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Fig4.Rmd
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---
title: "Figure 4"
output: html_notebook
---
```{r}
library(Nebulosa)
library(Seurat)
library(Signac)
library(scMEGA)
library(stringr)
```
```{r}
dir.create("Fig4")
output <- "Fig4/"
```
Figure 4.a (scMEGA data)
```{r}
obj <- readRDS("../scMega/aPTxPTS12_cor90_seed1234_DEC2.RDS") #aPTxPTS12_cor40_seed1234_NOV26.RDS
```
```{r}
cols <- c("#467a3c","#A82400","#F03B00" )
set.seed(1234)
```
# Figure 4b Diffusion Map (DM) cell type and trajectory from scMEGA
```{r,fig.keep='last'}
DefaultAssay(obj) <- 'RNA'
p1a <- DimPlot(obj, group.by = "subclass.l3", label = TRUE,
reduction = "dm", shuffle = TRUE, cols =cols )
p1b <- TrajectoryPlot(object = obj,trajectory ="Trajectory",
reduction = "dm",
continuousSet = "blueYellow",
size = 1,
addArrow = FALSE) +
xlab("DC_1") + ylab("DC_2") +
ggtitle("Trajectory")
plot(p1a) | plot(p1b)
```
Figure 4c Feature plots in DM
```{r,fig.keep='all', fig.show='hold'}
DefaultAssay(obj) <- 'RNA'
p2 <- list()
g <- c("ITGB3","PROM1","TPM1","PDZK1","SLC5A12","RXRA")
for (i in g){
p2[[i]] <- plot_density(obj,
features = i,
reduction = "dm",
pal = "magma")
plot(p2[[i]])
}
pdf("Fig4b_Fev13_2023.pdf",width = 15, height = 10)
plot(wrap_plots(p2,ncol = 3))
dev.off()
```
Figure 4a volcano plot DEGs aPTxPTS12
```{r}
date <- format(Sys.time(), "%a_%b_%d")
goal_gene <- unique(
c(
"ELF3",
"HNFA4",
"NFATC3",
"RREB1",
"PPRD",
"EGR1",
"ZEB1",
"MEF2A",
"FOXP2",
"SOX4",
"PROMO1",
"KLF6",
"ITGB3",
"KLF10",
"PROM1",
'LRP2',
'CUBN',
'SLC13A1',
'SLC5A12',
'PRODH2',
'SLC5A2',
'RXRA',
'SLC22A8',
'SLC13A3',
'SLC22A6',
'SLC34A1',
'SLC22A7',
'TPM1',
'VCAM1',
'CUBN',
'SLC13A1',
'SLC22A7',
'SLC7A13',
'SLC5A8',
'SATB2',
"MOGAT1",
'SLC13A1',
'SLC5A12',
'SLC3A1',
'SLC5A2',
"ABCC3",
'PDZK1'
)
)
data <- rio::import("data/MAP_aPTxPT_tripod_result.RDS")
df_1 <- data$DE_aPTxPTS12
pos <- 'aPT'
neg <- 'PT-S12'
color.df <-
rio::import_list("data/Epigenomic_paper_colors.xlsx")
color.df <- color.df$Sheet1
grey <- "#48494B"
color1 <- color.df[color.df$celltype == pos, 2]
color2 <- color.df[color.df$celltype == neg, 2]
colnames(df_1) <-
c("annot.symbol","p_val",
"log2FC",
"pct.1",
"pct.2",
"pvalue.t.test","UP_DE")
keyvals <- ifelse((df_1$log2FC < 0 &
df_1$pvalue.t.test < 0.05 & (df_1$pct.1>0.02 |df_1$pct.2>0.02 )),
color2,
ifelse((df_1$log2FC > 0 & df_1$pvalue.t.test < 0.05 & (df_1$pct.1>0.02 |df_1$pct.2>0.02 )), color1,
grey))
alphavp <- ifelse(
(
df_1$log2FC < 0 &
df_1$pvalue.t.test < 0.05 & df_1$annot.symbol %in% goal_gene & (df_1$pct.1>0.02 |df_1$pct.2>0.02 )
