rowData(sce_prot[grepl("PDL1|CD11b|CD206|PARP|CXCR2|CD11c|pS6|GrzB|IDO1|CD45RA|H3K27me3|TCF7|CD45RO|PD1|pERK|ICOS|Ki67", rownames(sce_prot)),])$heatmap_relevance <- "other"
# plot heatmap
h <- Heatmap(m, name = "Scaled Expression",
row_split = rowData(sce_prot[rowData(sce_prot)$good_marker,])$heatmap_relevance,
cluster_columns = TRUE,
show_column_dend = FALSE,
column_names_gp = gpar(fontsize=18),
column_names_rot = 45,
column_names_centered = TRUE,
show_column_names = TRUE,
row_names_gp = gpar(fontsize = 16),
row_title_gp = gpar(fontsize = 23),
top_annotation = ha,
col = colorRamp2(c(-2, 0, 2), c("blue", "white", "red")),
heatmap_legend_param = list(at = c(-2:2), legend_width = unit(6,"cm"),
direction="horizontal", title_gp = gpar(fontsize=16),
labels_gp = gpar(fontsize=12)),
column_names_side = "top",
height = unit(20, "cm"),
width = unit(17,"cm"))
draw(h, heatmap_legend_side = "bottom")cur_rna <- data.frame(colData(sce_rna))
cur_prot <- data.frame(colData(sce_prot))
rna_sum <- cur_rna %>%
group_by(Description, celltype) %>%
summarise(n = n()) %>%
mutate(fraction = n/sum(n)) %>%
reshape2::dcast(Description ~ celltype, value.var = "fraction", fill = 0)
prot_sum <- cur_prot %>%
group_by(Description, celltype) %>%
summarise(n = n()) %>%
mutate(fraction = n/sum(n)) %>%
reshape2::dcast(Description ~ celltype, value.var = "fraction", fill = 0)
# Correlation Plot - shared celltypes
shared <- c("Macrophage", "Neutrophil", "Tcytotoxic", "Tumor")
corrplot(cor(rna_sum[,-1], prot_sum[,-1], method = "pearson"),
addCoef.col = "white",
method = "circle",
tl.col="black",
tl.cex = 1.5)# Mean Correlation between shared celltypes
mean(c(cor(rna_sum$Macrophage, prot_sum$Macrophage, method = "pearson"),
cor(rna_sum$Neutrophil, prot_sum$Neutrophil, method = "pearson"),
cor(rna_sum$Tcytotoxic, prot_sum$Tcytotoxic, method = "pearson"),
cor(rna_sum$Tumor, prot_sum$Tumor, method = "pearson")))[1] 0.9389364all_mask <- loadImages(x = "data/full_data/protein/cpout/",
pattern = "ilastik_s2_Probabilities_equalized_cellmask.tiff")# we load the metadata for the images.
image_mat <- as.data.frame(read.csv(file = "data/data_for_analysis/protein/Image.csv",stringsAsFactors = FALSE))
# we extract only the FileNames of the masks as they are in the all_masks object
cur_df <- data.frame(cellmask = image_mat$FileName_cellmask,
ImageNumber = image_mat$ImageNumber,
Description = image_mat$Metadata_Description)
# we set the rownames of the extracted data to be equal to the names of all_masks
rownames(cur_df) <- gsub(pattern = ".tiff",replacement = "",image_mat$FileName_cellmask)
# we add the extracted information via mcols in the order of the all_masks object
mcols(all_mask) <- cur_df[names(all_mask),]all_mask <- scaleImages(all_mask,2^16-1)# subset masks
mask_sub <- all_mask[mcols(all_mask)$Description %in% c("H9", "L7", "H2")]
sce_prot_sub <- sce_prot[,sce_prot$Description %in% c("H9", "L7", "H2")]
# rename all cells that are not tumor and not tcytotoxic
#sce_prot_sub$celltype <- ifelse(sce_prot_sub$celltype %in% c("Tumor", "Tcytotoxic"), sce_prot_sub$celltype, "other")
col_list <- list()
col_list$`Cell Type` <- metadata(sce_prot)$colour_vectors$celltype
sce_prot_sub$`Cell Type` <- sce_prot_sub$celltype
plotCells(mask = mask_sub,
object = sce_prot_sub,
cell_id = "CellNumber", img_id = "Description",
colour_by = "Cell Type",
colour = col_list,
display = "single")# Create table with celltype fractions
