Figure 4
Last updated: 2021-12-17
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suppressMessages({
library(Seurat)
library(ggpubr)
library(ggridges)
library(patchwork)
library(dplyr)
library(ggupset)
library(tidyverse)
library(ggplot2)
library(xtable)
library(ComplexUpset)
library(SeuratData)
})Warning in if (is.na(desc)) {: the condition has length > 1 and only the first
element will be used
Warning in if (is.na(desc)) {: the condition has length > 1 and only the first
element will be used
Warning in if (is.na(desc)) {: the condition has length > 1 and only the first
element will be used
Warning in if (is.na(desc)) {: the condition has length > 1 and only the first
element will be used
Warning in if (is.na(desc)) {: the condition has length > 1 and only the first
element will be used
Warning in if (is.na(desc)) {: the condition has length > 1 and only the first
element will be used
Warning in if (is.na(desc)) {: the condition has length > 1 and only the first
element will be used
Warning in if (is.na(desc)) {: the condition has length > 1 and only the first
element will be used
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element will be used
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element will be used
Warning in if (is.na(desc)) {: the condition has length > 1 and only the first
element will be used
Warning in if (is.na(desc)) {: the condition has length > 1 and only the first
element will be useddata("bmcite")
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
theme_set(theme_pubr())Meta marker coverage
datasets <- read.csv(here::here("data/datasets.csv"))
n_markers <- readRDS(here::here("output/vargenes/n_markers.rds"))
# select datasets where we detect at least 3000 markers
n_markers_3k <- n_markers %>% filter(n_markers > 2999)
var_genes_df <- readRDS(here::here("output/vargenes/compare_variable_genes_df_long.rds"))
var_genes_df <- left_join(var_genes_df, datasets, by = "sample_name")
var_genes_df <- var_genes_df[var_genes_df$sample_name %in% n_markers_3k$sample_name,]
var_genes_df_summary2 <- var_genes_df %>%
group_by(technology, variable, rank) %>%
summarise(median_overlap = median(value)) %>%
ungroup()
var_genes_df_summary3 <- var_genes_df %>%
group_by(tissue, variable, rank) %>%
summarise(median_overlap = median(value)) %>%
ungroup()
var_genes_prop_df <- readRDS(here::here("output/vargenes/compare_variable_genes_prop_df_long.rds"))
var_genes_prop_df <- left_join(var_genes_prop_df, datasets, by = "sample_name")
var_genes_prop_df <- var_genes_prop_df[var_genes_prop_df$sample_name %in% n_markers_3k$sample_name,]
var_genes_prop_df_summary2 <- var_genes_prop_df %>%
group_by(technology, variable, rank) %>%
summarise(median_overlap = median(value)) %>%
ungroup()
var_genes_prop_df_summary3 <- var_genes_prop_df %>%
