I am using clusterProfiler to check whether a group of genes of interest is enriched in genes coming from a term from a custom ontology. I am following the outline provided by this article: "use clusterProfiler as an universal enrichment analysis tool", http://guangchuangyu.github.io/2015/05/use-clusterprofiler-as-an-universal-enrichment-analysis-tool/

The problem is that I run into an error:

No gene set have size > 10 ...
--> return NULL...

and I do not not understand this error. My googling and searching the code I assume that this is somehow related to DOSE and a variable "minGSSize: minimal size of genes annotated by Ontology term for testing", but I do not see what is wrong with my custom input ontology; all genes in the group to be tested are contained in the custom ontology, and the terms of the custom ontology which have these genes in them, all have more that 10 members respectively; and the genes_of_interest array also has more than 10 members.

My code is below. I cannot share the actual custom ontology. I tested with several hundred different groups of genes of interest against this custom ontology and had no problem. Only this particular group out of all groups tested triggers the error. However I will expand to several thousand groups in batch and must fix this error first. If I replace my custom ontology with another, custom-slimmed down version of Gene Ontology/Cellular Component, I have no problem as well. Example code (custom ontology cannot be shared):

library("clusterProfiler")

genes_of_interest <- c("MT-CO2","MT-CO3","COX5A","COX7C","COX5B","COX4I1","COX6A1","COX7A2","COX6B1","COX7B","COX8A","COX6C","MT-CO1")

# READ IN A GIVEN ONTOLOGY BUILD
# load the custom ontology
nestsystem2gene <- read.table("long_fuse81_nest_0.15.tsv", header=F)
# instead of the custom ontology I can also load a version of GO, Cellular Component
# nestsystem2gene <- read.table("collapsed_cc.symbol", header=F)

# convert everything to uppercase
df <- data.frame(lapply(nestsystem2gene, function(v) {
  if (is.character(v)) return(toupper(v))
  else return(v)
}))
# /READ IN A GIVEN ONTOLOGY BUILD

x = enricher(genes_of_interest, TERM2GENE=df)

And here is my sessionInfo:

​> sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.5 LTS

Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0

locale:
[1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8      
[4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8  
[7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C             
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] DOSE_3.6.1            clusterProfiler_3.8.1 BiocInstaller_1.30.0 

loaded via a namespace (and not attached):
[1] ggrepel_0.8.0        Rcpp_0.12.18         lattice_0.20-35      tidyr_0.8.1        
[5] GO.db_3.6.0          assertthat_0.2.0     digest_0.6.15        ggforce_0.1.3      
[9] R6_2.3.0             plyr_1.8.4           ggridges_0.5.1       stats4_3.5.1       
[13] RSQLite_2.1.1        ggplot2_3.0.0        pillar_1.3.0         rlang_0.2.2        
[17] lazyeval_0.2.1       rstudioapi_0.8       data.table_1.11.8    blob_1.1.1         
[21] S4Vectors_0.18.3     Matrix_1.2-14        qvalue_2.12.0        splines_3.5.1      
[25] BiocParallel_1.14.2  stringr_1.3.1        igraph_1.2.2         bit_1.1-14         
[29] munsell_0.5.0        fgsea_1.6.0          compiler_3.5.1       pkgconfig_2.0.1    
[33] BiocGenerics_0.26.0  tidyselect_0.2.5     tibble_1.4.2         gridExtra_2.3      
[37] IRanges_2.14.10      enrichplot_1.0.2     viridisLite_0.3.0    crayon_1.3.4       
[41] dplyr_0.7.7          MASS_7.3-50          grid_3.5.1           gtable_0.2.0       
[45] DBI_1.0.0            magrittr_1.5         units_0.6-1          scales_1.0.0       
[49] stringi_1.2.4        GOSemSim_2.6.0       farver_1.0           reshape2_1.4.3     
[53] viridis_0.5.1        bindrcpp_0.2.2       DO.db_2.9            rvcheck_0.1.1      
[57] cowplot_0.9.3        fastmatch_1.1-0      tools_3.5.1          bit64_0.9-7        
[61] Biobase_2.40.0       glue_1.3.0           tweenr_1.0.0         purrr_0.2.5        
[65] yaml_2.2.0           ggraph_1.0.2         parallel_3.5.1       AnnotationDbi_1.42.1
[69] colorspace_1.3-2     UpSetR_1.3.3         memoise_1.1.0        bindr_0.1.1