malhamdoosh / egsea Goto Github PK

View Code? Open in Web Editor NEW

9.0 5.0 4.0 5.74 MB

This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing.

R 98.57% TeX 1.43%

bioinformatics-pipeline pathway-analysis gene-set-enrichment bioconductor-package

egsea's Introduction

Ensemble of Gene Set Enrichment Analyses

_{Credit: Roberto Bonelli}

This package is part of the Bioconductor project and implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing.

Author: Monther Alhamdoosh, Luyi Tian, Milica Ng and Matthew Ritchie

Maintainer: Monther Alhamdoosh <m.hamdoosh at gmail.com>

Citation (from within R, enter citation("EGSEA")):

Alhamdoosh M, Ng M, Wilson N, Sheridan J, Huynh H, Wilson M and Ritchie M (2017). “Combining multiple tools outperforms individual methods in gene set enrichment analyses.” Bioinformatics, 33(3). doi: 10.1093/bioinformatics/btw623.

Alhamdoosh M, Law CW, Tian L et al. Easy and efficient ensemble gene set testing with EGSEA [version 1; peer review: 1 approved, 3 approved with reservations]. F1000Research 2017, 6:2010. doi: 10.12688/f1000research.12544.1

Installation

To install the stable release of this package, start R and enter:

## try http:// if https:// URLs are not supported
if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("EGSEA")

To install the development version of this package, start R and enter:

install.packages("devtools") # if you have not installed "devtools" package
devtools::install_github("malhamdoosh/EGSEA")

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("EGSEA")

egsea's People

Contributors

Stargazers

Watchers

Forkers

llrs scfurl bnanes olivervoogd

egsea's Issues

Feature Request: topSets enhancements

topSets returns information similar to the stats page in the html report but is missing the NumGenes and Contributor columns. Would like to see these columns also returned by topSets.

If I understand, the NumGenes column correctly, for X/Y numbers, Y corresponds to the number of genes annotated as members of a pathway and X represents the number of those genes present in the input dataset. It would also be useful to report the number of genes regulated based on a settable foldchange/p.adj thresholds. In addition, it would also be useful (perhaps optionally) to return the regulated gene symbols.

Specifically for the KEGG collection, it would be useful if topSets also returns the Type field (metabolism, signaling etc.).

John Thompson (BMS)

Dealing with 'NA' or absent entrezids for rownames in voom object

Within the voom Elist, I have rownames defined as gene names. Since the required input is entrezids, I added a gene list slot to the voom object. However the number fo entrezids < gene names (16451 vs 16573). When I replace the missing values with either 'NA' or '0', it gives an error stating duplicate row name:
Error in row.names<-.data.frame(*tmp*, value = value) : duplicate 'row.names' are not allowed

Is there a way to neglect 'NA' values within the egsea command ? Else how to subset the voom Elist to contain only non-missing values ?

Feature Request: More flexible data input

I would like to see the DGE calculations separated from the egsea analysis. It would be most convenient and flexible to simply supply a list of topTable dataframes as input for the enrichment analysis. For example, we often use the duplicateCorrelation method to account for repeated measures on subjects within a design. Unless I've missed it, the duplicateCorrelation method is not supported in the limma voom workflow within the current egsea function. Perhaps more importantly, a list of topTable DF as input would allow me to analyze and compare contrasts from different experiments.

Best Regards,
John Thompson, Ph.D.
Research Fellow
Translational Bioinformatics
Bristol-Myers Squibb

Typo in man page for showSetByName

showSetByName is listed as showSetByname on line 252 of EGSEA/man/EGSEAResults-methods.Rd. I just happened to copy that one from the help file and was confused at first as to why I was getting an error of could not find function "showSetByname".

Error in Show Pathway

Hello,

I have used EGSEA few times producing reports but I noticed that once I produce the Report , checking the Stats Table in KEGG Pathways and looking at pathways column, some of the Show Pathways are giving errors "File not found" which seems like those pathways files are missing in the corresponding folders. Have anyone noticed the same problem ?