group_by(tissue, variable, rank) %>%
summarise(median_overlap = median(value)) %>%
ungroup()
var_genes_prop_df_summary2$variable <- factor(as.character(var_genes_prop_df_summary2$variable),
levels = c("offset100", "offset10", "sct", "sct2")
)
var_genes_prop_df_summary3$variable <- factor(as.character(var_genes_prop_df_summary3$variable),
levels = c("offset100", "offset10", "sct", "sct2")
)
labels <- c(expression(theta == 100), expression(theta == 10), "SCT v1", "SCT v2")
names(labels) <- c("offset100", "offset10", "sct", "sct2")
p1.tech <- ggplot(var_genes_prop_df_summary2, aes(rank, median_overlap, color = variable)) +
geom_point(size = 0.1) +
facet_wrap(~technology, scales = "free_y") +
# geom_line() +
scale_color_brewer(type = "qual", palette = "Set1", name = "", labels = labels) +
guides(colour = guide_legend(override.aes = list(size = 2))) +
xlab("Variable feature rank") +
ylab("Median proportion of marker genes")
p1.system <- ggplot(var_genes_prop_df_summary3, aes(rank, median_overlap, color = variable)) +
geom_point(size = 0.1) +
facet_wrap(~tissue, scales = "free_y", ncol = 4) +
# geom_line() +
scale_color_brewer(type = "qual", palette = "Set2", name = "", labels = labels) +
guides(colour = guide_legend(override.aes = list(size = 2))) +
xlab("Variable feature rank") +
ylab("Median proportion of marker genes") #+ theme(legend.position = c(0.9, 0.24),legend.background=element_blank(), legend.text.align = 0)
p1.system
summary_df <- var_genes_prop_df_summary3[var_genes_prop_df_summary3$rank %in% c(1000,2000,3000), ]
print(xtable(summary_df, type = "latex", digits=3), include.rownames = FALSE)% latex table generated in R 4.1.2 by xtable 1.8-4 package
% Fri Dec 17 10:18:45 2021
\begin{table}[ht]
\centering
\begin{tabular}{llrr}
\hline
tissue & variable & rank & median\_overlap \\
\hline
Bone Marrow & offset100 & 1000 & 0.204 \\
Bone Marrow & offset100 & 2000 & 0.305 \\
Bone Marrow & offset100 & 3000 & 0.367 \\
Bone Marrow & offset10 & 1000 & 0.189 \\
Bone Marrow & offset10 & 2000 & 0.276 \\
Bone Marrow & offset10 & 3000 & 0.337 \\
Bone Marrow & sct & 1000 & 0.194 \\
Bone Marrow & sct & 2000 & 0.279 \\
Bone Marrow & sct & 3000 & 0.337 \\
Bone Marrow & sct2 & 1000 & 0.290 \\
Bone Marrow & sct2 & 2000 & 0.427 \\
Bone Marrow & sct2 & 3000 & 0.536 \\
Cortex & offset100 & 1000 & 0.263 \\
Cortex & offset100 & 2000 & 0.415 \\
Cortex & offset100 & 3000 & 0.544 \\
Cortex & offset10 & 1000 & 0.249 \\
Cortex & offset10 & 2000 & 0.402 \\
Cortex & offset10 & 3000 & 0.529 \\
Cortex & sct & 1000 & 0.216 \\
Cortex & sct & 2000 & 0.353 \\
Cortex & sct & 3000 & 0.470 \\
Cortex & sct2 & 1000 & 0.292 \\
Cortex & sct2 & 2000 & 0.474 \\
Cortex & sct2 & 3000 & 0.586 \\
Fetus & offset100 & 1000 & 0.210 \\
Fetus & offset100 & 2000 & 0.356 \\
