Last updated: 2019-12-02

Checks: 6 1

Knit directory: bentsen-rausch-2019/

This reproducible R Markdown analysis was created with workflowr (version 1.4.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

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.

Environment: objects present

The global environment had objects present when the code in the R Markdown file was run. These objects 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. Use wflow_publish or wflow_build to ensure that the code is always run in an empty environment.

The following objects were defined in the global environment when these results were created:

Name	Class	Size
data	environment	56 bytes
env	environment	56 bytes

Seed: set.seed(20191021)

The command set.seed(20191021) 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.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: acd3c85

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

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:    .Rproj.user/
    Ignored:    test_files/

Untracked files:
    Untracked:  Rplots.pdf
    Untracked:  analysis/dge_resample.pdf
    Untracked:  analysis/figure_6.Rmd
    Untracked:  analysis/figure_7.Rmd
    Untracked:  analysis/supp1.Rmd
    Untracked:  code/sc_functions.R
    Untracked:  data/bulk/
    Untracked:  data/fgf_filtered_nuclei.RDS
    Untracked:  data/figures/
    Untracked:  data/filtglia.RDS
    Untracked:  data/glia/
    Untracked:  data/lps1.txt
    Untracked:  data/mcao1.txt
    Untracked:  data/mcao_d3.txt
    Untracked:  data/mcaod7.txt
    Untracked:  data/mouse_data/
    Untracked:  data/neur_astro_induce.xlsx
    Untracked:  data/neuron/
    Untracked:  data/synaptic_activity_induced.xlsx
    Untracked:  neuron_clusters.csv
    Untracked:  olig_ttest_padj.csv
    Untracked:  output/agrp_pcgenes.csv
    Untracked:  output/all_wc_markers.csv
    Untracked:  output/allglia_wgcna_genemodules.csv
    Untracked:  output/bulk/
    Untracked:  output/fig.RData
    Untracked:  output/fig4_part2.RData
    Untracked:  output/glia/
    Untracked:  output/glial_markergenes.csv
    Untracked:  output/integrated_all_markergenes.csv
    Untracked:  output/integrated_neuronmarkers.csv
    Untracked:  output/neuron/

Unstaged changes:
    Modified:   analysis/10_wc_pseudobulk.Rmd
    Modified:   analysis/11_wc_astro_wgcna.Rmd
    Modified:   analysis/13_olig_pseudotime.Rmd
    Modified:   analysis/15_tany_wgcna_pseudo.Rmd
    Modified:   analysis/6_glial_dge.Rmd
    Modified:   analysis/7_ventricular_wgcna.Rmd
    Modified:   analysis/8_astro_wgcna.Rmd
    Modified:   analysis/9_wc_processing.Rmd

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 R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
html	942c6c2	Full Name	2019-12-02	Build site.
Rmd	81c758e	Full Name	2019-12-02	wflow_publish(“analysis/figure_2.Rmd”)

#Load Libraries

library(Seurat)
library(tidyverse)
library(DESeq2)
library(here)
library(future)
library(cluster)
library(parallelDist)
library(ggplot2)
library(cowplot)
library(ggrepel)
library(future.apply)
library(reshape2)
library(ggpubr)
library(ggsci)
library(ggExtra)
library(gProfileR)
#plan("multiprocess", workers = 40)
options(future.globals.maxSize = 4000 * 1024^2)

#Set chunk options

Load prepped data

fgf.agrp <- readRDS(here("data/neuron/agrp_neur.RDS"))
fgf.agrp@meta.data %>% select(sample, group, trt, day, batch)-> meta

#Extrad Agrp neuron embedding values

embed <- data.frame(Embeddings(fgf.agrp, reduction = "pca")[,1:10])
embed$sample <- meta$sample
embed$sample <- fct_reorder(embed$sample, meta$group)

Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
argument is not numeric or logical: returning NA

Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
argument is not numeric or logical: returning NA

Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
argument is not numeric or logical: returning NA

Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
argument is not numeric or logical: returning NA

Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
argument is not numeric or logical: returning NA

Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
argument is not numeric or logical: returning NA

Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
argument is not numeric or logical: returning NA

embed <- melt(embed, id.vars = "sample")
ggplot(embed, aes(x = sample, y=value)) + 
  geom_boxplot(aes(fill=sample)) + 
  facet_wrap(.~variable, scales="free") +
  scale_fill_jco()  + 
  theme_pubr() + 
  theme(legend.position = "none", 
        axis.text.x = element_text(size=6, angle=45, hjust=1, face="bold")) + 
  ylab("PC Embedding Value") + xlab(NULL) + theme_figure

