Functional Enrichment Analysis:

Aaron Brooks & Fang Yin Lo8/21/2012

Introduction to Systems Biology 2012

making sense of big data

Mischel et al, 2004

Experimental Design

Data collection

Data analysis

Insight

From knots to knowledge• What is functional enrichment?- Tools and caveats (e.g. DAVID and pvals)

• How can you apply these tools to large, complex analysis problems (i.e. automation)?

Why functional enrichment analysis?

Gene FunctionGeneGene

P(X q) q

m( k q

N m)( )k

N( )N: population sizem: # of positives in the populationk: # of drawsq: # of positives

Urn problem:

Total: 15 marbles (N):5 red (m)

10 black (N-m)

Draw 6 marbles (k)

How likely am I to draw 3 or more marbles?

Gene

Gene

GeneGene

CommonFunctions

CommonPathwayse.g. Glycolysis

e.g. sugar metabolism

Interesting glioblastoma study

The workflow of typical enrichment tools

Nucleic Acids Research, 2009, Vol. 37, No. 1 1–13

e.g. Gene Ontology (GO), KEGG Pathway, etc

e.g. GO Terms that are enriched in the input gene list

(the database for annotation, visualization and integrated discovery)

• Diverse, web-based functional analysis tool• Integrates a suite of databases and statistical tools

(GO, KEGG, Interpro, Disease)• User-friendly, • Problematic for large analysis problems (many

independent sets)

staRt your engineshttp://baliga.systemsbiology.net/events/sysbio/content/bicluster-307http://baliga.systemsbiology.net/events/sysbio/content/bicluster-353

What if you had many sets to

analyze?

AUTOMATION!!!

topGO• An R package that facilitates semi-automated enrichment

analysis for Gene Ontology• Three main Steps:

1. Prepare data- create a topGO object with list of genes identiers, gene-to-GO

annotations)

2. Run enrichment tests3. Display the results

Structured controlled vocabularies (ontologies) that describe relationships between gene products and their associated biological roles

• cellular components : the parts of a cell or its extracellular environment

• molecular functions: activities, such as catalytic or binding activities, that occur at the molecular level (e.g. catalytic activity, Toll receptor binding)

• biological processes: series of events accomplished by one or more ordered assemblies of molecular functions (e.g. signal transduction, pyrimidine metabolic process )

GO structure• Directed Acyclic Graph(DAG)

• Child terms are more specialized• Child can have more than one parent

Data preparation# Install topGO and Affymetrix Human Genome U133 Plus 2.0 Array

annotation data> source("http://bioconductor.org/biocLite.R")> biocLite("topGO")> biocLite("hgu133plus2.db")> geneSets # Input a list a genes

#load the library> library(topGO)> library(hgu133plus2.db)

#Boot the gaggle> library(gaggle)> gaggleInit()

### Initializing the analysis ###

# hgu133plus2ACCNUM: an R object that contains mappings between the manufacturers identifiers and gene names of Affymetrix Human Genome U133 Plus 2.0 Array

# all.genes: all background genes ( gene universe )

> all.genes <- ls(hgu133plus2ACCNUM)

Data preparation

Other annotation packages at Bioconductor

Other annotation packages can be found at: http://www.bioconductor.org/packages/release/data/annotation/

Data preparation: Input gene lists### Make gene lists #### We will make a list that includes two sets of genes of interest# Initialize the list:>glioblastoma.genes = list()

http://baliga.systemsbiology.net/events/sysbio/content/bicluster-307# broadcast 'bicluster 307 genes' to R>glioblastoma.genes[["bc307"]] = sapply(getNameList(),tolower)

Do the same for the other gene list: http://baliga.systemsbiology.net/events/sysbio/content/bicluster-353>glioblastoma.genes[["bc353"]] = sapply(getNameList(),tolower)

Data preparation: make topGO object## Analyze genes in bc353 first

> relevant.genes <- factor(as.integer(all.genes %in% glioblastoma.genes[["bc353"]]))

> names(relevant.genes) <- all.genes

# Construct the topGOdata object for automated analysis

>GOdata.BP <- new("topGOdata", ontology='BP', allGenes = relevant.genes,

annotationFun = annFUN.db, affyLib = 'hgu133plus2.db')

# ontology:'BP','MF; or 'CC'# allGenes: named vector of type numeric or factor. The names attribute contains the genes identifiers. The genes listed in this object define the gene universe.# annotationFun: function that maps gene identifiers to GO terms. # annFUN.db extracts the gene-to-GO mappings from the affyLib object# affyLib: character string containing the name of the Bioconductor annotaion package for a specific microarray chip.