Fetus & offset100 & 3000 & 0.453 \\
Fetus & offset10 & 1000 & 0.206 \\
Fetus & offset10 & 2000 & 0.352 \\
Fetus & offset10 & 3000 & 0.446 \\
Fetus & sct & 1000 & 0.222 \\
Fetus & sct & 2000 & 0.394 \\
Fetus & sct & 3000 & 0.520 \\
Fetus & sct2 & 1000 & 0.287 \\
Fetus & sct2 & 2000 & 0.455 \\
Fetus & sct2 & 3000 & 0.542 \\
PBMC & offset100 & 1000 & 0.190 \\
PBMC & offset100 & 2000 & 0.292 \\
PBMC & offset100 & 3000 & 0.385 \\
PBMC & offset10 & 1000 & 0.165 \\
PBMC & offset10 & 2000 & 0.264 \\
PBMC & offset10 & 3000 & 0.345 \\
PBMC & sct & 1000 & 0.145 \\
PBMC & sct & 2000 & 0.217 \\
PBMC & sct & 3000 & 0.287 \\
PBMC & sct2 & 1000 & 0.282 \\
PBMC & sct2 & 2000 & 0.459 \\
PBMC & sct2 & 3000 & 0.571 \\
\hline
\end{tabular}
\end{table}summary_df <- tidyr::spread(summary_df, key = "variable", value = "median_overlap")
print(xtable(summary_df, type = "latex", digits=3), include.rownames = FALSE)% latex table generated in R 4.1.2 by xtable 1.8-4 package
% Fri Dec 17 10:18:45 2021
\begin{table}[ht]
\centering
\begin{tabular}{lrrrrr}
\hline
tissue & rank & offset100 & offset10 & sct & sct2 \\
\hline
Bone Marrow & 1000 & 0.204 & 0.189 & 0.194 & 0.290 \\
Bone Marrow & 2000 & 0.305 & 0.276 & 0.279 & 0.427 \\
Bone Marrow & 3000 & 0.367 & 0.337 & 0.337 & 0.536 \\
Cortex & 1000 & 0.263 & 0.249 & 0.216 & 0.292 \\
Cortex & 2000 & 0.415 & 0.402 & 0.353 & 0.474 \\
Cortex & 3000 & 0.544 & 0.529 & 0.470 & 0.586 \\
Fetus & 1000 & 0.210 & 0.206 & 0.222 & 0.287 \\
Fetus & 2000 & 0.356 & 0.352 & 0.394 & 0.455 \\
Fetus & 3000 & 0.453 & 0.446 & 0.520 & 0.542 \\
PBMC & 1000 & 0.190 & 0.165 & 0.145 & 0.282 \\
PBMC & 2000 & 0.292 & 0.264 & 0.217 & 0.459 \\
PBMC & 3000 & 0.385 & 0.345 & 0.287 & 0.571 \\
\hline
\end{tabular}
\end{table}summary_df <- var_genes_df_summary3[var_genes_df_summary3$rank %in% c(1000,2000,3000), ]
summary_df <- tidyr::spread(summary_df, key = "variable", value = "median_overlap")
print(xtable(summary_df, type = "latex", digits=1), include.rownames = FALSE)% latex table generated in R 4.1.2 by xtable 1.8-4 package
% Fri Dec 17 10:18:45 2021
\begin{table}[ht]
\centering
\begin{tabular}{lrrrrr}
\hline
tissue & rank & offset100 & offset10 & sct & sct2 \\
\hline
Bone Marrow & 1000 & 611.0 & 567.0 & 582.0 & 871.0 \\
Bone Marrow & 2000 & 916.0 & 829.0 & 836.0 & 1281.0 \\
Bone Marrow & 3000 & 1100.0 & 1012.0 & 1012.0 & 1607.0 \\
Cortex & 1000 & 788.5 & 747.5 & 649.5 & 876.5 \\
Cortex & 2000 & 1245.0 & 1205.0 & 1058.5 & 1422.0 \\
Cortex & 3000 & 1633.0 & 1586.5 & 1408.5 & 1757.5 \\
Fetus & 1000 & 631.0 & 618.0 & 667.0 & 862.0 \\
Fetus & 2000 & 1068.0 & 1056.0 & 1182.0 & 1366.0 \\
Fetus & 3000 & 1360.0 & 1337.0 & 1559.0 & 1626.0 \\
PBMC & 1000 & 569.0 & 495.0 & 436.0 & 847.0 \\
PBMC & 2000 & 877.0 & 793.0 & 650.0 & 1376.0 \\
