Last updated: 2020-07-02

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DirSelectionKhan.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/dInforContent.Rmd
    Modified:   analysis/df_QC.Rmd
    Modified:   analysis/diffExpression.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/incorporateQTLsAncestral.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/miRNAanalysis.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/phastCon.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd
    Modified:   analysis/unliftedsites.Rmd

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Rmd 9ac7dbe brimittleman 2020-07-02 add sup pdfs
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Rmd f5a6b76 brimittleman 2020-06-08 add analysis of usage and significance and which site
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Rmd 3c19233 brimittleman 2020-05-14 add which PAS and seq between

Previously I mapped PAS to ortho exons. I want genes where the only PAS are those in the ortho UTRs:

library(workflowr)
This is workflowr version 1.6.0
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()
library(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave
library(ggpubr)
Loading required package: magrittr

Attaching package: 'magrittr'
The following object is masked from 'package:purrr':

    set_names
The following object is masked from 'package:tidyr':

    extract

Attaching package: 'ggpubr'
The following object is masked from 'package:cowplot':

    get_legend
OverlapOrtho=read.table("../data/orthoUTR/FilteredPASOverlapOrthoUTR.text", header = T,stringsAsFactors = F) 
PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",header = T,stringsAsFactors = F) 
OverlapOrtho_nper=  OverlapOrtho %>% group_by(gene) %>% summarise(nPASOrtho=n())


PASperGene= PASMeta %>% group_by(gene) %>% summarise(nPAS=n())

First inner join genes. Then those with the same nunmber of PAS:

PASperGene_inortho=PASperGene %>% inner_join(OverlapOrtho_nper,by="gene") %>% filter(nPAS==nPASOrtho)

Now i will grab these genes from the meta data:

use if else to get the first, middle, last..

Meta_allOrtho=PASMeta %>% filter(gene %in% PASperGene_inortho$gene) %>% group_by(gene) %>% arrange(start) %>% mutate(NumPAS=ifelse(strandFix=="+", 1:n(), n():1),number=n()) %>% mutate(WhichSite=ifelse(number==NumPAS, ifelse(NumPAS==1, "single", "last"), ifelse(NumPAS==1, "first", "middle")))
max(Meta_allOrtho$NumPAS)
[1] 8
Meta_allOrtho %>% ungroup() %>% group_by(WhichSite) %>% summarise(n())
# A tibble: 4 x 2
  WhichSite `n()`
  <chr>     <int>
1 first       480
2 last        480
3 middle      294
4 single      836

this means 836 have 1, 480 have more than one

Start to ask about which ones are dominant?, dAPA ect. filter diff used to ony those in the ortho exon only gene set.

PASGene=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",stringsAsFactors = F, header = T) %>% select(PAS, chr, start, end,loc)
DiffUsed=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt",header = T,stringsAsFactors = F) %>% inner_join(PASGene, by=c("chr",'start','end')) %>% filter(gene %in% PASperGene_inortho$gene) %>% select(PAS, SigPAU2)
useCOl <- c("#d73027", "#4575b4","#fee090")
Meta_allOrtho_SIG= Meta_allOrtho %>% inner_join(DiffUsed, by="PAS")

Meta_allOrtho_SIG$WhichSite=factor(Meta_allOrtho_SIG$WhichSite, levels=c("last", "middle", "first"))

Meta_allOrtho_SIG_num= Meta_allOrtho_SIG %>% group_by(SigPAU2,WhichSite ) %>% summarise(nPAS=n()) %>% ungroup() %>% group_by(SigPAU2) %>% mutate(Signum=sum(nPAS), Freq=nPAS/Signum,pos = cumsum(Freq) + (0.5 * Freq))

whichSiteplot=ggplot(Meta_allOrtho_SIG, aes(x=SigPAU2, by=WhichSite, fill=WhichSite)) + geom_bar(stat="count", position = "fill") + labs(x="Differentially used PAS",y="Proportion of sites", title="Differentially used PAS are more likely to be the \nfirst site in orthologous 3' UTRs") + scale_fill_manual(values=useCOl, name="") + theme_classic() + theme(legend.position = "top", axis.text.x = element_text(size=10),plot.title = element_text(hjust = 0.5, face="bold"),axis.text.y = element_text(size=10),text=element_text(size=10),plot.margin = unit(c(0,0,0,0), "cm"))

