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explore_scenic_epithelial_V2.R
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explore_scenic_epithelial_V2.R
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library(SCopeLoomR)
library(dplyr)
library(ggrepel)
library(RColorBrewer)
library(DT)
# load data
scenicLoomPath = "./Epithelial.pyscenic_output.updated.loom"
loom <- open_loom(scenicLoomPath)
regulons_incidMat <- get_regulons(loom, column.attr.name="Regulons")
regulonAUC <- get_regulons_AUC(loom, column.attr.name = "RegulonsAUC")
cell_annotation = get_cell_annotation(loom)
loom$close()
# load meta_data
meta_data = readRDS("all.clean.meta.rds")
meta_data.myeloid = meta_data[cell_annotation %>% rownames, ]
cell_annotation = cbind(cell_annotation, meta_data.myeloid)
cell_annotation$Label = NULL
cell_annotation$Percent_mito = NULL
cell_annotation$nGene = NULL
cell_annotation$nUMI = NULL
# compare regulon between healthy and UC
# Extended Data Figure 10e
if(T){
g1_cells = cell_annotation %>%
filter(stage == "adult") %>%
filter(disease == "UC_inflamed") %>% rownames
g1_cells %>% length # 19339
g2_cells = cell_annotation %>%
filter(stage == "adult") %>%
filter(disease == "Healthy") %>% rownames
g2_cells %>% length #
# select cells
group <- list(g1_cells = g1_cells, g2_cells = g2_cells)
regulonActivity_mean_byDisease = sapply(group,
function(cells) rowMeans(getAUC(regulonAUC)[,cells]))
tfs = rownames(regulonAUC)
# get result
result <- sapply(tfs, function(tf){
t_test_res = t.test( getAUC(regulonAUC)[tf, g1_cells] %>% as.vector(),
getAUC(regulonAUC)[tf, g2_cells] %>% as.vector())
res = c(t_test_res$estimate[1], t_test_res$estimate[2],
t_test_res$estimate[1]/(t_test_res$estimate[2]), t_test_res$p.value)
names(res) <- c("g1_avg","g2_avg","fold_change","p_value")
# return result
return(res)
}) %>% t %>% as.data.frame
# remove NaN, the mean value of two group are both 0
#result = result[!is.nan(result$p_value),]
# adjust p-value and round
result$p.adjust = p.adjust(result$p_value, "BH")
result$log2FoldChange = log2(result$fold_change)
#result$log2FoldChange = round(result$log2FoldChange, 6)
#result$g1_avg <- round(result$g1_avg, 6)
#result$g2_avg <- round(result$g2_avg, 6)
# write to file
colnames(result) = c("UC_inflamed", "Healthy", "FoldChange", "p-value", "p.adjust", "log2FoldChange")
#write.table(result, file = "Epithelial.diff_regulon.UC_vs_Healthy.txt",
# sep = "\t", quote = FALSE, row.names = TRUE, col.names = TRUE)
# set p.adjust to non-zero value
#result[result$p.adjust == 0,]$p.adjust = runif(sum(result$p.adjust == 0), min = 0.000001, max = 0.00001)
# set color
fc_cutoff = 1.25
result$Diff <- "NO"
result$Diff[result$log2FoldChange > log2(fc_cutoff) &
result$p.adjust < 0.01] <- "UP"
result$Diff[result$log2FoldChange < -1*log2(fc_cutoff) &
result$p.adjust < 0.01] <- "DOWN"
result$delabel <- NA
result$delabel[result$Diff != "NO"] <- rownames(result)[result$Diff != "NO"]
mycolors <- c("#E41A1C", "#377EB8", "grey50")
names(mycolors) <- c("UP", "DOWN", "NO")
# limit log2foldchange
result$x = result$log2FoldChange
thres_pos = 2
if( sum(result$x > thres_pos) > 0){
result[result$log2FoldChange > thres_pos, ]$x =
thres_pos + ( result[result$log2FoldChange > thres_pos, ]$log2FoldChange - thres_pos)/10
}
thres_neg = -1*thres_pos
if(sum(result$log2FoldChange < thres_neg) > 0){
result[result$log2FoldChange < thres_neg, ]$x =
thres_neg + (result[result$log2FoldChange < thres_neg, ]$log2FoldChange - thres_neg)/10
}
# In case of log2FoldChange equal to Inf or -Inf
if(sum(result$log2FoldChange == -Inf) > 0){
result[result$log2FoldChange == -Inf, ]$x = runif(sum(result$log2FoldChange == -Inf), -3, -2)
}
if(sum(result$log2FoldChange == Inf) > 0){
result[result$log2FoldChange == -Inf, ]$x = runif(sum(result$log2FoldChange == -Inf), 2, 3)
}
# limit -log10(adjusted p-value)
result$y = -log10(result$p.adjust)
result$y2 = result$y
y_cutoff = 5
if( sum(result$y > y_cutoff) > 0){
