yulab-smu/reactomepa

Reactome Pathway Analysis

ReactomePA: Reactome Pathway Analysis

This package provides functions for pathway analysis based on REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization.

For details, please visit https://yulab-smu.top/biomedical-knowledge-mining-book/.

✍️ Authors

Guangchuang YU

School of Basic Medical Sciences, Southern Medical University

https://yulab-smu.top

---

Please cite the following article when using ReactomePA:

G Yu, QY He^*. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 2016, 12(2):477-479.

reactomepa's Issues

installation from Github

The installation from Github may need the R package devtools.
How about

devtools::install_github(c("GuangchuangYu/DOSE", "GuangchuangYu/ReactomePA"))

?

support for plants?

Hi there,

Hope you can help me figure out what was wrong. I tried to use the gsePathway function to analyze some RNA-seq data from maize, I first ranked the genes (using metrics: -log10(padj) * sign(log2FoldChange)), then loaded the ReactomePA package, used command:

Reactomeresult <- gsePathway(genelist, organism = "zma", pAdjustMethod = "BH", minGSSize = 10, maxGSSize = 1000, pvalueCutoff=0.05)

I got an error:
character(0)
Failed with error: ‘'package' must be of length 1’

I googled the problem, seems like it's due to the lack of support for maize, but I see that the plant reactome is available,
https://plantreactome.gramene.org/index.php?lang=en
just wonder if the support for plant species will be added, is there any other ways to do this kind of analysis before that becomes available?

Thanks very much for your time!

No gene can be mapped, return NULL

Hi!

I am very new to Reactome and Pathway Analysis. I have a .csv file that contains only gene IDs in the Entrez form. I.e. "23956”, “56277”, “12268”, “20259”, “16157”, “11815". However, whenever I try to run the enrichPathway function, I get the error:
--> No gene can be mapped....
--> Expected input gene ID: 12491,74030,100502803,27801,12727,208092
--> return NULL...

The organism that I'm working with is mouse and the function I am using is:
x <- enrichPathway(gene=mydata,organism = "mouse", readable=T)

I have checked the IDs in KEGG and it does recognize the genes and the data associated with it. I'm really struggling to understand what could be causing the issue. Thanks!

I have also tried it with data that includes the logFC and p values, but get the same error.

NO plot output + enrichPathway error

Hi,
I wanted to see the GO terms for both up (log2FC>1) and down regulated genes (log2Fc<= -1). I am able to get results for the downregulated genes but not for the upregulated genes (FC >2). Also my code run perfectly with the downrgulated genes and with the upregulated gene I can get the results file if I export it in excel but not the plots. I have tried also to remove where there was NA.

my dataset:

# A tibble: 3 x 5
  ENSEMBL         ENTREZID  log2FC p_value    FC
  <chr>           <chr>      <dbl>   <dbl> <dbl>
1 ENSG00000236257 100129866   1.29  0.0136  2.44
2 ENSG00000267278 100133991   1.13  0.0433  2.19
3 ENSG00000236695 100287191   1.78  0.0206  3.43

then my code with the output for each line is:

#load packages
library(org.Hs.eg.db) #human genome
library(DOSE)
library(ReactomePA) # to perform enrichment analysis
library(enrichplot) # to plot the results
library(UpSetR)

genes <- d[[5]] #numeric vector #selected fc
    head(genes)
     [1] 2.444807 2.185109 3.430162 4.061950 2.766760
[6] 2.157705

names(genes) <- d[[2]] #named vector
head(genes)
100129866 100133991 100287191 100289099 
 2.444807  2.185109  3.430162  4.061950 
100418943 100421451 
 2.766760  2.157705

geneLIST <- sort(genes, decreasing = T) #decreasing order
head(geneLIST)
     <NA>      <NA>      <NA>    150677 
90.269867 57.499633 22.920100 11.773691 

de <- names(geneLIST)
head(de)
[1] NA       NA       NA       "150677" NA      
[6] "11067" 
x <- enrichPathway(gene=de,pvalueCutoff=0.05, readable=T)
head(as.data.frame(x))
[1] ID          Description GeneRatio  
[4] BgRatio     pvalue      p.adjust   
[7] qvalue      geneID      Count      
<0 rows> (or 0-length row.names)

result <- x@result #to see the result
head(result,2)
                        ID
R-HSA-196854   R-HSA-196854
R-HSA-5635838 R-HSA-5635838
                                       Description
R-HSA-196854  Metabolism of vitamins and cofactors
R-HSA-5635838                    Activation of SMO
              GeneRatio   BgRatio      pvalue
R-HSA-196854       5/58 189/10654 0.003567628
R-HSA-5635838      2/58  18/10654 0.004213869
               p.adjust    qvalue
R-HSA-196854  0.4646278 0.4646278
R-HSA-5635838 0.4646278 0.4646278
                                      geneID
R-HSA-196854  AKR1B10/AKR1C3/IDH1/SLC2A1/BTD
R-HSA-5635838                     GAS1/PTCH1
              Count
R-HSA-196854      5
R-HSA-5635838     2

so I can get the results file but not the plot it seems that there is a problem with enrichPathway but I don't know how to solve it.
thank you for the help!

sessionInfo:

R version 4.0.0 (2020-04-24)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
locale:
[1] LC_COLLATE=English_United Kingdom.1252 
[2] LC_CTYPE=English_United Kingdom.1252   
[3] LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C                           
[5] LC_TIME=English_United Kingdom.1252    