     



whichSiteplot

Version Author Date
afecfdb brimittleman 2020-05-20
ab6b356 brimittleman 2020-05-14

write out ortho utr PAS for use in another analysis:

write.table(Meta_allOrtho_SIG, "../data/orthoUTR/OrthoPASwithLocandSig.txt",col.names = T,row.names = F, quote = F)

pdf of figures

PropdAPA=Meta_allOrtho_SIG %>% group_by(SigPAU2, WhichSite) %>% summarise(n=n()) %>% ungroup() %>% group_by(SigPAU2) %>% mutate(nDapa=sum(n), prop=n/nDapa,dAPA=ifelse(SigPAU2=="Yes",1,0))

PropdAPA_no=as.vector(PropdAPA[1:3,5])

PropdAPA_yes=unlist(as.vector(PropdAPA[4:6,5]))

wilcox.test(PropdAPA$prop, PropdAPA$dAPA)
Warning in wilcox.test.default(PropdAPA$prop, PropdAPA$dAPA): cannot
compute exact p-value with ties

    Wilcoxon rank sum test with continuity correction

data:  PropdAPA$prop and PropdAPA$dAPA
W = 18, p-value = 1
alternative hypothesis: true location shift is not equal to 0
prop.test(x=c(47,442), n=c(84,1168))

    2-sample test for equality of proportions with continuity
    correction

data:  c(47, 442) out of c(84, 1168)
X-squared = 10.05, df = 1, p-value = 0.001523
alternative hypothesis: two.sided
95 percent confidence interval:
 0.06497125 0.29722705
sample estimates:
   prop 1    prop 2 
0.5595238 0.3784247

Test dominant:

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_10=BothRes %>% filter(Chimp_DiffDom >=0.1 | Human_DiffDom>=0.1) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=10) 
BothRes_20=BothRes %>% filter(Chimp_DiffDom >=0.2 | Human_DiffDom>=0.2) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=20)
BothRes_30=BothRes %>% filter(Chimp_DiffDom >=0.3 | Human_DiffDom>=0.3) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=30)
BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)
BothRes_50=BothRes %>% filter(Chimp_DiffDom >=0.5 | Human_DiffDom>=0.5) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=50)
BothRes_60=BothRes %>% filter(Chimp_DiffDom >=0.6 | Human_DiffDom>=0.6) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=60)
BothRes_70=BothRes %>% filter(Chimp_DiffDom >=0.7 | Human_DiffDom>=0.7) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=70)
BothRes_80=BothRes %>% filter(Chimp_DiffDom >=0.8 | Human_DiffDom>=0.8) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=80)
BothRes_90=BothRes %>% filter(Chimp_DiffDom >=0.9 | Human_DiffDom>=0.9) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=90)

BothResAll=BothRes_10 %>% bind_rows(BothRes_20) %>% bind_rows(BothRes_30) %>% bind_rows(BothRes_40) %>% bind_rows(BothRes_50) %>% bind_rows(BothRes_60) %>% bind_rows(BothRes_70) %>% bind_rows(BothRes_80) %>% bind_rows(BothRes_90)

Filter the genes in the utr only set:

BothRes_40_filt= BothRes_40 %>% filter(gene %in% PASperGene_inortho$gene )

nrow(BothRes_40_filt)
[1] 238
BothRes_40_filt %>% group_by(Set) %>% summarise(n())
# A tibble: 2 x 2
  Set       `n()`
  <chr>     <int>
1 Different    10
2 Same        228

Same:

Meta_allOrthoSm= Meta_allOrtho %>% ungroup() %>% select(PAS, WhichSite)

BothRes_40_filt_same= BothRes_40_filt %>% filter(Set=="Same") %>% dplyr::rename("PAS" = Human_PAS) %>% inner_join(Meta_allOrthoSm, by="PAS")

BothRes_40_filt_same$WhichSite= factor(BothRes_40_filt_same$WhichSite, levels=c("last", "middle","first"))
ggplot(BothRes_40_filt_same, aes(x=WhichSite, fill=WhichSite)) + geom_bar(stat="count" ) + scale_fill_manual(values=useCOl)

Version Author Date
b063d26 brimittleman 2020-06-08
afecfdb brimittleman 2020-05-20

Diff dominant:

