Last updated: 2019-12-02

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Knit directory: bentsen-rausch-2019/

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Load Libraries

library(Seurat)
library(WGCNA)
library(cluster)
library(genefilter)
library(tidyverse)
library(tidygraph)
library(ggraph)
library(reshape2)
library(parallelDist)
library(ggsci)
library(emmeans)
library(lme4)
library(ggbeeswarm)
library(ggpubr)
library(igraph)
library(RColorBrewer)
library(gProfileR)
library(here)
library(eulerr)
library(ggExtra)
library(cowplot)
library(future)
plan("multiprocess", workers=40)
options(future.globals.maxSize = 4000 * 1024^2)

Extract Astrocytes for WGCNA

Calculate softpower

enableWGCNAThreads()

Allowing parallel execution with up to 79 working processes.

datExpr <- as.matrix(t(astro[["SCT"]]@scale.data[astro[["SCT"]]@var.features,]))
powers <- c(c(1:10), seq(from = 12, to = 40, by = 2))
sft <- pickSoftThreshold(datExpr,
  dataIsExpr = TRUE, powerVector = powers, corOptions = list(use = "p"),
  networkType = "signed"
)

   Power SFT.R.sq  slope truncated.R.sq  mean.k. median.k.   max.k.
1      1  0.15300 105.00          0.626 2.50e+03  2.50e+03 2520.000
2      2  0.00719 -13.00          0.916 1.25e+03  1.25e+03 1280.000
3      3  0.49400 -62.20          0.691 6.29e+02  6.28e+02  658.000
4      4  0.76800 -48.60          0.749 3.16e+02  3.15e+02  349.000
5      5  0.96800 -34.90          0.963 1.59e+02  1.58e+02  190.000
6      6  0.97600 -23.30          0.988 8.05e+01  7.96e+01  107.000
7      7  0.95800 -15.70          0.974 4.07e+01  4.00e+01   62.600
8      8  0.94600 -11.40          0.951 2.07e+01  2.02e+01   38.300
9      9  0.48400 -13.50          0.347 1.06e+01  1.02e+01   24.600
10    10  0.48700 -10.40          0.358 5.41e+00  5.14e+00   16.500
11    12  0.48800  -6.67          0.376 1.45e+00  1.31e+00    8.350
12    14  0.96000  -3.21          0.951 4.03e-01  3.38e-01    4.770
13    16  0.93900  -2.50          0.922 1.20e-01  8.73e-02    2.950
14    18  0.97900  -2.00          0.973 3.99e-02  2.27e-02    1.940
15    20  0.97200  -1.65          0.964 1.55e-02  5.95e-03    1.330
16    22  0.91200  -1.49          0.891 7.22e-03  1.57e-03    0.949
17    24  0.31500  -2.03          0.122 3.97e-03  4.18e-04    0.768
18    26  0.94700  -1.27          0.941 2.48e-03  1.12e-04    0.630
19    28  0.40600  -1.58          0.302 1.68e-03  3.04e-05    0.521
20    30  0.40500  -1.50          0.299 1.21e-03  8.32e-06    0.434
21    32  0.40800  -1.44          0.297 9.15e-04  2.30e-06    0.363
22    34  0.37200  -1.33          0.192 7.10e-04  6.44e-07    0.305
23    36  0.37300  -1.32          0.195 5.65e-04  1.82e-07    0.256
24    38  0.35400  -1.59          0.224 4.58e-04  5.22e-08    0.216
25    40  0.33900  -1.85          0.153 3.77e-04  1.52e-08    0.190

cex1 <- 0.9
plot(sft$fitIndices[, 1], -sign(sft$fitIndices[, 3]) * sft$fitIndices[, 2], xlab = "Soft Threshold (power)", ylab = "Scale Free Topology Model Fit, signed R^2", type = "n", main = paste("Scale independence"))
text(sft$fitIndices[, 1], -sign(sft$fitIndices[, 3]) * sft$fitIndices[, 2], labels = powers, cex = cex1, col = "red")
abline(h = 0.80, col = "red")

