Last updated: 2020-04-19

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
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    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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Rmd 1ae3d26 brimittleman 2020-04-18 add prop utr

I looked at the ortho UTR PAS where there are only 2 for the gene. This was too restrictive. I am going to look at the percent of the ortho UTR for genes with only UTR PAS that are in the otho UTR file. I will do this because the are easier to interpret.

library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
OverlapOrtho=read.table("../data/orthoUTR/FilteredPASOverlapOrthoUTR.text", header = T,stringsAsFactors = F)

Look for genes that only have UTR pas.

PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",header = T, stringsAsFactors = F)
PASutr= PASMeta %>% group_by(gene) %>% 
  summarise(locString = toString(loc)) %>% 
  filter(!grepl("intron", locString)) %>% 
  filter(!grepl("cds", locString))  %>% 
  filter(!grepl("utr5", locString)) %>%
  filter(!grepl("end", locString)) %>% 
  mutate(numUTR= str_count(locString, pattern = "utr3"))

I need to make sure the number of UTR pas are in the ortho file:

OverlapOrtho_num= OverlapOrtho %>% group_by(gene) %>% summarise(nOrtho=n())

OrthoandMetha= OverlapOrtho_num %>% inner_join(PASutr, by="gene") %>% mutate(matched=ifelse(nOrtho==numUTR, "Yes","No"))
OrthoandMethayes= OrthoandMetha %>% filter(matched=="Yes")

OrthoandMetha %>% group_by(matched) %>% summarise(n())
# A tibble: 2 x 2
  matched `n()`
  <chr>   <int>
1 No        275
2 Yes      1272
OrthoandMetha_sm=OrthoandMetha %>% select(gene, numUTR)

This means the analysis will look at 1272 genes. I will filter these genes in the orthofile.

OverlapOrtho_filt= OverlapOrtho %>% inner_join(OrthoandMetha_sm, by="gene")
nrow(OverlapOrtho_filt)
[1] 2398

Plot number of PAS:

OrthoandMethayes$numUTR=as.factor(OrthoandMethayes$numUTR)
ggplot(OrthoandMethayes,aes(x=numUTR)) + geom_bar(stat="count")

There are 1961 PAS in the set.

Are any of these differentially used:

PASMetaSmall=PASMeta %>% select(PAS, chr, start, end)
DiffIso=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(PASMetaSmall,by=c("chr", "start", "end")) %>% select(PAS, SigPAU2)

DiffIsoSig= DiffIso %>% filter(SigPAU2=="Yes")

DiffIsoSigGene=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt",header = T, stringsAsFactors = F)
OverlapOrtho_filt %>% inner_join(DiffIsoSig, by="PAS") %>% nrow()
[1] 104
OverlapOrtho_filt %>% inner_join(DiffIsoSig, by="PAS") %>% select(gene) %>% unique() %>% nrow()
[1] 73

70 significant in 42 genes.

Seems worth it.

I will look at proportion of the UTR.

Overlapping= read.table("../data/orthoUTR/PASOverlapinDistal3UTR_bothWritten.bed", col.names = c("chrpas", "startpas", "endpas","PAS", "humanusage", "strandpas", "chrutr", "startutr", "endutr","geneUTR", "score","strand"),stringsAsFactors = F) %>% filter(PAS %in% OverlapOrtho_filt$PAS)
Overlapping_pos= Overlapping %>% filter(strand=="+") %>% mutate(length=endutr-startutr, center=endpas-100, cent2start=center-startutr, prop=cent2start/length)
Overlapping_neg= Overlapping %>% filter(strand=="-") %>% mutate(length=endutr-startutr, center=endpas-100, cent2start=endutr- center, prop=cent2start/length)
Overlapping_both=Overlapping_pos %>% bind_rows(Overlapping_neg) %>% mutate(SigPAS=ifelse(PAS %in% DiffIsoSig$PAS, "Yes","No"), sigGene=ifelse(geneUTR %in% DiffIsoSigGene$gene, "Yes","No"))  %>% rename(gene=geneUTR) %>% inner_join(OrthoandMetha_sm, by="gene")

plot:

ggplot(Overlapping_both,aes(x=prop, by=SigPAS, fill=SigPAS)) + geom_density(alpha=.4) + scale_fill_brewer(palette = "Dark2") + labs(title="UTR location for differentially used PAS",x="Proportion of 3' ortho exon UTR") 

ggplot(Overlapping_both,aes(x=prop, by=SigPAS, col=SigPAS)) + stat_ecdf()+scale_color_brewer(palette = "Dark2") + labs(title="UTR location for differentially used PAS",x="Proportion of 3' ortho exon UTR") 

Overlapping_both_yes= Overlapping_both %>% filter(SigPAS=="Yes")
nrow(Overlapping_both_yes)
[1] 104
Overlapping_both_no= Overlapping_both %>% filter(SigPAS=="No")
nrow(Overlapping_both_no)
[1] 2294
wilcox.test(Overlapping_both_yes$prop,Overlapping_both_no$prop)

    Wilcoxon rank sum test with continuity correction

data:  Overlapping_both_yes$prop and Overlapping_both_no$prop
W = 114140, p-value = 0.4563
alternative hypothesis: true location shift is not equal to 0

Not a significant difference in the distribution.


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_1.6.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   haven_1.1.2        lattice_0.20-38   
 [4] colorspace_1.3-2   generics_0.0.2     htmltools_0.3.6   
 [7] yaml_2.2.0         utf8_1.1.4         rlang_0.4.0       
[10] later_0.7.5        pillar_1.3.1       glue_1.3.0        
[13] withr_2.1.2        RColorBrewer_1.1-2 modelr_0.1.2      
[16] readxl_1.1.0       plyr_1.8.4         munsell_0.5.0     
[19] gtable_0.2.0       cellranger_1.1.0   rvest_0.3.2       
[22] evaluate_0.12      labeling_0.3       knitr_1.20        
[25] httpuv_1.4.5       fansi_0.4.0        broom_0.5.1       
[28] Rcpp_1.0.2         promises_1.0.1     scales_1.0.0      
[31] backports_1.1.2    jsonlite_1.6       fs_1.3.1          
[34] hms_0.4.2          digest_0.6.18      stringi_1.2.4     
[37] grid_3.5.1         rprojroot_1.3-2    cli_1.1.0         
[40] tools_3.5.1        magrittr_1.5       lazyeval_0.2.1    
[43] crayon_1.3.4       whisker_0.3-2      pkgconfig_2.0.2   
[46] xml2_1.2.0         lubridate_1.7.4    assertthat_0.2.0  
[49] rmarkdown_1.10     httr_1.3.1         rstudioapi_0.10   
[52] R6_2.3.0           nlme_3.1-137       git2r_0.26.1      
[55] compiler_3.5.1