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

other attached packages:
 [1] UpSetR_1.4.0         enrichplot_1.8.1    
 [3] ReactomePA_1.32.0    DOSE_3.14.0         
 [5] org.Hs.eg.db_3.11.4  AnnotationDbi_1.50.0
 [7] IRanges_2.22.2       S4Vectors_0.26.1    
 [9] Biobase_2.48.0       BiocGenerics_0.34.0 
[11] readxl_1.3.1        

loaded via a namespace (and not attached):
 [1] bit64_0.9-7         RColorBrewer_1.1-2 
 [3] progress_1.2.2      httr_1.4.1         
 [5] backports_1.1.7     tools_4.0.0        
 [7] R6_2.4.1            DBI_1.1.0          
 [9] colorspace_1.4-1    graphite_1.34.0    
[11] tidyselect_1.1.0    gridExtra_2.3      
[13] prettyunits_1.1.1   bit_1.1-15.2       
[15] compiler_4.0.0      graph_1.66.0       
[17] scatterpie_0.1.4    xml2_1.3.2         
[19] triebeard_0.3.0     checkmate_2.0.0    
[21] scales_1.1.1        ggridges_0.5.2     
[23] rappdirs_0.3.1      stringr_1.4.0      
[25] digest_0.6.25       pkgconfig_2.0.3    
[27] rlang_0.4.6         rstudioapi_0.11    
[29] RSQLite_2.2.0       gridGraphics_0.5-0 
[31] farver_2.0.3        generics_0.0.2     
[33] jsonlite_1.6.1      BiocParallel_1.22.0
[35] GOSemSim_2.14.0     dplyr_1.0.0        
[37] magrittr_1.5        ggplotify_0.0.5    
[39] GO.db_3.11.4        Matrix_1.2-18      
[41] Rcpp_1.0.4.6        munsell_0.5.0      
[43] viridis_0.5.1       lifecycle_0.2.0    
[45] stringi_1.4.6       ggraph_2.0.3       
[47] MASS_7.3-51.5       plyr_1.8.6         
[49] qvalue_2.20.0       grid_4.0.0         
[51] blob_1.2.1          ggrepel_0.8.2      
[53] DO.db_2.9           crayon_1.3.4       
[55] lattice_0.20-41     graphlayouts_0.7.0 
[57] cowplot_1.0.0       splines_4.0.0      
[59] hms_0.5.3           pillar_1.4.4       
[61] fgsea_1.14.0        igraph_1.2.5       
[63] reshape2_1.4.4      fastmatch_1.1-0    
[65] glue_1.4.1          data.table_1.12.8  
[67] BiocManager_1.30.10 vctrs_0.3.1        
[69] tweenr_1.0.1        urltools_1.7.3     
[71] cellranger_1.1.0    gtable_0.3.0       
[73] purrr_0.3.4         polyclip_1.10-0    
[75] tidyr_1.1.0         ggplot2_3.3.1      
[77] xfun_0.14           ggforce_0.3.1      
[79] europepmc_0.4       tidygraph_1.2.0    
[81] reactome.db_1.70.0  viridisLite_0.3.0  
[83] tibble_3.0.1        rvcheck_0.1.8      
[85] tinytex_0.23        memoise_1.1.0      
[87] ellipsis_0.3.1

problems with visualizing results

Hi Guangchuang,
I started exploring ReactomePA and have problems making graphs of the results; based on the error message it seems to me there is a bug.

Using he code from: http://www.bioconductor.org/packages/release/bioc/vignettes/ReactomePA/inst/doc/ReactomePA.html

> library(ReactomePA)
> data(geneList)
> de <- names(geneList)[abs(geneList) > 1.5]
> x <- enrichPathway(gene=de,pvalueCutoff=0.05, readable=T)
> barplot(x, showCategory=8)
Error in structure(list(family = family, face = face, colour = colour,  : 
  could not find function "margin"
> 

The same margin error happens when trying to plot the results from compareCluster:

> require(clusterProfiler)
> data(gcSample)
> res <- compareCluster(gcSample, fun="enrichPathway")
> plot(res)
Error in structure(list(family = family, face = face, colour = colour,  : 
  could not find function "margin"
In addition: Warning message:
In summary(model) :
  summary method to convert the object to data.frame is deprecated, please use as.data.frame instead.
> plot(res)

... and gseaplot()

> gseaplot(y, geneSetID = "1280215")
Error in structure(list(family = family, face = face, colour = colour,  : 
  could not find function "margin"

Please note that the above warning regarding the use of as.data.frame (instead of data.frame) is reported various times, also for example when using the plotting functions enrichMap(), and cnetplot().

> enrichMap(x, layout=igraph::layout.kamada.kawai, vertex.label.cex = 1)
Warning message:
In summary(x) :
  summary method to convert the object to data.frame is deprecated, please use as.data.frame instead.
>

Thanks,
Guido

> sessionInfo()
R version 3.3.1 Patched (2016-10-18 r71535)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

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

other attached packages:
 [1] graphite_1.20.1       org.Hs.eg.db_3.4.0    AnnotationDbi_1.36.0  IRanges_2.8.1        
 [5] S4Vectors_0.12.0      Biobase_2.34.0        BiocGenerics_0.20.0   ReactomePA_1.18.0    
 [9] clusterProfiler_3.2.2 DOSE_3.1.1            BiocInstaller_1.24.0 

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.7        plyr_1.8.4         tools_3.3.1        RSQLite_1.0.0     
 [5] tibble_1.2         gtable_0.2.0       graph_1.52.0       fastmatch_1.0-4   
 [9] igraph_1.0.1       DBI_0.5-1          fgsea_1.1.0.9000   gridExtra_2.2.1   
[13] stringr_1.1.0      rappdirs_0.3.1     grid_3.3.1         qvalue_2.6.0      
[17] data.table_1.9.6   BiocParallel_1.8.1 GOSemSim_2.0.0     reactome.db_1.58.0
[21] GO.db_3.4.0        ggplot2_2.2.0      DO.db_2.9          reshape2_1.4.2    
[25] tidyr_0.6.0        magrittr_1.5       scales_0.4.1       splines_3.3.1     
[29] assertthat_0.1     colorspace_1.3-0   stringi_1.1.2      lazyeval_0.2.0    
[33] munsell_0.4.3      chron_2.3-47      
> 

Support non decreasing sorts in reactome pathway analysis

Prerequisites

• Have you read Feedback and follow the guide?
  • make sure your are using the latest release version
  • read the documents
  • google your quesion/issue

Describe you issue

• Make a reproducible example (e.g. 1)
• your code should contain comments to describe the problem (e.g. what expected and actually happened?)