Version	Author	Date
942c6c2	Full Name	2019-12-02

ggsave(filename = here("output/neuron/agrp_pc_graph.png"), width = 10)

#Plot PCs which show greatest differences between groups

data.frame(Embeddings(fgf.agrp, reduction = "pca")[,4:5]) %>% 
  dplyr::rename(PC4 = PC_4, PC5 = PC_5) %>% mutate(group = fgf.agrp$group) %>%
  mutate(group = replace(group, group == "FGF_Day-5", "FGF_d5")) %>% 
  mutate(group = replace(group, group == "FGF_Day-1", "FGF_d1")) %>% 
  mutate(group = replace(group, group == "PF_Day-1", "Veh_d1")) %>%
  mutate(group = replace(group, group == "PF_Day-5", "Veh_d5")) %>%
  ggplot(aes(x=PC4, y=PC5, colour=group)) +
  geom_point(alpha=0.5) +
  scale_colour_jco(name="Treatment Group") + 
  guides(color = guide_legend(override.aes = list(size = 3))) + 
  theme_pubr() + theme(legend.position = c(0.85,0.15),
                       legend.key.size = unit(.5, "lines"), 
                       legend.background = element_blank(),
                       legend.title =element_blank(),
                       legend.text = element_text(size=8)) + theme_figure -> pcplot

# marginal density
pcplot2 <- ggMarginal(pcplot,type="boxplot",groupColour=T, groupFill=T)
pcplot2

Version	Author	Date
942c6c2	Full Name	2019-12-02

dev.off()

null device 
          1

#Test enrichment of pc5 genes

pc5 <- rownames(fgf.agrp@reductions$pca[order(fgf.agrp@reductions$pca[,5]),])[1:50]
gprofiler(pc5, organism = "mmusculus", significant = T, custom_bg = rownames(fgf.agrp),
                           src_filter = c("GO:BP","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) -> ego5
ego5 %>% 
  select(domain, term.name, p.value, overlap.size) %>% arrange(p.value) %>% top_n(5, -p.value) %>%
  mutate(x = fct_reorder(str_to_title(str_wrap(term.name,20)), -p.value)) %>% 
  mutate(y = -log10(p.value)) %>%
  ggplot(aes(x,y)) + 
  geom_col(colour="black", width = 1, fill="gray80", size=1) +
  theme_pubr(legend="none") + 
  theme(axis.text.y = element_text(size=8)) +
  scale_size(range = c(5,10)) + 
  ggsci::scale_fill_lancet() +
  coord_flip() +
  xlab(NULL) + ylab(expression(bold(-log[10]~pvalue))) +
  theme_figure -> pc5go

#Show Agrp/Npy changes

data.frame(t(fgf.agrp[["SCT"]]@data[c("Agrp","Npy"),])) %>% 
  mutate(group = fgf.agrp$group) %>%
  mutate(group = replace(group, group == "FGF_Day-5", "FGF_d5")) %>% 
  mutate(group = replace(group, group == "FGF_Day-1", "FGF_d1")) %>% 
  mutate(group = replace(group, group == "PF_Day-1", "Veh_d1")) %>%
  mutate(group = replace(group, group == "PF_Day-5", "Veh_d5")) %>%
  melt(id.vars = c("group")) %>% 
  ggplot(aes(x=group, y=value)) + 
  geom_boxplot(aes(fill=group),alpha=.5, notch=T) +
  facet_wrap(.~variable, nrow = 2) + theme_pubr() + 
  theme(axis.text.x = element_text(angle=45, hjust=1), legend.position = "none") + 
  ylab("Normalized Expression") + xlab(NULL) + scale_fill_jco() + theme_figure -> agrp_npy_exp

#Individual measurements of KK-Ay (supplementary)

readxl::read_xlsx(here("data/mouse_data/fig2/191116_Agouti_Mc4r_SHU.xlsx"), sheet = 1, range = "A6:V14", col_names = T) %>%
  melt(id.vars = "Days") %>%
  mutate(variable = c(rep(paste0("Veh", seq_len(9)), each = 8), rep(paste0("FGF-1", seq_len(12)), each = 8))) %>%
  mutate(trt = ifelse(grepl("Veh", variable), yes = "V", no = "F")) -> kk_bg

ggplot(kk_bg, aes(x = Days, y = value, color = variable)) + 
  geom_line() + geom_point() + theme_figure

