Ingenuity Pathway Analysis
Ha M. Tran
24/12/2021
Last updated: 2023-01-22
Checks: 7 0
Knit directory: SRB_2022/1_analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | eb10bef | Ha Manh Tran | 2023-01-22 | workflowr::wflow_publish(here::here("1_analysis/*.Rmd")) |
| Rmd | 8c9178d | tranmanhha135 | 2023-01-21 | added png |
| html | 8c9178d | tranmanhha135 | 2023-01-21 | added png |
| html | d578f46 | Ha Manh Tran | 2023-01-21 | Build site. |
| Rmd | 01a61cb | Ha Manh Tran | 2023-01-21 | workflowr::wflow_publish(here::here("1_analysis/*.Rmd")) |
| Rmd | 159f352 | tranmanhha135 | 2023-01-21 | adding for pathview |
| html | 159f352 | tranmanhha135 | 2023-01-21 | adding for pathview |
| html | 4d51a4e | tranmanhha135 | 2023-01-20 | test png |
| html | 691cf34 | Ha Manh Tran | 2023-01-20 | Build site. |
| Rmd | 3119fad | tranmanhha135 | 2022-11-05 | build website |
| html | 3119fad | tranmanhha135 | 2022-11-05 | build website |
Data Setup
# working with data
library(dplyr)
library(magrittr)
library(readr)
library(tibble)
library(reshape2)
library(tidyverse)
# Visualisation:
library(kableExtra)
library(ggplot2)
library(grid)
library(pander)
library(cowplot)
library(pheatmap)
library(viridis)
library(igraph)
library(ggalluvial)
# Custom ggplot
library(ggplotify)
library(ggbiplot)
library(ggrepel)
theme_set(theme_minimal())
pub <- readRDS(here::here("0_data/RDS_objects/pub.rds"))
palette <- readRDS(here::here("0_data/RDS_objects/palette.rds"))IPA analysis
Regulated Pathways
pathways <- read_csv(file = here::here("0_data/raw_data/IPA_pathways.csv"), col_names = T) %>% slice(1:14) %>% as.data.frame()
colnames(pathways) <- c("name", "logPval", "pval", "ratio", "zScore", "molecules")
# at the beginnning of a word (after 35 characters), add a newline. shorten the y axis for dot plot
pathways$name <- sub(
pattern = "(.{1,40})(?:$| )",
replacement = "\\1\n",
x = pathways$name
)
# remove the additional newline at the end of the string
pathways$name <- sub(
pattern = "\n$",
replacement = "",
x = pathways$name
)
pathways <- ggplot(data = pathways) +
geom_point(aes(
x = ratio,
y = reorder(name, logPval),
color = zScore,
size = logPval)) +
scale_size(range = c(2, 7)) +
scale_color_gradient(low = "dodgerblue3", high = "firebrick3", limits=c(0, NA)) +
ggtitle("Regulated Pathways") +
xlab(label = "Gene Ratio") +
ylab(label = "") +
labs(color = "Z-Score",
size = expression("-log"[10] * "P-value")) +
scale_x_continuous(expand = c(0,0.007))
# theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
ggsave(filename = "pathways.svg", plot = pathways, path = here::here("2_plots/ipa"), width = 250, height = 166, units = "mm")
pathways
Upstream Regulators
upstream <-
read_csv(file = here::here("0_data/raw_data/IPA_upstreamRegulators.csv"),
col_names = T,
skip = 1) %>% as.data.frame()
# colnames(upstream) <- c("regulator", "logRatio", "molecule", "activationState", "zScore", "flags", "pvalOverlap", "targetMolecule")
# upstream <- column_to_rownames(.data = upstream,var = "Upstream Regulator")
upstream <-
dplyr::filter(
.data = upstream,
`Molecule Type` == "enzyme" |
`Molecule Type` == "growth factor" |
`Molecule Type` == "cytokine" |
`Molecule Type` == "chemical - endogenous mammalian"
)
heatMatrix <-
upstream %>% select(c("Upstream Regulator", "Activation z-score")) %>% column_to_rownames("Upstream Regulator")
# %>% pivot_wider(names_from = `Upstream Regulator`, values_from = `Activation z-score`)
# my_palette <- colorRampPalette(c("dodgerblue3", "white", "firebrick3"))(n = 201)
# my_palette <- viridis_pal(option = "viridis")(300)
# df for heatmap annotation of sample group
anno <-
dplyr::select(.data = upstream, c(`Upstream Regulator`, `Molecule Type`))