Version	Author	Date
9cf1e45	Full Name	2019-10-28

# Mean Connectivity Plot
plot(sft$fitIndices[, 1], sft$fitIndices[, 5], xlab = "Soft Threshold (power)", ylab = "Mean Connectivity", type = "n", main = paste("Mean connectivity"))
text(sft$fitIndices[, 1], sft$fitIndices[, 5], labels = powers, cex = cex1, col = "red")

Version	Author	Date
9cf1e45	Full Name	2019-10-28

Generate TOM

softPower <- 5
SubGeneNames <- colnames(datExpr)
adj <- adjacency(datExpr, type = "signed", power = softPower)
diag(adj) <- 0
TOM <- TOMsimilarityFromExpr(datExpr, networkType = "signed", TOMType = "signed", power = softPower, maxPOutliers = 0.05)

TOM calculation: adjacency..
..will use 79 parallel threads.
 Fraction of slow calculations: 0.000000
..connectivity..
..matrix multiplication (system BLAS)..
..normalization..
..done.

colnames(TOM) <- rownames(TOM) <- SubGeneNames
dissTOM <- 1 - TOM
geneTree <- hclust(as.dist(dissTOM), method = "average") # use complete for method rather than average (gives better results)
plot(geneTree, xlab = "", sub = "", cex = .5, main = "Gene clustering", hang = .001)

Version	Author	Date
9cf1e45	Full Name	2019-10-28

Identify Modules

minModuleSize <- 15
x <- 4
dynamicMods <- cutreeDynamic(
  dendro = geneTree, distM = as.matrix(dissTOM),
  method = "hybrid", pamStage = F, deepSplit = x,
  minClusterSize = minModuleSize
)

 ..cutHeight not given, setting it to 0.968  ===>  99% of the (truncated) height range in dendro.
 ..done.

dynamicColors <- labels2colors(dynamicMods) # label each module with a unique color
plotDendroAndColors(geneTree, dynamicColors, "Dynamic Tree Cut",
  dendroLabels = FALSE, hang = 0.03, addGuide = TRUE, guideHang = 0.05,
  main = "Gene dendrogram and module colors"
) # plot the modules with colors

Version	Author	Date
9cf1e45	Full Name	2019-10-28

Calculate Eigengenes

MEs <- moduleEigengenes(datExpr, dynamicColors)$eigengenes
ME1 <- MEs
row.names(ME1) <- row.names(datExpr)
MEDiss <- 1 - cor(MEs)
METree <- hclust(as.dist(MEDiss), method = "average")
plot(METree, main = "Clustering of module eigengenes", xlab = "", sub = "")
MEDissThres <- 0.2
abline(h = MEDissThres, col = "red")

Version	Author	Date
9cf1e45	Full Name	2019-10-28

Merge modules

merge <- mergeCloseModules(datExpr, dynamicColors, cutHeight = MEDissThres, verbose = 3)

 mergeCloseModules: Merging modules whose distance is less than 0.2
   multiSetMEs: Calculating module MEs.
     Working on set 1 ...
     moduleEigengenes: Calculating 9 module eigengenes in given set.
   Calculating new MEs...
   multiSetMEs: Calculating module MEs.
     Working on set 1 ...
     moduleEigengenes: Calculating 9 module eigengenes in given set.

mergedColors <- merge$colors
mergedMEs <- merge$newMEs
moduleColors <- mergedColors
MEs <- mergedMEs
modulekME <- signedKME(datExpr, MEs)

Plot merged modules

plotDendroAndColors(geneTree, cbind(dynamicColors, mergedColors),
  c("Dynamic Tree Cut", "Merged dynamic"),
  dendroLabels = FALSE, hang = 0.03,
  addGuide = TRUE, guideHang = 0.05
)

Version	Author	Date
9cf1e45	Full Name	2019-10-28

moduleColors <- mergedColors
MEs <- mergedMEs
modulekME <- signedKME(datExpr, MEs)