),
1,
ifelse((
df_1$log2FC > 0 &
df_1$pvalue.t.test < 0.05 & df_1$annot.symbol %in% goal_gene & (df_1$pct.1>0.02 |df_1$pct.2>0.02 )
),
1,
1 / 10
)
)
lab1 <- ifelse(
(
df_1$log2FC < 0 &
df_1$pvalue.t.test < 0.05 & (df_1$annot.symbol %in% goal_gene) & (df_1$pct.1>0.02 |df_1$pct.2>0.02 )
),
df_1$annot.symbol,
ifelse((
df_1$log2FC > 0 &
df_1$pvalue.t.test < 0.05 & df_1$annot.symbol %in% goal_gene & (df_1$pct.1>0.02 |df_1$pct.2>0.02 )
),
df_1$annot.symbol,
""
)
)
keyvals[is.na(keyvals)] <- grey
names(keyvals)[keyvals == color1] <- pos
names(keyvals)[keyvals == grey] <- 'p-value NS'
names(keyvals)[keyvals == color2] <- neg
df_1$keyvals <- keyvals
df_1$alphavp <- alphavp
df_1$lab1 <- lab1
p3 <- EnhancedVolcano:: EnhancedVolcano(
df_1,
lab = df_1$lab1,
labSize = 3.5,
labCol = 'black',
colAlpha = alphavp,
labFace = 'bold',
selectLab = goal_gene,
colCustom = keyvals,
boxedLabels = F,
drawConnectors = T,
colConnectors = 'grey50',
arrowheads = FALSE,
widthConnectors = .5,
min.segment.length = .001,
maxoverlapsConnectors = 100,
ylim = 0.00000000001,
pointSize = .8,
legendLabSize = 10,
legendIconSize = 3,
x = 'log2FC',
y = 'pvalue.t.test',
pCutoff = .05,
FCcutoff = 0,
title = paste0(neg, ' ', pos),
xlab = bquote( ~ Log[2] ~ 'FC'),
ylab = bquote( ~ -Log[10] ~ 'p-value'),
gridlines.major = FALSE,
gridlines.minor = FALSE,
lengthConnectors = unit(0.05, "npc"),
)
plot(p3)
```
Fig4E
```{r}
date <- format(Sys.time(), "%a_%b_%d")
# #data <- rio::import("../scMega/Dec_30/TRIPOD.MAR.13.2023_aPTxPT.RDS")
# data <- rio::import("../../R_paper/scMega/Dec_30/TRIPOD.MAR.13.2023_aPTxPT_v2.RDS")
```
```{r}
df <- rio::import(paste0(output,"aPTxPT_annotatr.xlsx"))
df <- df[df$DNAme.Hypo.Dip_Annot!="FALSE",]
df <- df[!is.na(df$ACT_ENH_REP),]
df$volcano.fig4E_peak_Annot_by_Author <- paste0(df$Gene_Annot_by_Author,"_",str_split(df$name.pk_Annot ,'[.]',simplify = T)[,3])
goal_gene <- c("ITGB3","PROM1","TPM1","PDZK1","SLC5A12","RXRA","PRODH2")
df$keyvals <- ifelse(df$ACT_ENH_REP=="ACT", "darkgreen",ifelse(df$ACT_ENH_REP=="ENH","blue",ifelse(df$ACT_ENH_REP=="REP", "red","grey")))
df$vals <- ifelse(df$ACT_ENH_REP=="ACT", 2,ifelse(df$ACT_ENH_REP=="ENH",1,ifelse(df$ACT_ENH_REP=="REP", 3,0)))
df <- df[order(df$vals),]
df$alphavp <- ifelse(((df$Gene_Annot_by_Author %in% goal_gene & df$keyvals!="grey" & df$pv.adj_DA<0.05) ), 1,1/10)
df$lab1 <- ifelse(df$Gene_Annot_by_Author %in% goal_gene & df$vals!=0 & df$pv.adj_DA<0.05,df$volcano.fig4E,"")
df_1 <- df
alphavpv <- df_1$alphavp
keyvals <- df_1$keyvals
keyvals[is.na(keyvals)] <- 'grey'