PBMC & 3000 & 1155.0 & 1035.0 & 860.0 & 1712.0 \\
\hline
\end{tabular}
\end{table}Contrast feature lists
datasets <- read.csv(here::here("data/datasets.csv"))
read_gene_attr <- function(filepath) {
gene_attr <- read.csv(filepath, row.names = 1)
gene_attr$gene <- rownames(gene_attr)
gene_attr <- gene_attr %>%
arrange(desc(residual_variance)) %>%
pull(gene)
return(gene_attr[1:3000])
}
dataset_sub <- datasets[datasets$key == "PBMC__ChromiumV3", ]
overlap_list <- list()
overlap_list_prop <- list()
sample_name <- unique(dataset_sub$sample_name)
dataset <- unique(dataset_sub$key)
all_genes <- rownames(read.csv(here::here("output/snakemake_output/seurat_output", dataset, "glmGamPoi", "gene_attr.csv"), row.names = 1))
gene_attr <- read.csv(here::here("output/snakemake_output/seurat_output", dataset, "glmGamPoi", "gene_attr.csv"), row.names = 1)
gene_attr$gene <- rownames(gene_attr)
sct <- read_gene_attr(here::here("output/snakemake_output/seurat_output", dataset, "glmGamPoi", "gene_attr.csv"))
offset100 <- read_gene_attr(here::here("output/snakemake_output/seurat_output", dataset, "offset-100", "gene_attr.csv"))
offset10 <- read_gene_attr(here::here("output/snakemake_output/seurat_output", dataset, "offset-10", "gene_attr.csv"))
sct2 <- read_gene_attr(here::here("output/snakemake_output/seurat_output", dataset, "vst2", "gene_attr.csv"))
presto_output <- readRDS(here::here("output/snakemake_output/presto_output", dataset, "presto_markers.rds"))
#marker_genes <- unique(presto_output %>% filter(p_val_adj < 0.01) %>% filter(avg_log2FC > 0.5) %>% arrange(p_val_adj) %>% pull(gene))
presto_subset <- presto_output %>%
group_by(gene) %>%
arrange(desc(avg_log2FC)) %>%
filter(row_number() == 1) %>%
ungroup() %>%
filter(p_val < 0.05) %>%
filter(avg_log2FC > 0.25)
# marker_genes <- unique(presto_output %>% filter(p_val_adj < 0.01) %>% filter(avg_log2FC > 0.5) %>% arrange(p_val_adj) %>% pull(gene))
marker_genes <- presto_subset %>%
arrange(desc(avg_log2FC), p_val_adj) %>%
pull(gene)
if (length(marker_genes) > length(sct2)) {
marker_genes <- marker_genes[1:length(sct2)]
}
all_methods <- list(sct2 = sct2, offset10 = offset10, offset100 = offset100, sct = sct)
gene_methods_list <- list()
index <- 1
for (gene in unique(unlist(all_methods))) {
gene_methods <- c()
for (method in names(all_methods)) {
if (gene %in% all_methods[[method]]) {
gene_methods <- c(gene_methods, method)
}
}
gene_methods_list[[index]] <- list(gene = gene, methods = gene_methods)
index <- index + 1
}
gene_methods_list_df <- data.frame(gene = names(gene_methods_list))
gene_methods_list_df$methods <- gene_methods_list[gene_methods_list_df$gene]
gene_methods_df <- data.frame(gene = unique(unlist(all_methods)))
rownames(gene_methods_df) <- gene_methods_df$gene
for (method in names(all_methods)) {