Generate function to look up genes in each module

modules <- MEs
c_modules <- data.frame(moduleColors)
row.names(c_modules) <- colnames(datExpr) 
module.list.set1 <- substring(colnames(modules), 3) 
index.set1 <- 0
Network <- list() 
for (i in 1:length(module.list.set1)) {
  index.set1 <- which(c_modules == module.list.set1[i])
  Network[[i]] <- row.names(c_modules)[index.set1]
}
names(Network) <- module.list.set1
lookup <- function(gene, network) {
  return(network[names(network)[grep(gene, network)]])
} # load function

MEs %>% dplyr::select(-MEgrey) -> MEs
data <- data.frame(MEs,
  day = astro$day, trt = astro$trt,
  sample = as.factor(astro$sample), group = astro$group,
  batch = astro$batch, celltype = Idents(astro),
  groupall = paste0(Idents(astro), astro$group)
)


mod<-lapply(colnames(MEs), function(me) {
  mod<-lmer(MEs[[me]] ~ group + (1|batch) + (1|sample), data=data)
  pairwise<-emmeans(mod, pairwise ~ group)
  plot<-data.frame(plot(pairwise, plotIt=F)$data)
  sig<-as.data.frame(pairwise$contrasts)
  sig%>%separate(contrast, c("start", "end"), sep = " - ") -> sig
  yvals<-unlist(lapply(unique(sig$celltype), function(x) {
    x<-as.character(x)
    y<-data[data$celltype==x,]
    z<-max(as.numeric(y[[me]]))
    names(z)<-x
    return(z)
  }))
  sig$yvals<-yvals[match(sig$celltype, names(yvals))]
  sig$yvals[duplicated(sig$yvals)]<-sig$yvals[duplicated(sig$yvals)]+.004
  sig$yvals[duplicated(sig$yvals)]<-sig$yvals[duplicated(sig$yvals)]+.004
  sig$yvals[duplicated(sig$yvals)]<-sig$yvals[duplicated(sig$yvals)]+.004
  return(sig)
})

names(mod) <- colnames(MEs)
sig <- bind_rows(mod, .id="id")
sig$symbol <- sig$p.value
sig$symbol[findInterval(sig$symbol, c(0.1,2)) == 1L] <-NA
sig$symbol[findInterval(sig$symbol, c(0.01,0.1)) == 1L] <- "*"
sig$symbol[findInterval(sig$symbol, c(0.001,0.01)) == 1L] <- "**"

Warning in findInterval(sig$symbol, c(0.001, 0.01)): NAs introduced by
coercion

sig$symbol[findInterval(sig$symbol, c(1e-200,0.001)) == 1L] <- "***"

Warning in findInterval(sig$symbol, c(1e-200, 0.001)): NAs introduced by
coercion

sig %>% group_by(id) %>% dplyr::summarise(max=max(estimate)) %>% arrange(-max) %>% mutate(pubname = paste0("sn-astro-M", seq_len(8))) -> name_mods

rename_mods <- name_mods$id
names(rename_mods) <- name_mods$pubname
write_csv(data.frame(x=names(rename_mods), y=as.character(rename_mods)), 
          path = here("output/glia/wgcna/sn_astro_translatelabels.csv"))

MEs <- rename(MEs, !!!rename_mods)
data <- rename(data, !!!rename_mods)

lapply(unique(colnames(MEs)), function(me) {
  tryCatch({
   print(ggplot(data = data[sample(nrow(data)), ], aes(x = group, y = get(me))) +
      geom_quasirandom(aes(fill = sample), shape = 21, size = 2, alpha = .75) +
      scale_fill_manual(values = pal_jco()(10)) + ylab(NULL) + xlab(NULL) +
      theme_pubr() + theme(
        axis.text.x = element_text(angle = 45, hjust = 1, face = "bold"),
        plot.title = element_text(hjust = 0.5)
      ) +
      scale_y_continuous(aes(name = "", limits = c(min(get(me)) - .02, max(get(me))) + .02)) +
      ggtitle(me))
  },
  error = function(err) {
    print(err)
  }
  )
})