Hi, I have been exploring this package and once again stumbled on the strict decreasing only sort. Here is how to reproduce:

data("geneList", package="DOSE")
non_decreasing <-sort(geneList, decreasing=FALSE)
ReactomePA::gsePathway(non_decreasing, 
                pvalueCutoff = 0.2,
                pAdjustMethod = "BH", 
                verbose = FALSE)

The above will throw the following error:

Error in GSEA_internal(geneList = geneList, exponent = exponent, minGSSize = minGSSize, :
geneList should be a decreasing sorted vector...

This is related to YuLab-SMU/DOSE#58. I also see that in a comment, there is some suggestion that this was fixed but looking at the code in DOSE, isSorted still only allows for a decreasing sort.

This is also related to PR YuLab-SMU/DOSE#59 where I had attempted to fix this.

Thank you,

NelsonGon

Ask in right place

• for bugs or feature requests, post here (github issue)
• for questions, please post to Bioconductor or Biostars with tag ReactomePA

No overrepresented genes (but I see significant p-values)

Hello!

First I need to say that I am really enjoying your packages for my RNASeq data! :) Thank you

I am trying out ReactomePA to look at my differentially expressed genes. I have previously used your other packages and was successful with this same dataset. I run the script:

x <- enrichPathway(gene=defM.EB$table$ENTREZID,organism = "fly",qvalueCutoff=0.05, readable=T)

when I just look at x I get no significance
x

"#...0 enriched terms found"

BUT also tried
head(x)

and it gave me an output with significant p and q values.
ID Description GeneRatio BgRatio pvalue p.adjust
R-DME-72706 R-DME-72706 GTP hydrolysis and joining of the 60S ribosomal subunit 35/399 88/4280 8.672619e-15 4.995429e-12

(sorry, I dont know how to add R outputs nicely here)

I played around with changing the cutoff for the P/Q values, and I kept getting nothing being overrepresented.

I just updated my R version to the latest (3.5.0)... would that be an issue?

thank you!

one gene kills the pathway

enrichPathway(gene=gene, readable=TRUE)
finds something by
gene <- c(10144, 80230,26240,152926,2020,26011,51133,152742)
but not with
gene <- c(10144, 80230,26240,152926,2020,26011,51133,152742,284076)

Cannot change cnetplot edge thickness and color

Dear Guangchuang,
How to increase cnetplot edge thickness and change edge colour to black?
I am using ReactomePA_1.24.0 on R version 3.5.1 (2018-07-02)
Thanks
Sincerely,
Wasim

Prerequisites

• Have you read Feedback and follow the guide?
  • make sure your are using the latest release version
  • read the documents
  • google your quesion/issue

Describe you issue

• Make a reproducible example (e.g. 1)
• your code should contain comments to describe the problem (e.g. what expected and actually happened?)

Ask in right place

• for bugs or feature requests, post here (github issue)
• for questions, please post to Bioconductor or Biostars with tag ReactomePA

WES data

Hello,

I was wondering if it's possible to apply the package on WES VCF or MAF files?

Kind regards

Ivana

feature request: Mapping of Metabolites

Dear Guangchuang,
thanks for giving this nice package to the world ;-)

However, to be only able to map genes/proteins deprecates the full power of Reactome.
Could you think of a possibility to map metabolites as well?

Best, Tobias

Error in ReactomePA

I am using R package for ReactomePA. But I am getting this type of error after reading each command perfectly also.

Error in x[seq_len(n)] : object of type 'S4' is not subsettable

Thanks!

gsePathway giving "Error in check_gene_id(geneList, geneSets)"

Hello,

I have tried looking through previous issue reports and have not found this issue addressed, yet.

I am using enrichPathway and gsePathway in a custom loop to analyze a large dataset. Typically, when an enrichment (ORA/GSEA) result yields a NULL value, the loop will simply check for the NULL and skip to the next iteration. However, when I run gsePathway on these genes: 100101640 and 321166, instead of simply returning a NULL, gsePathway interrupts the loop entirely, preventing further iteration. That is, even when I assign the results of gsePathway to a variable, that variable is not created at all due this error.

Here is a basic version of the code:

genes <- c("100101640", "321166")
geneList <- c(0.6, -0.5)
names(geneList) <- genes

reac_gse <- gsePathway(geneList,
organism = "zebrafish",
eps = 1e-300,
pvalueCutoff = 0.05)

This is the error:

--> Expected input gene ID: 565971,565155,100317496,550134,447850,100334231
Error in check_gene_id(geneList, geneSets) :
--> No gene can be mapped....

If I run these two genes using enrichPathway in the same manner as:

reac_ora <- enrichPathway(gene = genes,
organism = "zebrafish",
pvalueCutoff = 0.05)

"reac_ora" will be a NULL value, which is good because my loop is not interrupted by this, and this is somewhat expected given that there are only 2 genes, here.

Can you please suggest a solution to this? I haven't checked the source code for this function, but it seems like a NULL should be returned in this case.

Additionally, (this is just a curiosity) could you explain why the error lists the "expected" genes?

Thank you for your time,
Peter

Unable to produce graphics with enrichMap()

Hi Guangchuang

Thank you very much for the package, and for your great support.

I was using ReactomePA, and followed your instruction regarding genelist production and etc. Everything worked well, including all the graphic features - barplot(), dotplot(), cnetplot(), gseaplot(), and viewPathway().

However, when I apply for the same data enrichMap(), I get the following error:
"Error in symbols(x = coords[, 1], y = coords[, 2], bg = vertex.color, :
invalid symbol parameter"

Could you please advice?

Thank you!
Amit

gene symbols not being seen with viewPathway calls using openCPU+apache+R3.5+ubuntu18.04

I have an issue to report using opencpu 2.0.7 with Ubuntu 18.04, for R version 3.5.
Gene symbols are not being shown using the viewPathway function when using the openCPU. It works fine locally on RStudio using my laptop, but using the opencpu + apache server, somehow the gene symbols are not being shown with the new version of ReactomePA. However, I have to mention that it worked fine with the previous versions of ReactomePA and clusterProfiler, i.e., I was able to see the gene names on the pathway.