Version	Author	Date
942c6c2	Full Name	2019-12-02

ggsave(filename = here("data/figures/fig2/fig2supp_kk_indiv_bg.tiff"), width = 8, h=4, compression="lzw")

readxl::read_xlsx(here("data/mouse_data/fig2/191116_Agouti_Mc4r_SHU.xlsx"), sheet = 1, range = "A20:V28", col_names = T) %>%
  melt(id.vars = "Days") %>%
  mutate(variable = c(rep(paste0("Veh", seq_len(9)), each = 8), rep(paste0("FGF-1", seq_len(12)), each = 8))) %>%
  mutate(trt = ifelse(grepl("Veh", variable), yes = "V", no = "F")) -> kk_fi

ggplot(kk_fi, aes(x = Days, y = value, color = variable)) + geom_line() +
  geom_point()  + theme_figure

Version	Author	Date
942c6c2	Full Name	2019-12-02

ggsave(filename = here("data/figures/fig2/fig2supp_kk_indiv_fi.tiff"), width = 8, h=4, compression="lzw")

#Group measurements of KK-Ay (Fig 2E)

kk_bg %>% dplyr::group_by(Days, trt) %>% dplyr::summarise(mean = mean(value), sd = sd(value), se=sd/length(value)) %>%
  mutate(trt = ifelse(grepl("F", trt), yes = "FGF-1", no = "Veh")) %>%
  ggplot(aes(x=Days, y=mean, color=trt)) + geom_point(size=0.5) + geom_line() + 
  geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.5) + ggpubr::theme_pubr() + 
  scale_color_manual(name=NULL, values = c("gray30","gray80")) +
  ylab("Blood glucose (mg/dL)") + xlab("Days") + ylim(c(0,600)) +
  scale_x_continuous(breaks=c(0,1,7,14,21,28)) +
  theme(legend.direction = "vertical", legend.position = c(.15,.95), 
        legend.background = element_blank()) + theme_figure -> kk_bg_plot

kk_fi %>% dplyr::group_by(Days, trt) %>% dplyr::summarise(mean = mean(value), sd = sd(value), se=sd/length(value)) %>%
  mutate(trt = ifelse(grepl("F", trt), yes = "FGF-1", no = "Veh")) %>%
  ggplot(aes(x=Days, y=mean, color=trt)) + geom_point(size=0.5) + geom_line() + 
  geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.5) + ggpubr::theme_pubr() + 
  scale_color_manual(name=NULL, values = c("gray30","gray80")) + ylim(c(0,10)) +
  scale_x_continuous(breaks=c(0,1,7,14,21,28)) +
  ylab("Daily food intake (g)") + xlab("Days") +
  theme(legend.direction = "vertical", legend.position = c(.15,.9), 
        legend.background = element_blank()) + theme_figure -> kk_fi_plot

#Individual measurements of Mc4r -/- (supplementary)

readxl::read_xlsx(here("data/mouse_data/fig2/191116_Agouti_Mc4r_SHU.xlsx"), sheet = 2, range="A6:Q14", col_names = T) %>% 
  melt(id.vars="Days") %>% mutate(variable = c(rep(paste0("Veh", seq_len(8)), each=8), rep(paste0("FGF-1", seq_len(8)), each=8))) %>% 
  mutate(trt = ifelse(grepl("Veh", variable), yes = "V", no = "F"))-> mc4_bg

ggplot(mc4_bg, aes(x=Days, y=value, color=variable)) + geom_line() + geom_point()

Version	Author	Date
942c6c2	Full Name	2019-12-02

ggsave(filename = here("data/figures/fig2/fig2supp_mc4_indiv_bg.tiff"), width = 8, h=4, compression="lzw")

readxl::read_xlsx(here("data/mouse_data/fig2/191116_Agouti_Mc4r_SHU.xlsx"), sheet = 2, range="A21:Q29", col_names = T) %>% 
  melt(id.vars="Days") %>% mutate(variable = c(rep(paste0("Veh", seq_len(8)), each=8), rep(paste0("FGF-1", seq_len(8)), each=8))) %>% 
  mutate(trt = ifelse(grepl("Veh", variable), yes = "V", no = "F"))-> mc4_fi

ggplot(mc4_fi, aes(x=Days, y=value, color=variable)) + geom_line() + geom_point()