[[1]]


[[2]]


[[3]]


[[4]]


[[5]]


[[6]]


[[7]]


[[8]]

write_csv(sig, path=here("output/glia/wgcna/astro_wgcna_linearmodel_testing.csv"))

Filter metadata table and correlate with eigengenes

nGenes <- ncol(datExpr)
nSamples <- nrow(datExpr) 
MEs <- orderMEs(MEs)
astro$group <- paste0(astro$trt, "_", astro$day)

MEs %>%
  as.data.frame() %>%
  mutate(sample = astro$sample, day = astro$day) %>%
  melt() %>%
  dplyr::group_by(sample, variable) %>%
  dplyr::summarise(mean_mod = median(value)) -> me_heatmap
me_heatmap %>%
  dplyr::group_by(variable) %>%
  mutate(scaled_mod = scale(mean_mod)) -> me_heatmap

sigmods <- paste0("sn-astro-M",seq_len(4))

me_heatmap$day <- as.character(astro$day[match(me_heatmap$sample, astro$sample)])
me_heatmap$day <- gsub(me_heatmap$day, pattern="Day-", replacement = "D")
me_heatmap$trt <- as.character(astro$trt[match(me_heatmap$sample, astro$sample)])
me_heatmap$trt <- gsub(me_heatmap$trt, pattern="PF", replacement = "Veh")
me_heatmap$sample <- fct_relevel(me_heatmap$sample, "1_FGF", "2_FGF", 
                                 "3_FGF", "1_Veh", "2_Veh", "3_Veh", "37_FGF", "45_FGF", "28_Veh", "38_Veh")

Warning: Unknown levels in `f`: 1_Veh, 2_Veh, 3_Veh, 28_Veh, 38_Veh

me_heatmap <- me_heatmap[me_heatmap$variable %in% c(sigmods), ]
me_heatmap$order <- paste0(me_heatmap$trt,"_",me_heatmap$day)
me_heatmap$order <- fct_relevel(me_heatmap$order, c("Veh_Day1","FGF_Day1","Veh_Day5","FGF_Day5"))

Warning: Unknown levels in `f`: Veh_Day1, FGF_Day1, Veh_Day5, FGF_Day5

diffmod_heatmap <- ggplot(me_heatmap, aes(variable, sample)) +
  geom_tile(aes(fill = scaled_mod), colour = "white", size=.5) + ylab(NULL) + xlab(NULL) +
  scale_fill_gsea(limits=c(-2,3), name="Scaled ME\nExpression") +
  facet_grid(order ~ ., scales = "free", switch="y") + theme_pubr(border = T, legend="right") + ggpubr::labs_pubr() +
  scale_x_discrete(position="top") +
  theme(axis.text.y = element_blank(), panel.spacing = unit(.25, "lines"),  axis.ticks.y = element_blank(), 
        axis.ticks.x = element_blank(), axis.text.x = element_blank(),
        plot.margin = unit(c(0, 0, 0, 0), "cm"), legend.title = element_text(size=8, hjust=0.5, angle=90), 
        legend.text = element_text(size=7, face="bold")) + 
  guides(fill = guide_colorbar(barwidth = .75, barheight = 5, title.position = "left")) +
  theme_figure
diffmod_heatmap

Version	Author	Date
9cf1e45	Full Name	2019-10-28

Get hubgenes and kME

hubgenes <- lapply(seq_len(length(Network)-1), function(x) {
  dat <- modulekME[Network[[x]], ]
  dat <- dat[order(-dat[paste0("kME", names(Network)[x])]), ]
  gene <- data.frame(gene=rownames(dat),kme=dat[,x]) 
  return(gene)
})
names(hubgenes) <-  names(rename_mods)[match(names(Network)[-length(Network)], 
                                             gsub(as.character(rename_mods),pattern = "ME",replacement = ""))]
d <- bind_rows(hubgenes, .id="id")

Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

write_csv(d, path = here("output/glia/wgcna/astro_wgcna_genemodules.csv"))