Best,
Monika

Error when sort.by not pvals

Error in egsea.comparison[, sort.by] : subscript out of bounds

Error in initialize(value, ...): l'argomento "go_id" non è specificato e non ha un valore predefinito

Error in initialize(value, ...): l'argomento "go_id" non è specificato e non ha un valore predefinito
Traceback:

buildIdx(entrezIDs = orthologs_biomarkers_monkey_delta_entrez_human$entrezgene_human,
. species = "human", gsdb.gsets = "all", msigdb.gsets = "none",
. kegg.exclude = c("Metabolism"))
buildGeneSetDBIdx(entrezIDs = entrezIDs, species = species, geneSets = gsdb.gsets,
. go.part = go.part, min.size = min.size)
xx[[go.terms[i]]]
xx[[go.terms[i]]]
.doubleBracketSub(x, i, j, ...)
mget(i, envir = x, ifnotfound = NA)
mget(i, envir = x, ifnotfound = NA)
as.list(envir)
as.list(envir)
.toListOfLists(y, mode = 1, makeGONode)
do.call(FUN, slice_one)
do.call(FUN, slice_one)
(function (go_id, Term, Ontology, Definition, ...)
. {
. new("GOTerms", GOID = go_id[1], Term = Term[1], Ontology = Ontology[1],
. Definition = Definition[1], ...)
. })(go_id..2 = c("GO:0032201", "GO:0032201", "GO:0032201"), Term = c("telomere maintenance via semi-conservative replication",
. "telomere maintenance via semi-conservative replication", "telomere maintenance via semi-conservative replication"
. ), Ontology = c("BP", "BP", "BP"), Definition = c("The process in which telomeric DNA is synthesized semi-conservatively by the conventional replication machinery and telomeric accessory factors as part of cell cycle DNA replication.",
. "The process in which telomeric DNA is synthesized semi-conservatively by the conventional replication machinery and telomeric accessory factors as part of cell cycle DNA replication.",
. "The process in which telomeric DNA is synthesized semi-conservatively by the conventional replication machinery and telomeric accessory factors as part of cell cycle DNA replication."
. ), Synonym = c("equal telomere replication", "telomeric fork progression",
. "telomeric replication fork progression"), Secondary = c(NA_character_,
. NA_character_, NA_character_))
new("GOTerms", GOID = go_id[1], Term = Term[1], Ontology = Ontology[1],
. Definition = Definition[1], ...)
initialize(value, ...)
initialize(value, ...)

Feature Request: Improvements to graphic handling

It seems the plotting tools only support output to file. In a markdown context the user is thus forced to use markdown syntax:

 ![graphname](path/filename)

to import the graphics into a markdown report. This image import tag lacks control over image size and I find KEGG graphs import in postage stamp size. Sending output directly to the console would allow utilization of chunk options to control the output and resolution when including graphics in markdown reports.

Along the same lines, wherever ggplot is used for the graphics, returning the ggplot object would be most convenient. This would give the user the opportunity to further customize/annotate these plots before printing/saving and would allow the use of the ggsave function to provide finer control over size, dpi and file format.

Many of the dotplots would be improved by utilization of transparency to better interpret regions where many pathway points overlap.

Lastly, for interactive plots, showing the pathway name in a mouseover would be highly desirable.

Best,
John Thompson (BMS)

defensive programming for buildIdx to avoid bad gene sets

gs.annots = buildIdx(entrezIDs = NULL, species = "mouse",
msigdb.gsets = gsdb.gsets, kegg.exclude = "all")

add this to function
stopifnot(!is.null(entrezIDs))

One of the base methods failed on this dataset

Hello,
I get plenty of these errors while it was not happening before.

Log fold changes are estimated using limma package ... 
limma DE analysis is carried out ... 
EGSEA is running on the provided data and h collection

EGSEA is running on the provided data and c1 collection

Error in runegsea(voom.results = voom.results, contrast = contrast, limma.tops = limma.tops,  : 
  ERROR: One of the base methods failed on this dataset (zscore).
Remove it and try again.
See error messages for more information.