# anno %>% column_to_rownames("Upstream Regulator")
anno$`Molecule Type` <- str_to_title(anno$`Molecule Type`)
anno$`Molecule Type` <- as.factor(anno$`Molecule Type`)
anno <- column_to_rownames(.data = anno, var = "Upstream Regulator")
anno_colours <- c("#d7191c", "#fdae61", "#abd9e9", "#2c7bb6")
names(anno_colours) <- levels(anno$`Molecule Type`)
upstream <- pheatmap(
mat = heatMatrix,
cluster_rows = F,
cluster_cols = F,
show_colnames = F,
show_rownames = T,
legend = T,
annotation_legend = T,
annotation_row = anno,
annotation_names_row = F,
annotation_colors = list("Molecule Type" = anno_colours),
annotation_names_col = F,
# annotation = F,
color = palette,
fontsize = 8,
fontsize_col = 6,
fontsize_number = 5 ,
fontsize_row = 8,
legend_breaks = c(seq(-3, 11, by = 1)),
legend_labels = c(seq(-3, 11, by = 1)),
border_color = "grey85",
angle_col = 90,
gaps_row = c(8, 13, 16)
) %>% as.ggplot() # upstream <- upstream + theme(legend.box.margin = margin(0,0,-150,0))
ggsave(filename = "upstream_2.svg", plot = upstream, path = here::here("2_plots/ipa"), width = 200, height = 133, units = "mm")upstream
Disease and Function
categories <- c("Cellular Movement", "Cardiovascular System", "Cell-To-Cell Signaling")
tittle <- c("Cellular Movement", "Cardiovascular System Development and Function", "Cell-to-Cell Signaling and Interaction")
disease_function <- read_csv(file = here::here("0_data/raw_data/IPA_diseaseAndFunction.csv"), col_names = T, skip = 1)
disease_function <- drop_na(data = disease_function, "Predicted Activation State")
# disease_function <- dplyr::filter(disease_function, grepl(c(categories), x = disease_function$Categories))
funct=list()
funct_bar=list()
for (i in 1:length(categories)) {
x <- categories[i] %>% as.character()
funct[[x]] <- dplyr::filter(.data = disease_function, grepl(categories[i], x = disease_function$Categories))
# at the beginnning of a word (after 35 characters), add a newline. shorten the y axis for dot plot
funct[[x]]$`Diseases or Functions Annotation` <- sub(
pattern = "(.{1,40})(?:$| )",
replacement = "\\1\n",
x = funct[[x]]$`Diseases or Functions Annotation`
)
# remove the additional newline at the end of the string
funct[[x]]$`Diseases or Functions Annotation` <- sub(
pattern = "\n$",
replacement = "",
x = funct[[x]]$`Diseases or Functions Annotation`
)
funct_bar[[x]] <- ggplot(data = funct[[x]]) +
geom_point(aes(
x = `# Molecules`,
y = reorder(`Diseases or Functions Annotation`, desc(`p-value`)),
colour = `Activation z-score`,
size = -log(`p-value`, 10))) +
scale_size(range = c(2, 7)) +
scale_color_gradient(low = "dodgerblue3", high = "firebrick3", limits=c(0, NA)) +
ggtitle(tittle[i]) +
xlab(label = "Count") +
ylab(label = "") +
labs(colour = "Z-score",
size = expression("-log"[10] * "P-value")) +
scale_x_continuous(expand = c(0,5))
ggsave(filename = paste0(x, ".svg"), plot = funct_bar[[i]], path = here::here("2_plots/ipa"), width = 250, height = 166, units = "mm")
}
funct <- do.call(rbind, lapply(funct, as.data.frame)) %>% dplyr::select(-Categories) %>% rownames_to_column("Categories")
funct$Categories <- gsub(pattern = "\\..*", "", funct$Categories) %>% as.factor()
funct_dot <- ggplot(funct) +
geom_point(aes(
x = `# Molecules`,
y = reorder(`Diseases or Functions Annotation`, desc(`p-value`)),
colour = `Activation z-score`,
size = -log(`p-value`, 10),
shape = `Categories`
)) +
facet_grid(vars(`Categories`), scales = "free_y", shrink = T) +
scale_color_gradient(low = "dodgerblue3", high = "firebrick3", limits=c(0,NA)) +
xlab(label = "Count") + ylab("") +
labs(colour = "Z-score",
size = expression("-log"[10] * "p-value"),
shape = "Categories") +
scale_x_continuous(expand = c (0,10)) +
scale_size(range = c(2,5))
