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go_enrich_dot.R
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go_enrich_dot.R
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#' @title GO enrichment analysis and dot plot (None/Exist Reference Genome).
#' @description GO enrichment analysis and dot plot (None/Exist Reference Genome).
#' @author benben-miao
#'
#' @return Plot: GO enrichment analysis and dot plot (None/Exist Reference Genome).
#' @param go_anno Dataframe: GO and KEGG annotation of background genes (1st-col: Genes, 2nd-col: biological_process, 3rd-col: cellular_component, 4th-col: molecular_function, 5th-col: kegg_pathway).
#' @param degs_list Dataframe: degs list.
#' @param padjust_method Character: P-value adjust to Q-value. Default: "fdr" (false discovery rate), options: "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".
#' @param pvalue_cutoff Numeric: P-value cutoff. Recommend: small than 0.05.
#' @param qvalue_cutoff Numeric: Q-value cutoff. Recommend: small than 0.05.
#' @param sign_by Character: significant by. Default: "p.adjust", options: "pvalue", "p.adjust", "qvalue".
#' @param category_num Numeric: categories number to display. Default: 30, min: 1, max: NULL.
#' @param font_size Numeric: category font size. Default: 12.
#' @param low_color Character: low value (p-value or q-value) color (color name or hex value).
#' @param high_color Character: high value (p-value or q-value) color (color name or hex value).
#' @param ggTheme Character: ggplot2 themes. Default: "theme_light", options: "theme_default", "theme_bw", "theme_gray", "theme_light", "theme_linedraw", "theme_dark", "theme_minimal", "theme_classic", "theme_void"
#'
#' @import ggplot2
#' @import ggsci
#' @importFrom reshape2 melt
#' @importFrom tidyr separate_rows separate drop_na
#' @importFrom clusterProfiler enricher
#' @importFrom dplyr distinct
#' @import enrichplot
#' @export
#'
#' @examples
#' # 1. Library TOmicsVis package
#' library(TOmicsVis)
#'
#' # 2. Use example dataset
#' data(gene_go_kegg)
#' head(gene_go_kegg)
#'
#' # 3. Default parameters
#' go_enrich_dot(gene_go_kegg[,-5], gene_go_kegg[100:200,1])
#'
#' # 4. Set padjust_method = "BH"
#' go_enrich_dot(gene_go_kegg[,-5], gene_go_kegg[100:200,1], padjust_method = "BH")
#'
#' # 5. Set category_num = 10
#' go_enrich_dot(gene_go_kegg[,-5], gene_go_kegg[100:200,1], category_num = 10)
#'
#' # 6. Set ggTheme = "theme_bw"
#' go_enrich_dot(gene_go_kegg[,-5], gene_go_kegg[100:200,1], ggTheme = "theme_bw")
#'
go_enrich_dot <- function(go_anno,
degs_list,
padjust_method = "fdr",
pvalue_cutoff = 0.05,
qvalue_cutoff = 0.05,
sign_by = "p.adjust",
category_num = 30,
font_size = 12,
low_color = "#ff0000aa",
high_color = "#008800aa",
ggTheme = "theme_light"
){
# -> 2. Data Parameters
# padjust_method <- "fdr"
# ChoiceBox: "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"
# pvalue_cutoff <- 0.30
# Slider: 0.30, 0.00, 0.01, 1.00
# qvalue_cutoff <- 0.50
# Slider: 0.50, 0.00, 0.01, 1.00
# <- 2. Data Parameters
# -> 3. Data
gene_go <- go_anno
degs_list <- degs_list
# deg_fc["log2FC"] <- 2^(deg_fc["log2FC"])
# deg_list <- with(deg_fc, setNames(log2FC, id))
gene_go1 <- melt(gene_go,