names(keyvals)[keyvals == "red"] <- "Repressed Pro"
names(keyvals)[keyvals == "darkgreen"] <- "Active Pro"
names(keyvals)[keyvals == "blue"] <- "Predicted Enh"
p5 <- EnhancedVolcano:: EnhancedVolcano(df_1,
lab = df_1$lab1,
labSize = 3.5, #3.5
labCol = 'black',
colAlpha = alphavpv,
labFace = 'bold',
#selectLab = genegoal,
colCustom = keyvals,
boxedLabels = F,
drawConnectors = T,
colConnectors = 'grey50',
arrowheads = FALSE,
widthConnectors = .4,
min.segment.length=.001,
maxoverlapsConnectors = 800000000,
directionConnectors = 'both',
#ylim = c(0,200),
xlim=c(-6,6),
pointSize = 1,
legendLabSize = 10,
legendIconSize = 1,
x = 'avg.log2FC_DA',
y = 'pv.adj_DA',
pCutoff = .05,
#pCutoff = .05,
FCcutoff = 0,
title="",
xlab = bquote(~Log[2]~ 'FC'),
ylab = bquote(~-Log[10]~'p-value'),
gridlines.major = FALSE,
gridlines.minor = FALSE,
lengthConnectors = unit(0.05, "npc")
)
plot(p5)
```
Fig4CDips DNAm
```{r}
df <- rio::import(paste0(output,"aPTxPT_annotatr.xlsx"))
df <- df[df$DNAme.Hypo.Dip_Annot!="FALSE",]
goal_gene <- c("ITGB3","PROM1","TPM1","PDZK1","SLC5A12","RXRA","PRODH2")
df$volcano.fig4C_peak_Annot_by_Author <- paste0(df$Gene_Annot_by_Author,"_",str_split(df$name.pk_Annot,'[.]',simplify = T)[,3])
color1 <- c("#467a3c")
color2 <- c("#EB7D28")
color3 <- "#BB001E"
grey <- "grey80" #48494B"
pos <- "aPT"
neg <- "PT-S12"
df$keyvals <- ifelse(df$DNAme.Hypo.Dip_Annot=="TRUE" & df$curated_Annot_by_Author=="promoter" & df$pv.adj_DA<0.05 , color3,ifelse(df$DNAme.Hypo.Dip_Annot=="TRUE" & df$curated_Annot_by_Author!="promoter" & df$pv.adj_DA<0.05, "blue",grey))
df$alphavp <- ifelse(df$DNAme.Hypo.Dip_Annot=="FALSE",1/10,ifelse((df$Gene_Annot_by_Author %in% goal_gene & df$DNAme.Hypo.Dip_Annot=="TRUE" & df$pv.adj_DA<0.05),1,1/10))
genegoal <- ifelse(df$DNAme.Hypo.Dip_Annot=="TRUE" & (df$Gene_Annot_by_Author %in% goal_gene)& df$pv.adj_DA<0.05 ,df$volcano.fig4C_peak_Annot_by_Author,"")
df$lab1 <-genegoal
dfa <- df[df$keyvals==grey,]
dfb <- df[df$keyvals!=grey & df$alphavp==1/10,]
dfb <- dfb[order(dfb$curated_Annot_by_Author,decreasing = F),]
dfc <- df[df$keyvals!=grey & df$alphavp==1,]
df_1 <-do.call("rbind", list(dfa, dfb, dfc))
keyvals <- df_1$keyvals
keyvals[is.na(keyvals)] <- grey
names(keyvals)[keyvals == color3] <- "DNAm Dip Promoter"
names(keyvals)[keyvals == "blue"] <- "DNAm Dip"
names(keyvals)[keyvals == grey] <- "No DNAm Dip"
alphavp <- df_1$alphavp
p4 <- EnhancedVolcano:: EnhancedVolcano(df_1,
lab = df_1$lab1,
labSize = 3.5,
labCol = 'black',
colAlpha = alphavp,
labFace = 'bold',
colCustom = keyvals,
boxedLabels = F,