Version	Author	Date
942c6c2	Full Name	2019-12-02

ggsave(filename = here("data/figures/fig2/fig2supp_mc4_indiv_fi.tiff"), width = 8, h=4, compression="lzw")

#Group measurements of Mc4r -/- (Fig 2E)

mc4_bg %>% dplyr::group_by(Days, trt) %>% dplyr::summarise(mean = mean(value), sd = sd(value), se=sd/length(value)) %>%
  mutate(trt = ifelse(grepl("F", trt), yes = "FGF-1", no = "Veh-PF")) %>%
  ggplot(aes(x=Days, y=mean, color=trt)) + geom_point(size=0.5) + geom_line() + 
  geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.5) + ggpubr::theme_pubr() + 
  scale_color_manual(name=NULL, values = c("gray30","gray80")) +
  ylab("Blood glucose (mg/dL)") + xlab("Days") + ylim(c(0,600)) +
  scale_x_continuous(breaks=c(0,1,7,14,21,28)) +
  theme(legend.direction = "vertical", legend.position = c(.15,.95), legend.background = element_blank()) + theme_figure -> mc4_bg_plot

mc4_fi %>% dplyr::group_by(Days, trt) %>% dplyr::summarise(mean = mean(value), sd = sd(value), se=sd/length(value)) %>%
  mutate(trt = ifelse(grepl("F", trt), yes = "FGF-1", no = "Veh-PF")) %>%
  ggplot(aes(x=Days, y=mean, color=trt)) + geom_point(size=0.5) + geom_line() + 
  geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.5) + ggpubr::theme_pubr() + 
  scale_color_manual(name=NULL, values = c("gray30","gray80")) +
  ylab("Daily food intake (g)") + xlab("Days") + ylim(c(0,10)) +
  scale_x_continuous(breaks=c(0,1,7,14,21,28)) +
  theme(legend.direction = "vertical", legend.position = c(.15,.9), legend.background = element_blank()) + theme_figure -> mc4_fi_plot

#Individual measurements of Shu (supplementary)

readxl::read_xlsx(here("data/mouse_data/fig2/191116_Agouti_Mc4r_SHU.xlsx"), sheet = 3, range="A6:AF12", col_names = T) %>% 
  melt(id.vars="Days") %>% 
  mutate(variable = c(rep(paste0("Veh+Veh_", seq_len(8)), each=6), rep(paste0("FGF-1+Veh_", seq_len(9)), each=6), 
                      rep(paste0("FGF-1+Shu_", seq_len(8)), each=6), rep(paste0("Veh+Shu_", seq_len(6)), each=6))) %>% 
  separate(variable, sep="_", into="group",remove = F)-> shu_bg

Warning: Expected 1 pieces. Additional pieces discarded in 186 rows [1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].

ggplot(shu_bg, aes(x=Days, y=value, color=variable)) + geom_line() + geom_point()

Version	Author	Date
942c6c2	Full Name	2019-12-02

ggsave(filename = here("data/figures/fig2/fig2supp_shu_indiv_bg.tiff"), width = 8, h=4, compression="lzw")


readxl::read_xlsx(here("data/mouse_data/fig2/191116_Agouti_Mc4r_SHU.xlsx"), sheet = 3, range="A19:AF27", col_names = T) %>% 
  melt(id.vars="Days") %>% 
  mutate(variable = c(rep(paste0("Veh+Veh_", seq_len(8)), each=8), rep(paste0("FGF-1+Veh_", seq_len(9)), each=8), 
                      rep(paste0("FGF-1+Shu_", seq_len(8)), each=8), rep(paste0("Veh+Shu_", seq_len(6)), each=8))) %>% 
  separate(variable, sep="_", into="group",remove = F)-> shu_fi

Warning: Expected 1 pieces. Additional pieces discarded in 248 rows [1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].

ggplot(shu_fi, aes(x=Days, y=value, color=variable)) + geom_line() + geom_point()

Version	Author	Date
942c6c2	Full Name	2019-12-02

ggsave(filename = here("data/figures/fig2/fig2supp_shu_indiv_fi.tiff"), width = 8, h=4, compression="lzw")

