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go_enrich_net.R
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go_enrich_net.R
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#' @title GO enrichment analysis and net plot (None/Exist Reference Genome).
#' @description GO enrichment analysis and net plot (None/Exist Reference Genome).
#' @author benben-miao
#'
#' @return PLot: GO enrichment analysis and net 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 category_num Numeric: categories number to display. Default: 20, min: 1, max: NULL.
#' @param net_layout Character: network layout. Default: "circle", options: 'star', 'circle', 'gem', 'dh', 'graphopt', 'grid', 'mds', 'randomly', 'fr', 'kk', 'drl' or 'lgl'.
#' @param net_circular Logical: network circular. Default: TRUE, options: TRUE, FALSE.
#' @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).
#'
#' @import ggplot2
#' @import ggsci
#' @importFrom reshape2 melt
#' @importFrom tidyr separate_rows separate drop_na
#' @importFrom clusterProfiler enricher
#' @importFrom dplyr distinct
#' @import enrichplot
#' @import ggnewscale
#' @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_net(gene_go_kegg[,-5], gene_go_kegg[100:200,1])
#'
go_enrich_net <- function(go_anno,
degs_list,
padjust_method = "fdr",
pvalue_cutoff = 0.05,
qvalue_cutoff = 0.05,
category_num = 20,
net_layout = "circle",
net_circular = TRUE,
low_color = "#ff0000aa",
high_color = "#008800aa"
){
# -> 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"
# category_num <- 30
# ChoiceBox: 30, 10, 1, 50
# low_color <- "#ff0000aa"
# ColorPicker
# high_color <- "#0000ffaa"
# ColorPicker
# net_layout <- "circle"
# ChoiceBox: 'star', 'circle', 'gem', 'dh', 'graphopt', 'grid', 'mds', 'randomly', 'fr', 'kk', 'drl' or 'lgl'.
# isCircular <- "circular"
# if (isCircular == "circular") {
# circular <- TRUE
# } else if (isCircular == "scattered") {
# circular <- FALSE
# }
# # ChoiceBox: "circular", "scattered"
cateLabelScale <- 0.8
# Slider: 0.8, 0.1, 0.1, 2.0
geneLabelScale <- 0.8
# Slider: 0.8, 0.1, 0.1, 2.0
# <- 4. Plot parameters
# -> 5. Plot
p <- cnetplot(
x = enrich_results,
showCategory = category_num,
color.params = list(foldChange = NULL,
edge = TRUE),
# foldChange = deg_list,
# colorEdge = TRUE,
cex.params = list(category_node = 1,
gene_node = 1,
category_label = cateLabelScale,
gene_label = geneLabelScale),
# cex_category = 1,
# cex_gene = 1,
# cex_label_category = cateLabelScale,
# cex_label_gene = geneLabelScale,
layout = net_layout, # 'star', 'circle', 'gem', 'dh', 'graphopt', 'grid', 'mds', 'randomly', 'fr', 'kk', 'drl' or 'lgl'.
circular = net_circular,
node_label = "all",
# color_category = "#ff0000",
# color_gene = "#008000",
shadowtext = "all"
) +
# guides(color = guide_legend(title="New Legend Title")) +
labs(color = "Genes") +
theme(
# text = element_text(family = fonts)
) +
scale_fill_gradient(low = low_color, high = high_color,
space = "Lab",
guide = "colourbar", aesthetics = "fill")
# p
# <- 5. Plot
return(p)
invisible()
}