wc pseudobulk
Last updated: 2019-10-28
Checks: 6 1
Knit directory: fgf_alldata/
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Load libraries
library(DESeq2)
library(Seurat)
library(future.apply)
library(ggplot2)
library(ggpubr)
library(RColorBrewer)
library(here)
library(reshape2)
library(tidyverse)
library(cowplot)
library(ggupset)
library(gProfileR)
library(here)
library(ggrepel)
plan(multiprocess, workers = 30)
source(here("code/sc_functions.R"))Load updated functions
rundeseq <- function(pb) {
future_lapply(pb, function(x) {
tryCatch({
trt <- ifelse(grepl("FGF", colnames(x)), yes = "F", no = "P")
sample <- as.factor(sapply(strsplit(colnames(x), "_"), "[", 1))
batch <- batch_df[match(sample, batch_df$samp), "batch"]
meta <- data.frame(trt = trt, batch = factor(batch))
dds <- DESeqDataSetFromMatrix(
countData = x,
colData = meta,
design = ~ batch + trt
)
keep <- rowSums(counts(dds) >= 5) > 5
dds <- dds[keep, ]
dds <- DESeq(dds)
res <- results(dds, contrast = c("trt", "F", "P"))
return(list(dds, res))
}, error = function(err) {
print(err)
})
})
}Load data
glia_sub <- readRDS(here("data/filtglia.RDS"))Generate Pseudo Counts
batch_df <- data.frame(
samp = c(7, 12, 29, 28, 4, 27, 37, 22, 6, 30, 20, 21, 35, 10, 3, 25, 36, 34),
batch = rep(1:6, each = 3)
)
split_mats <- splitbysamp(glia_sub, split_by = "orig.ident")
names(split_mats) <- unique(Idents(glia_sub))
test <- replicate(100, gen_pseudo_counts(split_mats, ncells = 30))
names(test) <- paste0(rep(names(split_mats)), "_", rep(1:100, each = length(names(split_mats))))
res <- rundeseq(test)Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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geth, : Estimated rdf < 1.0; not estimating variance
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geth, : Estimated rdf < 1.0; not estimating variance
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geth, : Estimated rdf < 1.0; not estimating variance
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geth, : Estimated rdf < 1.0; not estimating variance
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geth, : Estimated rdf < 1.0; not estimating variance
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geth, : Estimated rdf < 1.0; not estimating variance
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geth, : Estimated rdf < 1.0; not estimating varianceIdentify most responsive cell types
degenes <- lapply(res, function(x) {
tryCatch({
y <- x[[2]]
y <- na.omit(y)
data.frame(y) %>%
filter(padj < 0.1) %>%
nrow()
},
error = function(err) {
NA
}
)
})
boxplot <- lapply(unique(Idents(glia_sub)), function(x) {
y <- paste0("^", x)
z <- unlist(degenes[grep(y, names(degenes))])
})
names(boxplot) <- unique(Idents(glia_sub))
genenum <- melt(boxplot)
colnames(genenum) <- c("number", "CellType")
write_csv(genenum, here("output/glia/wc_resamplingresults.csv"))
ggplot(genenum, aes(x = reorder(CellType, -number), y = number, fill = CellType)) +
geom_boxplot(notch = T) + theme_pubr() +
theme(
legend.position = "none", axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = .5, face = "bold")
) +
xlab("Cell Type") + ylab("Number of DE Genes")
ggsave("dge_resample.pdf", w = 20)Generate Pseudo Counts