gene_methods_df[, method] <- 0
gene_methods_df[all_methods[[method]], method] <- 1
}
gene_methods_df <- left_join(gene_methods_df, gene_attr, by = "gene")
gene_methods_df$marker_prop <- 0
gene_methods_df$marker_prop[gene_methods_df$gene %in% marker_genes] <- 100*1/length(marker_genes)
gene_methods_df$is_marker <- "No"
gene_methods_df$is_marker[gene_methods_df$gene %in% marker_genes] <- "Yes"
gene_methods_df <- as_tibble(gene_methods_df)
gene_methods_df[, "SCT v1"] <- gene_methods_df$sct
gene_methods_df[, "SCT v2"] <- gene_methods_df$sct2
gene_methods_df[, "Offset 100"] <- gene_methods_df$offset100
gene_methods_df[, "Offset 10"] <- gene_methods_df$offset10
sct_sp_genes <- setdiff(sct, union(sct2, union(offset10, offset100)))
summary(gene_methods_df[gene_methods_df$gene %in% sct_sp_genes,] %>% pull(gmean)) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.003539 0.004248 0.004958 0.005965 0.006795 0.028845 sct2_sp_genes <- setdiff(sct2, union(sct, union(offset10, offset100)))
summary(gene_methods_df[gene_methods_df$gene %in% sct2_sp_genes,] %>% pull(gmean)) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.02746 0.06597 0.10432 0.12552 0.16410 0.43309 length(intersect(sct2_sp_genes, marker_genes))[1] 269p2 <- upset(
gene_methods_df,
c("SCT v1", "SCT v2", "Offset 100", "Offset 10"),
base_annotations=list(
'Intersection size'=intersection_size(
counts=TRUE,
mapping=aes(fill=is_marker),
) + scale_fill_brewer(type="qual", palette = "Dark2", name = "Top 3000 marker")
),
annotations = list(
"log2(1 + Gene mean)" = ggplot(mapping = aes(x = intersection, y = log(1+gmean, base = 2)))
+
ggbeeswarm::geom_quasirandom(groupOnX = TRUE, size = 0.1) +
stat_summary(
geom = "crossbar", color = "red",
fun = median, fun.min = median, fun.max = median,
fatten = 1.2, width = 0.5
) + ylim(0,3)
),
min_size = 10,
width_ratio = 0.1,
set_sizes = FALSE
)
p2 
all_fdr_tpr_df_melt <- readRDS(here::here("output/muscat_simulation/results/all_fdr_tpr_df_melt.rds"))
all_fdr_tpr_df_melt[all_fdr_tpr_df_melt$method == "SCT2", "method"] <- "SCT v2"
all_fdr_tpr_df_melt[all_fdr_tpr_df_melt$method == "SCT", "method"] <- "SCT v1"
all_fdr_tpr_df_melt$method <- factor(all_fdr_tpr_df_melt$method, levels = c("lognorm", "scran", "SCT v1", "SCT v2"))
labels <- c("LogNorm", "Scran", "SCT v1", "SCT v2")
names(labels) <- c("LogNorm", "Scran", "SCT v1", "SCT v2")
p3 <- ggplot(all_fdr_tpr_df_melt[all_fdr_tpr_df_melt$de_percent > 0.01, ], aes(FDR, TPR, color = method)) +
geom_point() +
geom_line() +
geom_vline(xintercept = c(0.01, 0.05, 0.1), linetype = "dashed") +
xlim(0, 1) +
scale_x_continuous(trans = "log", breaks = c(0.01, 0.05, 0.1, 0.5,1), guide = guide_axis(angle = 50)) +
facet_wrap(~de_percent, ncol = 4) +
theme_pubr() +
theme(
panel.grid.minor = element_line(size = (0.2), colour = "grey")