result[result$y > y_cutoff, ]$y2 =
y_cutoff + ( result[result$y > y_cutoff, ]$y - y_cutoff )/100
}
result[result$y == Inf, ]$y2 = runif(sum(result$y == Inf), 8, 10)
ggplot(data=result, aes(x=x, y=y2, col=Diff, label=delabel)) +
geom_point(size = 1, alpha = 0.5) +
xlim(-3,3) +
ylim(0, 10) +
scale_color_manual(values=mycolors)+
geom_text_repel(max.overlaps = 10, label.size = 0.1) +
geom_vline(xintercept=c(-log2(fc_cutoff), log2(fc_cutoff)), col="black", linetype="dotted") +
geom_hline(yintercept=2, col="black", linetype="dotted") +
theme_classic() +
xlab("log2FoldChange") +
ylab("-log10(adjustd p-value") +
ggtitle('Epithelial') +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5))
ggsave(filename = 'Epithelial.regulon_diff.UC_VS_healthy.pdf', width = 8, height = 8, dpi = 600)
}
# compare regulon between healthy and CD
# Extended Data Figure 10f
if(T){
g1_cells = cell_annotation %>%
filter(stage == "adult") %>%
filter(disease == "CD_inflamed") %>% rownames
g1_cells %>% length # 19339
g2_cells = cell_annotation %>%
filter(stage == "adult") %>%
filter(disease == "Healthy") %>% rownames
g2_cells %>% length #
# select cells
group <- list(g1_cells = g1_cells, g2_cells = g2_cells)
regulonActivity_mean_byDisease = sapply(group,
function(cells) rowMeans(getAUC(regulonAUC)[,cells]))
tfs = rownames(regulonAUC)
# get result
result <- sapply(tfs, function(tf){
t_test_res = t.test( getAUC(regulonAUC)[tf, g1_cells] %>% as.vector(),
getAUC(regulonAUC)[tf, g2_cells] %>% as.vector())
res = c(t_test_res$estimate[1], t_test_res$estimate[2],
t_test_res$estimate[1]/(t_test_res$estimate[2]), t_test_res$p.value)
names(res) <- c("g1_avg","g2_avg","fold_change","p_value")
# return result
return(res)
}) %>% t %>% as.data.frame
# remove NaN, the mean value of two group are both 0
#result = result[!is.nan(result$p_value),]
# adjust p-value and round
result$p.adjust = p.adjust(result$p_value, "BH")
result$log2FoldChange = log2(result$fold_change)
#result$log2FoldChange = round(result$log2FoldChange, 6)
#result$g1_avg <- round(result$g1_avg, 6)
#result$g2_avg <- round(result$g2_avg, 6)
# write to file
colnames(result) = c("CD_inflamed", "Healthy", "FoldChange", "p-value", "p.adjust", "log2FoldChange")
#write.table(result, file = "Epithelial.diff_regulon.UC_vs_Healthy.txt",
# sep = "\t", quote = FALSE, row.names = TRUE, col.names = TRUE)
# set p.adjust to non-zero value
#result[result$p.adjust == 0,]$p.adjust = runif(sum(result$p.adjust == 0), min = 0.000001, max = 0.00001)
# set color
fc_cutoff = 1.25
result$Diff <- "NO"
result$Diff[result$log2FoldChange > log2(fc_cutoff) &
result$p.adjust < 0.01] <- "UP"
result$Diff[result$log2FoldChange < -1*log2(fc_cutoff) &
result$p.adjust < 0.01] <- "DOWN"
result$delabel <- NA
result$delabel[result$Diff != "NO"] <- rownames(result)[result$Diff != "NO"]
mycolors <- c("#E41A1C", "#377EB8", "grey50")
names(mycolors) <- c("UP", "DOWN", "NO")
# limit log2foldchange
result$x = result$log2FoldChange
thres_pos = 2
if( sum(result$x > thres_pos) > 0){
result[result$log2FoldChange > thres_pos, ]$x =
thres_pos + ( result[result$log2FoldChange > thres_pos, ]$log2FoldChange - thres_pos)/10
}
thres_neg = -1*thres_pos
if(sum(result$log2FoldChange < thres_neg) > 0){
result[result$log2FoldChange < thres_neg, ]$x =
thres_neg + (result[result$log2FoldChange < thres_neg, ]$log2FoldChange - thres_neg)/10
}
# In case of log2FoldChange equal to Inf or -Inf
if(sum(result$log2FoldChange == -Inf) > 0){
result[result$log2FoldChange == -Inf, ]$x = runif(sum(result$log2FoldChange == -Inf), -3, -2)
}
if(sum(result$log2FoldChange == Inf) > 0){
result[result$log2FoldChange == -Inf, ]$x = runif(sum(result$log2FoldChange == -Inf), 2, 3)
}
# limit -log10(adjusted p-value)
result$y = -log10(result$p.adjust)
result$y2 = result$y
y_cutoff = 5
if( sum(result$y > y_cutoff) > 0){
result[result$y > y_cutoff, ]$y2 =
y_cutoff + ( result[result$y > y_cutoff, ]$y - y_cutoff )/100
}
result[result$y == Inf, ]$y2 = runif(sum(result$y == Inf), 8, 10)