Could you please add a running example in the README file here, so users can check whether the baseline algorithm is working, and check the input formats.

egsea(
					voom.results=v, 
					contrasts=contrasts, 
					gs.annots=buildIdx(entrezIDs=rownames(v), species="human"), 
					symbolsMap=
						v %$% 
						genes %>% 
						dplyr::select(1:2) %>%
						setNames(c("FeatureID", "Symbols")),
					baseGSEAs = egsea.base()[-c(8, 5, 4)],
					sort.by="med.rank",
					num.threads = 16
				)


Browse[1]> sessionInfo()
R version 3.5.0 (2018-04-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS release 6.4 (Final)

Matrix products: default
BLAS: /wehisan/general/system/bioinf-software/bioinfsoftware/R/R-3.5.0/lib64/R/lib/libRblas.so
LAPACK: /wehisan/general/system/bioinf-software/bioinfsoftware/R/R-3.5.0/lib64/R/lib/libRlapack.so

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

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] EGSEA_1.9.1          EGSEAdata_1.8.0      foreach_1.4.6        RColorBrewer_1.1-2   pathview_1.20.0      topGO_2.32.0        
 [7] SparseM_1.77         GO.db_3.6.0          graph_1.58.0         gage_2.30.0          BiocInstaller_1.30.0 bindrcpp_0.2.2      
[13] ggdendro_0.1-20      ruv_0.9.7            sva_3.28.0           BiocParallel_1.13.3  genefilter_1.61.1    mgcv_1.8-23         
[19] nlme_3.1-137         magrittr_1.5         forcats_0.3.0        stringr_1.3.1        dplyr_0.7.6          purrr_0.2.5         
[25] readr_1.1.1          tidyr_0.8.1          tibble_1.4.2         ggplot2_3.0.0.9000   tidyverse_1.2.1      hgu95av2.db_3.2.3   
[31] org.Hs.eg.db_3.6.0   AnnotationDbi_1.42.1 IRanges_2.14.11      S4Vectors_0.18.3     Biobase_2.40.0       BiocGenerics_0.26.0 
[37] edgeR_3.22.3         limma_3.36.3        

loaded via a namespace (and not attached):
  [1] utf8_1.1.3               R.utils_2.6.0            tidyselect_0.2.4         RSQLite_2.1.0            htmlwidgets_1.2         
  [6] grid_3.5.0               KEGG.db_3.2.3            R2HTML_2.3.2             devtools_1.13.6          munsell_0.5.0           
 [11] codetools_0.2-15         DT_0.4                   KEGGdzPathwaysGEO_1.18.0 withr_2.1.2              colorspace_1.4-0        
 [16] knitr_1.20               rstudioapi_0.7           gbRd_0.4-11              Rdpack_0.8-0             labeling_0.3            
 [21] git2r_0.21.0             KEGGgraph_1.40.0         org.Rn.eg.db_3.6.0       mnormt_1.5-5             hwriter_1.3.2           
 [26] bit64_0.9-7              R6_2.2.2                 Glimma_1.8.2             locfit_1.5-9.1           bitops_1.0-6            
 [31] assertthat_0.2.0         promises_1.0.1           scales_1.0.0             gtable_0.2.0             org.Mm.eg.db_3.6.0      
 [36] rlang_0.2.1              splines_3.5.0            lazyeval_0.2.1           PADOG_1.22.0             broom_0.4.4             
 [41] yaml_2.1.18              reshape2_1.4.3           modelr_0.1.2             httpuv_1.4.5             tools_3.5.0             
 [46] psych_1.8.3.3            gplots_3.0.3             HTMLUtils_0.1.7          Rcpp_0.12.18             plyr_1.8.4              
 [51] zlibbioc_1.25.0          RCurl_1.95-4.10          hgu133plus2.db_3.2.3     haven_1.1.1              ggrepel_0.7.0           
 [56] data.table_1.10.4-3      R.cache_0.13.0           matrixStats_0.53.1       hms_0.4.2                mime_0.5                
 [61] GSVA_1.28.0              xtable_1.8-3             globaltest_5.33.0        XML_3.98-1.11            hgu133a.db_3.2.3        
 [66] readxl_1.1.0             gridExtra_2.3            compiler_3.5.0           safe_3.20.0              KernSmooth_2.23-15      
 [71] crayon_1.3.4             R.oo_1.22.0              htmltools_0.3.6          later_0.7.3              geneplotter_1.57.0      
 [76] lubridate_1.7.4          DBI_1.0.0                MASS_7.3-50              Matrix_1.2-14            cli_1.0.0               
 [81] R.methodsS3_1.7.1        gdata_2.18.0             metap_1.0                bindr_0.1.1              pkgconfig_2.0.1         
 [86] registry_0.5             foreign_0.8-70           plotly_4.8.0             xml2_1.2.0               annotate_1.57.3         
 [91] rngtools_1.2.4           pkgmaker_0.25.8          XVector_0.19.9           bibtex_0.4.2             rvest_0.3.2             
 [96] R.rsp_0.42.0             doRNG_1.6.6              digest_0.6.16            Biostrings_2.47.12       cellranger_1.1.0        
[101] GSEABase_1.42.0          curl_3.2                 shiny_1.1.0              gtools_3.5.0             GSA_1.03                
[106] jsonlite_1.5             viridisLite_0.3.0        pillar_1.2.2             lattice_0.20-35          KEGGREST_1.19.2         
[111] httr_1.3.1               survival_2.42-3          glue_1.3.0               png_0.1-7                shinythemes_1.1.1       
[116] iterators_1.0.9          bit_1.1-13               Rgraphviz_2.23.0         stringi_1.2.3            blob_1.1.1              
[121] caTools_1.17.1           memoise_1.1.0

