Why pathview plot shows both colors(red and green) in up-regulated pathways?
Entering edit mode
nhwoo • 0
Last seen 3.9 years ago

Hello friends,

I'm trying to use Pathview for pathway analysis of my differentially expressed genes. I used the below command

pathview(gene.data=foldchanges, pathway.id=keggresids, species="hsa", limit = c(-10,10))

before this I run gage function to get up-regulated pathways from my all differentially expressed genes. Then, I extracted kegg ids and gave as input to pathview function.

keggres = gage(foldchanges, gsets=kegg.sets.hs, same.dir=TRUE)
keggrespathways <- rownames(keggres$greater)
keggresids = substr(keggrespathways, start=1, stop=8)

Finally, I got KEGG pathway plots that have both red and green colors. My question is : 1) If it is up-regulated pathway, why do I get both red and green colors. I know that red color indicates positive fold value, so I thought I should get mostly red colored genes for up-regulated pathway.

2) Also, in this keggres$greater, greater than what? I do not clearly understand the meaning of up-regulated pathway. Tutorial says that gage performs some pair-wise comparisons for all proper gene sets, and I do not get the meaning.

Here is my R session info :

> sessionInfo()
R version 3.6.2 (2019-12-12)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

Matrix products: default

[1] LC_COLLATE=Korean_Korea.949  LC_CTYPE=Korean_Korea.949   
[3] LC_MONETARY=Korean_Korea.949 LC_NUMERIC=C                
[5] LC_TIME=Korean_Korea.949    

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

other attached packages:
 [1] gageData_2.24.0             gage_2.36.0                
 [3] pathview_1.26.0             org.Hs.eg.db_3.10.0        
 [5] AnnotationDbi_1.48.0        DESeq2_1.26.0              
 [7] SummarizedExperiment_1.16.1 DelayedArray_0.12.2        
 [9] BiocParallel_1.20.1         matrixStats_0.55.0         
[11] Biobase_2.46.0              GenomicRanges_1.38.0       
[13] GenomeInfoDb_1.22.0         IRanges_2.20.2             
[15] S4Vectors_0.24.3            BiocGenerics_0.32.0        

loaded via a namespace (and not attached):
 [1] httr_1.4.1             bit64_0.9-7            splines_3.6.2         
 [4] Formula_1.2-3          assertthat_0.2.1       latticeExtra_0.6-29   
 [7] blob_1.2.1             GenomeInfoDbData_1.2.2 yaml_2.2.0            
[10] pillar_1.4.3           RSQLite_2.2.0          backports_1.1.5       
[13] lattice_0.20-38        glue_1.3.2             digest_0.6.25         
[16] RColorBrewer_1.1-2     XVector_0.26.0         checkmate_2.0.0       
[19] colorspace_1.4-1       htmltools_0.4.0        Matrix_1.2-18         
[22] XML_3.99-0.3           pkgconfig_2.0.3        genefilter_1.68.0     
[25] zlibbioc_1.32.0        purrr_0.3.3            xtable_1.8-4          
[28] scales_1.1.0           jpeg_0.1-8.1           htmlTable_1.13.3      
[31] tibble_2.1.3           annotate_1.64.0        KEGGREST_1.26.1       
[34] ggplot2_3.3.0          nnet_7.3-12            survival_3.1-8        
[37] magrittr_1.5           crayon_1.3.4           KEGGgraph_1.46.0      
[40] memoise_1.1.0          foreign_0.8-72         graph_1.64.0          
[43] tools_3.6.2            data.table_1.12.8      lifecycle_0.1.0       
[46] stringr_1.4.0          locfit_1.5-9.1         munsell_0.5.0         
[49] cluster_2.1.0          Biostrings_2.54.0      compiler_3.6.2        
[52] rlang_0.4.4            grid_3.6.2             RCurl_1.98-1.1        
[55] rstudioapi_0.11        htmlwidgets_1.5.1      bitops_1.0-6          
[58] base64enc_0.1-3        gtable_0.3.0           DBI_1.1.0             
[61] R6_2.4.1               gridExtra_2.3          knitr_1.28            
[64] dplyr_0.8.4            bit_1.1-15.2           Hmisc_4.3-1           
[67] Rgraphviz_2.30.0       stringi_1.4.6          Rcpp_1.0.4            
[70] geneplotter_1.64.0     vctrs_0.2.3            rpart_4.1-15          
[73] acepack_1.4.1          png_0.1-7              tidyselect_1.0.0      
[76] xfun_0.12

Please kindly let me know some answers. Thank you.

pathveiw R gage • 1.2k views
Entering edit mode
Last seen 19 minutes ago
United States

For any gene set test, the goal is to say if the gene set in aggregate is up or down-regulated. This is different from saying that every single gene in the pathway is up or down. Depending on the test (and here I am not talking about gage in particular) you have to have some measure of the aggregate signal from a gene set, and how you interpret that is dependent on what the statistic is.

In general I would caution against using a method blindly, without understanding what is happening. How else can you explain to others what you have done?

Entering edit mode

Thank you for kind answer. This helps me a lot. I agree that understanding is important.

Entering edit mode

Thank you for kind answer. This helps me a lot. I agree that understanding is important.


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