split_mats <- lapply(unique(Idents(glia_sub)), function(x) {
sub <- subset(glia_sub, idents = x)
DefaultAssay(sub) <- "SCT"
list_sub <- SplitObject(sub, split.by = "orig.ident")
return(list_sub)
})
names(split_mats) <- unique(Idents(glia_sub))
pseudo_counts <- lapply(split_mats, function(x) {
lapply(x, function(y) {
DefaultAssay(y) <- "SCT"
mat <- GetAssayData(y, slot = "counts")
counts <- Matrix::rowSums(mat)
}) %>%
do.call(rbind, .) %>%
t() %>%
as.data.frame()
})
names(pseudo_counts) <- names(split_mats)Generate DESeq2 Objects
dds_list <- lapply(pseudo_counts, function(x) {
tryCatch({
trt <- ifelse(grepl("FGF", colnames(x)), yes = "F", no = "P")
sample <- as.factor(sapply(strsplit(colnames(x), "_"), "[", 1))
batch <- batch_df[match(sample, batch_df$samp), "batch"]
meta <- data.frame(trt = trt, batch = factor(batch))
dds <- DESeqDataSetFromMatrix(
countData = x,
colData = meta,
design = ~ batch + trt
)
keep <- rowSums(counts(dds) >= 5) > 5
dds <- dds[keep, ]
dds <- DESeq(dds)
res <- results(dds, contrast = c("trt", "F", "P"))
return(list(dds, res))
}, error = function(err) {
print(err)
})
})Generate Volcano Plots
volc_list <- lapply(dds_list, function(x) {
x[[2]] %>%
na.omit() %>%
data.frame() %>%
add_rownames("gene") %>%
mutate(siglog = ifelse(padj < 0.05 & abs(log2FoldChange) > .5, yes = T, no = F)) %>%
mutate(onlysig = ifelse(padj < 0.05 & abs(log2FoldChange) < .5, yes = T, no = F)) %>%
mutate(onlylog = ifelse(padj > 0.05 & abs(log2FoldChange) > .5, yes = T, no = F)) %>%
mutate(col = ifelse(siglog == T, yes = "1", no =
ifelse(onlysig == T, yes = "2", no =
ifelse(onlylog == T, yes = "3", no = "4")
)
)) %>%
arrange(padj) %>%
mutate(label = case_when(
min(padj) > 0.05 ~ "",
min_rank(padj) <= 10 ~ gene,
TRUE ~ NA_character_
)) %>%
dplyr::select(gene, log2FoldChange, padj, col, label)
})
mapply(x = volc_list, y = names(volc_list), function(x, y) {
write_csv(x, path = sprintf(here("output/glia/wc_%s_pseudobulk_dge.csv"), y))
}) Peri Astro Tany Endo
gene Character,970 Character,10979 Character,9879 Character,3122
log2FoldChange Numeric,970 Numeric,10979 Numeric,9879 Numeric,3122
padj Numeric,970 Numeric,10979 Numeric,9879 Numeric,3122
col Character,970 Character,10979 Character,9879 Character,3122
label Character,970 Character,10979 Character,9879 Character,3122
OPC Olig Epend COP
gene Character,9158 Character,6372 Character,6047 Character,548
log2FoldChange Numeric,9158 Numeric,6372 Numeric,6047 Numeric,548
padj Numeric,9158 Numeric,6372 Numeric,6047 Numeric,548
col Character,9158 Character,6372 Character,6047 Character,548
label Character,9158 Character,6372 Character,6047 Character,548
Micro VLMC Macro SMC
gene Character,897 Character,1497 Character,872 Character,237
log2FoldChange Numeric,897 Numeric,1497 Numeric,872 Numeric,237
padj Numeric,897 Numeric,1497 Numeric,872 Numeric,237
col Character,897 Character,1497 Character,872 Character,237
label Character,897 Character,1497 Character,872 Character,237
ABC
gene Character,22
log2FoldChange Numeric,22
padj Numeric,22
col Character,22
label Character,22volc_list <- volc_list[as.logical(unlist(lapply(volc_list, function(x) !min(x$padj > 0.05))))]
plotlist <- mapply(x = volc_list, y = names(volc_list), function(x, y) {
tryCatch({
