I'm using enrichGO function from clusterProfiler package to run a functional enrichment analysis using a set of annotated peaks from a chip-seq experiment (the input is the list of genes obtained by the annotation of peaks, human).
Using the same input data, I'm getting different results from enrichGO function using the latest version when compared to a previous one from beginning 2017. From the older version, I get much more GO terms, with a count value very high for each term (for example, biological process appears as enriched) and now I get less GO terms with a count value quite low. I didn't change function parameters. It seems like now more specific GO terms are returned instead of all enriched terms regardless of the level of GO hierarchy. What was the change in the behavior of enrichGO function?
Thanks,
Javier
> sessionInfo() R version 3.4.3 (2017-11-30) Platform: x86_64-redhat-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core) Matrix products: default BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=es_ES.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=es_ES.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] stats4 parallel stats graphics grDevices utils datasets [8] methods base other attached packages: [1] clusterProfiler_3.6.0 [2] DOSE_3.4.0 [3] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 [4] GenomicFeatures_1.30.3 [5] AnnotationDbi_1.40.0 [6] Biobase_2.38.0 [7] GenomicRanges_1.30.3 [8] GenomeInfoDb_1.14.0 [9] IRanges_2.12.0 [10] S4Vectors_0.16.0 [11] BiocGenerics_0.24.0 [12] ChIPseeker_1.14.2 loaded via a namespace (and not attached): [1] httr_1.3.1 tidyr_0.8.0 [3] RMySQL_0.10.14 bit64_0.9-7 [5] splines_3.4.3 gtools_3.5.0 [7] assertthat_0.2.0 DO.db_2.9 [9] rvcheck_0.0.9 blob_1.1.0 [11] GenomeInfoDbData_1.0.0 Rsamtools_1.30.0 [13] progress_1.1.2 pillar_1.2.1 [15] RSQLite_2.0 lattice_0.20-35 [17] glue_1.2.0 digest_0.6.15 [19] RColorBrewer_1.1-2 XVector_0.18.0 [21] qvalue_2.10.0 colorspace_1.3-2 [23] Matrix_1.2-12 plyr_1.8.4 [25] XML_3.98-1.10 pkgconfig_2.0.1 [27] biomaRt_2.34.2 zlibbioc_1.24.0 [29] purrr_0.2.4 GO.db_3.5.0 [31] scales_0.5.0 gdata_2.18.0 [33] BiocParallel_1.12.0 tibble_1.4.2 [35] ggplot2_2.2.1 UpSetR_1.3.3 [37] SummarizedExperiment_1.8.1 lazyeval_0.2.1 [39] magrittr_1.5 memoise_1.1.0 [41] gplots_3.0.1 tools_3.4.3 [43] data.table_1.10.4-3 prettyunits_1.0.2 [45] gridBase_0.4-7 matrixStats_0.53.1 [47] stringr_1.3.0 munsell_0.4.3 [49] plotrix_3.7 DelayedArray_0.4.1 [51] bindrcpp_0.2 Biostrings_2.46.0 [53] compiler_3.4.3 caTools_1.17.1 [55] rlang_0.2.0 grid_3.4.3 [57] RCurl_1.95-4.10 igraph_1.2.1 [59] bitops_1.0-6 boot_1.3-20 [61] gtable_0.2.0 DBI_0.8 [63] reshape2_1.4.3 R6_2.2.2 [65] GenomicAlignments_1.14.1 gridExtra_2.3 [67] dplyr_0.7.4 rtracklayer_1.38.3 [69] bit_1.1-12 bindr_0.1.1 [71] fastmatch_1.1-0 fgsea_1.4.1 [73] KernSmooth_2.23-15 GOSemSim_2.4.1 [75] stringi_1.1.7 Rcpp_0.12.16