# funct_dot <- funct_dot +
# theme(
# panel.background = element_rect(fill='transparent'), #transparent panel bg
# plot.background = element_rect(fill='transparent', color=NA), #transparent plot bg
# # panel.grid.major = element_blank(), #remove major gridlines
# # panel.grid.minor = element_blank(), #remove minor gridlines
# legend.background = element_rect(fill='transparent'), #transparent legend bg
# legend.box.background = element_rect(fill='transparent', color=NA) #transparent legend panel
# )
funct_dot
ggsave(filename = "diseaseAndFunction.svg", plot = funct_dot + theme_bw(), path = here::here("2_plots/ipa"), width = 200, height = 300, units = "mm")upstream_filtered <- subset(upstream[c(1,3,19,21,2,18,10,13,11),])
upstream_filtered <- upstream_filtered[order(upstream_filtered$`Molecule Type`),]
test <- separate_rows(data = upstream_filtered, `Target Molecules in Dataset`, sep = ",")
colnames(test)[c(1,8,3)] <- c("name", "molecule", "type")
pathways_filtered <- subset(pathways[c(11,4, 6, 10, 7, 8),])
test1 <- separate_rows(data = pathways_filtered, molecules, sep = ",")
test1[,7] <- "enriched pathways"
colnames(test1)[c(1,6,7)] <- c("name", "molecule", "type" )
funct_filtered <- subset(funct[c(2,3,5,6,7,9,10:21),])
test2 <- separate_rows(data = funct, Molecules, sep = ",")
colnames(test2)[c(2,6,1)] <- c("name", "molecule", "type")
# test_com <- do.call(rbind, lapply(list(test[, c(1, 8, 3)],
# test1[, c(1, 6, 7)]), as.data.frame))
# write.csv(test_com, here::here("C:\\Users/tranm/Desktop/test_com.csv"))
#
# testGraph <- graph.data.frame(test_com, directed = T)
# # testReverse <- as_data_frame(testGraph)
# # E(testGraph)$color <- 'grey'
# # V(testGraph)$color <- 'grey'
# summary(testGraph)
# write_graph(simplify(testGraph), "C:\\Users/tranm/Desktop/testGraph.gml", format = "gml")
# tkplot(testGraph)
merged <- list()
for (i in 1:length(upstream_filtered$`Upstream Regulator`)) {
x <- upstream_filtered$`Upstream Regulator`[i]
for (j in 1:length(funct_filtered$`Diseases or Functions Annotation`)) {
y <- paste0("funct",j)
merged[[x]][[y]] <- length(intersect(unlist(
strsplit(upstream_filtered$`Target Molecules in Dataset`[i], split = ",")
), unlist(strsplit(funct_filtered$Molecules[j], split = ","))))
}
merged[[x]] <- do.call(rbind, lapply(merged[[x]], as.data.frame)) %>% remove_rownames()
merged[[x]][, c( "funct", "funct_cat")] <-
c(funct_filtered$`Diseases or Functions Annotation`,
funct_filtered$Categories %>% as.character()
)
print(i)
}
merged <- do.call(rbind, lapply(merged, as.data.frame)) %>% rownames_to_column("upstream")
merged$upstream <- gsub(pattern = "\\..*", "",merged$upstream) %>% as.factor()
merged$funct_cat <- as.factor(merged$funct_cat)
colnames(merged) <- c("upstream","intersect","funct","funct_cat")
levels(merged$upstream) <- c("beta-estradiol","progesterone","prostaglandin E2","IL1B","IL6","TNF","EGF","VEGFA","BMP2")
####THIS is really weird
# merged$upstream <- gsub(pattern = "protagladin E2",replacement = "prostaglandin E2", merged$upstream)
merged$up_cat <- upstream_filtered$`Molecule Type`[match(merged$upstream, upstream_filtered$`Upstream Regulator`)]
merged$funct <- factor(merged$funct, levels = unique(merged$funct[order(merged$funct_cat)]))
is_alluvia_form(as.data.frame(merged), silent = T)
ggplot(
as.data.frame(merged),
aes(
y = intersect,
# axis1 = up_cat,
axis2 = upstream,
axis3 = funct
# axis4 = funct_cat
)
) +
geom_alluvium(
aes(fill = upstream),
alpha = 0.5,
width = 1 / 250,
curve_type = "quintic"
) +
geom_stratum(fill = "#193e3f",
width = 1 / 35,
color = "#fffaf2") +
# geom_flow() +
geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
scale_x_discrete(
limits =
c(
# "Molecule Type",
"Upstream Regulator",
"Disease and Function"
# "Category"
),
expand = c(.05, .05)
) +
