Hi,
I’m writing about your very useful and complete packages for functional analysis.
In particular I often use DOSE and clusterProfiler and ChipSeeker.
I’m writing about the possibility to perform leading edge analysis in GSEA.
I found on your github page you have already developed this
for gseDO() function in DOSE.
I’m interested in performing GSEA analysis using custom database and would like to perform also leading edge analysis.
I tried GSEA() in clusterProfiler but this does not perform leading edge analysis (in the stable bioconductor version)
and GSEA_internal() in DOSE, here I had additional problem because I cannot give a TERM2GENE table, instead
the option in the function is USER_DATA, which I was not able to reproduce and I did not found an explanation on how
should this field be provided.
Anyhow
could you suggest me the more practical way to be able to perform
custom GSEA analyis with leading edge analysis, perhaps without need to install the Development version of Bioconductor?
I thank you very much and look forward to your answer.
Michela Riba
R version 3.3.1 (2016-06-21)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.11.6 (El Capitan)
locale:
[1] it_IT.UTF-8/it_IT.UTF-8/it_IT.UTF-8/C/it_IT.UTF-8/it_IT.UTF-8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] clusterProfiler_3.0.5 DOSE_2.10.7 pander_0.6.0
[4] DT_0.2 plyr_1.8.4 VennDiagram_1.6.17
[7] futile.logger_1.4.3 gplots_3.0.1 limma_3.28.21
loaded via a namespace (and not attached):
[1] Rcpp_0.12.7 bitops_1.0-6 futile.options_1.0.0
[4] tools_3.3.1 digest_0.6.10 lattice_0.20-34
[7] tibble_1.2 annotate_1.50.0 RSQLite_1.0.0
[10] gtable_0.2.0 graph_1.50.0 igraph_1.0.1
[13] DBI_0.5-1 parallel_3.3.1 SparseM_1.72
[16] topGO_2.24.0 stringr_1.1.0 knitr_1.14
[19] htmlwidgets_0.7 S4Vectors_0.10.3 gtools_3.5.0
[22] caTools_1.17.1 IRanges_2.6.1 stats4_3.3.1
[25] GSEABase_1.34.1 qvalue_2.4.2 Biobase_2.32.0
[28] AnnotationDbi_1.34.4 XML_3.98-1.4 GOSemSim_1.30.3
[31] gdata_2.17.0 tidyr_0.6.0 reshape2_1.4.1
[34] GO.db_3.3.0 DO.db_2.9 ggplot2_2.1.0
[37] lambda.r_1.1.9 magrittr_1.5 matrixStats_0.50.2
[40] splines_3.3.1 scales_0.4.0 htmltools_0.3.5
[43] BiocGenerics_0.18.0 assertthat_0.1 xtable_1.8-2
[46] colorspace_1.2-6 KernSmooth_2.23-15 stringi_1.1.1
[49] munsell_0.4.3
Do you really read my blog post?
Can you copy the sentence that mentioned leading edge analysis and paste it here?
I *think* the OP has this link in mind: here. With the current release version of R/BioC, generating a
cnetplot
of the leading edge genes does indeed not work when using the example dataset/code provided on that page; likely because the output ofgseDO()
has been modified in development *only* to accommodate this functionality.