ggplot(x, aes(y = (-log10(padj)), x = log2FoldChange, fill = factor(col), label = label)) +
xlab(expression(Log[2] * ~ Fold * ~Change)) + ylab(expression(-Log[10] * ~pvalue)) +
geom_point(shape = 21, size = 3, alpha = 0.75) + geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
geom_vline(xintercept = c(-.5, .5), linetype = "dashed") + geom_text_repel() + theme_pubr() +
labs_pubr() + theme(legend.position = "none") +
scale_fill_manual(values = wes_palette("Royal1", 4, type = "discrete")) + ggtitle(y)
},
error = function(err) {
print(err)
}
)
}, SIMPLIFY = F)<simpleError in wes_palette("Royal1", 4, type = "discrete"): could not find function "wes_palette">
<simpleError in wes_palette("Royal1", 4, type = "discrete"): could not find function "wes_palette">
<simpleError in wes_palette("Royal1", 4, type = "discrete"): could not find function "wes_palette">
<simpleError in wes_palette("Royal1", 4, type = "discrete"): could not find function "wes_palette">
<simpleError in wes_palette("Royal1", 4, type = "discrete"): could not find function "wes_palette">
<simpleError in wes_palette("Royal1", 4, type = "discrete"): could not find function "wes_palette">
<simpleError in wes_palette("Royal1", 4, type = "discrete"): could not find function "wes_palette">
<simpleError in wes_palette("Royal1", 4, type = "discrete"): could not find function "wes_palette">plot_grid(plotlist = plotlist, ncol = 3)
ggsave("wc_de.pdf", w = 20, h = 20)Overlap
res_glia <- lapply(dds_list, function(x) {
data.frame(x[[2]]) %>%
add_rownames("gene") %>%
na.omit(x) %>%
filter(padj < 0.05) %>%
arrange(padj) %>%
select(gene) -> x
})Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.resglia <- bind_rows(res_glia, .id = "id")
resglia %>%
group_by(gene) %>%
summarize(Celltype = list(id)) -> resglia
ggplot(resglia, aes(x = Celltype)) +
geom_bar() + theme_pubr() +
scale_x_upset(n_intersections = 10)Warning: Removed 98 rows containing non-finite values (stat_count).
GO terms up
res_up <- lapply(dds_list, function(x) {
data.frame(x[[2]]) %>%
add_rownames("gene") %>%
na.omit(x) %>%
filter(padj < 0.05) %>%
filter(log2FoldChange > .5) %>%
arrange(padj) %>%
select(gene) -> x
})Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.goup <- lapply(names(dds_list), function(x) {
gprofiler(res_up[[x]]$gene,
organism = "mmusculus", significant = T, custom_bg = rownames(dds_list[[x]][[1]]),
src_filter = c("GO:BP", "GO:MF", "REAC", "KEGG"),
hier_filtering = "strong",
min_isect_size = 3,
sort_by_structure = T, exclude_iea = T,
min_set_size = 10, max_set_size = 300, correction_method = "fdr"
) %>% arrange(p.value)
})
names(goup) <- names(dds_list)
bind_rows(goup, .id = "id") %>%
group_by(id) %>%
top_n(5, -p.value) %>%
ggplot(aes(x = str_wrap(term.name, 30), y = -log10(p.value), fill = domain)) + geom_col() + facet_wrap(. ~ id, scales = "free_y") +
coord_flip() + theme_pubr()
ggsave("gotermup.pdf", w = 20)
mapply(x = goup, y = names(goup), function(x, y) {
write_csv(x, path = sprintf(here("output/glia/wc_up_goterm_%s.csv"), y))
}) Peri Astro Tany Endo
query.number Logical,0 Integer,50 Logical,0 Integer,2
significant Logical,0 Logical,50 Logical,0 Logical,2
p.value Logical,0 Numeric,50 Logical,0 Numeric,2
term.size Logical,0 Integer,50 Logical,0 Integer,2