scale_fill_brewer(type = "qual", palette = "Set1") +
theme_void() +
theme(legend.position = "none")
# ylab(" ")
ggsave(filename = "upstream_funct_alluvial.svg",path = here::here("2_plots/ipa/"), width = 450, height = 800, units = "mm")
ggplot(
as.data.frame(merged),
aes(
y = intersect,
axis1 = funct,
axis2 = funct_cat
# axis3 = funct_cat
)
) +
geom_alluvium(aes(fill = funct_cat), alpha = 0.5, width = 1 / 250, curve_type = "quintic") +
geom_stratum(fill = "#193e3f", width = 1 / 35, color = "#fffaf2") +
# geom_flow() +
# geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
scale_x_discrete(
limits = c("Upstream Regulator", "Disease and Function"),
expand = c(.05, .05)
) +
scale_fill_brewer(type = "qual", palette = "Set2") +
theme_void() +
theme(legend.position = "none")
# ylab(" ")
ggsave(filename = "funct_cat_alluvial.svg",path = here::here("2_plots/ipa/"), width = 450, height = 800, units = "mm")
gephi_colours <- colorRampPalette(c("#00c7ff","#ff7045","#8cb900","black"))
ggplot(
as.data.frame(merged),
aes(
y = intersect,
axis1 = up_cat,
axis2 = upstream
)
) +
geom_alluvium(aes(fill = up_cat), alpha = 0.5, width = 1 / 250, curve_type = "quintic") +
geom_stratum(fill = "#193e3f", width = 1 / 35, color = "#fffaf2") +
# geom_flow() +
# geom_text(stat = "stratum", aes(label = after_stat(stratum))) +
scale_x_discrete(
limits = c("Upstream Regulator", "Disease and Function"),
expand = c(.05, .05)
) +
scale_fill_manual(c("#c5da79","#ffb59c","#7fe1f9")) +
theme_void() +
theme(legend.position = "none")
# ylab(" ")
ggsave(filename = "up_cat_alluvial.svg",path = here::here("2_plots/ipa/"), width = 450, height = 800, units = "mm")Network plot
DE genes regulated by predicted upstream regulators and pathways
Alluvial plot
Proportion of DE genes regulated by predicted upstream regulators & functional terms
sessionInfo()R version 4.2.1 (2022-06-23 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=English_Australia.utf8 LC_CTYPE=English_Australia.utf8
[3] LC_MONETARY=English_Australia.utf8 LC_NUMERIC=C
[5] LC_TIME=English_Australia.utf8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ggrepel_0.9.1 ggbiplot_0.55 scales_1.2.1 plyr_1.8.7
[5] ggplotify_0.1.0 ggalluvial_0.12.3 igraph_1.3.5 viridis_0.6.2
[9] viridisLite_0.4.1 pheatmap_1.0.12 cowplot_1.1.1 pander_0.6.5
[13] kableExtra_1.3.4 forcats_0.5.2 stringr_1.4.1 purrr_0.3.5
[17] tidyr_1.2.1 ggplot2_3.3.6 tidyverse_1.3.2 reshape2_1.4.4
[21] tibble_3.1.8 readr_2.1.3 magrittr_2.0.3 dplyr_1.0.10
loaded via a namespace (and not attached):
[1] fs_1.5.2 bit64_4.0.5 lubridate_1.8.0
[4] webshot_0.5.4 RColorBrewer_1.1-3 httr_1.4.4
[7] rprojroot_2.0.3 tools_4.2.1 backports_1.4.1
[10] bslib_0.4.0 utf8_1.2.2 R6_2.5.1
[13] DBI_1.1.3 colorspace_2.0-3 withr_2.5.0
[16] tidyselect_1.2.0 gridExtra_2.3 bit_4.0.4
[19] compiler_4.2.1 git2r_0.30.1 textshaping_0.3.6
[22] cli_3.4.1 rvest_1.0.3 xml2_1.3.3
[25] labeling_0.4.2 sass_0.4.2 yulab.utils_0.0.5
[28] systemfonts_1.0.4 digest_0.6.29 rmarkdown_2.17
[31] svglite_2.1.0 pkgconfig_2.0.3 htmltools_0.5.3
[34] highr_0.9 dbplyr_2.2.1 fastmap_1.1.0
[37] rlang_1.0.6 readxl_1.4.1 rstudioapi_0.14
[40] farver_2.1.1 gridGraphics_0.5-1 jquerylib_0.1.4
[43] generics_0.1.3 jsonlite_1.8.2 vroom_1.6.0
[46] googlesheets4_1.0.1 Rcpp_1.0.9 munsell_0.5.0
[49] fansi_1.0.3 lifecycle_1.0.3 stringi_1.7.8
[52] whisker_0.4 yaml_2.3.5 parallel_4.2.1
[55] promises_1.2.0.1 crayon_1.5.2 haven_2.5.1
[58] hms_1.1.2 knitr_1.40 pillar_1.8.1
[61] reprex_2.0.2 glue_1.6.2 evaluate_0.17
[64] modelr_0.1.9 vctrs_0.4.2 tzdb_0.3.0
[67] httpuv_1.6.6 cellranger_1.1.0 gtable_0.3.1
[70] assertthat_0.2.1 cachem_1.0.6 xfun_0.33
[73] broom_1.0.1 later_1.3.0 ragg_1.2.3
[76] googledrive_2.0.0 gargle_1.2.1 workflowr_1.7.0
[79] ellipsis_0.3.2 here_1.0.1