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Downstream proportions are not identical in vennpie and upsetplot results
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12 months ago
afli • 180
China, Beijing, IGDB

enter image description here

Hi, I find that the peaks in downstream region is zero in upsetplot, but it take up some part in ven plot. Where is this difference come from? Thank you!

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any reproducible example?

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Yes, I used the example in your tutorial, and the same issue occur. My command lines are as follows:

library(ChIPseeker)

files <- getSampleFiles()
peak <- readPeakFile(files[[4]])

require(TxDb.Hsapiens.UCSC.hg19.knownGene)

txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
peakAnno <- annotatePeak(files[[4]], tssRegion=c(-3000, 3000),TxDb=txdb)

pdf("upsetplot_test.pdf",width =13, height=7)
upsetplot(peakAnno, vennpie=TRUE)
dev.off()

In the pdf file, downstream region is always False, and be zero.My session information is as follows:

sessionInfo()

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: /mnt/tiger/afli/softwares/R3.5.0_afli/lib/R/lib/libRblas.so
LAPACK: /mnt/tiger/afli/softwares/R3.5.0_afli/lib/R/lib/libRlapack.so

locale:

 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] 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   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

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

other attached packages:
 [1] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
 [2] GenomicFeatures_1.32.0                 
 [3] AnnotationDbi_1.42.1                   
 [4] Biobase_2.40.0                         
 [5] GenomicRanges_1.32.3                   
 [6] GenomeInfoDb_1.16.0                    
 [7] IRanges_2.14.10                        
 [8] S4Vectors_0.18.3                       
 [9] BiocGenerics_0.26.0                    
[10] ChIPseeker_1.16.0

loaded via a namespace (and not attached):
 [1] bitops_1.0-6                matrixStats_0.53.1         
 [3] enrichplot_1.0.2            bit64_0.9-7                
 [5] RColorBrewer_1.1-2          progress_1.2.0             
 [7] httr_1.3.1                  UpSetR_1.3.3               
 [9] tools_3.5.0                 R6_2.2.2                   
[11] KernSmooth_2.23-15          DBI_1.0.0                  
[13] lazyeval_0.2.1              colorspace_1.3-2           
[15] tidyselect_0.2.4            gridExtra_2.3              
[17] prettyunits_1.0.2           bit_1.1-14                 
[19] compiler_3.5.0              DelayedArray_0.6.1         
[21] labeling_0.3                rtracklayer_1.40.3         
[23] caTools_1.17.1              scales_0.5.0               
[25] ggridges_0.5.0              stringr_1.3.1              
[27] digest_0.6.15               Rsamtools_1.32.0           
[29] DOSE_3.6.1                  XVector_0.20.0             
[31] pkgconfig_2.0.1             plotrix_3.7-2              
[33] rlang_0.2.1                 RSQLite_2.1.1              
[35] bindr_0.1.1                 gtools_3.8.1               
[37] BiocParallel_1.14.1         GOSemSim_2.6.0             
[39] dplyr_0.7.6                 RCurl_1.95-4.10            
[41] magrittr_1.5                GO.db_3.6.0                
[43] GenomeInfoDbData_1.1.0      Matrix_1.2-14
[45] Rcpp_0.12.17                munsell_0.5.0              
[47] viridis_0.5.1               stringi_1.2.3              
[49] ggraph_1.0.2                MASS_7.3-50                
[51] SummarizedExperiment_1.10.1 zlibbioc_1.26.0            
[53] gplots_3.0.1                plyr_1.8.4                 
[55] qvalue_2.12.0               grid_3.5.0                 
[57] blob_1.1.1                  gdata_2.18.0               
[59] ggrepel_0.8.0               DO.db_2.9                  
[61] crayon_1.3.4                lattice_0.20-35            
[63] Biostrings_2.48.0           cowplot_0.9.2              
[65] splines_3.5.0               hms_0.4.2                  
[67] pillar_1.2.3                fgsea_1.6.0                
[69] igraph_1.2.1                boot_1.3-20                
[71] reshape2_1.4.3              biomaRt_2.36.1             
[73] fastmatch_1.1-0             XML_3.98-1.11              
[75] glue_1.2.0                  data.table_1.11.4          
[77] tweenr_0.1.5                gtable_0.2.0               
[79] purrr_0.2.5                 assertthat_0.2.0           
[81] ggplot2_3.0.0               gridBase_0.4-7             
[83] ggforce_0.1.3               viridisLite_0.3.0          
[85] tibble_1.4.2                GenomicAlignments_1.16.0   
[87] memoise_1.1.0               units_0.6-0                
[89] bindrcpp_0.2.2

This is my upsetplot pdf file:

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12 months ago
WCIP | Glasgow | UK

I haven't thought carefully about your question but the first explanation that comes to my mind is that proportional Venn diagrams with 3 or more sets are not always possible. See this post Venn/Euler Diagram Of Four Or More Sets.

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Thanks my friend, I see the post and UpSetR is recommended in the reply, I do not quite understand it why proportional Venn diagrams with 3 or more sets are not always possible, because UpSetR can easily handle six sets. Maybe I should think your advice further.

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13 months ago
Guangchuang Yu ♦ 2.2k
China/Guangzhou/Southern Medical Univer…

thanks for reporting this issue. It has been fixed in v=1.16.1.

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