ggplot(data=result, aes(x=x, y=y2, col=Diff, label=delabel)) +
geom_point(size = 1, alpha = 0.5) +
xlim(-3,3) +
ylim(0, 10) +
scale_color_manual(values=mycolors)+
geom_text_repel(max.overlaps = 15, label.size = 0.1) +
geom_vline(xintercept=c(-log2(fc_cutoff), log2(fc_cutoff)), col="black", linetype="dotted") +
geom_hline(yintercept=2, col="black", linetype="dotted") +
theme_classic() +
xlab("log2FoldChange") +
ylab("-log10(adjustd p-value") +
ggtitle('Epithelial') +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5))
ggsave(filename = 'Epithelial.regulon_diff.CD_VS_healthy.pdf', width = 8, height = 8, dpi = 600)
}
# compare regulon between healthy and CD
if(F){
g1_cells = cell_annotation %>%
filter(stage == "adult") %>%
filter(disease == "CD_inflamed") %>% rownames
g1_cells %>% length # 1123
g2_cells = cell_annotation %>%
filter(stage == "adult") %>%
filter(disease == "Healthy") %>% rownames
g2_cells %>% length # 63411
# select cells
group <- list(g1_cells = g1_cells, g2_cells = g2_cells)
regulonActivity_mean_byDisease = sapply(group,
function(cells) rowMeans(getAUC(regulonAUC)[,cells]))
tfs = rownames(regulonAUC)
# get result
result <- sapply(tfs, function(tf){
t_test_res = t.test( getAUC(regulonAUC)[tf,g1_cells] %>% as.vector(),
getAUC(regulonAUC)[tf,g2_cells] %>% as.vector())
res = c(t_test_res$estimate[1], t_test_res$estimate[2],
t_test_res$estimate[1]/(t_test_res$estimate[2]+0.001), t_test_res$p.value)
names(res) <- c("g1_avg","g2_avg","fold_change","p_value")
# return result
return(res)
}) %>% t %>% as.data.frame
# remove NaN, the mean value of two group are both 0
result = result[!is.nan(result$p_value),]
# remove regulons with too low activity
result = result[ result$fold_change != 0, ]
# adjust p-value and round
result$p.adjust = round(p.adjust(result$p_value, "BH"), 6)
result$log2FoldChange = log2(result$fold_change)
result$log2FoldChange = round(result$log2FoldChange, 6)
result$g1_avg <- round(result$g1_avg, 6)
result$g2_avg <- round(result$g2_avg, 6)
# write to file
colnames(result) = c("UC_inflamed", "Healthy", "FoldChange", "p-value", "p.adjust", "log2FoldChange")
#write.table(result, file = "Epithelial.diff_regulon.CD_vs_Healthy.txt",
# sep = "\t", quote = FALSE, row.names = TRUE, col.names = TRUE)
# set p.adjust to non-zero value
result[result$p.adjust == 0,]$p.adjust = runif(sum(result$p.adjust == 0),
min = 0.000001, max = 0.00001)
# limit log2foldchange
thres_pos = 1.2
if( sum(result$log2FoldChange > thres_pos) > 0){
result[result$log2FoldChange > thres_pos, ]$log2FoldChange =
thres_pos + ( result[result$log2FoldChange > thres_pos, ]$log2FoldChange - thres_pos)/10
}
thres_neg = -1*thres_pos
if(sum(result$log2FoldChange < thres_neg) > 0){
result[result$log2FoldChange < thres_neg, ]$log2FoldChange =
thres_neg + (result[result$log2FoldChange < thres_neg, ]$log2FoldChange - thres_neg)/10
}
result$Diff <- "NO"
result$Diff[result$log2FoldChange > log2(thres_pos) &
result$p.adjust < 0.01] <- "UP"
result$Diff[result$log2FoldChange < -log2(thres_pos) &
result$p.adjust < 0.01] <- "DOWN"
result$delabel <- NA
#result[rownames(result) == 'PITX1',"delabel"] = 'PITX1'
result$delabel[result$Diff != "NO"] <- rownames(result)[result$Diff != "NO"]
mycolors <- c("#E41A1C", "#377EB8", "grey50")
names(mycolors) <- c("UP", "DOWN", "NO")
ggplot(data=result, aes(x=log2FoldChange, y=-log10(p.adjust), col=Diff, label=delabel)) +
geom_point(size = 1, alpha = 0.5) +
xlim(-3,3) +
ylim(0, max(-log10(result$p.adjust))) +
scale_color_manual(values=mycolors)+
geom_text_repel(max.overlaps = 20, label.size = 0.1) +
geom_vline(xintercept=c(-1*log2(thres_pos), log2(thres_pos)), col="black", linetype="dotted") +
geom_hline(yintercept=2, col="black", linetype="dotted") +
theme_classic() +
ggtitle('Epithelial') +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5))
outfile = paste0("Epithelial.CD_VS_Healthy.regulon_diff.pdf")
ggsave(filename = outfile, width = 5, height = 5, dpi = 600)
}