Run Enrichment Analysis

> results <- runTest(GOdata.BP, algorithm = 'classic', statistic = 'fisher’)

Analysis of results: summary

# generate a summary of the enrichment analysis> results.table <- GenTable(GOdata.BP, results, topNodes =

length(results@score))

# How many GO terms were tested?> dim(results.table)[1]

# reduce results to GO terms passing Benjamini-Hochberg multiple hypothesis corrected pval <= 0.05, FDR <= 5%

>results.table.bh <- results.table[which(p.adjust(results.table[,"result1"],method="BH")<=0.05),]

Analysis of results: get significant GO terms

# reduce results to GO terms passing Benjamini-Hochberg multiple hypothesis corrected pval <= 0.05, FDR <= 5%

>results.table.bh <- results.table[which(p.adjust(results.table[,"result1"],method="BH")<=0.05),]

# How many terms are enriched?>dim(results.table.bh)[1]

# What are the top ten terms?>results.table.bh[1:10,]

Analysis of results: get genes in top GO terms

# Get all the genes annotated to a specific GO term of interest:>GOid.of.interest = results.table.bh[1,"GO.ID"]>all.term.genes = genesInTerm(GOdata.BP,GOid.of.interest)[[1]]

# Which of these genes is in the bicluster?>genes.of.interest <- intersect(glioblastoma.genes[["bc353"]],all.term.genes)

# print table with probe ID and gene symbol >gene.symbol= toTable(hgu133plus2SYMBOL[genes.of.interest]) # print table with probe ID and gene names >gene.name= toTable(hgu133plus2GENENAME[genes.of.interest]) # Combine the information of the genes, output to csvfile: >cbind(gene.symbol,gene.name[,2]) >write.csv(cbind(gene.symbol,gene.name[,2]), file =

"glioblastoma.genes_bc353_in_immune response.csv“)

Automation

results <- list()for( bc in names(glioblastoma.genes) ) { cat(paste("Computing functional enrichment for...",bc,"\n"))

relevant.genes <- factor(as.integer(all.genes %in% glioblastoma.genes[[bc]]))

names(relevant.genes) <- all.genes GOdata.BP <- new("topGOdata", ontology='BP', allGenes =

relevant.genes, annotationFun = annFUN.db, affyLib = 'hgu133plus2.db')

results[[bc]] <- GenTable(GOdata.BP,runTest(GOdata.BP, algorithm = 'classic', statistic = 'fisher'),topNodes = length(results@score))

}

Questions?

Other algorithms supported by topGO• Standard implementations of GO testing compute the

significance of a node independent of the significance of the neighboring nodes (‘classic’)

• Other algorithms take into considerations of the GO structure and try to find more specific GO terms (e.g.’elim’, ‘weight’, ‘weight01’,etc) Alexa et al. (2006). Bioinformatics (2006) 22 (13): 1600-1607.

Other algorithms supported by topGO# Try running other algorithms and compare the results:>r1.BP.elim = runTest(GOdata.BP, algorithm = 'elim', statistic = 'fisher')>r1.BP.weight = runTest(GOdata.BP, algorithm = 'weight', statistic = 'fisher‘)# This will take a while…

# After the runs are done, visually compare resulting p values from different algorithms:>pValue.classic <- score(r1.BP)>pValue.elim <- score(r1.BP.elim)[names(pValue.classic)]>pValue.weight <- score(r1.BP.weight)[names(pValue.classic)]>gstat <- termStat(GOdata.BP, names(pValue.classic))>gSize <- gstat$Annotated / max(gstat$Annotated) * 4>colMap <- function(x) { .col <- rep(rev(heat.colors(length(unique(x)))), time = table(x)) return(.col[match(1:length(x), order(x))])}>gCol <- colMap(gstat$Significant)>plot(pValue.classic, pValue.elim, xlab = "p-value classic", ylab = "p-value elim",pch = 19,

cex = gSize, col = gCol)

Broadcasting gene list to DAVID1. In R: > broadcast(geneSetGenes)

2.

3. Select target: DAVID

4. Broadcast to DAVID

Broadcasting gene list to DAVID

Broadcasting gene list to DAVID

Broadcasting gene list to DAVID

Gene list and population background being analyzed

Original database/resources where the terms orient

Enriched terms associate with input gene list

Modified Fisher Exact p-values

Corrected p-values

A group of terms having similar biological meaning due to sharing similar gene members

Clustering options and contingency

Some remaining challenges

Limitation of multiple testing correction on enrichment P-values • common multiple testing correction techniques maybe overly conservative approaches if there are thousands or

even more annotation terms involved in the analysis

Genome Inform. 2005;16:106-115.; Nucleic Acids Research, 2009, Vol. 37, No. 1 1–13