) +
scale_color_brewer(type = "qual", palette = "Dark2", name = "", labels = labels) +
theme(axis.text.x=element_text(size=rel(0.7), angle=45))
p3
nk_markers <- readRDS(here::here("output/simulation_NK_downsampling/NK_downsampling_DE_sig.rds"))
nk_markers$method <- factor(nk_markers$method, levels = c("LogNorm", "Scran", "SCT v1", "SCT v2"))
nk_markers_summary <- nk_markers %>%
group_by(method) %>%
summarise(count = n())
nk_markers_empty <- data.frame(method = c("LogNorm", "Scran", "SCT v1", "SCT v2"))
nk_markers_empty$count <- 1
nk_markers_summary2 <- rbind(nk_markers_empty, nk_markers_summary)
nk_markers_summary2 <- nk_markers_summary2 %>%
group_by(method) %>%
summarise(count = sum(count))
p4 <- ggplot(nk_markers_summary2, aes(method, y = count, fill = method)) +
geom_bar(width = 0.5, stat = "identity") +
scale_y_log10() +
scale_fill_brewer(type = "qual", palette = "Dark2", name = "") +
ylab("Number of DE genes") +
xlab("") +
scale_x_discrete(guide = guide_axis(angle = 30)) +
NoLegend()
p4
layout <- "
AAABB
CCDBB"
p <- p1.system + p2 + p3 + p4
p + plot_layout(design = layout) & plot_annotation(tag_levels = "A") & theme(plot.tag = element_text(face = "bold"))
ggsave(here::here("output/figures/Figure4.pdf"))#, width = 14, height = 8)
ggsave(here::here("output/figures/Figure4.png"))#, width = 12, height = 8)nbfits.sct <- readRDS(here::here("output/11C_VST/nbfits.sct.rds"))
nbfits.sct2 <- readRDS(here::here("output/11C_VST/nbfits.sct2.rds"))
pl <- VlnPlot(bmcite, "RP11-290C10.1", slot = "counts", group.by = "celltype.l2") + NoLegend()
pr <- VlnPlot(bmcite, "CD86", slot = "counts", group.by = "celltype.l2") + NoLegend()
px <- pl | pr
p1 <- ggplot(nbfits.sct, aes(umi, res, color = gene)) +
geom_jitter(alpha = 0.5, size=0.5) +
scale_color_manual(values = RColorBrewer::brewer.pal(3, "Set1"), name = "") +
ylab("Pearson residual") + xlab("UMI")
p3 <- ggplot(nbfits.sct, aes(sd, res, color = gene)) +
geom_jitter(alpha = 0.5, size=0.5) +
scale_color_manual(values = RColorBrewer::brewer.pal(3, "Set1"), name = "") +
ylab("Pearson residual") + xlab("Standard deviation (model)")
pup <- p1 | p3 + patchwork::plot_annotation(title = "sctransform (v1)")
p1 <- ggplot(nbfits.sct2, aes(umi, res, color = gene)) +
geom_jitter(alpha = 0.5, size=0.5) +
scale_color_manual(values = RColorBrewer::brewer.pal(3, "Set1"), name = "") +
ylab("Pearson residual") + xlab("UMI")
p3 <- ggplot(nbfits.sct2, aes(sd, res, color = gene)) +
geom_jitter(alpha = 0.5, size=0.5) +
scale_color_manual(values = RColorBrewer::brewer.pal(3, "Set1"), name = "") +
ylab("Pearson residual") + xlab("Standard deviation (model)")
pdown <- p1 | p3 + patchwork::plot_annotation(title = "sctransform (v2)")
#px / pup / pdown
layout <- "
AB
CD
EF"
p <- px / pup / pdown