Thanks

Rename entrezIDs into geneIDs in egsea.ora

egsea.ora fails when 'title' has spaces

To fix this error message when running egsea.ora with the parameter 'title' contains white spaces

Error in egsea.main(voom.results, contrasts, gs.annots, baseGSEAs, combineMethod, : logFC should be a matrix object with column names equal to the (column) names of the argument 'contrast'.

It mainly occurs because spaces and special characters are removed from contrast names.

Conversion between the different ID annotations

Hello,

Many a times, RNAseq data is annotated with ensembl ids. Though there are external tools available, It would be great if there are some internal function for doing transformation ensembl to entrez conversion.

Cheers,
Piyush

Is it possible to use pre-ranked gene lists in EGSEA?

Hi,

I don't know if this is the proper channel to make this question, but I was wondering if it is possible to use a pre-ranked list in EGSEA or we need a countmatrix or voom object to start with? I have some colleagues who work on single-cell data, and the structure is a bit different. It is not possible to get a countmtx, and using voom doesn't make sense since Seurat works differently than bulk-RNAseq and microarray (EdgeR, limma, voom).
So would it be possible to get a list of differentially expressed genes (findAllmarkers function from Seurat) and input that pre-ranked list in EGSEA, such as it is possible using GSEA?

Thanks in advance!
Tiago

Integrating EGSEA plotSummary in Rmarkdown

Hi Monther,
Thanks for your work on EGSEA. I think its a great tool for gene enrichment analyses, and started using it recently. I am following up on our email conversation regarding the following issue.
I want to use EGSEA in a pipeline that I run in Rmarkdown, wherein the output figures are generated in a html file (our a pdf file) report. When I use plotSummary, the plots are saved in the working directory instead of being printed in the html file. Is there a way such that plotSummary will print the plot to the open graphic device instead of saving the file. I tried leaving file.name empty, but by default the plot is saved in the current working directory.

It will be great to add this feature. Thanks.

EGSEA::egsea - Parallelization issue and "Error in array(col.rgb[, i], dim(node.rgb)[3:1]) : negative length vectors are not allowed"

1.) EGSEA testing with report generation enabled consistently gives me the following error:
Error in array(col.rgb[, i], dim(node.rgb)[3:1]) : negative length vectors are not allowed
This error occurs for both the report=TRUE setting or using the manual S4 method generateReport() function.

2.) Parallelization does not seem to work regardless of how many threads my processor has available. This is the consistent message seen at the beginning of the EGSEA analysis:

Number of used cores has changed to ... in order to avoid CPU overloading.