query.size Logical,0 Integer,50 Logical,0 Integer,2
overlap.size Logical,0 Integer,50 Logical,0 Integer,2
precision Logical,0 Numeric,50 Logical,0 Numeric,2
recall Logical,0 Numeric,50 Logical,0 Numeric,2
term.id Logical,0 Character,50 Logical,0 Character,2
domain Logical,0 Character,50 Logical,0 Character,2
subgraph.number Logical,0 Integer,50 Logical,0 Integer,2
term.name Character,0 Character,50 Character,0 Character,2
relative.depth Logical,0 Integer,50 Logical,0 Integer,2
intersection Logical,0 Character,50 Logical,0 Character,2
OPC Olig Epend COP
query.number Integer,37 Integer,4 Integer,5 Integer,42
significant Logical,37 Logical,4 Logical,5 Logical,42
p.value Numeric,37 Numeric,4 Numeric,5 Numeric,42
term.size Integer,37 Integer,4 Integer,5 Integer,42
query.size Integer,37 Integer,4 Integer,5 Integer,42
overlap.size Integer,37 Integer,4 Integer,5 Integer,42
precision Numeric,37 Numeric,4 Numeric,5 Numeric,42
recall Numeric,37 Numeric,4 Numeric,5 Numeric,42
term.id Character,37 Character,4 Character,5 Character,42
domain Character,37 Character,4 Character,5 Character,42
subgraph.number Integer,37 Integer,4 Integer,5 Integer,42
term.name Character,37 Character,4 Character,5 Character,42
relative.depth Integer,37 Integer,4 Integer,5 Integer,42
intersection Character,37 Character,4 Character,5 Character,42
Micro VLMC Macro SMC
query.number Integer,12 Logical,0 Logical,0 Logical,0
significant Logical,12 Logical,0 Logical,0 Logical,0
p.value Numeric,12 Logical,0 Logical,0 Logical,0
term.size Integer,12 Logical,0 Logical,0 Logical,0
query.size Integer,12 Logical,0 Logical,0 Logical,0
overlap.size Integer,12 Logical,0 Logical,0 Logical,0
precision Numeric,12 Logical,0 Logical,0 Logical,0
recall Numeric,12 Logical,0 Logical,0 Logical,0
term.id Character,12 Logical,0 Logical,0 Logical,0
domain Character,12 Logical,0 Logical,0 Logical,0
subgraph.number Integer,12 Logical,0 Logical,0 Logical,0
term.name Character,12 Character,0 Character,0 Character,0
relative.depth Integer,12 Logical,0 Logical,0 Logical,0
intersection Character,12 Logical,0 Logical,0 Logical,0
ABC
query.number Logical,0
significant Logical,0
p.value Logical,0
term.size Logical,0
query.size Logical,0
overlap.size Logical,0
precision Logical,0
recall Logical,0
term.id Logical,0
domain Logical,0
subgraph.number Logical,0
term.name Character,0
relative.depth Logical,0
intersection Logical,0 GO terms down
res_down <- lapply(dds_list, function(x) {
data.frame(x[[2]]) %>%
add_rownames("gene") %>%
na.omit(x) %>%
filter(padj < 0.05) %>%
filter(log2FoldChange < (-0.5)) %>%
arrange(padj) %>%
select(gene) -> x
})Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.godown <- lapply(names(dds_list), function(x) {
gprofiler(res_down[[x]]$gene,
organism = "mmusculus", significant = T, custom_bg = rownames(dds_list[[x]][[1]]),
src_filter = c("GO:BP", "GO:MF", "REAC", "KEGG"),
hier_filtering = "strong",
min_isect_size = 3,
sort_by_structure = T, exclude_iea = T,
min_set_size = 10, max_set_size = 300, correction_method = "fdr"
) %>% arrange(p.value)
})
names(godown) <- names(dds_list)
bind_rows(godown, .id = "id") %>%
group_by(id) %>%
top_n(5, -p.value) %>%
ggplot(aes(x = str_wrap(term.name, 30), y = -log10(p.value), fill = domain)) + geom_col() + facet_wrap(. ~ id, scales = "free_y") +