Cross-comparing enrichment analysis results derived from multiple gene lists• the size of the gene list impacts the absolute enrichment P-values, thereforedifficult to directly compare the absolute enrichment P-values across gene lists

Realistically positioning the role of enrichment P-values in the current data-mining environment:

• high-throughput enrichment data-mining environment is extremely complicated

• Variations of the user gene list size

• deviation of the number of genes associated with each annotation

• gene overlap between annotation

• incompleteness of annotation content

• strong connectivity/dependency among genes

• unbalanced distributions of annotation content

some may treat the resulting enrichment P-values as a scoring system that plays a advisory role

more of an exploratory procedure, with the aid of enrichment P-value, rather than a pure statistical solution.

the specificity of enrichment analysis is more impacted by non-statistical layers than it is by statistical methods alone

Nat. Protoc. 2008. doi: 10.1038/nprot.2008.211

Nucleic Acids Research, 2009, Vol. 37, No. 1 1–13

Some remaining challenges

Working with topGO data object

# work with Godata.BP# obtaining all genes> a = genes(GOdata.BP)> str(a) chr [1:31777] "1007_s_at" "1053_at" "117_at" "121_at" "1255_g_at" "1294_at"

...# number of genes> numGenes(GOdata.BP)[1] 31777# The list of significant genes can be accessed using the method sigGenes()> sg = sigGenes(GOdata.BP)> str(sg) chr [1:22] "201291_s_at" "202095_s_at" "202589_at" "202705_at"

"203213_at" ...

# accessing information related to GO and its structure# which GO terms are available for analysis:> ug = usedGO(GOdata.BP)> str(ug) chr [1:10921] "GO:0000002" "GO:0000003" "GO:0000012" "GO:0000018" "GO:0000019" ...

# select some random GO terms: (1). count the number of annotated genes and (2) obtain their annotation> sel.terms <- sample(usedGO(GOdata.BP), 10)> sel.terms [1] "GO:0032913" "GO:0043372" "GO:0044259" "GO:0032700" "GO:0043122" "GO:2001141" "GO:0060587" [8] "GO:0071352" "GO:0007256" "GO:0051343“

# Check what are the genes annotated to a specific GO term:> genesInTerm(GOdata.BP, "GO:0032913" )$`GO:0032913`[1] "208650_s_at" "208651_x_at" "209771_x_at" "209772_s_at" "216379_x_at" "266_s_at"

# Number of genes annotated to the selected GO terms:> num.ann.genes <- countGenesInTerm(GOdata.BP, sel.terms)> num.ann.genesGO:0032913 GO:0043372 GO:0044259 GO:0032700 GO:0043122 GO:2001141 GO:0060587 GO:0071352 GO:0007256 6 36 111 12 454 6809 2 5 11 GO:0051343 2

Working with topGO data object

> ann.genes <- genesInTerm(GOdata.BP, sel.terms)> str(ann.genes)List of 10 $ GO:0032913: chr [1:6] "208650_s_at" "208651_x_at" "209771_x_at" "209772_s_at" ... $ GO:0043372: chr [1:36] "1554519_at" "1555689_at" "1565358_at" "1569748_at" ... $ GO:0044259: chr [1:111] "1554383_a_at" "1555540_at" "1555896_a_at" "1556499_s_at" ... $ GO:0032700: chr [1:12] "1552798_a_at" "1556190_s_at" "201300_s_at" "207160_at" ... $ GO:0043122: chr [1:454] "1552360_a_at" "1552703_s_at" "1552798_a_at" "1552804_a_at" ... $ GO:2001141: chr [1:6809] "121_at" "1316_at" "1405_i_at" "1487_at" ... $ GO:0060587: chr [1:2] "201525_at" "207092_at" $ GO:0071352: chr [1:5] "201940_at" "201941_at" "201942_s_at" "201943_s_at" ... $ GO:0007256: chr [1:11] "1558984_at" "203652_at" "206362_x_at" "207347_at" ... $ GO:0051343: chr [1:2] "207514_s_at" "214286_at"

Working with topGO data object

Analysis of results: get genes in top GO terms

#We can also look at multiple GO terms at the same time:> GOids.of.interest = results.table.bh[c(1:10),"GO.ID"]> all.term.genes = genesInTerm(GOdata.BP,GOids.of.interest)

# Which of these genes is in the bicluster?> genes.of.interest <- sapply(names(all.term.genes),function(x)

{intersect(all.term.genes[[x]],glioblastoma.genes[["bc353"]])})

# print table with probe ID and gene symbol:> geneSynmol.of.interest <- lapply(names(genes.of.interest),function(x)

{toTable(hgu133plus2SYMBOL[genes.of.interest[[x]]])})> names(geneSynmol.of.interest)<- GOids.of.interest