na.rm = FALSE,
id.vars = c("Genes"),
measure.vars = c("biological_process", "cellular_component", "molecular_function"),
variable.name = "ontology",
value.name = "term",
factorsAsStrings = TRUE
)
gene_go2 <- separate_rows(data = gene_go1,
"term",
sep = ";"
)
gene_go3 <- separate(gene_go2,
"term",
c("term", "description"),
"\\("
)
gene_go4 <- drop_na(gene_go3)
gene_go4["description"] <- gsub(")", "", gene_go4$description)
gene_go4["ontology"] <- gsub("_", " ", gene_go4$ontology)
gene_go5 <- data.frame(gene_go4["Genes"],
gene_go4["term"],
gene_go4["ontology"],
gene_go4["description"]
)
enrich_results <- enricher(gene = degs_list,
TERM2GENE = data.frame(gene_go5[,2],gene_go5[,1]),
TERM2NAME = data.frame(gene_go5[,2],gene_go5[,4]),
pvalueCutoff = pvalue_cutoff,
pAdjustMethod = padjust_method, # "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"
qvalueCutoff = qvalue_cutoff,
minGSSize = 1,
maxGSSize = 1000
)
enrich_result <- enrich_results@result
gene_go6 <- data.frame(gene_go5["term"], gene_go5["ontology"])
gene_go6 <- distinct(gene_go6, .keep_all = TRUE)
enrich_table <- merge(gene_go6,
enrich_result,
by.x = "term",
by.y = "ID"
)
colnames(enrich_table)[1] <- "ID"
# write.table(enrich_table,
# file = "Results.txt",
# append = FALSE,
# sep = "\t",
# quote = TRUE,
# na = "NA"
# )
# <- 3. Data
# -> 4. Plot parameters
# fonts <- "Times"
# ChoiceBox: "Times", "Palatino", "Bookman", "Courier", "Helvetica", "URWGothic", "NimbusMon", "NimbusSan"
# ggTheme <- "theme_bw"
# ChoiceBox: "theme_default", "theme_bw", "theme_gray", "theme_light", "theme_linedraw", "theme_dark", "theme_minimal", "theme_classic", "theme_void"
if (ggTheme == "theme_default") {
gg_theme <- theme()
} else if (ggTheme == "theme_bw") {
gg_theme <- theme_bw()
} else if (ggTheme == "theme_gray") {
gg_theme <- theme_gray()
} else if (ggTheme == "theme_light") {
gg_theme <- theme_light()
} else if (ggTheme == "theme_linedraw") {
gg_theme <- theme_linedraw()
} else if (ggTheme == "theme_dark") {
gg_theme <- theme_dark()
} else if (ggTheme == "theme_minimal") {
gg_theme <- theme_minimal()
} else if (ggTheme == "theme_classic") {
gg_theme <- theme_classic()
} else if (ggTheme == "theme_void") {
gg_theme <- theme_void()
} else if (ggTheme == "theme_test") {
gg_theme <- theme_test()
}
# sign_by <- "p.adjust"
# ChoiceBox: "pvalue", "p.adjust", "qvalue"
# category_num <- 30
# ChoiceBox: 30, 10, 1, 50
# low_color <- "#ff0000aa"
# ColorPicker
# high_color <- "#0000ffaa"
# ColorPicker
# font_size <- 12
# Slider: 12, 2, 2, 30
# <- 4. Plot parameters
# -> 5. Plot
p <- dotplot(
enrich_results,
x = "GeneRatio",
color = sign_by,
showCategory = category_num,
size = NULL,
split = NULL,
font.size = font_size,
title = "",
orderBy = "x",
label_format = 200
) +
# geom_text(aes(label = Count),
# vjust = 0.3,
# hjust = -0.5,
# size = 3,
# color = "#ffffff") +
ylab("GO terms") +
# geom_point(alpha = 0.5) +
gg_theme +
theme(
# text = element_text(family = fonts),
axis.text = element_text(colour = "#000000")
) +
scale_color_gradient(low = low_color, high = high_color, space = "Lab")
# p
# <- 5. Plot
return(p)
invisible()
}