Last updated: 2020-04-20

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Knit directory: Comparative_APA/analysis/

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Rmd b00be28 brimittleman 2020-04-20 add new dom to anaylses
html edd8940 brimittleman 2020-04-13 Build site.
Rmd 6997920 brimittleman 2020-04-13 initial miRNA

In this analysis I will ask about conservation and number of conserved miRNA sites.

I have the miRNA target info from TargetScanHuman. I downloaded the predicted targets for conserved targets from conserved miRNA families.

I will look from the human perspective and ask if genes with conserved vs non concerved sites have different numbers of miRNA binding sites. I will standaradize this by length of 3’ UTR. This means I can only look at those with orthologous utrs.

library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(ggpubr)
Loading required package: ggplot2
Loading required package: magrittr
library(tidyverse)
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OrthoUTR=read.table("../data/orthoUTR/HumanDistal3UTR.sort.bed", col.names = c("chr",'start','end','gene','score','strand'),stringsAsFactors = F) %>% mutate(length=end-start) %>% select(gene, length)
miRNADB=read.table("../data/miRNA/Conserved_Family_Info.txt", header= T, stringsAsFactors = F,sep="\t") 
miRNADBgenes= miRNADB %>% group_by(Gene.Symbol) %>% summarise(nSites=n()) %>% rename(gene= Gene.Symbol) %>% inner_join(OrthoUTR, by="gene") %>% mutate(density=nSites/length)

Look at my set and overlap these.

DiffIsoGene=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header = T,stringsAsFactors = F)

DiffIso=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% select(gene) %>% unique() %>% mutate(Conserved=ifelse(gene %in% DiffIsoGene$gene, "No", "Yes")) %>% inner_join(miRNADBgenes,by="gene")

Plot this:

ggplot(DiffIso, aes(x=Conserved, y=log10(density),fill=Conserved)) +geom_boxplot() + stat_compare_means(method.args = list(alternative = "greater")) +labs(title="Number of annotated miRNA sites\nConserved = no differnetially used PAS") + theme(legend.position = "none")+ scale_fill_brewer(palette = "Dark2")

Version Author Date
edd8940 brimittleman 2020-04-13 
DiffIso %>% group_by(Conserved) %>% summarise(mean(density))
# A tibble: 2 x 2
  Conserved `mean(density)`
  <chr>               <dbl>
1 No                  0.595
2 Yes                 0.526

Look specifically at genes with differentially used 3’ UTR PAS:

PASINFO=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",stringsAsFactors = F, header = T) %>% select(PAS, chr, start, end, loc)
DiffIsoUTR=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(PASINFO,by=c("chr", "start", "end")) %>% filter(SigPAU2=="Yes", loc=='utr3') %>% select(gene) %>% unique()

DiffIsoUTRall=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% select(gene) %>% unique() %>% mutate(Conserved=ifelse(gene %in% DiffIsoUTR$gene, "No", "Yes")) %>% inner_join(miRNADBgenes,by="gene")

DiffIsoIntron=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(PASINFO,by=c("chr", "start", "end")) %>% filter(SigPAU2=="Yes", loc=='intron') %>% select(gene) %>% unique()

DiffIsoIntronall=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% select(gene) %>% unique() %>% mutate(Conserved=ifelse(gene %in% DiffIsoIntron$gene, "No", "Yes")) %>% inner_join(miRNADBgenes,by="gene")
ggplot(DiffIsoUTRall, aes(x=Conserved, y=log10(density),fill=Conserved)) +geom_boxplot() + stat_compare_means(method.args = list(alternative = "greater")) +labs(title="Number of annotated miRNA sites\nConserved = no differnetially used 3' UTR PAS") + theme(legend.position = "none")+ scale_fill_brewer(palette = "Dark2")

Version Author Date
edd8940 brimittleman 2020-04-13 
ggplot(DiffIsoIntronall, aes(x=Conserved, y=log10(density),fill=Conserved)) +geom_boxplot() + stat_compare_means(method.args = list(alternative = "greater")) +labs(title="Number of annotated miRNA sites\nConserved = no differnetially used Intronic PAS") + theme(legend.position = "none")+ scale_fill_brewer(palette = "Dark2")

Version Author Date
edd8940 brimittleman 2020-04-13

Can I look at conserved another way. Same dominant:

