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10-figureS2.Rmd
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10-figureS2.Rmd
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---
title: "Figure S2 (Genome regulatory elements)"
author: "Tu Hu"
date: "06/07/2022"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Genome regulatory elements (Figure S2)
### Transcription factor enrichment (Figure S2a)
```{r gsea - regulatory elements}
# gene set regulatory
gsea_r_p <-
gsea_res_filt %>% filter(pathway %in% gs_r,
!contrast %>% str_detect("PSO"),
padj < .05,
!contrast %in% c("Adult_LSvsAdult_NN",
"Adult_NLvsAdult_NN",
"Adult_NLvsAdult_NN",
"Children_NLvsChildren_NN",
"Children_LSvsChildren_NN",
"Adult_LSvsChildren_LS",
"Adult_NLvsChildren_NL",
"LS/NN_derm",
"LS/NN_epi",
"LS/NN_FT",
"NL/NN_derm",
"NL/NN_FT")) %>%
mutate(project = ifelse(project == "multi-omics",
"GENAD",
project),
project = factor(project, levels = c("gse121212",
"acute_chronic",
"gse107361",
"gse120721",
"GENAD"))) %>%
ggplot(aes(contrast, des, size = `-log10p`, color = NES)) +
geom_point() +
scale_size_area(max_size = 6) +
scale_color_gradient2(low="blue", mid = "white", high="red") +
theme_bw() +
facet_grid(~project, scales = "free", space = "fixed") +
theme(axis.title = element_blank(), axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("data/supplementary/figure_s2_a.png", gsea_r_p,
width = 6.5,
height = 3.5,
dpi = 300)
```
```{r figure 5c boxplot - ncRNA}
toptopncRNA <-
vp_core_t %>%
filter(gene_name %>% str_detect("LINC"),
skin_type > .05) %>%
mutate(group = gene_name %>% str_extract("LINC")) %>%
group_by(group) %>%
slice_max(skin_type, n = 5) %>% pull(gene_name)
toptopncRNA_boxplot <-
se[toptopncRNA,] %>% as_tibble() %>%
mutate(feature = feature %>% forcats::fct_relevel(toptopncRNA)) %>%
ggboxplot(x = "skin_type", y = "counts_scaled", add = "jitter",
facet.by = "feature", xlab = FALSE,
ylab = FALSE, color = "skin_type",
palette = pca_color, legend.title = "",
font.legend = c(14),
nrow = 1,
yscale = "log10")
ggsave("data/supplementary/figure_s2_b.png",
toptopncRNA_boxplot,
width = 6.5,
height = 3.5,
dpi = 300)
```
#### LINC
```{r}
LINC_g <-
de_v1_c %>% pull(gene_name) %>%
grep("LINC", ., value = T) %>% unique
MIR_g <-
de_v1_c %>% pull(gene_name) %>%
grep("MIR", ., value = T) %>% unique
```
```{r heatmap-linc}
se_linc <- se[LINC_g,]
heatmap_linc_data <-
se_linc
heatmap_linc_data_m <-
scale(assay(heatmap_linc_data, 2) %>% t() %>% log1p(),
center = TRUE, scale = TRUE) %>% t()
sample_site_color <-
colData(heatmap_linc_data) %>% as_tibble() %>% pull(biopsy_area) %>% unique()
color <- RColorBrewer::brewer.pal(length(sample_site_color), "Pastel2")
names(color) <- sample_site_color
heatmap_linc_annotation <-
HeatmapAnnotation(
`Tissue type` = colData(heatmap_linc_data) %>% as_tibble() %>% pull(skin_type),
`Anatomic region` = colData(heatmap_linc_data) %>% as_tibble() %>% pull(biopsy_area),
col = list(`Tissue type` = pca_color,
`Anatomic region` = color),
annotation_legend_param = list(`Tissue type` = list(nrow = 1,
title_gp = gpar(fontsize = 16),
labels_gp = gpar(fontsize = 16)),
`Anatomic region` = list(nrow = 1,
title_gp = gpar(fontsize = 16),
labels_gp = gpar(fontsize = 16))
))
library(dendsort)