BothRes_40_filt_diff= BothRes_40_filt%>% filter(Set=="Different") %>% select(gene, Human_PAS, Chimp_PAS) %>% gather("Species","PAS", -gene) %>%   inner_join(Meta_allOrthoSm, by="PAS")

ggplot(BothRes_40_filt_diff, aes(x=Species,by=Species, fill=WhichSite)) + geom_bar(stat="count",position = "dodge" ) + scale_fill_manual(values=useCOl)

Version Author Date
b063d26 brimittleman 2020-06-08
afecfdb brimittleman 2020-05-20
ab6b356 brimittleman 2020-05-14

This may be due to the low numbers: try different cutoffs:

number=seq(1,9,1)
#BothResAll
plotlist=list()
for (i in number){
  val=i *10
  df=BothResAll %>% filter(Set=="Same", cut==val) %>% dplyr::rename("PAS" = Human_PAS) %>% inner_join(Meta_allOrthoSm, by="PAS")
  df$WhichSite= factor(df$WhichSite, levels=c("first", "middle","last"))
  plotlist[[i]]=ggplot(df, aes(x=WhichSite, fill=WhichSite)) + geom_bar(stat="count" ) + scale_fill_manual(values=useCOl) + labs(title=paste(val, "Same Dominant" , nrow(df), sep="_")) +theme(legend.position = "bottom")
}
plot_grid(plotlist[[1]], plotlist[[2]], plotlist[[3]], plotlist[[4]], plotlist[[5]], plotlist[[6]], plotlist[[7]], plotlist[[8]], plotlist[[9]])

Version Author Date
b063d26 brimittleman 2020-06-08
afecfdb brimittleman 2020-05-20
ab6b356 brimittleman 2020-05-14 
#BothResAll
plotlistdiff=list()
for (i in number){
  val=i *10
  df=BothResAll %>% filter(Set=="Different", cut==val) %>% select(gene, Human_PAS, Chimp_PAS) %>% gather("Species","PAS", -gene) %>%   inner_join(Meta_allOrthoSm, by="PAS")
  df$WhichSite= factor(df$WhichSite, levels=c("first", "middle","last"))
  plotlistdiff[[i]]=ggplot(df, aes(x=Species,by=Species, fill=WhichSite)) + geom_bar(stat="count",position = "dodge" ) + scale_fill_manual(values=useCOl) + labs(title=paste(val, "Different Dominant" , nrow(df), sep="_")) +theme(legend.position = "bottom")
}
plot_grid(plotlistdiff[[1]], plotlistdiff[[2]], plotlistdiff[[3]], plotlistdiff[[4]], plotlistdiff[[5]], plotlistdiff[[6]], plotlistdiff[[7]], plotlistdiff[[8]], plotlistdiff[[9]])

Version Author Date
b063d26 brimittleman 2020-06-08
afecfdb brimittleman 2020-05-20
ab6b356 brimittleman 2020-05-14

Seperate by DE:

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
DE= read.table("../data/DiffExpression/DEtested_allres.txt",header=F, stringsAsFactors = F,col.names = c('Gene_stable_ID', 'logFC' ,'AveExpr', 't', 'P.Value', 'adj.P.Val', 'B')) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(-Gene_stable_ID, -Source_of_gene_name) %>% dplyr::rename("gene"=Gene.name) %>% mutate(DE=ifelse(adj.P.Val<=.05, "Yes","No")) %>% select(gene,DE)
DE_yes= DE %>% filter(DE=="Yes")

numbersmall=seq(1,4)
#BothResAll
plotlistdiffDE=list()
for (i in numbersmall){
  val=i *10
  df=BothResAll %>% filter(Set=="Different", cut==val) %>% select(gene, Human_PAS, Chimp_PAS) %>% gather("Species","PAS", -gene) %>%   inner_join(Meta_allOrthoSm, by="PAS") %>% mutate(DEgenes=ifelse(gene %in%DE_yes$gene, "DE", "Not"))
  df$WhichSite= factor(df$WhichSite, levels=c("first", "middle","last"))
  plotlistdiffDE[[i]]=ggplot(df, aes(x=Species,by=Species, fill=WhichSite)) + geom_bar(stat="count",position = "dodge" ) + scale_fill_manual(values=useCOl) + labs(title=paste(val, "Different Dominant" , nrow(df), sep="_")) +theme(legend.position = "bottom") + facet_grid(~DEgenes)
}
plot_grid(plotlistdiffDE[[1]], plotlistdiffDE[[2]], plotlistdiffDE[[3]], plotlistdiffDE[[4]])

