Information content of gene ontology
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Entering edit mode
12 months ago
Yean ▴ 130

Hi all

I am struggling with calculating the information content of the individuals GO term.

As far as i know, most R packages deal with the semantic similarity of GO term such as GOSemSim

but I didn't find any tools calculating IC of individuals GO term yet

Does anyone know the tools for doing this job directly ?

thanks

gene ontology information content • 420 views
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Entering edit mode
8 months ago
Zuguang Gu ▴ 200

A quick look at the code of GOSemSim::godata() shows the IC slot of the object returned by this function contains the IC for all GO terms:

> GOSemSim:::computeIC
function (goAnno, ont) 
{
    if (!exists(".GOSemSimEnv")) 
        .initial()
    .GOSemSimEnv <- get(".GOSemSimEnv", envir = .GlobalEnv)
    godata <- get("gotbl", envir = .GOSemSimEnv)
    goids <- unique(godata[godata$Ontology == ont, "go_id"])
    goterms = goAnno$GO
    gocount <- table(goterms)
    goname <- names(gocount)
    go.diff <- setdiff(goids, goname)
    m <- double(length(go.diff))
    names(m) <- go.diff
    gocount <- as.vector(gocount)
    names(gocount) <- goname
    gocount <- c(gocount, m)
    Offsprings <- switch(ont, MF = AnnotationDbi::as.list(GOMFOFFSPRING), 
        BP = AnnotationDbi::as.list(GOBPOFFSPRING), CC = AnnotationDbi::as.list(GOCCOFFSPRING))
    cnt <- gocount[goids] + sapply(goids, function(i) sum(gocount[Offsprings[[i]]], 
        na.rm = TRUE))
    names(cnt) <- goids
    p <- cnt/sum(gocount)
    IC <- -log(p)
    return(IC)
}

E.g.:

semData <- godata(db, ont = ont)
semData@IC
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Entering edit mode
12 months ago
Yean ▴ 130

I think I found the answer which is using the annotation file (GO.db) from bioconductor

This is an example code I have used (sorry for a bit messy)

#if (!requireNamespace("BiocManager", quietly = TRUE))
#  install.packages("BiocManager")
#BiocManager::install("GO.db")

library(GO.db)

P <- toTable(GOBPOFFSPRING)
names(P) <- c("child","parent")
P_count <- as.data.frame(table(P$parent))
P_count$type <- "BP"

C <- toTable(GOCCOFFSPRING)
names(C) <- c("child","parent")
C_count <- as.data.frame(table(C$parent))
C_count$type <- "CC"

M <- toTable(GOMFOFFSPRING) 
names(M) <- c("child","parent")
M_count <- as.data.frame(table(M$parent))
M_count$type <- "MF"

ref <- rbind(M_count,C_count,P_count)
IC <- function(id,onto){
  #count child  
  cnt <- filter(ref, Var1 == id) %>% dplyr::select(Freq) %>% as.numeric()
  #all GO term in select ontology
  df <- select(GO.db, keys(GO.db, "GOID"),  columns = c("ONTOLOGY"))
  df_02 <- as.data.frame(table(df$ONTOLOGY)) %>% filter(.,Var1 == onto) 
  all_onto <- as.numeric(df_02[,2])
  prob <- cnt/all_onto
  IC <- -log2(prob)
  return(IC)
}

IC("GO:0001895","MF")
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