For example, I followed a demonstration similar to the posted guide and vignette:

# HTML report generation and parallel processing do not work 
# Packages needed
if (!requireNamespace("BiocManager"))
    install.packages("BiocManager")
BiocManager::install(c("edgeR", "EGSEA"))

library(edgeR)
library(EGSEA)

# Load and read in example voom object and contrast matrix from wd()
# https://github.com/nztao/EGSEA_issue_example-/blob/main/egsea_issue_example.RData
load("egsea_issue_example.RData")

# EGSEA Configuration
# Example: Contrast Matrix
example.cf.matrix <- cf.matrices.combined.inter$BPA$'10'

# Example: buildIdx uses Entrez IDs for Hallmark Collection and KEGG pathways
example.gs.annots <-  buildIdx(entrezIDs= rownames(komen.voomqw.egsea$E),
                          species="human", 
                          msigdb.gsets= "h",
                          kegg.updated = T)

# Example: base methods
example.egsea.base <- egsea.base()[c(1,2,12)]

# Ensemble testing with EGSEA
example.egsea.issue<- egsea(voom.results = komen.voomqw.egsea, 
        contrasts = example.cf.matrix,
        gs.annots= example.gs.annots, 
        baseGSEAs = example.egsea.base, 
        sort.by="med.rank",  
        symbolsMap= komen.voomqw.egsea$genes,
        #Issue 1: Parallel substantially underutilizes threads
        num.threads = 16,
        verbose = T,
        #Issue 2: HTML 'report = T' and 'generateReport()' give the same error:
        # Error in array(col.rgb[, i], dim(node.rgb)[3:1]) :
        # negative length vectors are not allowed 
        report = T, report.dir = "./example_report_issue")

I am wondering if it's an issue with my data, but I am at a loss at where to diagnose and how to fix it. I tried troubleshooting and verifying for these issues such as:

Issue 1: Error in array(col.rgb[, i], dim(node.rgb)[3:1]) : negative length vectors are not allowed

any(is.na(komen.voomqw.egsea$genes$FeatureID)),
any(is.na(komen.voomqw.egsea$genes$Symbols)),
symbolsMap= c("FeatureID", "Symbols") in the colnames of komen.voomqw.egsea$genes[,1:2]
Trying at least 3 or more base methods and different combinations of kegg and MSigDB gs.annots

Issue 2: Number of used cores has changed to ... in order to avoid CPU overloading.

Using different processors and contrast matrices (i.e. i9-9900K, 16 cores changes to 4 when processing all the contrast matrices; AMD Ryzen Threadripper PRO 3975WX 32-Cores, 60 cores changes to 11 when processing all the contrast matrices, only 2-5% CPU is utilized)

My apologies if these are two separate issues, the lack of parallelization and report issues both contribute to significant time losses. Any suggestions?

Thank you!

Here is my sessioninfo:

R version 4.2.2 (2022-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.utf8  LC_CTYPE=English_United States.utf8    LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                           LC_TIME=English_United States.utf8    

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] EGSEA_1.26.0         pathview_1.38.0      topGO_2.50.0         SparseM_1.81         GO.db_3.16.0        
 [6] graph_1.76.0         AnnotationDbi_1.60.2 IRanges_2.32.0       S4Vectors_0.36.1     gage_2.48.0         
[11] Biobase_2.58.0       BiocGenerics_0.44.0  edgeR_3.40.2         limma_3.54.1        

