Hi, everyone.
I've been using the amazing package clusterProfiler and I have a question about visualizing the results.
Well, I've been using the enrichGo() function and plotting its results (enrichResult obj.) with barplot() and dotplot() functions. Now, I'm using gseGo (another test, similar but not the same) and I'd like to plot the results via barplot() and dotplot() again, but I'm getting an error stating:
barplot(gsea_BP) Error in barplot.default(gsea_BP) : 'height' must be a vector or a matrix
or..
dotplot(gsea_BP) Error in (function (classes, fdef, mtable) : unable to find an inherited method for function 'dotplot' for signature '"gseaResult"'
The function works flawlessly for the enrichResult object.
Is there some way to tweak theses functions in order to ge them working? In any case, I thought about extracting the data from those objects and just use ggplot2 by itself.
Thank you all.
> sessionInfo() R version 3.3.2 (2016-10-31) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows >= 8 x64 (build 9200) locale: [1] LC_COLLATE=Portuguese_Brazil.1252 LC_CTYPE=Portuguese_Brazil.1252 [3] LC_MONETARY=Portuguese_Brazil.1252 LC_NUMERIC=C [5] LC_TIME=Portuguese_Brazil.1252 attached base packages: [1] parallel stats graphics grDevices utils datasets methods base other attached packages: [1] clusterProfiler_3.2.14 DOSE_3.0.10 affy_1.52.0 [4] Biobase_2.34.0 BiocGenerics_0.20.0 loaded via a namespace (and not attached): [1] Category_2.40.0 bitops_1.0-6 [3] RColorBrewer_1.1-2 httr_1.2.1 [5] GenomeInfoDb_1.10.3 tools_3.3.2 [7] backports_1.0.5 affyio_1.44.0 [9] R6_2.2.0 rpart_4.1-10 [11] Hmisc_4.0-2 DBI_0.6-1 [13] lazyeval_0.2.0 colorspace_1.3-2 [15] nnet_7.3-12 gridExtra_2.2.1 [17] GGally_1.3.0 DESeq2_1.14.1 [19] preprocessCore_1.36.0 graph_1.52.0 [21] htmlTable_1.9 rtracklayer_1.34.2 [23] ggbio_1.22.4 scales_0.4.1 [25] checkmate_1.8.2 genefilter_1.56.0 [27] RBGL_1.50.0 stringr_1.2.0 [29] digest_0.6.12 Rsamtools_1.26.2 [31] foreign_0.8-67 R.utils_2.5.0 [33] AnnotationForge_1.16.1 XVector_0.14.1 [35] base64enc_0.1-3 dichromat_2.0-0 [37] htmltools_0.3.5 BSgenome_1.42.0 [39] ensembldb_1.6.2 limma_3.30.13 [41] htmlwidgets_0.8 PFAM.db_3.4.0 [43] RSQLite_1.1-2 BiocInstaller_1.24.0 [45] shiny_1.0.1 GOstats_2.40.0 [47] hwriter_1.3.2 BiocParallel_1.8.2 [49] R.oo_1.21.0 acepack_1.4.1 [51] GOSemSim_2.0.4 VariantAnnotation_1.20.3 [53] RCurl_1.95-4.8 magrittr_1.5 [55] GO.db_3.4.0 Formula_1.2-1 [57] Matrix_1.2-8 Rcpp_0.12.10 [59] munsell_0.4.3 S4Vectors_0.12.2 [61] R.methodsS3_1.7.1 yaml_2.1.14 [63] stringi_1.1.5 edgeR_3.16.5 [65] SummarizedExperiment_1.4.0 zlibbioc_1.20.0 [67] plyr_1.8.4 qvalue_2.6.0 [69] AnnotationHub_2.6.5 grid_3.3.2 [71] DO.db_2.9 ReportingTools_2.14.0 [73] lattice_0.20-35 Biostrings_2.42.1 [75] splines_3.3.2 GenomicFeatures_1.26.4 [77] annotate_1.52.1 locfit_1.5-9.1 [79] knitr_1.15.1 fgsea_1.0.2 [81] igraph_1.0.1 GenomicRanges_1.26.4 [83] geneplotter_1.52.0 reshape2_1.4.2 [85] biomaRt_2.30.0 stats4_3.3.2 [87] fastmatch_1.1-0 XML_3.98-1.6 [89] biovizBase_1.22.0 latticeExtra_0.6-28 [91] data.table_1.10.4 httpuv_1.3.3 [93] tidyr_0.6.1 gtable_0.2.0 [95] reshape_0.8.6 ggplot2_2.2.1 [97] mime_0.5 xtable_1.8-2 [99] survival_2.41-3 OrganismDbi_1.16.0 [101] tibble_1.3.0 GenomicAlignments_1.10.1 [103] AnnotationDbi_1.36.2 memoise_1.0.0 [105] IRanges_2.8.2 cluster_2.0.6 [107] interactiveDisplayBase_1.12.0 GSEABase_1.36.0
please also checkout my blog post and also the homepage of clusterProfiler.