drawConnectors = T,
colConnectors = 'grey50',
arrowheads = FALSE,
widthConnectors = .5,
min.segment.length=.001,
maxoverlapsConnectors = 500000,
directionConnectors = 'both',
#ylim = c(0,200),
xlim=c(-6,6),
pointSize = 1,
legendLabSize = 10,
legendIconSize = 1,
x = 'avg.log2FC_DA',
y = 'pv.adj_DA',
pCutoff = .05,
#pCutoff = .05,
FCcutoff = 0,
title="",
xlab = bquote(~Log[2]~ 'FC'),
ylab = bquote(~-Log[10]~'p-value'),
gridlines.major = FALSE,
gridlines.minor = FALSE,
lengthConnectors = unit(0.05, "npc")
)
plot(p4)
```
```{r}
df_1 <- rio::import(paste0(output,"Sup_Table_6_TRIPOD_aPTxPT_annotatr.xlsx"))
df_1$volcano.fig4G_Gene.link_TP <- paste0(df_1$Gene.link_TP,"_",str_split(df_1$name.pk_Annot,'[.]',simplify = T)[,3])
df_1 <- df_1[df_1$Gene.link_TP_DE=="UP_PT",]
df_1 <- df_1[,c(1,3,4,14:20,30,53)]
df_1 <- unique(df_1)
grey <- "#48494b"
cut1 <- 0.02
cutoff <- 0.05
looking <- 'down_aPTxup_PTS12'
#output <- './'
goal_gene <- unique(c("ITGB3","PROM1","TPM1","PDZK1","SLC5A12","RXRA","PRODH2",'LRP2','SLC5A12',"HNF4A"))
#grey all NO_NP and DA bigger in PT and adjpv>0.05
keyvals <- ifelse(
(df_1$pct.aPT_DA < cut1 & df_1$`pct.PT-S12_DA`> cut1 & df_1$NewPeak_DA =="NP" & df_1$avg.log2FC_DA < 0 & df_1$pv.adj_DA<cutoff), color2,grey)
df_1$keyvals <- keyvals
# alphavp <- ifelse(
# (df_1$pct.aPT_DA< cut1 & df_1$`pct.PT-S12_DA`> cut1 & df_1$avg.log2FC_DA< 0 & df_1$pv.adj_DA<cutoff & (df_1$Gene.link_TP %in% goal_gene)), 1,1/5) #df_1$pct.2 > cut2 & df_1$pct.1 < cut1 &
alphavp <- ifelse( df_1$keyvals == color2 & df_1$Gene.link_TP %in% goal_gene, 1,1/5)
df_1$alphavp <- alphavp
keyvals[is.na(keyvals)] <- grey
names(keyvals)[keyvals == color1] <- pos
names(keyvals)[keyvals == grey] <- ''
names(keyvals)[keyvals == grey] <-"DA"
names(keyvals)[keyvals == color2] <- paste0('DA and New Peaks')
df_1$lab1 <- ifelse((df_1$keyvals == color2 & df_1$Gene.link_TP %in% goal_gene),df_1$volcano.fig4G_Gene.link_TP,'')
#saveRDS(df_1,"../scMega/Dec_30/Fig4G_UPPT.RDS")
#write.csv(df_1, file=paste0(output,looking,"_volcano_FEV17_2023.csv"),row.names=FALSE)
# df_1$lab1 <- ifelse((df_1$pct.aPT_DA < cut1 &df_1$`pct.PT-S12_DA`>cut1 & df_1$Gene.link_TP %in% goal_gene & df_1$avg.log2FC_DA<0),df_1$volcano,'')
#dfff <- readRDS("../scMega/Dec_30/Fig4G_UPPT.RDS")
p2 <- EnhancedVolcano:: EnhancedVolcano(df_1,
lab = df_1$lab1,
labSize = 3.5, #3.5
labCol = 'black',
colAlpha = alphavp,
labFace = 'bold',
#selectLab = df_1$lab1,
colCustom = keyvals,
boxedLabels = F,
drawConnectors = T,
colConnectors = 'grey50',
arrowheads = FALSE,
widthConnectors = .2,
