Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE,
warning = TRUE,
comment = "#>")
run_vignette <- requireNamespace("GSEABase", quietly = TRUE) &&
requireNamespace("GO.db", quietly = TRUE) &&
requireNamespace("reactome.db", quietly = TRUE) &&
requireNamespace("org.Hs.eg.db", quietly = TRUE) &&
requireNamespace("ggplot2", quietly = TRUE) &&
requireNamespace("forcats", quietly = TRUE)
## ----setupr, message=FALSE----------------------------------------------------
library("BaseSet", quietly = TRUE)
library("dplyr", quietly = TRUE)
## ----prepare_GO, message=FALSE, eval=run_vignette-----------------------------
# # We load some libraries
# library("org.Hs.eg.db", quietly = TRUE)
# library("GO.db", quietly = TRUE)
# library("ggplot2", quietly = TRUE)
# # Prepare the data
# h2GO_TS <- tidySet(org.Hs.egGO)
# h2GO <- as.data.frame(org.Hs.egGO)
## ----evidence_ontology, eval=run_vignette-------------------------------------
# library("forcats", include.only = "fct_reorder2", quietly = TRUE)
# h2GO %>%
# group_by(Evidence, Ontology) %>%
# count(name = "Freq") %>%
# ungroup() %>%
# mutate(Evidence = fct_reorder2(Evidence, Ontology, -Freq),
# Ontology = case_match(Ontology,
# "CC" ~ "Cellular Component",
# "MF" ~ "Molecular Function",
# "BP" ~ "Biological Process",
# .default = NA)) %>%
# ggplot() +
# geom_col(aes(Evidence, Freq)) +
# facet_grid(~Ontology) +
# theme_minimal() +
# coord_flip() +
# labs(x = element_blank(), y = element_blank(),
# title = "Evidence codes for each ontology")
## ----nEvidence_plot, eval=run_vignette----------------------------------------
# h2GO_TS %>%
# relations() %>%
# group_by(elements, sets) %>%
# count(sort = TRUE, name = "Annotations") %>%
# ungroup() %>%
# count(Annotations, sort = TRUE) %>%
# ggplot() +
# geom_col(aes(Annotations, n)) +
# theme_minimal() +
# labs(x = "Evidence codes", y = "Annotations",
# title = "Evidence codes for each annotation",
# subtitle = "in human") +
# scale_x_continuous(breaks = 1:7)
## ----numbers, eval=run_vignette-----------------------------------------------
# # Add all the genes and GO terms
# h2GO_TS <- add_elements(h2GO_TS, keys(org.Hs.eg.db)) %>%
# add_sets(grep("^GO:", keys(GO.db), value = TRUE))
#
# sizes_element <- element_size(h2GO_TS) %>%
# arrange(desc(size))
# sum(sizes_element$size == 0)
# sum(sizes_element$size != 0)
#
# sizes_set <- set_size(h2GO_TS) %>%
# arrange(desc(size))
# sum(sizes_set$size == 0)
# sum(sizes_set$size != 0)
## ----plots_GO, eval=run_vignette----------------------------------------------
# sizes_element %>%
# filter(size != 0) %>%
# ggplot() +
# geom_histogram(aes(size), binwidth = 1) +
# theme_minimal() +
# labs(x = "# sets per element", y = "Count")
#
# sizes_set %>%
# filter(size != 0) %>%
# ggplot() +
# geom_histogram(aes(size), binwidth = 1) +
# theme_minimal() +
# labs(x = "# elements per set", y = "Count")
## ----distr_sizes, eval=run_vignette-------------------------------------------
# head(sizes_set, 10)
## ----fuzzy_setup, eval=run_vignette-------------------------------------------
# nr <- h2GO_TS %>%
# relations() %>%
# dplyr::select(sets, Evidence) %>%
# distinct() %>%
# mutate(fuzzy = case_match(Evidence,
# "EXP" ~ 0.9,
# "IDA" ~ 0.8,
# "IPI" ~ 0.8,
# "IMP" ~ 0.75,
# "IGI" ~ 0.7,
# "IEP" ~ 0.65,
# "HEP" ~ 0.6,
# "HDA" ~ 0.6,
# "HMP" ~ 0.5,
# "IBA" ~ 0.45,
# "ISS" ~ 0.4,
# "ISO" ~ 0.32,
# "ISA" ~ 0.32,
# "ISM" ~ 0.3,
# "RCA" ~ 0.2,
# "TAS" ~ 0.15,
# "NAS" ~ 0.1,
# "IC" ~ 0.02,
# "ND" ~ 0.02,
# "IEA" ~ 0.01,
# .default = 0.01)) %>%
# dplyr::select(sets = "sets", elements = "Evidence", fuzzy = fuzzy)
## ----fuzzy_setup2, eval=run_vignette------------------------------------------
# ts <- h2GO_TS %>%
# relations() %>%
# dplyr::select(-Evidence) %>%
# rbind(nr) %>%
# tidySet() %>%
# mutate_element(Type = ifelse(grepl("^[0-9]+$", elements), "gene", "evidence"))
## ----cardinality, eval=run_vignette-------------------------------------------
# ts %>%
# dplyr::filter(Type != "Gene") %>%
# cardinality() %>%
# arrange(desc(cardinality)) %>%
# head()
## ----size_go, eval=run_vignette-----------------------------------------------
# ts %>%
# filter(sets %in% c("GO:0008152", "GO:0003674", "GO:0005575"),
# Type != "gene") %>%
# set_size()
## ----evidence_go, eval=run_vignette-------------------------------------------
# go_terms <- c("GO:0008152", "GO:0003674", "GO:0005575")
# ts %>%
# filter(sets %in% go_terms & Type != "gene")
## ----prepare_reactome, eval=run_vignette--------------------------------------
# # We load some libraries
# library("reactome.db")
#
# # Prepare the data (is easier, there isn't any ontoogy or evidence column)
# h2p <- as.data.frame(reactomeEXTID2PATHID)
# colnames(h2p) <- c("sets", "elements")
# # Filter only for human pathways
# h2p <- h2p[grepl("^R-HSA-", h2p$sets), ]
#
# # There are duplicate relations with different evidence codes!!:
# summary(duplicated(h2p[, c("elements", "sets")]))
# h2p <- unique(h2p)
# # Create a TidySet and
# h2p_TS <- tidySet(h2p) %>%
# # Add all the genes
# add_elements(keys(org.Hs.eg.db))
## ----numbers_pathways, eval=run_vignette--------------------------------------
# sizes_element <- element_size(h2p_TS) %>%
# arrange(desc(size))
# sum(sizes_element$size == 0)
# sum(sizes_element$size != 0)
#
# sizes_set <- set_size(h2p_TS) %>%
# arrange(desc(size))
## ----pathways_plots, eval=run_vignette----------------------------------------
# sizes_element %>%
# filter(size != 0) %>%
# ggplot() +
# geom_histogram(aes(size), binwidth = 1) +
# scale_y_log10() +
# theme_minimal() +
# labs(x = "# sets per element", y = "Count")
#
# sizes_set %>%
# ggplot() +
# geom_histogram(aes(size), binwidth = 1) +
# scale_y_log10() +
# theme_minimal() +
# labs(x = "# elements per set", y = "Count")
## ----distr_sizes_pathways, eval=run_vignette----------------------------------
# head(sizes_set, 10)
## ----sessionInfo, echo=FALSE--------------------------------------------------
sessionInfo()
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