p & plot_annotation(tag_levels = "A") & theme(plot.tag = element_text(face = "bold"))
ggsave(here::here("output/figures/varcomp.pdf"))
ggsave(here::here("output/figures/varcomp.png"))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: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] thp1.eccite.SeuratData_3.1.5 pbmcsca.SeuratData_3.0.0
[3] pbmc3k.SeuratData_3.1.4 panc8.SeuratData_3.0.2
[5] hcabm40k.SeuratData_3.0.0 bmcite.SeuratData_0.3.0
[7] SeuratData_0.2.1 ComplexUpset_1.3.3
[9] xtable_1.8-4 forcats_0.5.1
[11] stringr_1.4.0 purrr_0.3.4
[13] readr_2.1.1 tidyr_1.1.4
[15] tibble_3.1.6 tidyverse_1.3.1
[17] ggupset_0.3.0 dplyr_1.0.7
[19] patchwork_1.1.1 ggridges_0.5.3
[21] ggpubr_0.4.0 ggplot2_3.3.5
[23] SeuratObject_4.0.4 Seurat_4.0.5
[25] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.4.1 plyr_1.8.6
[4] igraph_1.2.9 lazyeval_0.2.2 splines_4.1.2
[7] listenv_0.8.0 scattermore_0.7 digest_0.6.29
[10] htmltools_0.5.2 fansi_0.5.0 magrittr_2.0.1
[13] tensor_1.5 cluster_2.1.2 ROCR_1.0-11
[16] tzdb_0.2.0 globals_0.14.0 modelr_0.1.8
[19] matrixStats_0.61.0 spatstat.sparse_2.0-0 colorspace_2.0-2
[22] rappdirs_0.3.3 rvest_1.0.2 ggrepel_0.9.1
[25] haven_2.4.3 xfun_0.28 crayon_1.4.2
[28] jsonlite_1.7.2 spatstat.data_2.1-0 survival_3.2-13
[31] zoo_1.8-9 glue_1.5.1 polyclip_1.10-0
[34] gtable_0.3.0 leiden_0.3.9 car_3.0-12
[37] future.apply_1.8.1 abind_1.4-5 scales_1.1.1
[40] DBI_1.1.1 rstatix_0.7.0 miniUI_0.1.1.1
[43] Rcpp_1.0.7 viridisLite_0.4.0 reticulate_1.22
[46] spatstat.core_2.3-2 htmlwidgets_1.5.4 httr_1.4.2
[49] RColorBrewer_1.1-2 ellipsis_0.3.2 ica_1.0-2
[52] farver_2.1.0 pkgconfig_2.0.3 sass_0.4.0
[55] uwot_0.1.11 dbplyr_2.1.1 deldir_1.0-6
[58] here_1.0.1 utf8_1.2.2 labeling_0.4.2
[61] tidyselect_1.1.1 rlang_0.4.12 reshape2_1.4.4
[64] later_1.3.0 cellranger_1.1.0 munsell_0.5.0
[67] tools_4.1.2 cli_3.1.0 generics_0.1.1
[70] broom_0.7.10 evaluate_0.14 fastmap_1.1.0
[73] yaml_2.2.1 goftest_1.2-3 knitr_1.36
[76] fs_1.5.2 fitdistrplus_1.1-6 RANN_2.6.1
[79] pbapply_1.5-0 future_1.23.0 nlme_3.1-152
[82] whisker_0.4 mime_0.12 xml2_1.3.3
[85] rstudioapi_0.13 compiler_4.1.2 beeswarm_0.4.0
[88] plotly_4.10.0 png_0.1-7 ggsignif_0.6.3
[91] spatstat.utils_2.3-0 reprex_2.0.1 bslib_0.3.1
[94] stringi_1.7.6 highr_0.9 lattice_0.20-45
[97] Matrix_1.4-0 vctrs_0.3.8 pillar_1.6.4
[100] lifecycle_1.0.1 spatstat.geom_2.3-1 lmtest_0.9-39
[103] jquerylib_0.1.4 RcppAnnoy_0.0.19 data.table_1.14.2
[106] cowplot_1.1.1 irlba_2.3.5 httpuv_1.6.3
[109] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20
[112] gridExtra_2.3 vipor_0.4.5 parallelly_1.29.0
[115] codetools_0.2-18 MASS_7.3-54 assertthat_0.2.1
[118] rprojroot_2.0.2 withr_2.4.3 sctransform_0.3.2.9008
[121] mgcv_1.8-38 parallel_4.1.2 hms_1.1.1
[124] grid_4.1.2 rpart_4.1-15 rmarkdown_2.11