Calculate GO enrichment

goterms <- lapply(hubgenes[c(sigmods)], function(x) {
  x <- gprofiler(x,
    ordered_query = T, organism = "mmusculus", significant = T, custom_bg = colnames(datExpr),
    src_filter = c("GO:BP", "GO:MF", "REAC", "KEGG"), hier_filtering = "strong",
    min_isect_size = 2,
    sort_by_structure = T, exclude_iea = T,
    min_set_size = 10, max_set_size = 300, correction_method = "fdr"
  )
  x <- x[order(x$p.value), ]
  return(x)
})

goterms %>% bind_rows(.id="id") %>%
  mutate(padj=p.adjust(p.value, "fdr")) -> godat

write_csv(godat, path=here("output/glia/wgcna/astrocyte_wgcna_goterms.csv"))

goterms %>%
  bind_rows(.id = "id") %>%
  mutate(padj = p.adjust(p.value, "fdr")) %>%
  group_by(id) %>%
  top_n(5, -padj) %>%
  select(p.value, padj, term.name, domain, id) %>%
  arrange(id) -> goplot

goterm <- ggplot(goplot, aes(x = str_to_title(str_wrap(term.name, 15)), y = -log10(padj), fill = domain)) + 
  geom_col() + scale_fill_npg() +
  facet_wrap(. ~ id, scales = "free_x", ncol = 2) + 
  theme_pubr(legend = "right") + 
  theme(
    text = element_text(size = 8),
    legend.text = element_text(size=8, face="bold"),
    legend.title = element_text(size=12, face="bold"), 
    axis.text.x = element_text(angle = 45, hjust = 1),
    strip.text.x = element_text(face="bold", size=8)
  ) +
  xlab(NULL) + geom_hline(yintercept = -log10(0.05), linetype = "dashed", size = .75)

Plot gene networks

lapply(sigmods, function(col) {
  maxsize <- 15
  hubs <- data.frame(genes=hubgenes[[col]]$gene[1:maxsize], kme = hubgenes[[col]]$kme[1:maxsize], mod = rep(col,15))
}) %>% bind_rows() -> hub_plot

Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

adj[hub_plot$genes, hub_plot$genes] %>% 
  graph.adjacency(mode = "undirected", weighted = T, diag = FALSE) %>%
  as_tbl_graph(g1) %>% upgrade_graph() %>% activate(nodes) %>% dplyr::mutate(mod=hub_plot$mod) %>% 
  dplyr::mutate(kme=hub_plot$kme) %>% activate(edges) %>% dplyr::filter(weight>.15) %>% activate(nodes) %>% 
  filter(!node_is_isolated()) %>% 
  dplyr::mutate(color = ifelse(name %in% c("Npas3","Sparcl1","Ndrg2","Aqp4","Apoe","Gfap"), yes="red", no="black")) -> hub_plot

set.seed("21")
geneplot <- ggraph(hub_plot,  layout = 'kk') + 
  geom_edge_link(color="darkgrey", aes(alpha = weight), show.legend = F)  + 
  scale_edge_width(range = c(0.2, 1)) + geom_node_text(aes(label = name, color=color), fontface="bold", size=3, repel=T) +
  geom_node_point(aes(fill = mod, size=kme), shape=21, alpha=0.5) + 
  scale_size(range = c(1,5), name = "kME") +
  ggsci::scale_fill_npg(name="Module") +
  guides(fill = guide_legend(override.aes = list(size=5)),
         size = guide_legend(override.aes = list(size=c(1,2,3,4)))) + 
  theme_graph() + theme(legend.title.align=0.5,
                        legend.box = "horizontal", 
                        legend.position = c(0.1, 0.9),
                        legend.margin = margin(-0.5,0,0,0, unit="cm"),
                        legend.title = element_text(size=10, face="bold"),
                        legend.text = element_text(size=8, face="bold")) +
  scale_color_manual(values=c("gray0","red")) + guides(color=FALSE) +
  coord_cartesian(clip="off")
geneplot