#Group measurements of Mc4r -/- (Fig 2E)

shu_bg %>% dplyr::group_by(Days, group) %>% dplyr::summarise(mean = mean(value), sd = sd(value), se=sd/length(value)) %>%
  ggplot(aes(x=Days, y=mean, color=group)) + geom_point(size=0.5) + geom_line() + 
  geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.5) + ggpubr::theme_pubr() + 
  scale_color_manual(name=NULL, values = c("#E64B35B2","gray30", "#35C488B2","gray80")) +
  ylab("Blood glucose (mg/dL)") + xlab("Days") + ylim(c(0,600)) +
  scale_x_continuous(breaks=c(0,1,7,14,21,28)) +
  guides(color=guide_legend(ncol=2)) +
  theme(legend.position = c(.3,.85), legend.background = element_blank()) + theme_figure -> shu_bg_plot

shu_fi %>% dplyr::group_by(Days, group) %>% dplyr::summarise(mean = mean(value), sd = sd(value), se=sd/length(value)) %>%
  ggplot(aes(x=Days, y=mean, color=group)) + geom_point(size=0.5) + geom_line() + 
  geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.5) + ggpubr::theme_pubr() + 
  scale_color_manual(name=NULL, values = c("#E64B35B2","gray30", "#35C488B2","gray80")) +
  ylab("Daily food intake (g)") + xlab("Days") + ylim(c(0,10)) +
  scale_x_continuous(breaks=c(0,1,7,14,21,28)) +
  guides(color=guide_legend(ncol=2)) +
  theme(legend.position = c(.3,.95), legend.background = element_blank()) + theme_figure -> shu_fi_plot

top <- plot_grid(pcplot2, pc5go, agrp_npy_exp, nrow=1, labels=c("auto"), scale=0.95, 
                 rel_widths = c(2,1.5,1), align="hv", axis = "tb")

mc4title <- ggdraw() + draw_label(expression(Mc4r^{"-/-"}),fontface = 'bold', x = 0, hjust = 0) +  theme(plot.margin = margin(0, 0, 0, 25))
mc4 <- plot_grid(mc4_bg_plot, mc4_fi_plot, scale=0.9)
mc4plot <- plot_grid(mc4title,mc4, ncol=1, rel_heights = c(0.1,1), labels = c("d"))

kktitle <- ggdraw() + draw_label("KK-Ay", x = 0, hjust = 0) +  theme(plot.margin = margin(0, 0, 0, 25))
kk_ay <- plot_grid(kk_bg_plot, kk_fi_plot, scale=0.9)
kkplot <- plot_grid(kktitle,kk_ay, ncol=1, rel_heights = c(0.1,1), labels = c("e"))

shutitle <- ggdraw() + draw_label("SHU9119", x = 0, hjust = 0) +  theme(plot.margin = margin(0, 0, 0, 25))
shucomp <- plot_grid(shu_bg_plot, shu_fi_plot, scale=0.9)
shuplot <- plot_grid(shutitle,shucomp, ncol=1, rel_heights = c(0.1,1),  labels = c("f"))

plot_grid(top, mc4plot, kkplot, shuplot, ncol=1, rel_heights = c(1.25,1,1,1))

Version	Author	Date
942c6c2	Full Name	2019-12-02

ggsave(filename = here("data/figures/fig2/fig2.tiff"), width = 9, h=10, compression="lzw")

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] gProfileR_0.6.7             ggExtra_0.9                
 [3] ggsci_2.9                   ggpubr_0.2.1               
 [5] magrittr_1.5                reshape2_1.4.3             
 [7] future.apply_1.3.0          ggrepel_0.8.0.9000         
 [9] cowplot_1.0.0               parallelDist_0.2.4         
[11] cluster_2.1.0               future_1.14.0              
[13] here_0.1                    DESeq2_1.22.2              
[15] SummarizedExperiment_1.12.0 DelayedArray_0.8.0         
[17] BiocParallel_1.16.6         matrixStats_0.54.0         
[19] Biobase_2.42.0              GenomicRanges_1.34.0       
[21] GenomeInfoDb_1.18.2         IRanges_2.16.0             
[23] S4Vectors_0.20.1            BiocGenerics_0.28.0        
[25] forcats_0.4.0               stringr_1.4.0              
[27] dplyr_0.8.3                 purrr_0.3.2                
[29] readr_1.3.1.9000            tidyr_0.8.3                
[31] tibble_2.1.3                ggplot2_3.2.1              
[33] tidyverse_1.2.1             Seurat_3.0.3.9036          