coord_flip() + theme_pubr()
ggsave("gotermdown.pdf", w = 20)
mapply(x = godown, y = names(godown), function(x, y) {
write_csv(x, path = sprintf(here("output/glia/wc_down_goterm_%s.csv"), y))
}) Peri Astro Tany Endo
query.number Logical,0 Integer,66 Integer,57 Integer,10
significant Logical,0 Logical,66 Logical,57 Logical,10
p.value Logical,0 Numeric,66 Numeric,57 Numeric,10
term.size Logical,0 Integer,66 Integer,57 Integer,10
query.size Logical,0 Integer,66 Integer,57 Integer,10
overlap.size Logical,0 Integer,66 Integer,57 Integer,10
precision Logical,0 Numeric,66 Numeric,57 Numeric,10
recall Logical,0 Numeric,66 Numeric,57 Numeric,10
term.id Logical,0 Character,66 Character,57 Character,10
domain Logical,0 Character,66 Character,57 Character,10
subgraph.number Logical,0 Integer,66 Integer,57 Integer,10
term.name Character,0 Character,66 Character,57 Character,10
relative.depth Logical,0 Integer,66 Integer,57 Integer,10
intersection Logical,0 Character,66 Character,57 Character,10
OPC Olig Epend COP
query.number Logical,0 Integer,37 Integer,7 Logical,0
significant Logical,0 Logical,37 Logical,7 Logical,0
p.value Logical,0 Numeric,37 Numeric,7 Logical,0
term.size Logical,0 Integer,37 Integer,7 Logical,0
query.size Logical,0 Integer,37 Integer,7 Logical,0
overlap.size Logical,0 Integer,37 Integer,7 Logical,0
precision Logical,0 Numeric,37 Numeric,7 Logical,0
recall Logical,0 Numeric,37 Numeric,7 Logical,0
term.id Logical,0 Character,37 Character,7 Logical,0
domain Logical,0 Character,37 Character,7 Logical,0
subgraph.number Logical,0 Integer,37 Integer,7 Logical,0
term.name Character,0 Character,37 Character,7 Character,0
relative.depth Logical,0 Integer,37 Integer,7 Logical,0
intersection Logical,0 Character,37 Character,7 Logical,0
Micro VLMC Macro SMC
query.number Integer,4 Logical,0 Logical,0 Logical,0
significant Logical,4 Logical,0 Logical,0 Logical,0
p.value Numeric,4 Logical,0 Logical,0 Logical,0
term.size Integer,4 Logical,0 Logical,0 Logical,0
query.size Integer,4 Logical,0 Logical,0 Logical,0
overlap.size Integer,4 Logical,0 Logical,0 Logical,0
precision Numeric,4 Logical,0 Logical,0 Logical,0
recall Numeric,4 Logical,0 Logical,0 Logical,0
term.id Character,4 Logical,0 Logical,0 Logical,0
domain Character,4 Logical,0 Logical,0 Logical,0
subgraph.number Integer,4 Logical,0 Logical,0 Logical,0
term.name Character,4 Character,0 Character,0 Character,0
relative.depth Integer,4 Logical,0 Logical,0 Logical,0
intersection Character,4 Logical,0 Logical,0 Logical,0
ABC
query.number Logical,0
significant Logical,0
p.value Logical,0
term.size Logical,0
query.size Logical,0
overlap.size Logical,0
precision Logical,0
recall Logical,0
term.id Logical,0
domain Logical,0
subgraph.number Logical,0
term.name Character,0
relative.depth Logical,0
intersection Logical,0 Cell Numbers
colourCount <- length(unique(Idents(glia_sub)))
getPalette <- colorRampPalette(brewer.pal(9, "Set1"))
table(Idents(glia_sub), glia_sub$orig.ident) %>%
prop.table(margin = 2) %>%
as.data.frame.matrix() %>%
rownames_to_column("celltype") %>%
melt() %>%
separate(variable, into = c(NA, "treat"), remove = F) %>%
ggplot(aes(x = variable, y = value, fill = factor(celltype))) + geom_col() +