cur_df <- data.frame(celltype = sce_prot$celltype,
Description = sce_prot$Description,
Location = sce_prot$Location)
# remove control samples
cur_df <- cur_df %>%
filter(Location != "CTRL") %>%
group_by(Description, celltype) %>%
summarise(n=n()) %>%
group_by(Description) %>%
mutate(fraction = n / sum(n)) %>%
reshape2::dcast(Description ~ celltype, value.var = "fraction", fill=0)
matrixrownames <- cur_df$Description
# now we create a matrix from the data and cluster the data based on the cell fractions
hm_dat = as.matrix(cur_df[,-1])
rownames(hm_dat) <- as.character(matrixrownames)
# calculate distance and then cluster images based on cluster fraction
dd <- dist((hm_dat), method = "euclidean")
hc <- hclust(dd, method = "ward.D2")
# order optimal orders the "leafs" of the cluster tree optimally to achieve smooth transitions
hc$order = order.optimal(dd, hc$merge)$order
row_sorted <- hc$labels[hc$order]
# now we generate the clinical metadata and order it in the same way as the celltype data
patient_meta <- metadata(sce_prot)
patient_meta <- data.frame(patient_meta[c("Description", "MM_location_simplified", "Location")])
patient_meta <- patient_meta %>%
filter(Location != "CTRL")
mrownames <- patient_meta$Description
patient_meta <- as.matrix(patient_meta)
rownames(patient_meta) <- mrownames
patient_meta <- data.frame(patient_meta[row_sorted,])
# generate the barplot. this is generated as the annotation for the heatmap of the Patient_ID that is generated below.
hm_dat <- hm_dat[row_sorted,]
# bring cell types in order (column order)
col_order <- c("Tumor", "Macrophage", "Neutrophil", "Tcytotoxic", "Thelper", "Tregulatory", "Bcell", "BnTcell", "pDC", "Stroma", "unknown")
hm_dat <- hm_dat[, col_order]
col_vector <- metadata(sce_prot)$colour_vector$celltype[colnames(hm_dat)]
col_vector_location <- structure(c("blue", "red", "green"),
names = c("LN", "other", "skin"))
# rename punch locations
patient_meta[patient_meta$Location == "M", ]$Location <- "margin"
patient_meta[patient_meta$Location == "C", ]$Location <- "core"
col_vector_punch <- structure(c("black", "grey"),
names = c("core", "margin"))
# create annotation with cell type proportions
ha <- rowAnnotation(`Punch Location` = patient_meta[,c("Location")],
`Cell Type Proportion` =
anno_barplot(hm_dat,
gp=gpar(fill=col_vector),
bar_width = 1,
height = unit(30,"cm"),
width = unit(15,"cm"),
show_row_names = FALSE),
col = list(`Punch Location` = col_vector_punch),
show_legend = FALSE)
dend <- as.dendrogram(hclust(dist(hm_dat, method = "euclidean"), method = "ward.D2"), ylim = c(0,10))
dend <- color_branches(dend, k = 4, groupLabels = TRUE) # `color_branches()` returns a dendrogram object
#plot(dend)
# heatmap consisting of the patient_IDs. one color per patient
h1 = Heatmap(patient_meta[,c("MM_location_simplified")],
col = col_vector_location,
width = unit(0.5, "cm"),
cluster_rows = dend,
row_dend_width = unit(3, "cm"),
height = unit(30, "cm"),
show_heatmap_legend = FALSE,
heatmap_legend_param = list(title = "Met Location"),
row_names_gp = gpar(cex=0.5),
show_row_names = TRUE,
right_annotation = ha,
column_labels = "Met Location")
# plot the data
ht = grid.grabExpr(draw(h1))
grid.newpage()
pushViewport(viewport(angle = 270))
grid.draw(ht)lgd1 <- Legend(labels = names(col_vector),
title = "Cell Type",
legend_gp = gpar(fill = col_vector),
ncol = 1)