Version	Author	Date
f4dd96b	Full Name	2019-10-29

Plot figure

plot_grid(diffmod_heatmap, geneplot, rel_widths = c(1,1.5), align="hv", axis="tb", labels="auto", scale=0.95)

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Read in gene sets

lps1 <- read_tsv(here("data/lps1.txt"))
mcao1 <- read_tsv(here("data/mcao1.txt"))
mcao3 <- read_tsv(here("data/mcao_d3.txt"))
mcao7 <- read_tsv(here("data/mcaod7.txt"))
ni <- readxl::read_xlsx(here("data/neur_astro_induce.xlsx"))
na <- readxl::read_xlsx(here("data/synaptic_activity_induced.xlsx"))

ni %>%
  select(gene_name, `Fold Change`, padj_deseq2) %>%
  filter(`Fold Change` > 2, padj_deseq2 < 0.05) -> ni
na %>%
  select(gene_name, Fold_Change, DESeq2_padj) %>%
  filter(Fold_Change > 2, DESeq2_padj < 0.05) -> na


mcao1 %>%
  filter(logFC < (-2)) %>%
  arrange(logFC) %>%
  distinct(Gene.symbol) %>%
  filter(!grepl("///", Gene.symbol)) -> mcao_gene

lps1 %>%
  filter(logFC < (-2)) %>%
  arrange(logFC) %>%
  distinct(Gene.symbol) %>%
  filter(!grepl("///", Gene.symbol)) -> lps_gene

mcao3 %>%
  filter(logFC < (-2)) %>%
  arrange(logFC) %>%
  distinct(Gene.symbol) %>%
  filter(!grepl("///", Gene.symbol)) -> mcao3_gene

mcao7 %>%
  filter(logFC < (-2)) %>%
  arrange(logFC) %>%
  distinct(Gene.symbol) %>%
  filter(!grepl("///", Gene.symbol)) -> mcao7_gene

Filter gene sets

intersect(lps_gene$Gene.symbol, mcao_gene$Gene.symbol) -> panreact
lps_uniq <- lps_gene$Gene.symbol[!lps_gene$Gene.symbol %in% mcao_gene$Gene.symbol]
mcao_uniq <- mcao_gene$Gene.symbol[!mcao_gene$Gene.symbol %in% lps_gene$Gene.symbol]
mcao3_uniq <- mcao3_gene$Gene.symbol[!mcao3_gene$Gene.symbol %in% lps_gene$Gene.symbol]
mcao7_uniq <- mcao7_gene$Gene.symbol[!mcao7_gene$Gene.symbol %in% lps_gene$Gene.symbol]

Test module enrichment in gene sets

d %>%
  filter(id %in% sigmods) %>%
  group_by(id) -> astro_mod
astro_mod %>%
  group_split() %>%
  map("gene") -> astro_gene
group_keys(astro_mod) %>% pull(id) -> mod_names

lapply(astro_gene, function(x) {
  a <- 1 - phyper(sum(x %in% lps_uniq), length(lps_uniq), 5000, length(x), log.p = F)
  b <- 1 - phyper(sum(x %in% mcao_uniq), length(mcao_uniq), 5000, length(x), log.p = F)
  c <- 1 - phyper(sum(x %in% mcao3_uniq), length(mcao3_uniq), 5000, length(x), log.p = F)
  d <- 1 - phyper(sum(x %in% mcao7_uniq), length(mcao7_uniq), 5000, length(x), log.p = F)
  e <- 1 - phyper(sum(x %in% panreact), length(panreact), 5000, length(x), log.p = F)
  f <- 1 - phyper(sum(x %in% ni$gene_name), length(ni$gene_name), 5000, length(x), log.p = F)
  g <- 1 - phyper(sum(x %in% na$gene_name), length(na$gene_name), 5000, length(x), log.p = F)
  return(data.frame(A1 = a, A2 = b, PAN = e, NeurI = f, `NeurA` = g))
}) %>% bind_rows() -> overlap_test

