Upsetter plot with all phenotypes

Last updated: 2020-01-10

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
Rmd	3aee984	brimittleman	2020-01-10	delta pau and sig
html	417783c	brimittleman	2020-01-10	Build site.
Rmd	521dc81	brimittleman	2020-01-10	update dPAS > 0.2
html	4c87e96	brimittleman	2020-01-07	Build site.
Rmd	6e3027a	brimittleman	2020-01-07	upset plots for comparisons

In this analysis I will use the UpSetR package to look at all of the differential gene regulation phenotype results in one plot. This should be easier to visualize than the venn diagrams.

library(UpSetR)
library(workflowr)

This is workflowr version 1.5.0
Run ?workflowr for help getting started

library(tidyverse)

── Attaching packages ───────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ──────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

Input the datasets:

#protein
Protein=read.table("../data/Khan_prot/HC_SigProtein.txt", header = T, stringsAsFactors = F)
#trans
Translation=read.table("../data/Wang_ribo/HC_SigTranslation.txt", header = T, stringsAsFactors = F)
#expression
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
DEgenes=read.table("../data/DiffExpression/DE_genes.txt", header = F,col.names = c("Gene_stable_ID"),stringsAsFactors = F) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(Gene.name)
#nuclear apa  
NucAPA=read.table("../data/DiffIso_Nuclear/SignifianceEitherGENES_Nuclear.txt",header = T,stringsAsFactors = F)
#totalAPA  
totAPA=read.table("../data/DiffIso_Total/SignifianceEitherGENES_Total.txt",header = T,stringsAsFactors = F)
DSgenes=read.table("../data/DiffSplice_liftedJunc/orderedGeneListFixed.txt",stringsAsFactors = F, col.names = "DS")

Create a named list object

# example of list input (list of named vectors)
listInput <- list(DE=DEgenes$Gene.name, DS=DSgenes$DS, DapaNuc=NucAPA$gene, DapaTot=totAPA$gene, DT=Translation$Gene, DP=Protein$gene.symbol)

Create plot:

upset(fromList(listInput), order.by = "freq", keep.order = T,empty.intersections = "on")

Version	Author	Date
417783c	brimittleman	2020-01-10
4c87e96	brimittleman	2020-01-07

Do this without total APA:

listInput_nucOnly <- list(DE=DEgenes$Gene.name, DS=DSgenes$DS, DAPA=NucAPA$gene, DT=Translation$Gene, DP=Protein$gene.symbol)

upset(fromList(listInput_nucOnly), order.by = "freq", keep.order = T,empty.intersections = "on")

Version	Author	Date
417783c	brimittleman	2020-01-10
4c87e96	brimittleman	2020-01-07

Add colors for certain queries:

#upset(movies, queries = list(list(query = intersects, params = list("Drama", 
#    "Comedy", "Action"), color = "orange", active = T), list(query = intersects, 
 #   params = list("Drama"), color = "red", active = F), list(query = intersects, 
 #   params = list("Action", "Drama"), active = T)))


upset(fromList(listInput_nucOnly), queries = list(list(query=intersects, params=list("DAPA", "DT", "DP"), color="red", active=T,query.name="APA,Ribo, Protein"),list(query=intersects, params=list("DE", "DT", "DP"), color="orange", active=T, query.name="Expression,Ribo, Protein"), list(query=intersects, params=list("DS", "DT", "DP"), color="green", active=T,query.name="Splicing ,Ribo, Protein"),list(query=intersects, params=list("DAPA", "DT"), color="blue", active=T, query.name="APA,Ribo") ,list(query=intersects, params=list("DAPA", "DP"), color="purple", active=T, query.name="APA, Protein")), order.by = "freq", query.legend = "bottom")

Version	Author	Date
417783c	brimittleman	2020-01-10
4c87e96	brimittleman	2020-01-07

Can I do this by proportion???

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1  
 [9] tidyverse_1.2.1 workflowr_1.5.0 UpSetR_1.3.3   

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 haven_1.1.2      lattice_0.20-38  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       rlang_0.4.0     
 [9] later_0.7.5      pillar_1.3.1     withr_2.1.2      glue_1.3.0      
[13] modelr_0.1.2     readxl_1.1.0     plyr_1.8.4       munsell_0.5.0   
[17] gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2      evaluate_0.12   
[21] labeling_0.3     knitr_1.20       httpuv_1.4.5     broom_0.5.1     
[25] Rcpp_1.0.2       promises_1.0.1   scales_1.0.0     backports_1.1.2 
[29] jsonlite_1.6     fs_1.3.1         gridExtra_2.3    hms_0.4.2       
[33] digest_0.6.18    stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2 
[37] cli_1.1.0        tools_3.5.1      magrittr_1.5     lazyeval_0.2.1  
[41] crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0      
[45] lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1      
[49] rstudioapi_0.10  R6_2.3.0         nlme_3.1-137     git2r_0.26.1    
[53] compiler_3.5.1

Upsetter plot with all phenotypes

Briana Mittleman

1/7/2020