[127] carData_3.0-4 Rtsne_0.15 git2r_0.29.0
[130] shiny_1.7.1 lubridate_1.8.0 ggbeeswarm_0.6.0
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: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] thp1.eccite.SeuratData_3.1.5 pbmcsca.SeuratData_3.0.0
[3] pbmc3k.SeuratData_3.1.4 panc8.SeuratData_3.0.2
[5] hcabm40k.SeuratData_3.0.0 bmcite.SeuratData_0.3.0
[7] SeuratData_0.2.1 ComplexUpset_1.3.3
[9] xtable_1.8-4 forcats_0.5.1
[11] stringr_1.4.0 purrr_0.3.4
[13] readr_2.1.1 tidyr_1.1.4
[15] tibble_3.1.6 tidyverse_1.3.1
[17] ggupset_0.3.0 dplyr_1.0.7
[19] patchwork_1.1.1 ggridges_0.5.3
[21] ggpubr_0.4.0 ggplot2_3.3.5
[23] SeuratObject_4.0.4 Seurat_4.0.5
[25] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.4.1 plyr_1.8.6
[4] igraph_1.2.9 lazyeval_0.2.2 splines_4.1.2
[7] listenv_0.8.0 scattermore_0.7 digest_0.6.29
[10] htmltools_0.5.2 fansi_0.5.0 magrittr_2.0.1
[13] tensor_1.5 cluster_2.1.2 ROCR_1.0-11
[16] tzdb_0.2.0 globals_0.14.0 modelr_0.1.8
[19] matrixStats_0.61.0 spatstat.sparse_2.0-0 colorspace_2.0-2
[22] rappdirs_0.3.3 rvest_1.0.2 ggrepel_0.9.1
[25] haven_2.4.3 xfun_0.28 crayon_1.4.2
[28] jsonlite_1.7.2 spatstat.data_2.1-0 survival_3.2-13
[31] zoo_1.8-9 glue_1.5.1 polyclip_1.10-0
[34] gtable_0.3.0 leiden_0.3.9 car_3.0-12
[37] future.apply_1.8.1 abind_1.4-5 scales_1.1.1
[40] DBI_1.1.1 rstatix_0.7.0 miniUI_0.1.1.1
[43] Rcpp_1.0.7 viridisLite_0.4.0 reticulate_1.22
[46] spatstat.core_2.3-2 htmlwidgets_1.5.4 httr_1.4.2
[49] RColorBrewer_1.1-2 ellipsis_0.3.2 ica_1.0-2
[52] farver_2.1.0 pkgconfig_2.0.3 sass_0.4.0
[55] uwot_0.1.11 dbplyr_2.1.1 deldir_1.0-6
[58] here_1.0.1 utf8_1.2.2 labeling_0.4.2
[61] tidyselect_1.1.1 rlang_0.4.12 reshape2_1.4.4
[64] later_1.3.0 cellranger_1.1.0 munsell_0.5.0
[67] tools_4.1.2 cli_3.1.0 generics_0.1.1
[70] broom_0.7.10 evaluate_0.14 fastmap_1.1.0
[73] yaml_2.2.1 goftest_1.2-3 knitr_1.36
[76] fs_1.5.2 fitdistrplus_1.1-6 RANN_2.6.1
[79] pbapply_1.5-0 future_1.23.0 nlme_3.1-152
[82] whisker_0.4 mime_0.12 xml2_1.3.3
[85] rstudioapi_0.13 compiler_4.1.2 beeswarm_0.4.0
[88] plotly_4.10.0 png_0.1-7 ggsignif_0.6.3
[91] spatstat.utils_2.3-0 reprex_2.0.1 bslib_0.3.1
[94] stringi_1.7.6 highr_0.9 lattice_0.20-45
[97] Matrix_1.4-0 vctrs_0.3.8 pillar_1.6.4
[100] lifecycle_1.0.1 spatstat.geom_2.3-1 lmtest_0.9-39
[103] jquerylib_0.1.4 RcppAnnoy_0.0.19 data.table_1.14.2
[106] cowplot_1.1.1 irlba_2.3.5 httpuv_1.6.3
[109] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20
[112] gridExtra_2.3 vipor_0.4.5 parallelly_1.29.0
[115] codetools_0.2-18 MASS_7.3-54 assertthat_0.2.1
[118] rprojroot_2.0.2 withr_2.4.3 sctransform_0.3.2.9008
[121] mgcv_1.8-38 parallel_4.1.2 hms_1.1.1
[124] grid_4.1.2 rpart_4.1-15 rmarkdown_2.11
[127] carData_3.0-4 Rtsne_0.15 git2r_0.29.0
[130] shiny_1.7.1 lubridate_1.8.0 ggbeeswarm_0.6.0