as.data.frame(sapply(overlap_test, function(x) p.adjust(x, n = dim(overlap_test)[1] * dim(overlap_test)[2]))) -> overlap_test
overlap_test$mod <- mod_names
overlap_pval <- reshape2::melt(overlap_test)
set_plot <- ggplot(overlap_pval, aes(y = mod, 
                                     x = fct_relevel(variable,"NeurA","NeurI","PAN","A2","A1"))) + 
  geom_tile(size = 1, color = "white", fill="grey99") +
  geom_point(aes(size = if_else(-log10(value)<1.3,true = 0, false = -log10(value))), alpha=0.25) + 
  scale_size(name= expression(bold(-log[10] ~ pvalue)), breaks = c(2,4,8)) + 
  scale_fill_manual(values=c("black","red"), guide=F) + coord_flip() + 
  theme_pubr(legend = "right") + xlab(NULL) + ylab(NULL) + 
  theme(plot.margin = unit(c(0, 0, 0, 0), "cm"),
        legend.title = element_text(size=8), 
        legend.text = element_text(size=7, face="bold"),
        axis.text.x = element_text(angle=45, hjust=1)) + theme_figure
set_plot

Version	Author	Date
9cf1e45	Full Name	2019-10-28

Generate top half of figure

topleft <- plot_grid(diffmod_heatmap, set_plot, align = "v", axis="lr", labels="a", ncol=1, rel_heights = c(3,1), scale=0.95)
topright <- plot_grid(geneplot, labels=c("b"), scale=0.95)

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Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
font family 'Arial Narrow' not found in PostScript font database

top_astro <- plot_grid(topleft,topright, ncol=2, rel_widths = c(1,1.25))
save(top_astro, file = here("data/figures/fig4_nuc_top.RData"))
ggsave(here("data/figures/fig4_nuc_astro.png"), h=6,w=8)

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] future_1.14.0         cowplot_1.0.0         ggExtra_0.9          
 [4] eulerr_5.1.0          here_0.1              gProfileR_0.6.7      
 [7] RColorBrewer_1.1-2    igraph_1.2.4.1        ggpubr_0.2.1         
[10] magrittr_1.5          ggbeeswarm_0.6.0      lme4_1.1-21          
[13] Matrix_1.2-17         emmeans_1.3.5.1       ggsci_2.9            
[16] parallelDist_0.2.4    reshape2_1.4.3        ggraph_1.0.2         
[19] tidygraph_1.1.2       forcats_0.4.0         stringr_1.4.0        
[22] dplyr_0.8.3           purrr_0.3.2           readr_1.3.1.9000     
[25] tidyr_0.8.3           tibble_2.1.3          ggplot2_3.2.1        
[28] tidyverse_1.2.1       genefilter_1.64.0     cluster_2.1.0        
[31] WGCNA_1.68            fastcluster_1.1.25    dynamicTreeCut_1.63-1
[34] Seurat_3.0.3.9036    