This is the call I am using: viewPathway(pathName, organism = colloquialName, readable = TRUE, foldChange = geneList, vertex.label.font = 3, vertex.label.color = "#000000", vertex.label.cex = 1)
I also tried using, just the viewPathway(pathName, organism = colloquialName, readable = TRUE, foldChange = geneList) and a simple viewPathway(pathName) call.

Is this a known issue? These are the ReactomePA and clusterProfiler versions.
Reactome PA 1.24.0
clusterProfiler 3.8.1
DOSE 3.6.0
This is how the geneList looks like:

head(geneList)
229700 229706 78935 14317 14025 66356
6.305014 3.172351 2.276868 2.212017 -1.810016 -1.938765
typeof(geneList)
[1] "double"

can Arabidopsis run this function?

I run the function "enrichPathway", the organism can only be chosen as one of "human", "rat", "mouse", "celegans", "yeast", "zebrafish", "fly".
So, what can i do if I want to analysis Arabidopsis database?

Input structure of Gene Set Enrichment Analysis

I'm trying to write a shiny app using this package, however, I am unable to identify the input structure required to run GSEA. More specifically, I need to know what is the first argument for gsePathway(). I am aware it must be an order rank geneList, but how would this translate into a sample .csv file to be used as input file?

For example, I'm using a list of genes (EntrezID) all in one column, written as .csv file to run enrichPathway() and that works perfectly. How would you prepare an input file for gsePathway()?

As a side note: I am able to run the analysis using the sample dataset embedded in the package.

Best regards,
Sajjad Abedian
New York City College of Technology

Difference between Kobas-i, Reactome website and ReactomePA (may caused by duplicated background genes)

Hello,

I have 482 ensembl genes (411 tranformed into entrz using bitr) to perform Reactome pathway gene erichment analysis.

I used the ReactomePA and kobas-i at the same time, with the q value <0.1, I got 7 pathways by kobas-i

ID                                    Description GeneRatio     Bg       pvalue   p.adjust

1 R-HSA-3700989 Transcriptional_Regulation_by_TP53 19/482 356 5.228496e-06 0.00383667
2 R-HSA-74160 Gene_expression_(Transcription) 43/482 1448 4.022863e-05 0.02108555
3 R-HSA-1362409 Mitochondrial_iron-sulfur_cluster_biogenesis 4/482 11 6.167911e-05 0.02828758
4 R-HSA-73857 RNA_Polymerase_II_Transcription 38/482 1316 1.990795e-04 0.06086855
5 R-HSA-212436 Generic_Transcription_Pathway 35/482 1193 2.684548e-04 0.07035433
6 R-HSA-5689896 Ovarian_tumor_domain_proteases 5/482 38 4.633071e-04 0.09443742
7 R-HSA-2426168 Activation_of_gene_expression_by_SREBF_(SREBP) 5/482 40 5.736862e-04 0.09567521

The ReactomePA gave 4 pathways with q value <0.5
ID Description GeneRatio BgRatio pvalue p.adjust
R-HSA-1362409 R-HSA-1362409 Mitochondrial iron-sulfur cluster biogenesis 4/215 13/10856 9.296164e-05 0.0487148
R-HSA-3700989 R-HSA-3700989 Transcriptional Regulation by TP53 19/215 365/10856 1.153013e-04 0.0487148
R-HSA-5689896 R-HSA-5689896 Ovarian tumor domain proteases 5/215 38/10856 8.571709e-04 0.2414365
R-HSA-2426168 R-HSA-2426168 Activation of gene expression by SREBF (SREBP) 5/215 42/10856 1.362514e-03 0.2878311

The R-HSA-74160, R-HSA-73857 and R-HSA-212436 were not calculated in the analysis by ReactomePA. At the meantime, I had the same enrichment results as kobas-i using the reactome website. To find reasons, I checked three aspects:

First, I checked if the pathway exist in the reactome.db.

get("R-HSA-74160", reactomePATHID2NAME)
[1] "Homo sapiens: Gene expression (Transcription)"
get("R-HSA-212436", reactomePATHID2NAME)
[1] "Homo sapiens: Generic Transcription Pathway"
get("R-HSA-73857",reactomePATHID2NAME)
[1] "Homo sapiens: RNA Polymerase II Transcription"

Then, I excluded the possiblity that the changes caused by the gene ID transformation from ENSEMBL to ENTRZ

(df[2,8]%>%strsplit("\|"))[[1]] %in% (entrz2$ENSEMBL%>%as.vector())%>%table()
FALSE TRUE
1 42
There were 42 enriched genes in ENTRZ ID

Third, I checked the background gene numbers in reactome.db

length(get("R-HSA-74160", reactomePATHID2EXTID))
[1] 1837
length(get("R-HSA-74160", reactomePATHID2EXTID)%>%unique())
[1] 1506
It seemed that the background genes are duplicated.

My question is:
I suspected the difference were caused by the duplicate genes in reactome.db. How to avoid this?
I wanted to draw the cneplot of reactome enrichment results by kobas-i, if the duplicated problem could not be solved, how can I achieved the drawing purpose?

