1, obtain input genes

Question

issues regarding topGO usage

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Entering edit mode

6.5 years ago

prakashpandey1111 ▴ 10

I have a expression set of 91 uniprot ids across three time points. I performed fuzzy c-means clustering using Mfuzz & now i wish to perform enrichment analysis using topGO but i am confused with the input data required and the steps involved in to use topGO. I referred topGO manual but i'm confused with the type of input data required & steps to perform it.please help me with this

topGO • 4.8k views

ADD COMMENT • link updated 6.5 years ago by Kevin Blighe 88k • written 6.5 years ago by prakashpandey1111 ▴ 10

score 9 · Answer 1 · 2018-07-08

Greetings,

At which part are you having difficulty with the function, exactly?

The function can be used in different ways but the input gene list should be a name-value pairing. The values can be a rank (#1, highest) or a list of p-values, e.g., from differential expression. In this example, I supply a list of ranked genes:

1, obtain input genes

rankedGenes <- c(1:10)
names(rankedGenes) <- c("BRCA1","BRCA2","TP53", "ATM", "ERCC1", "BRCC3", "TP63", "ERCC2", "ERCC3", "ERCC4")

rankedGenes
BRCA1 BRCA2  TP53   ATM ERCC1 BRCC3  TP63 ERCC2 ERCC3 ERCC4 
    1     2     3     4     5     6     7     8     9    10

So, in my genes, BRCA1 is ranked highest... ERCC4 is lowest.

Edit April 25, 2019:

One can also supply name-value pairings with p-values, in which case the lowest p-value is ranked highest.
You can also just supply a vector where everything has the same value, in which case everything has equal rank

2, set-up the enrichment object based on your genes

require(topGO)
require(org.Hs.eg.db)

selection <- function(x) TRUE
allGO2genes <- annFUN.org(whichOnto="BP", feasibleGenes=NULL, mapping="org.Hs.eg.db", ID="symbol")
GOdata <- new("topGOdata", ontology="BP", allGenes=rankedGenes,
  annot=annFUN.GO2genes, GO2genes=allGO2genes,
  geneSel=selection, nodeSize=5)

Depending on the format of your input genes, you can change the value of ID, which is passed to annFUN.org(). This is to allow for gene mapping to GO terms to take place.

Also note that whichOnto and ontology can be BP, MF, or CC, relating to the GO categories.

It's up to you to ensure that the gene list that you're providing is the correct list of genes. The geneSel parameter of new() (above) can be used to filter your list in real time, e.g., filtering on P<0.01 if supplying genes and their respective p-values; otherwise, you can just set this to geneSel=TRUE and use everything that's supplied (as I have).

3, perform gene enrichment. Here, we make use of the rank information by using the Kolmogorov-Smirnov (K-S) test on the enriched terms

results.ks <- runTest(GOdata, algorithm="classic", statistic="ks")
goEnrichment <- GenTable(GOdata, KS=results.ks, orderBy="KS", topNodes=10)
goEnrichment$KS <- as.numeric(goEnrichment$KS)
goEnrichment <- goEnrichment[goEnrichment$KS<0.05,]
goEnrichment <- goEnrichment[,c("GO.ID","Term","KS")]
goEnrichment

goEnrichment
        GO.ID                                        Term    KS
1  GO:0010212              response to ionizing radiation 0.015
2  GO:0007154                          cell communication 0.041
3  GO:0007165                         signal transduction 0.041
4  GO:0008630 intrinsic apoptotic signaling pathway in... 0.041
5  GO:0016569             covalent chromatin modification 0.041
6  GO:0016570                        histone modification 0.041
7  GO:0023052                                   signaling 0.041
8  GO:0030330 DNA damage response, signal transduction... 0.041
9  GO:0035556           intracellular signal transduction 0.041
10 GO:0042127            regulation of cell proliferation 0.041

See a related thread where I also produce an enrichment plot using ggplot2: A: GO analysis using topGO

Kevin