loaded via a namespace (and not attached):
  [1] utf8_1.2.3                  tidyselect_1.2.0            RSQLite_2.3.0               htmlwidgets_1.6.2          
  [5] grid_4.2.2                  BiocParallel_1.32.5         R2HTML_2.3.3                munsell_0.5.0              
  [9] ScaledMatrix_1.6.0          codetools_0.2-19            mutoss_0.1-13               DT_0.27                    
 [13] KEGGdzPathwaysGEO_1.36.0    colorspace_2.1-0            knitr_1.42                  rstudioapi_0.14            
 [17] SingleCellExperiment_1.20.1 MatrixGenerics_1.10.0       Rdpack_2.4                  KEGGgraph_1.58.3           
 [21] BiocSet_1.12.1              org.Rn.eg.db_3.16.0         GenomeInfoDbData_1.2.9      mnormt_2.1.1               
 [25] hwriter_1.3.2.1             bit64_4.0.5                 rhdf5_2.42.1                vctrs_0.5.2                
 [29] generics_0.1.3              TH.data_1.1-1               xfun_0.37                   R6_2.5.1                   
 [33] doParallel_1.0.17           GenomeInfoDb_1.34.9         clue_0.3-64                 rsvd_1.0.5                 
 [37] locfit_1.5-9.7              bitops_1.0-7                rhdf5filters_1.10.1         cachem_1.0.6               
 [41] DelayedArray_0.23.0         BiocIO_1.8.0                scales_1.2.1                multcomp_1.4-23            
 [45] gtable_0.3.3                beachmat_2.14.2             org.Mm.eg.db_3.16.0         sandwich_3.0-2             
 [49] rlang_1.1.0                 GlobalOptions_0.1.2         splines_4.2.2               lazyeval_0.2.2             
 [53] PADOG_1.40.0                checkmate_2.1.0             yaml_2.3.7                  backports_1.4.1            
 [57] tools_4.2.2                 ggplot2_3.4.2               gplots_3.1.3                RColorBrewer_1.1-3         
 [61] HTMLUtils_0.1.8             sparrow_1.4.0               TFisher_0.2.0               Rcpp_1.0.10                
 [65] plyr_1.8.8                  sparseMatrixStats_1.10.0    zlibbioc_1.44.0             purrr_1.0.1                
 [69] RCurl_1.98-1.10             GetoptLong_1.0.5            viridis_0.6.2               hgu133plus2.db_3.13.0      
 [73] zoo_1.8-11                  SummarizedExperiment_1.28.0 cluster_2.1.4               magrittr_2.0.3             
 [77] data.table_1.14.8           circlize_0.4.15             mvtnorm_1.1-3               matrixStats_0.63.0         
 [81] evaluate_0.20               GSVA_1.46.0                 xtable_1.8-4                globaltest_5.52.1          
 [85] XML_3.99-0.13               hgu133a.db_3.13.0           gridExtra_2.3               EGSEAdata_1.26.0           
 [89] shape_1.4.6                 compiler_4.2.2              safe_3.38.0                 tibble_3.1.8               
 [93] KernSmooth_2.23-20          crayon_1.5.2                htmltools_0.5.4             tidyr_1.3.0                
 [97] DBI_1.1.3                   ComplexHeatmap_2.14.0       MASS_7.3-58.3               babelgene_22.9             
[101] Matrix_1.5-3                cli_3.6.0                   rbibutils_2.2.13            parallel_4.2.2             
[105] metap_1.8                   qqconf_1.3.1                GenomicRanges_1.50.2        pkgconfig_2.0.3            
[109] sn_2.1.1                    numDeriv_2016.8-1.1         plotly_4.10.1               foreach_1.5.2              
[113] annotate_1.76.0             rngtools_1.5.2              multtest_2.54.0             XVector_0.38.0             
[117] doRNG_1.8.6                 digest_0.6.29               Biostrings_2.66.0           rmarkdown_2.21             
[121] DelayedMatrixStats_1.20.0   GSEABase_1.60.0             curl_5.0.0                  gtools_3.9.4               
[125] rjson_0.2.21                GSA_1.03.2                  lifecycle_1.0.3             nlme_3.1-162               
[129] jsonlite_1.8.4              Rhdf5lib_1.20.0             viridisLite_0.4.1           fansi_1.0.4                
[133] pillar_1.9.0                ontologyIndex_2.10          lattice_0.20-45             KEGGREST_1.38.0            
[137] fastmap_1.1.0               httr_1.4.5                  plotrix_3.8-2               survival_3.5-5             
[141] glue_1.6.2                  png_0.1-8                   iterators_1.0.14            bit_4.0.5                  
[145] Rgraphviz_2.42.0            stringi_1.7.12              HDF5Array_1.26.0            blob_1.2.4                 
[149] org.Hs.eg.db_3.16.0         BiocSingular_1.14.0         caTools_1.18.2              memoise_2.0.1              
[153] mathjaxr_1.6-0              dplyr_1.1.0                 irlba_2.3.5.1

Implement additional visualizations using joyplots

Nice viz for gene set analysis from

https://www.r-bloggers.com/joyplot-for-gsea-result/