Attached is my R sessionInfo:

R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)

Matrix products: default

locale:
[1] LC_COLLATE=Chinese (Simplified)_China.936
[2] LC_CTYPE=Chinese (Simplified)_China.936
[3] LC_MONETARY=Chinese (Simplified)_China.936
[4] LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_China.936

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

other attached packages:
[1] reactome.db_1.76.0 graphite_1.38.0
[3] org.Hs.eg.db_3.13.0 AnnotationDbi_1.54.1
[5] IRanges_2.26.0 S4Vectors_0.30.2
[7] Biobase_2.52.0 BiocGenerics_0.38.0
[9] ReactomePA_1.36.0 clusterProfiler_4.0.5
[11] ggplot2_3.3.5

loaded via a namespace (and not attached):
[1] fgsea_1.18.0
[2] colorspace_2.0-2
[3] ggtree_3.0.4
[4] ellipsis_0.3.2
[5] qvalue_2.24.0
[6] XVector_0.32.0
[7] aplot_0.1.1
[8] rstudioapi_0.13
[9] farver_2.1.0
[10] graphlayouts_0.7.1
[11] ggrepel_0.9.1
[12] bit64_4.0.5
[13] fansi_0.5.0
[14] scatterpie_0.1.7
[15] splines_4.1.1
[16] cachem_1.0.6
[17] GOSemSim_2.18.1
[18] polyclip_1.10-0
[19] jsonlite_1.7.2
[20] GO.db_3.13.0
[21] png_0.1-7
[22] graph_1.70.0
[23] ggforce_0.3.3
[24] BiocManager_1.30.16
[25] compiler_4.1.1
[26] httr_1.4.2
[27] backports_1.2.1
[28] assertthat_0.2.1
[29] Matrix_1.3-4
[30] fastmap_1.1.0
[31] lazyeval_0.2.2
[32] tweenr_1.0.2
[33] tools_4.1.1
[34] igraph_1.2.6
[35] gtable_0.3.0
[36] glue_1.4.2
[37] GenomeInfoDbData_1.2.6
[38] reshape2_1.4.4
[39] DO.db_2.9
[40] dplyr_1.0.7
[41] rappdirs_0.3.3
[42] fastmatch_1.1-3
[43] Rcpp_1.0.7
[44] enrichplot_1.12.3
[45] vctrs_0.3.8
[46] Biostrings_2.60.2
[47] ape_5.5
[48] nlme_3.1-153
[49] ggraph_2.0.5
[50] stringr_1.4.0
[51] lifecycle_1.0.1
[52] DOSE_3.18.3
[53] zlibbioc_1.38.0
[54] MASS_7.3-54
[55] scales_1.1.1
[56] tidygraph_1.2.0
[57] RColorBrewer_1.1-2
[58] curl_4.3.2
[59] memoise_2.0.0
[60] gridExtra_2.3
[61] downloader_0.4
[62] ggfun_0.0.4
[63] yulab.utils_0.0.4
[64] stringi_1.7.5
[65] RSQLite_2.2.8
[66] tidytree_0.3.5
[67] checkmate_2.0.0
[68] BiocParallel_1.26.2
[69] GenomeInfoDb_1.28.4
[70] rlang_0.4.11
[71] pkgconfig_2.0.3
[72] bitops_1.0-7
[73] lattice_0.20-45
[74] purrr_0.3.4
[75] labeling_0.4.2
[76] treeio_1.16.2
[77] patchwork_1.1.1
[78] cowplot_1.1.1
[79] shadowtext_0.0.9
[80] bit_4.0.4
[81] tidyselect_1.1.1
[82] plyr_1.8.6
[83] magrittr_2.0.1
[84] R6_2.5.1
[85] generics_0.1.0
[86] DBI_1.1.1
[87] pillar_1.6.3
[88] withr_2.4.2
[89] KEGGREST_1.32.0
[90] RCurl_1.98-1.5
[91] tibble_3.1.4
[92] crayon_1.4.1
[93] utf8_1.2.2
[94] viridis_0.6.2
[95] grid_4.1.1
[96] data.table_1.14.2
[97] blob_1.2.2
[98] digest_0.6.28
[99] tidyr_1.1.4
[100] gridGraphics_0.5-1
[101] munsell_0.5.0
[102] viridisLite_0.4.0
[103] ggplotify_0.1.0

sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)

Matrix products: default

locale:
[1] LC_COLLATE=Chinese (Simplified)_China.936 LC_CTYPE=Chinese (Simplified)_China.936
[3] LC_MONETARY=Chinese (Simplified)_China.936 LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_China.936

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

other attached packages:
[1] reactome.db_1.76.0 graphite_1.38.0 org.Hs.eg.db_3.13.0 AnnotationDbi_1.54.1
[5] IRanges_2.26.0 S4Vectors_0.30.2 Biobase_2.52.0 BiocGenerics_0.38.0
[9] ReactomePA_1.36.0 clusterProfiler_4.0.5 ggplot2_3.3.5

loaded via a namespace (and not attached):
[1] fgsea_1.18.0 colorspace_2.0-2 ggtree_3.0.4 ellipsis_0.3.2
[5] qvalue_2.24.0 XVector_0.32.0 aplot_0.1.1 rstudioapi_0.13
[9] farver_2.1.0 graphlayouts_0.7.1 ggrepel_0.9.1 bit64_4.0.5
[13] fansi_0.5.0 scatterpie_0.1.7 splines_4.1.1 cachem_1.0.6
[17] GOSemSim_2.18.1 polyclip_1.10-0 jsonlite_1.7.2 GO.db_3.13.0
[21] png_0.1-7 graph_1.70.0 ggforce_0.3.3 BiocManager_1.30.16
[25] compiler_4.1.1 httr_1.4.2 backports_1.2.1 assertthat_0.2.1
[29] Matrix_1.3-4 fastmap_1.1.0 lazyeval_0.2.2 tweenr_1.0.2
[33] tools_4.1.1 igraph_1.2.6 gtable_0.3.0 glue_1.4.2
[37] GenomeInfoDbData_1.2.6 reshape2_1.4.4 DO.db_2.9 dplyr_1.0.7
[41] rappdirs_0.3.3 fastmatch_1.1-3 Rcpp_1.0.7 enrichplot_1.12.3
[45] vctrs_0.3.8 Biostrings_2.60.2 ape_5.5 nlme_3.1-153
[49] ggraph_2.0.5 stringr_1.4.0 lifecycle_1.0.1 DOSE_3.18.3
[53] zlibbioc_1.38.0 MASS_7.3-54 scales_1.1.1 tidygraph_1.2.0
[57] RColorBrewer_1.1-2 curl_4.3.2 memoise_2.0.0 gridExtra_2.3
[61] downloader_0.4 ggfun_0.0.4 yulab.utils_0.0.4 stringi_1.7.5
[65] RSQLite_2.2.8 tidytree_0.3.5 checkmate_2.0.0 BiocParallel_1.26.2
[69] GenomeInfoDb_1.28.4 rlang_0.4.11 pkgconfig_2.0.3 bitops_1.0-7
[73] lattice_0.20-45 purrr_0.3.4 labeling_0.4.2 treeio_1.16.2
[77] patchwork_1.1.1 cowplot_1.1.1 shadowtext_0.0.9 bit_4.0.4
[81] tidyselect_1.1.1 plyr_1.8.6 magrittr_2.0.1 R6_2.5.1
[85] generics_0.1.0 DBI_1.1.1 pillar_1.6.3 withr_2.4.2
[89] KEGGREST_1.32.0 RCurl_1.98-1.5 tibble_3.1.4 crayon_1.4.1
[93] utf8_1.2.2 viridis_0.6.2 grid_4.1.1 data.table_1.14.2
[97] blob_1.2.2 digest_0.6.28 tidyr_1.1.4 gridGraphics_0.5-1
[101] munsell_0.5.0 viridisLite_0.4.0 ggplotify_0.1.0

