gsEasy

Daniel Greene

2016-08-06

Calculate p-values for enrichment of set

gsEasy has a function gset for calculating p-values of enrichment for sets (of genes) in ranked/scored lists (of genes) by permutation (see ‘Gene Set Enrichment Analysis’ described by Subramanian et al, 2005). The arguments of gset are named as in the paper:

Say we had a set of 5 genes which appeared at the top five ranks out of 1000 (i.e. highly enriched at the high ranks!). We could calculate an enrichment p-value simply as:

gset(S=1:5, N=1000)
## [1] 9.9999e-06

So the p-value is close to zero. However for random sets, the p-values are distributed uniformly:

replicate(n=10, expr=gset(S=sample.int(n=1000, size=5), N=1000))
##  [1] 0.9303483 0.2039801 0.9004975 0.5472637 0.2736318 0.7014925 0.1791045
##  [8] 0.9950249 0.6517413 0.9502488

Alternatively, you can pass the names of genes as S with a sorted list of gene names as r (in which case the scores default to the ranks in the list), or a numeric vector of scores named by genes as r.

gset(S=c("gene 1", "gene 5", "gene 40"), r=paste("gene", 1:100))
## [1] 0.07587383

Multiple gene sets can thus be tested for enrichment with a single call to a high level function such as sapply (or, if you have many sets to test and multiple cores available, mclapply), for instance:

gene_sets <- c(list(1:5), replicate(n=10, simplify=FALSE, expr=sample.int(n=1000, size=5)))
names(gene_sets) <- c("enriched set", paste("unenriched set", 1:10))
gene_sets
## $`enriched set`
## [1] 1 2 3 4 5
## 
## $`unenriched set 1`
## [1] 922 424 840 133 849
## 
## $`unenriched set 2`
## [1] 110 297 737 702 284
## 
## $`unenriched set 3`
## [1] 257 168 249 999 411
## 
## $`unenriched set 4`
## [1]  23 663 500 876  31
## 
## $`unenriched set 5`
## [1] 111 896 219 923 888
## 
## $`unenriched set 6`
## [1]  19 143 590 107 588
## 
## $`unenriched set 7`
## [1] 223 402 696 273 102
## 
## $`unenriched set 8`
## [1] 187 242 200 458 531
## 
## $`unenriched set 9`
## [1]  39 407 709 188 235
## 
## $`unenriched set 10`
## [1]  93 238 820 675 368
sapply(gene_sets, function(set) gset(S=set, N=1000))
##      enriched set  unenriched set 1  unenriched set 2  unenriched set 3 
##      0.0000099999      0.6567164179      0.3482587065      0.1194029851 
##  unenriched set 4  unenriched set 5  unenriched set 6  unenriched set 7 
##      0.0778846154      0.0852359209      0.0914893617      0.2537313433 
##  unenriched set 8  unenriched set 9 unenriched set 10 
##      0.4079601990      0.2985074627      0.4676616915

Ontological annotations

gsEasy has a function get_ontological_gene_sets for creating lists of gene sets defined by annotation with ontological terms, such that ontological is-a relations are propagated. get_ontological_gene_sets accepts an ontological_index argument and two character vectors, corresponding to genes and terms respectively, whereby the n-th element in each vector corresponds to one annotation pair. The result, a list of character vectors of gene names, can then be used as an argument of gset.

library(ontologyIndex)
data(hpo)
df <- data.frame(
    gene=c("gene 1", "gene 2"), 
    term=c("HP:0000598", "HP:0000118"), 
    name=hpo$name[c("HP:0000598", "HP:0000118")], 
    stringsAsFactors=FALSE,
    row.names=NULL)
df
##     gene       term                   name
## 1 gene 1 HP:0000598 Abnormality of the ear
## 2 gene 2 HP:0000118 Phenotypic abnormality
get_ontological_gene_sets(hpo, gene=df$gene, term=df$term)
## $`HP:0000001`
## [1] "gene 1" "gene 2"
## 
## $`HP:0000118`
## [1] "gene 1" "gene 2"
## 
## $`HP:0000598`
## [1] "gene 1"

Gene Ontology (GO) annotations

gsEasy comes with a list of GO annotations, GO_gene_sets [based on annotations downloaded from geneontology.org on 07/08/2016], which can be loaded with data. This comprises a list of all gene sets (i.e. character vectors of gene names) associated with each GO term, for GO terms being annotated with at most 500 genes.

data(GO_gene_sets)
GO_gene_sets[1:6]
## $`GO:0000002`
##  [1] "AKT3"     "LONP1"    "MEF2A"    "MGME1"    "MPV17"    "MRPL17"  
##  [7] "MRPL39"   "OPA1"     "PIF1"     "SLC25A33" "SLC25A36" "SLC25A4" 
## [13] "TYMP"    
## 
## $`GO:0000003`
##  [1] "EIF4H"  "IL12B"  "LEP"    "LEPR"   "MMP23A" "RHOXF1" "SEPP1" 
##  [8] "STAT3"  "TNP1"   "VGF"    "WDR43" 
## 
## $`GO:0000009`
## [1] "ALG12"
## 
## $`GO:0000010`
## [1] "PDSS1" "PDSS2"
## 
## $`GO:0000011`
## [1] "RBSN"
## 
## $`GO:0000012`
##  [1] "APLF"   "APTX"   "E9PQ18" "LIG4"   "M0R2N6" "Q6ZNB5" "SIRT1" 
##  [8] "TDP1"   "TNP1"   "XRCC1"

It also has a function get_GO_gene_sets which is a specialisation of get_ontological_gene_sets for the Gene Ontology (GO) which can be called passing just a file path to the annotation file (official version available at http://geneontology.org/), should an up-to-date version be required.