col_dend <- heatmap_linc_data_m %>% t() %>% dist() %>% hclust
col_dend_sort <- dendsort(col_dend)
heatmap_linc <- ComplexHeatmap::Heatmap(heatmap_linc_data_m,
name = "gene expression",
column_dend_reorder = TRUE,
row_dend_reorder = TRUE,
show_column_names = FALSE,
show_row_names = T,
show_row_dend = FALSE,
# cluster_columns = col_dend_sort,
row_names_gp = gpar(fontsize = min(8, 800 * 6 /dim(heatmap_linc_data)[1])),
top_annotation = heatmap_linc_annotation,
heatmap_legend_param = list(
title_gp = gpar(fontsize = 16),
labels_gp = gpar(fontsize = 14)
))
draw(heatmap_linc, heatmap_legend_side = "right",
annotation_legend_side = "top", merge_legend = FALSE)
```
```{r}
png("data/supplementary/figure_s5.png",
width = 480*6, height = 480*11, res=300)
draw(heatmap_linc, heatmap_legend_side = "right",
annotation_legend_side = "top", merge_legend = FALSE)
dev.off()
```
```{r heatmap-mir}
se_mir <- se[MIR_g,]
heatmap_mir_data <-
se_mir
heatmap_mir_data_m <-
scale(assay(heatmap_mir_data, 2) %>% t() %>% log1p(),
center = TRUE, scale = TRUE) %>% t()
sample_site_color <-
colData(heatmap_mir_data) %>% as_tibble() %>% pull(biopsy_area) %>% unique()
color <- RColorBrewer::brewer.pal(length(sample_site_color), "Pastel2")
names(color) <- sample_site_color
heatmap_mir_annotation <-
HeatmapAnnotation(
`Tissue type` = colData(heatmap_mir_data) %>% as_tibble() %>% pull(skin_type),
`Anatomic region` = colData(heatmap_mir_data) %>% as_tibble() %>% pull(biopsy_area),
col = list(`Tissue type` = pca_color,
`Anatomic region` = color),
# simple_anno_size = unit(1, "cm"),
annotation_legend_param = list(`Tissue type` = list(nrow = 1,
# grid_height = unit(2, "cm"),
# grid_width = unit(1, "cm"),
title_gp = gpar(fontsize = 16),
labels_gp = gpar(fontsize = 16)),
`Anatomic region` = list(nrow = 1,
# grid_height = unit(2, "cm"),
# grid_width = unit(1, "cm"),
title_gp = gpar(fontsize = 16),
labels_gp = gpar(fontsize = 16))
))
heatmap_mir <- ComplexHeatmap::Heatmap(heatmap_mir_data_m,
name = "gene expression",
# column_dend_reorder = TRUE,
# row_dend_reorder = TRUE,
# clustering_method_columns = "complete",
cluster_columns = heatmap_mir_data_m %>% t() %>% dist() %>% hclust() %>% dendsort(),
cluster_rows = heatmap_mir_data_m %>% dist() %>% hclust() %>% dendsort(),
show_column_names = FALSE,
show_row_names = T,
show_row_dend = FALSE,
row_names_gp = gpar(fontsize = 10),
top_annotation = heatmap_mir_annotation,
heatmap_legend_param = list(
title_gp = gpar(fontsize = 16),
labels_gp = gpar(fontsize = 14)
))
draw(heatmap_mir, heatmap_legend_side = "right",
annotation_legend_side = "top", merge_legend = FALSE)
```
```{r save heatmap}
png("data/supplementary/figure_s2_c.png",
width = 480*6.5,
height = 480*4,
res=300)
draw(heatmap_mir, heatmap_legend_side = "right",
annotation_legend_side = "top", merge_legend = FALSE)
dev.off()
```
```{r MIR31 and CLDN1}
MIR31_CLDN1 <-
se[c("MIR31HG", "CLDN1"),] %>% as_tibble() %>%
filter(skin_type == "LS") %>%
select(feature, sample, counts_scaled, skin_type) %>% pivot_wider(names_from = feature, values_from = counts_scaled) %>%
ggscatter(x = "CLDN1",
y = "MIR31HG",
ylab = "MIR31HG",
xscale = "log10",
add = "reg.line",
) +
stat_cor()
ggsave("data/supplementary/figure_s2_d.png",
MIR31_CLDN1, width = 7, height = 5, dpi = 300)
```