ReactomePA plot label text overlap

Prerequisites

• Have you read Feedback and follow the guide?
  • make sure your are using the latest release version
  • read the documents
  • google your quesion/issue

Describe you issue

• Make a reproducible example (e.g. 1)
• your code should contain comments to describe the problem (e.g. what expected and actually happened?)

Hi, I have ran ReactomePA on ChIP-Seq data and wanted to plot the result with dotplot and barplot. However, the label text between the lines seem to overlap and I am not sure why.

The code I ran is:

pathways <- enrichPathway(gene = common_genes, organism = "human", universe = background_genes)

pathways_dotplot <- dotplot(pathways, showCategory=10)
pathways_dotplot

pathways_barplot <- barplot(pathways)
pathways_barplot

The plots generated are:

This issue does not appear with other Gene Ontology/KEGG plots. Could you advise? Thanks!

Ask in right place

• for bugs or feature requests, post here (github issue)
• for questions, please post to Bioconductor or Biostars with tag ReactomePA

Exporting getDb

Hi @GuangchuangYu,

Is the function getDb meant to be exported? It is not currently in the NAMESPACE and it seems like it should since other packages are depending on it. I got an error message that said:

Error : object ‘getDb’ is not exported by 'namespace:GOSemSim' ERROR: lazy loading failed for package ‘ReactomePA’

when installing ReactomePA as a package dependency.

Regards,
Marcel

enrichPathway

Hi,
I'm running the enrichPathway function:
x<-enrichPathway(gene=df$name, organism = "fly", pvalueCutoff = 0.05,
pAdjustMethod = "BH", qvalueCutoff = 0.2, universe, minGSSize = 10,
maxGSSize = 500, readable = FALSE)
and I get the errro:
universe is not in character and will be ignored...
How is the universe parameter set, my understanding is that I don't have to pass any arguments but it seems that the genes that the background genes it is obtaining ( from where??) are are not in character format?

Prerequisites

• Have you read Feedback and follow the guide?
  • make sure your are using the latest release version
  • read the documents
  • google your quesion/issue

Describe you issue

• Make a reproducible example (e.g. 1)
• your code should contain comments to describe the problem (e.g. what expected and actually happened?)

Ask in right place

• for bugs or feature requests, post here (github issue)
• for questions, please post to Bioconductor or Biostars with tag ReactomePA

"No gene can be mapped" when running enrichPathway

Hi,

Thanks for establishing the R-based Reactome tool.

I want to do a pathway analysis based on a list of mouse genes, but when I tried the function enrichPathway, an error pops out as
"--> No gene can be mapped....
--> Expected input gene ID: 5660,5695,23295,25915,161,3433
--> return NULL..."

I used the entrez id associated with the geneset as the function input; I also tried to upload this entrez id list to Reactome website for analysis. For the web-based analysis, there are some results popping out. Therefore I am wondering what could be the reason for the error? Is it because no pathways are found or because the entrez id from mouse are not accepted by ReactomePA?

for reference the code I used to run pathway analysis is:
x <- enrichPathway(gene=geneset_entrez,pvalueCutoff=1, readable=T)

Looking forward to your reply!

Ruiyang

bump version

Your version number hasn't been bumped since Dan bumped it automatically in March.

r117512 | d.tenenbaum | 2016-05-15 16:14:22 -0400 (Sun, 15 May 2016) | 6 lines

This means that the changes you have made via SVN have not been propagated in the server.
In other words, BiocInstaller::biocLite("ReactomePA") is not working properly from the devel branch.

enrichPathway make readable does not work for yeast genome

Issue stems from enrichPathway looking for a column named SYMBOL which does not exist in the org.Sc.sgd.db object (see columns(org.Sc.sgd.db)). Instead for yeast this should use the "GENENAME" column. Alternately, it would be nice to be able to pass which column to use for the Readable option instead of just T/F (ie so I could say convert IDs to GENENAME or ENTREZID or whatever), and also give it a specific org.db instead of just "yeast"/"fly"/"human" so I know what database is explicitly being used.

EXAMPLE:

lookup <- select(org.Sc.sgd.db, keys = c("AAD10","AAD15","ALD3"), keytype = "GENENAME", columns = c("ENTREZID", "GENENAME"))

# works
enrichPathway(lookup$ENTREZID, organism = "yeast")

# does not work
enrichPathway(lookup$ENTREZID, organism = "yeast", readable = T)

Fails with:

Error in .testForValidCols(x, cols) : 
  Invalid columns: SYMBOL. Please use the columns method to see a listing of valid arguments.