loaded via a namespace (and not attached):
  [1] reticulate_1.13        R.utils_2.9.0          tidyselect_0.2.5      
  [4] RSQLite_2.1.1          AnnotationDbi_1.44.0   htmlwidgets_1.3       
  [7] grid_3.5.3             Rtsne_0.15             munsell_0.5.0         
 [10] codetools_0.2-16       ica_1.0-2              miniUI_0.1.1.1        
 [13] withr_2.1.2            colorspace_1.4-1       highr_0.8             
 [16] knitr_1.23             rstudioapi_0.10        ROCR_1.0-7            
 [19] ggsignif_0.5.0         gbRd_0.4-11            listenv_0.7.0         
 [22] labeling_0.3           Rdpack_0.11-0          git2r_0.25.2          
 [25] GenomeInfoDbData_1.2.0 bit64_0.9-7            rprojroot_1.3-2       
 [28] vctrs_0.2.0            generics_0.0.2         xfun_0.8              
 [31] R6_2.4.0               rsvd_1.0.2             locfit_1.5-9.1        
 [34] bitops_1.0-6           assertthat_0.2.1       promises_1.0.1        
 [37] SDMTools_1.1-221.1     scales_1.0.0           nnet_7.3-12           
 [40] gtable_0.3.0           npsurv_0.4-0           globals_0.12.4        
 [43] workflowr_1.4.0        rlang_0.4.0            zeallot_0.1.0         
 [46] genefilter_1.64.0      splines_3.5.3          lazyeval_0.2.2        
 [49] acepack_1.4.1          broom_0.5.2            checkmate_1.9.4       
 [52] yaml_2.2.0             modelr_0.1.4           backports_1.1.4       
 [55] httpuv_1.5.1           Hmisc_4.2-0            tools_3.5.3           
 [58] ellipsis_0.2.0.1       gplots_3.0.1.1         RColorBrewer_1.1-2    
 [61] ggridges_0.5.1         Rcpp_1.0.2             plyr_1.8.4            
 [64] base64enc_0.1-3        zlibbioc_1.28.0        RCurl_1.95-4.12       
 [67] rpart_4.1-15           pbapply_1.4-1          zoo_1.8-6             
 [70] haven_2.1.0            fs_1.3.1               data.table_1.12.2     
 [73] lmtest_0.9-37          RANN_2.6.1             whisker_0.3-2         
 [76] fitdistrplus_1.0-14    mime_0.7               hms_0.5.0             
 [79] lsei_1.2-0             evaluate_0.14          xtable_1.8-4          
 [82] XML_3.98-1.20          readxl_1.3.1           gridExtra_2.3         
 [85] compiler_3.5.3         KernSmooth_2.23-15     crayon_1.3.4          
 [88] R.oo_1.22.0            htmltools_0.3.6        later_0.8.0           
 [91] Formula_1.2-3          geneplotter_1.60.0     RcppParallel_4.4.3    
 [94] lubridate_1.7.4        DBI_1.0.0              MASS_7.3-51.4         
 [97] Matrix_1.2-17          cli_1.1.0              R.methodsS3_1.7.1     
[100] gdata_2.18.0           metap_1.1              igraph_1.2.4.1        
[103] pkgconfig_2.0.2        foreign_0.8-71         plotly_4.9.0          
[106] xml2_1.2.0             annotate_1.60.1        XVector_0.22.0        
[109] rematch_1.0.1          bibtex_0.4.2           rvest_0.3.4           
[112] digest_0.6.20          sctransform_0.2.0      RcppAnnoy_0.0.12      
[115] tsne_0.1-3             rmarkdown_1.13         cellranger_1.1.0      
[118] leiden_0.3.1           htmlTable_1.13.1       uwot_0.1.3            
[121] shiny_1.3.2            gtools_3.8.1           nlme_3.1-140          
[124] jsonlite_1.6           viridisLite_0.3.0      pillar_1.4.2          
[127] lattice_0.20-38        httr_1.4.1             survival_2.44-1.1     
[130] glue_1.3.1             png_0.1-7              bit_1.1-14            
[133] stringi_1.4.3          blob_1.1.1             latticeExtra_0.6-28   
[136] caTools_1.17.1.2       memoise_1.1.0          irlba_2.3.3           
[139] ape_5.3

Figure 2

Load prepped data