SameDom=read.table("../data/DominantPAS_DF/Nuclear_SameDom.txt",header = T, stringsAsFactors = F)
Allgenes=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T,stringsAsFactors = F) %>% select(gene) %>% unique() %>% mutate(ConservedDom=ifelse(gene %in% SameDom$gene, "Yes","No")) %>% inner_join(miRNADBgenes,by="gene")


ggplot(Allgenes, aes(x=ConservedDom, y=log10(density),fill=ConservedDom)) +geom_boxplot() + stat_compare_means() +labs(title="Number of annotated miRNA sites by Conservation of Dominant PAS", x="Same Dominant PAS") + theme(legend.position = "none")+ scale_fill_brewer(palette = "Dark2")

Version Author Date
edd8940 brimittleman 2020-04-13 
Allgenes %>% group_by(ConservedDom) %>% summarise(mean(density))
# A tibble: 2 x 2
  ConservedDom `mean(density)`
  <chr>                  <dbl>
1 No                     0.618
2 Yes                    0.471

Different dominant PAS more dense with miRNA binding sites

Differentially used conditioned on dominant:

Look at the differentially used PAS when the differentially used PAS is the dominant PAS.

Usage=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",stringsAsFactors = F, header = T)
ChimpDom= Usage %>% 
  group_by(gene) %>%
  top_n(1,Chimp) %>% 
  select(PAS,gene)

HumanDom= Usage %>% 
  group_by(gene) %>%
  top_n(1,Human) %>% 
  select(PAS,gene)


DomEither= ChimpDom %>% bind_rows(HumanDom) %>% unique()
DiffIsoDom=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(PASINFO,by=c("chr", "start", "end"))  %>% mutate(Dom=ifelse(PAS %in% DomEither$PAS, "Yes", "No"))%>% filter(Dom =="Yes",SigPAU2=="Yes") %>% select(gene) %>% unique()

AllgenesDom=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T,stringsAsFactors = F) %>% select(gene) %>% unique() %>% mutate(DiffUsed=ifelse(gene %in% DiffIsoDom$gene, "Yes","No")) %>% inner_join(miRNADBgenes,by="gene")

ggplot(AllgenesDom, aes(x=DiffUsed, y=log10(density),fill=DiffUsed)) +geom_boxplot() + stat_compare_means() +labs(title="Number of annotated miRNA sites by Conservation Gene with Diff used dominant PAS", x="Same Dominant PAS") + theme(legend.position = "none")+ scale_fill_brewer(palette = "Dark2")

Version Author Date
edd8940 brimittleman 2020-04-13 
AllgenesDom %>% group_by(DiffUsed) %>% summarise(n(), mean(density))
# A tibble: 2 x 3
  DiffUsed `n()` `mean(density)`
  <chr>    <int>           <dbl>
1 No        6761           0.501
2 Yes       1230           0.590

When the gene has a differentially used pas that is dominant we see the effect.

New way to call dominance:

HumanRes=read.table("../data/DomDefGreaterX/Human_AllGenes_DiffTop.txt", col.names = c("Human_PAS", "gene","Human_DiffDom"),stringsAsFactors = F)

ChimpRes=read.table("../data/DomDefGreaterX/Chimp_AllGenes_DiffTop.txt", col.names = c("Chimp_PAS", "gene","Chimp_DiffDom"),stringsAsFactors = F)

BothRes=HumanRes %>% inner_join(ChimpRes,by="gene")

BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)  %>% inner_join(miRNADBgenes, by="gene")
ggplot(BothRes_40, aes(x=Set, y=log10(density),fill=Set)) +geom_boxplot() + stat_compare_means(method.args = list(alternative = "greater")) +labs(title="Number of annotated miRNA sites\n Dominance Structure") + theme(legend.position = "none")+ scale_fill_brewer(palette = "Dark2")

No difference here. Look at if there is a domiant PAS or not:

miRNADBgenes_dom= miRNADBgenes %>% mutate(Dom=ifelse(gene %in% BothRes$gene, "Yes", "No"))

ggplot(miRNADBgenes_dom, aes(x=Dom, y=log10(density),fill=Dom)) +geom_boxplot() + stat_compare_means() +labs(title="Density of annotated miRNA sites") + theme(legend.position = "none")+ scale_fill_brewer(palette = "Dark2")

This shows that genes with a domiant PAS have higher density of miRNA.


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    tidyverse_1.2.1
 [9] ggpubr_0.2      magrittr_1.5    ggplot2_3.1.1   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        lazyeval_0.2.1     crayon_1.3.4      
[43] whisker_0.3-2      pkgconfig_2.0.2    xml2_1.2.0        
[46] lubridate_1.7.4    assertthat_0.2.0   rmarkdown_1.10    
[49] httr_1.3.1         rstudioapi_0.10    R6_2.3.0          
[52] nlme_3.1-137       git2r_0.26.1       compiler_3.5.1