No results returned

Hello,
Trying to run ReactomePA and the sample run returns no result.
Thank you.

gene <- c("11171", "8243", "112464", "2194")
> yy = enrichPathway(gene, pvalueCutoff=0.05)
Loading required package: org.Hs.eg.db

> head(summary(yy))
[1] ID          Description GeneRatio   BgRatio     pvalue      p.adjust    qvalue      geneID      Count      
<0 rows> (or 0-length row.names)

> sessionInfo()
R version 3.3.1 (2016-06-21)
Platform: i386-w64-mingw32/i386 (32-bit)
Running under: Windows 7 (build 7601) Service Pack 1

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

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

other attached packages:
 [1] org.Hs.eg.db_3.3.0   ReactomePA_1.16.2    DOSE_2.10.7          reactome.db_1.55.0   AnnotationDbi_1.34.4
 [6] IRanges_2.6.1        S4Vectors_0.10.3     Biobase_2.32.0       BiocGenerics_0.18.0  BiocInstaller_1.22.3

loaded via a namespace (and not attached):
 [1] graph_1.50.0     igraph_1.0.1     Rcpp_0.12.6      magrittr_1.5     rappdirs_0.3.1   splines_3.3.1   
 [7] munsell_0.4.3    colorspace_1.2-6 stringr_1.1.0    plyr_1.8.4       tools_3.3.1      grid_3.3.1      
[13] gtable_0.2.0     DBI_0.5          GOSemSim_1.30.3  DO.db_2.9        reshape2_1.4.1   ggplot2_2.1.0   
[19] qvalue_2.4.2     RSQLite_1.0.0    stringi_1.1.1    GO.db_3.3.0      scales_0.4.0     graphite_1.18.0 

organism avaialble?

Hi,
I want to run ReactomePA on my chick dataset but the organism (Gallus gallus) is not available. Would be available in the future or not?

thanks
Camilla

No gene can be mapped with gsePathway

Hello,

I am trying to run reactomePA for my DE genes. I manage to run enrichPathway successfully (yy = enrichPathway(genes$entrezgene_id, pvalueCutoff=0.05)),
but when I do the following:

sorted_genes = sort(genes$entrezgene_id, decreasing = TRUE)
gseP = gsePathway(sorted_genes, organism = "human", pvalueCutoff = 0.05, pAdjustMethod = "BH", verbose = TRUE)

I am getting this error:

preparing geneSet collections...
--> Expected input gene ID: 1072,2768,3804,3479,3659,5742
Error in check_gene_id(geneList, geneSets) :
--> No gene can be mapped....

What could cause this issue?

Also the reactomePA manual page is not available: https://guangchuangyu.github.io/ReactomePA/ Is there somewhere else the manual?
And a last question: Could I run the reactomePA analysis with my background set of genes?

Thank you in advance!
Kind regards,
Alex

very different results with upgrade of R

I recently updated from R 3.2.2 to 3.3.1 and the enrichPathway result is very very different for the identical list of genes. The P-values are different by about 3 orders of magnitude and while the returned result is similar, the order is quite a bit different. The problem is very difficult to figure out however because I can reproduce 2 totally different results on 3 different computers using the same gene set, and yet, it's not even clear to me exactly what package being used by enrichPathway is causing the issue. Any suggestions?

Here are three different sessionInfos with 2 different results:

Original EnrichPathway Result (result 1) (R3.2.2 running on computer 1)
AnnotationDbi_1.32.3
Biobase_2.30.0
BiocGenerics_0.16.1
BiocInstaller_1.20.3
DOSE_2.9.1
IRanges_2.4.8
ReactomePA_1.14.4
clusterProfiler_2.4.3
org.Mm.eg.db_3.2.3

New different EnrichPathway Result (result 2) (R3.2.2 running on computer 2)
AnnotationDbi_1.32.3
Biobase_2.30.0
BiocGenerics_0.16.1
BiocInstaller_1.20.3
DOSE_2.8.3
IRanges_2.4.8
ReactomePA_1.14.4
clusterProfiler_2.4.3
org.Mm.eg.db_3.2.3

New different EnrichPathway Result (result 2) (R3.2.3 running on computer 3)
AnnotationDbi_1.35.4
Biobase_2.33.3
BiocGenerics_0.19.2
BiocInstaller_1.22.3
DOSE_2.11.12
IRanges_2.7.16
ReactomePA_1.17.4
clusterProfiler_3.1.8
org.Mm.eg.db_3.4.0

Working with more than one gene list

Dear Developers.
The package is perfect. I can have it up and running for my own list of genes.
What i would like to know is if there is a straightforward means to to sample more than 1 gene list and compare the pathways and visualiise them on barplot or dotplot. This is because I have gene lists for condition 1, condition 2, condition 3, etc.

Would you happen to have a script that I can test out for more than 1 gene lists (that i can adapt) so that they can be compared? This would be super helpful.
I am excited to use this tool.
Thanks, Kaz

Cannot extract results from enrichPathway

I have some code to analyze a dataset that I used in 2016 and that produced great results..

genes <- geneNames[bss$ix[1:100],1]
genes <- as.character(genes)
hugoNames <- lapply(genes, function(t) substr(t, 1, regexpr("\|", t) - 1)) # extract HUGO names
entrezNames <- lapply(genes, function(t) substr(t, regexpr("\|", t) + 1, nchar(t))) # extract Entrez names
paths = enrichPathway(gene=unlist(entrezNames), pvalueCutoff=.9, readable=T)

With the latest version of ReactomePA (1.26.0) and DOSE (1.38) for R 3.5.1 under Windows x64, I see that I am getting results in the 'paths' variable, but cannot extract them or visualize them - all figures are blank. See below.

head(as.data.frame(paths))

enrichMap(paths, layout=igraph::layout.kamada.kawai, vertex.label.cex = 1)

Please help. Thank you. Isabelle

install issue

HI,
When i install this package in R/3.5.1, some issue happened.
BiocManager::install("ReactomePA", version = "3.8")
Bioconductor version 3.8 (BiocManager 1.30.3), R 3.5.1 (2018-07-02)
Installing package(s) 'ReactomePA'
trying URL 'https://bioconductor.org/packages/3.8/bioc/src/contrib/ReactomePA_1.26.0.tar.gz'
Content type 'application/x-gzip' length 3001858 bytes (2.9 MB)

downloaded 2.9 MB

• installing source package ‘ReactomePA’ ...
  ** R
  ** inst
  ** byte-compile and prepare package for lazy loading
  Warning in gzfile(file, "rb") :
  cannot open compressed file '/home/chenx/R/x86_64-pc-linux-gnu-library/3.5/graphite/R/sysdata.rdx', probable reason 'No such file or directory'
  Error in gzfile(file, "rb") : cannot open the connection
  ERROR: lazy loading failed for package ‘ReactomePA’
• removing ‘/home/chenx/R/x86_64-pc-linux-gnu-library/3.5/ReactomePA’
  Could you help me ?