min.segment.length=.001,
maxoverlapsConnectors = 500000000,
# ylim = c(0.,100),
xlim=c(-4,4),
pointSize = .8,
legendLabSize = 10,
legendIconSize = 1,
x = 'avg.log2FC_DA',
y = 'pv.adj_DA',
pCutoff = .05,
#pCutoff = .05,
FCcutoff = 0,
title=paste0(neg,' ',pos,'pct.aPT<',cut1),
xlab = bquote(~Log[2]~ 'FC'),
ylab = bquote(~-Log[10]~'p-value'),
gridlines.major = FALSE,
gridlines.minor = FALSE,
lengthConnectors = unit(0.05, "npc"),
)
plot(p2)
```
aPT
```{r}
df_1 <- rio::import(paste0(output,"Sup_Table_6_TRIPOD_aPTxPT_annotatr.xlsx"))
df_1$volcano.fig4G_Gene.link_TP <- paste0(df_1$Gene.link_TP,"_",str_split(df_1$name.pk_Annot,'[.]',simplify = T)[,3])
df_1 <- df_1[df_1$Gene.link_TP_DE=="UP_aPT",]
df_1 <- df_1[,c(1,3,4,14:20,30,53)]
#df_1 <- df_1[,c(2,3,4,14:20,30,53)]
df_1 <- unique(df_1)
cut1 <- 0.02
cutoff <- 0.05
looking <- 'up_aPTxdown_PTS12'
#output <- './'
goal_gene <- c("ELF3","KLF6","ITGB3","KLF10","PROM1", 'TPM1', 'VCAM1')
keyvals <- ifelse(
( df_1$`pct.PT-S12_DA`< cut1 &df_1$pct.aPT_DA > cut1 & df_1$NewPeak_DA =="NP" & df_1$avg.log2FC_DA > 0 & df_1$pv.adj_DA<cutoff), color1,grey)
df_1$keyvals <- keyvals
alphavp <- ifelse( df_1$keyvals == color1 & (df_1$Gene.link_TP %in% goal_gene), 1,1/5)
df_1$alphavp <- alphavp
keyvals[is.na(keyvals)] <- grey
names(keyvals)[keyvals == color1] <- paste0('DA and New Peaks')
names(keyvals)[keyvals == grey] <- ''
names(keyvals)[keyvals == grey] <-"DA"
names(keyvals)[keyvals == color2] <- neg
df_1$lab1 <- ifelse((df_1$keyvals == color1 & df_1$Gene.link_TP %in% goal_gene),df_1$volcano.fig4G,'')
#write.csv(df_1, file=paste0(output,looking,"_volcano_FEV16_2023.csv"),row.names=FALSE)
#saveRDS(df_1,"../scMega/Dec_30/Fig4J_UPaPT.RDS")
#dfapt <- readRDS("../scMega/Dec_30/Fig4J_UPaPT.RDS")
p2 <- EnhancedVolcano:: EnhancedVolcano(df_1,
lab = df_1$lab1,
labSize = 3.5, #3.5
labCol = 'black',
colAlpha = alphavp,
labFace = 'bold',
#selectLab = df_1$lab1,
colCustom = keyvals,
boxedLabels = F,
drawConnectors = T,
colConnectors = 'grey50',
arrowheads = FALSE,
widthConnectors = .2,
min.segment.length=.001,
maxoverlapsConnectors = 500000000,
# ylim = c(0.,100),
xlim=c(-3,5),
pointSize = .8,
legendLabSize = 10,
legendIconSize = 1,
x = 'avg.log2FC_DA',
y = 'pv.adj_DA',
pCutoff = .05,
#pCutoff = .05,
FCcutoff = 0,
title=paste0(neg,' ',pos,'pct.aPT<',cut1),
xlab = bquote(~Log[2]~ 'FC'),
ylab = bquote(~-Log[10]~'p-value'),
gridlines.major = FALSE,
gridlines.minor = FALSE,
lengthConnectors = unit(0.05, "npc"),
)
pdf(paste0(output,looking,'volcano_NP_TRIPOD1_',date,'.pdf'),width = 4.5,height = 6)
plot(p2)
dev.off()
```