loaded via a namespace (and not attached):
  [1] estimability_1.3      R.methodsS3_1.7.1     coda_0.19-3          
  [4] acepack_1.4.1         bit64_0.9-7           knitr_1.23           
  [7] irlba_2.3.3           multcomp_1.4-10       R.utils_2.9.0        
 [10] data.table_1.12.2     rpart_4.1-15          RCurl_1.95-4.12      
 [13] doParallel_1.0.14     generics_0.0.2        metap_1.1            
 [16] BiocGenerics_0.28.0   preprocessCore_1.44.0 TH.data_1.0-10       
 [19] RSQLite_2.1.1         RANN_2.6.1            bit_1.1-14           
 [22] xml2_1.2.0            lubridate_1.7.4       httpuv_1.5.1         
 [25] assertthat_0.2.1      viridis_0.5.1         xfun_0.8             
 [28] hms_0.5.0             evaluate_0.14         promises_1.0.1       
 [31] DEoptimR_1.0-8        caTools_1.17.1.2      readxl_1.3.1         
 [34] DBI_1.0.0             htmlwidgets_1.3       stats4_3.5.3         
 [37] backports_1.1.4       annotate_1.60.1       gbRd_0.4-11          
 [40] RcppParallel_4.4.3    vctrs_0.2.0           Biobase_2.42.0       
 [43] ROCR_1.0-7            withr_2.1.2           ggforce_0.3.0.9000   
 [46] robustbase_0.93-5     checkmate_1.9.4       sctransform_0.2.0    
 [49] ape_5.3               lazyeval_0.2.2        crayon_1.3.4         
 [52] labeling_0.3          pkgconfig_2.0.2       tweenr_1.0.1         
 [55] nlme_3.1-140          vipor_0.4.5           nnet_7.3-12          
 [58] rlang_0.4.0           globals_0.12.4        miniUI_0.1.1.1       
 [61] sandwich_2.5-1        modelr_0.1.4          rsvd_1.0.2           
 [64] cellranger_1.1.0      rprojroot_1.3-2       polyclip_1.10-0      
 [67] matrixStats_0.54.0    lmtest_0.9-37         boot_1.3-22          
 [70] zoo_1.8-6             base64enc_0.1-3       beeswarm_0.2.3       
 [73] whisker_0.3-2         ggridges_0.5.1        png_0.1-7            
 [76] viridisLite_0.3.0     bitops_1.0-6          R.oo_1.22.0          
 [79] KernSmooth_2.23-15    blob_1.1.1            workflowr_1.4.0      
 [82] robust_0.4-18.1       S4Vectors_0.20.1      ggsignif_0.5.0       
 [85] scales_1.0.0          memoise_1.1.0         plyr_1.8.4           
 [88] ica_1.0-2             gplots_3.0.1.1        bibtex_0.4.2         
 [91] gdata_2.18.0          compiler_3.5.3        lsei_1.2-0           
 [94] rrcov_1.4-7           fitdistrplus_1.0-14   cli_1.1.0            
 [97] listenv_0.7.0         pbapply_1.4-1         htmlTable_1.13.1     
[100] Formula_1.2-3         MASS_7.3-51.4         tidyselect_0.2.5     
[103] stringi_1.4.3         highr_0.8             yaml_2.2.0           
[106] latticeExtra_0.6-28   ggrepel_0.8.0.9000    grid_3.5.3           
[109] tools_3.5.3           future.apply_1.3.0    parallel_3.5.3       
[112] rstudioapi_0.10       foreach_1.4.4         foreign_0.8-71       
[115] git2r_0.25.2          gridExtra_2.3         farver_1.1.0         
[118] Rtsne_0.15            digest_0.6.20         shiny_1.3.2          
[121] Rcpp_1.0.2            broom_0.5.2           SDMTools_1.1-221.1   
[124] later_0.8.0           RcppAnnoy_0.0.12      httr_1.4.1           
[127] AnnotationDbi_1.44.0  npsurv_0.4-0          Rdpack_0.11-0        
[130] colorspace_1.4-1      rvest_0.3.4           XML_3.98-1.20        
[133] fs_1.3.1              reticulate_1.13       IRanges_2.16.0       
[136] splines_3.5.3         uwot_0.1.3            plotly_4.9.0         
[139] fit.models_0.5-14     xtable_1.8-4          jsonlite_1.6         
[142] nloptr_1.2.1          zeallot_0.1.0         R6_2.4.0             
[145] Hmisc_4.2-0           pillar_1.4.2          htmltools_0.3.6      
[148] mime_0.7              glue_1.3.1            minqa_1.2.4          
[151] codetools_0.2-16      tsne_0.1-3            pcaPP_1.9-73         
[154] mvtnorm_1.0-11        lattice_0.20-38       pbkrtest_0.4-7       
[157] leiden_0.3.1          gtools_3.8.1          GO.db_3.7.0          
[160] survival_2.44-1.1     rmarkdown_1.13        munsell_0.5.0        
[163] iterators_1.0.10      impute_1.56.0         haven_2.1.0          
[166] gtable_0.3.0

Astrocyte WGCNA