facet_wrap(. ~ treat, scales = "free") + theme_pubr() +
scale_fill_manual(values = getPalette(colourCount)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "right")
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Storage
Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.so
locale:
[1] LC_CTYPE=en_DK.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_DK.UTF-8 LC_COLLATE=en_DK.UTF-8
[5] LC_MONETARY=en_DK.UTF-8 LC_MESSAGES=en_DK.UTF-8
[7] LC_PAPER=en_DK.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_DK.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ggrepel_0.8.1 gProfileR_0.6.7
[3] ggupset_0.1.0.9000 cowplot_1.0.0
[5] forcats_0.4.0 stringr_1.4.0
[7] dplyr_0.8.3 purrr_0.3.2
[9] readr_1.3.1.9000 tidyr_0.8.3
[11] tibble_2.1.3 tidyverse_1.2.1
[13] reshape2_1.4.3 here_0.1
[15] RColorBrewer_1.1-2 ggpubr_0.2.1
[17] magrittr_1.5 ggplot2_3.2.1
[19] future.apply_1.3.0 future_1.14.0
[21] Seurat_3.0.3.9036 DESeq2_1.22.2
[23] SummarizedExperiment_1.12.0 DelayedArray_0.8.0
[25] BiocParallel_1.16.6 matrixStats_0.54.0
[27] Biobase_2.42.0 GenomicRanges_1.34.0
[29] GenomeInfoDb_1.18.2 IRanges_2.16.0
[31] S4Vectors_0.20.1 BiocGenerics_0.28.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.4 Hmisc_4.2-0
[4] workflowr_1.4.0 plyr_1.8.4 igraph_1.2.4.1
[7] lazyeval_0.2.2 splines_3.5.3 listenv_0.7.0
[10] digest_0.6.20 htmltools_0.3.6 gdata_2.18.0
[13] checkmate_1.9.4 memoise_1.1.0 cluster_2.1.0
[16] ROCR_1.0-7 globals_0.12.4 annotate_1.60.1
[19] modelr_0.1.4 RcppParallel_4.4.3 R.utils_2.9.0
[22] colorspace_1.4-1 rvest_0.3.4 blob_1.1.1
[25] haven_2.1.0 xfun_0.8 crayon_1.3.4
[28] RCurl_1.95-4.12 jsonlite_1.6 genefilter_1.64.0
[31] zeallot_0.1.0 survival_2.44-1.1 zoo_1.8-6
[34] ape_5.3 glue_1.3.1 gtable_0.3.0
[37] zlibbioc_1.28.0 XVector_0.22.0 leiden_0.3.1
[40] scales_1.0.0 DBI_1.0.0 bibtex_0.4.2
[43] Rcpp_1.0.2 metap_1.1 viridisLite_0.3.0
[46] xtable_1.8-4 htmlTable_1.13.1 reticulate_1.13
[49] foreign_0.8-71 bit_1.1-14 rsvd_1.0.2
[52] SDMTools_1.1-221.1 Formula_1.2-3 tsne_0.1-3
[55] htmlwidgets_1.3 httr_1.4.1 gplots_3.0.1.1
[58] acepack_1.4.1 ica_1.0-2 pkgconfig_2.0.2
[61] XML_3.98-1.20 R.methodsS3_1.7.1 nnet_7.3-12
[64] uwot_0.1.3 locfit_1.5-9.1 labeling_0.3
[67] tidyselect_0.2.5 rlang_0.4.0 AnnotationDbi_1.44.0
[70] cellranger_1.1.0 munsell_0.5.0 tools_3.5.3
[73] cli_1.1.0 generics_0.0.2 RSQLite_2.1.1
[76] broom_0.5.2 ggridges_0.5.1 evaluate_0.14
[79] yaml_2.2.0 npsurv_0.4-0 knitr_1.23
[82] bit64_0.9-7 fs_1.3.1 fitdistrplus_1.0-14
[85] caTools_1.17.1.2 RANN_2.6.1 pbapply_1.4-1
[88] nlme_3.1-140 whisker_0.3-2 R.oo_1.22.0
[91] xml2_1.2.0 compiler_3.5.3 rstudioapi_0.10
[94] plotly_4.9.0 png_0.1-7 ggsignif_0.5.0
[97] lsei_1.2-0 geneplotter_1.60.0 stringi_1.4.3
[100] highr_0.8 lattice_0.20-38 Matrix_1.2-17
[103] vctrs_0.2.0 pillar_1.4.2 Rdpack_0.11-0
[106] lmtest_0.9-37 RcppAnnoy_0.0.12 data.table_1.12.2
[109] bitops_1.0-6 irlba_2.3.3 gbRd_0.4-11
[112] R6_2.4.0 latticeExtra_0.6-28 KernSmooth_2.23-15
[115] gridExtra_2.3 codetools_0.2-16 MASS_7.3-51.4
[118] gtools_3.8.1 assertthat_0.2.1 rprojroot_1.3-2
[121] withr_2.1.2 sctransform_0.2.0 GenomeInfoDbData_1.2.0
[124] hms_0.5.0 grid_3.5.3 rpart_4.1-15
[127] rmarkdown_1.13 Rtsne_0.15 git2r_0.25.2
[130] lubridate_1.7.4 base64enc_0.1-3