P-value correction for multiple testing necessary?

Hey,

first: thank you for the package along with the data comprehensive book/vignette, it's great!

I was digging a bit deeper in the original Reactome analysis provided by the web-tool and there p-values are corrected by benjamini-hochberg and can be reported and subsequently filtered. Is this correction already implemented in the reactomePA::enrichPathway() function and hence pAdjustMethod = "BH" would be 'double correction'?
I am asking, because setting pAdjustMehthod = "none" give very comparable results to the webtool result, whereas leaving it to "BH" seems to be much more stringent.

What would you suggest?

Kind regards
Thorben

fold change color palette on viewPathway is inconsistent

The color palette that displays the fold change of the input gene lists seem not to be consistent with the actual values in the input vector (i.e., geneList), when using viewPathway.

Specify minimal package versions in the Description file

Prerequisites

• Have you read Feedback and follow the guide?
  • make sure your are using the latest release version
  • read the documents
  • google your quesion/issue

Describe you issue

Hi,

This is more of a developer focused issue. When running devtools::check, I noticed that some checks are skipped due to missing minimal package versions. I was wondering if it could be possible to specify minimal package versions in the Description file.

Thanks,

NelsonGon

• Make a reproducible example (e.g. 1)
• your code should contain comments to describe the problem (e.g. what expected and actually happened?)

Ask in right place

• for bugs or feature requests, post here (github issue)
• for questions, please post to Bioconductor or Biostars with tag ReactomePA

gnutls_handshake() failed: Handshake failed

Dear Dr. Yu,

I was using ReacomePA to reporduce the results in your tutorial (https://bioconductor.org/packages/devel/bioc/vignettes/ReactomePA/inst/doc/ReactomePA.html#references). However, i have encountered an error while running the following command.

viewPathway("E2F mediated regulation of DNA replication", readable=TRUE, foldChange=geneList)

Error in curl::curl_fetch_disk(url, x$path, handle = handle) : 
  gnutls_handshake() failed: Handshake failed
Error in fetchRemote(name, path) : 
  cannot download pathway data: are you offline?

I am wondering it could be the url the command tries to access has failed or changed.
Any suggestion on this error? Thank you very much.

Further investigation leads me to the source of the error coming from the function convertIdentifiers in graphite. It seems it cannot properly convert the Uniport IDs into Gene symbols.

if (readable) { p <- convertIdentifiers(p, "symbol") if (!is.null(foldChange)) { stopifnot(!any(duplicated(names(foldChange)))) OrgDb <- getDb(organism) names(foldChange) <- EXTID2NAME(OrgDb, names(foldChange), "ENTREZID") } }

R version 3.5.0 (2018-04-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.5 LTS

Matrix products: default
BLAS: /home/eberhamn/Libs/R-3.5.0/lib/libRblas.so
LAPACK: /home/eberhamn/Libs/R-3.5.0/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] clusterProfiler_3.8.1 org.Hs.eg.db_3.6.0    AnnotationDbi_1.42.1  IRanges_2.14.10       S4Vectors_0.18.3     
 [6] Biobase_2.40.0        BiocGenerics_0.26.0   ReactomePA_1.24.0     graphite_1.26.1       BiocInstaller_1.30.0 

loaded via a namespace (and not attached):
 [1] viridis_0.5.1       httr_1.3.1          tidyr_0.8.1         bit64_0.9-7         viridisLite_0.3.0   splines_3.5.0      
 [7] ggraph_1.0.2        assertthat_0.2.0    DO.db_2.9           rvcheck_0.1.0       blob_1.1.1          ggrepel_0.8.0      
[13] pillar_1.3.0        RSQLite_2.1.1       backports_1.1.2     lattice_0.20-35     glue_1.3.0          digest_0.6.15      
[19] checkmate_1.8.5     qvalue_2.12.0       colorspace_1.3-2    cowplot_0.9.3       Matrix_1.2-14       plyr_1.8.4         
[25] pkgconfig_2.0.2     reactome.db_1.64.0  purrr_0.2.5         GO.db_3.6.0         scales_1.0.0        tweenr_0.1.5       
[31] enrichplot_1.0.2    BiocParallel_1.14.2 ggforce_0.1.3       tibble_1.4.2        ggplot2_3.0.0       UpSetR_1.3.3       
[37] lazyeval_0.2.1      magrittr_1.5        crayon_1.3.4        memoise_1.1.0       DOSE_3.6.1          MASS_7.3-50        
[43] graph_1.58.0        tools_3.5.0         data.table_1.11.4   stringr_1.3.1       munsell_0.5.0       bindrcpp_0.2.2     
[49] compiler_3.5.0      rlang_0.2.2         units_0.6-0         grid_3.5.0          ggridges_0.5.0      rstudioapi_0.7     
[55] rappdirs_0.3.1      igraph_1.2.2        gtable_0.2.0        DBI_1.0.0           reshape2_1.4.3      R6_2.2.2           
[61] gridExtra_2.3       dplyr_0.7.6         bit_1.1-14          bindr_0.1.1         fastmatch_1.1-0     fgsea_1.6.0        
[67] GOSemSim_2.6.2      stringi_1.2.4       Rcpp_0.12.18        tidyselect_0.2.4