lgd2 <- Legend(labels = names(col_vector_location),
legend_gp = gpar(fill = unname(col_vector_location)),
title = "Met Location",
ncol = 1)
lgd3 <- Legend(labels = names(col_vector_punch),
legend_gp = gpar(fill = unname(col_vector_punch)),
title = "Punch Location",
ncol = 1)
pd = packLegend(lgd1, lgd2, lgd3, direction = "vertical",
column_gap = unit(0.5, "cm"))
draw(pd)
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04 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=C
[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 parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] dendextend_1.14.0 ggbeeswarm_0.6.0
[3] circlize_0.4.12 ggrepel_0.9.0
[5] cytomapper_1.3.1 EBImage_4.32.0
[7] rms_6.1-0 SparseM_1.78
[9] Hmisc_4.4-2 Formula_1.2-4
[11] survival_3.2-7 lattice_0.20-41
[13] forcats_0.5.0 stringr_1.4.0
[15] purrr_0.3.4 readr_1.4.0
[17] tidyr_1.1.2 tibble_3.0.4
[19] tidyverse_1.3.0 reshape2_1.4.4
[21] dplyr_1.0.2 corrplot_0.84
[23] ComplexHeatmap_2.4.3 cba_0.2-21
[25] proxy_0.4-24 dittoSeq_1.0.2
[27] scater_1.16.2 ggplot2_3.3.3
[29] data.table_1.13.6 SingleCellExperiment_1.12.0
[31] SummarizedExperiment_1.20.0 Biobase_2.50.0
[33] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2
[35] IRanges_2.24.1 S4Vectors_0.28.1
[37] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
[39] matrixStats_0.57.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1
[3] systemfonts_0.3.2 plyr_1.8.6
[5] sp_1.4-5 shinydashboard_0.7.1
[7] splines_4.0.3 BiocParallel_1.22.0
[9] TH.data_1.0-10 digest_0.6.27
[11] htmltools_0.5.0 tiff_0.1-6
[13] viridis_0.5.1 fansi_0.4.1
[15] magrittr_2.0.1 checkmate_2.0.0
[17] cluster_2.1.0 limma_3.44.3
[19] modelr_0.1.8 svgPanZoom_0.3.4
[21] svglite_1.2.3.2 sandwich_3.0-0
[23] jpeg_0.1-8.1 colorspace_2.0-0
[25] rvest_0.3.6 haven_2.3.1
[27] xfun_0.20 crayon_1.3.4
[29] RCurl_1.98-1.2 jsonlite_1.7.2
[31] zoo_1.8-8 glue_1.4.2
[33] gtable_0.3.0 zlibbioc_1.36.0
[35] XVector_0.30.0 MatrixModels_0.4-1
[37] GetoptLong_1.0.5 DelayedArray_0.16.0
[39] BiocSingular_1.4.0 shape_1.4.5
[41] abind_1.4-5 scales_1.1.1
[43] mvtnorm_1.1-1 pheatmap_1.0.12
[45] DBI_1.1.0 edgeR_3.30.3
[47] Rcpp_1.0.5 xtable_1.8-4
[49] viridisLite_0.3.0 htmlTable_2.1.0
[51] clue_0.3-58 foreign_0.8-81
[53] rsvd_1.0.3 htmlwidgets_1.5.3
[55] httr_1.4.2 RColorBrewer_1.1-2
[57] ellipsis_0.3.1 pkgconfig_2.0.3
[59] nnet_7.3-14 dbplyr_2.0.0
[61] locfit_1.5-9.4 tidyselect_1.1.0
[63] rlang_0.4.10 later_1.1.0.1
[65] munsell_0.5.0 cellranger_1.1.0
[67] tools_4.0.3 cli_2.2.0
[69] generics_0.1.0 broom_0.7.3
[71] ggridges_0.5.3 fastmap_1.0.1
[73] fftwtools_0.9-9 evaluate_0.14
[75] yaml_2.2.1 knitr_1.30
[77] fs_1.5.0 nlme_3.1-151
[79] mime_0.9 quantreg_5.82
[81] whisker_0.4 xml2_1.3.2
[83] compiler_4.0.3 rstudioapi_0.13
[85] beeswarm_0.2.3 png_0.1-7
[87] reprex_0.3.0 stringi_1.5.3
[89] gdtools_0.2.3 Matrix_1.3-2
[91] vctrs_0.3.6 pillar_1.4.7
[93] lifecycle_0.2.0 GlobalOptions_0.1.2
[95] BiocNeighbors_1.6.0 conquer_1.0.2
[97] cowplot_1.1.1 bitops_1.0-6
[99] irlba_2.3.3 raster_3.4-5
[101] httpuv_1.5.4 R6_2.5.0
[103] latticeExtra_0.6-29 promises_1.1.1
[105] gridExtra_2.3 vipor_0.4.5
[107] codetools_0.2-18 polspline_1.1.19
[109] MASS_7.3-53 assertthat_0.2.1
[111] rprojroot_2.0.2 rjson_0.2.20
[113] withr_2.3.0 multcomp_1.4-15
[115] GenomeInfoDbData_1.2.4 hms_0.5.3
[117] rpart_4.1-15 rmarkdown_2.6
[119] DelayedMatrixStats_1.10.1 git2r_0.28.0
[121] shiny_1.5.0 lubridate_1.7.9.2
[123] base64enc_0.1-3