Version Author Date
b063d26 brimittleman 2020-06-08
afecfdb brimittleman 2020-05-20 
metaPAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",stringsAsFactors = F, header = T) %>% mutate(midpoint=start+100)
metaPAS_sm= metaPAS %>% select(PAS, gene, midpoint)
BothResallDiff= BothResAll %>%
  filter(Set=="Different") %>% 
  select(gene, Chimp_PAS, Human_PAS, cut) %>% 
  gather("species", "PAS", -gene, -cut) %>% 
  inner_join(metaPAS_sm, by=c("gene", "PAS")) %>% 
  mutate(extra="PAS") %>% 
  spread(extra,midpoint) %>% 
  group_by(cut, gene) %>% 
  summarise(minPAS=min(PAS), maxPAS=max(PAS)) %>% 
  mutate(length= maxPAS-minPAS) %>% 
  filter(gene %in% PASperGene_inortho$gene)
BothResallDiff$cut=factor(BothResallDiff$cut)


ggplot(BothResallDiff, aes(x=cut, y=length,fill=cut)) +geom_boxplot() + theme(legend.position = "none")+ scale_fill_brewer(palette = "Set1")  + geom_jitter() + labs(title="Distance between center of different Dominant PAS",y="basepairs",x="cutoff")

Version Author Date
b063d26 brimittleman 2020-06-08 
BothResallDiff %>% group_by(cut) %>% summarise(n())
# A tibble: 8 x 2
  cut   `n()`
  <fct> <int>
1 10       37
2 20       24
3 30       17
4 40       10
5 50        8
6 60        5
7 70        2
8 80        2

Run nuc for these:

metaPAS_bed= metaPAS %>% select(chr, gene, strandFix) %>% unique()
BothResallDiff_Bed= BothResallDiff%>% inner_join(metaPAS_bed, by="gene") %>% select(chr, minPAS, maxPAS, gene, cut, strandFix ) %>% arrange(chr, minPAS)

write.table(BothResallDiff_Bed, "../data/DistTwoDom/SeqBetweenDom_Allcutt.bed", quote = F, col.names = F, row.names = F, sep="\t")

bedtools nuc -s -seq -fi /project2/gilad/kenneth/References/human/genome/hg38.fa -bed ../data/DistTwoDom/SeqBetweenDom_Allcutt.bed > ../data/DistTwoDom/SeqBetweenDom_Allcutt_nuc.bed
SeqBetween=read.table("../data/DistTwoDom/SeqBetweenDom_Allcutt_nuc.bed", col.names = c(colnames(BothResallDiff_Bed),"AT", "GC", "A", "C", "G", "T","N", "other", "len", "seq" )) %>% mutate(DEgenes=ifelse(gene %in%DE_yes$gene, "DE", "Not")) 

SeqBetween %>% group_by(cut) %>% summarise(n())
# A tibble: 8 x 2
    cut `n()`
  <int> <int>
1    10    37
2    20    24
3    30    17
4    40    10
5    50     8
6    60     5
7    70     2
8    80     2
SeqBetween$cut=as.factor(SeqBetween$cut)
ggplot(SeqBetween, aes(y=len, by=DEgenes,fill=DEgenes, x=cut))+geom_boxplot() + geom_jitter()+scale_fill_brewer(palette = "Set1")+ labs(y="Nucleotides", title="Distance between Dominant PAS")+ theme(legend.position = "bottom")

Version Author Date
b063d26 brimittleman 2020-06-08 
atplot=ggplot(SeqBetween, aes(by=DEgenes, y=AT, x=cut,fill=DEgenes)) +geom_boxplot()+geom_jitter() +scale_fill_brewer(palette = "Set1")+ labs(y="AT proportion", title="AT proportion for Seq between dominat")+ theme(legend.position = "bottom") 
gcplot=ggplot(SeqBetween, aes(by=DEgenes, y=GC, x=cut,fill=DEgenes)) +geom_boxplot()+geom_jitter() +scale_fill_brewer(palette = "Set1")+ theme(legend.position = "bottom") +labs(y="GC proportion", title="GC proportion for Seq between dominat")
plot_grid(atplot,gcplot)

Version Author Date
b063d26 brimittleman 2020-06-08 

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] ggpubr_0.2      magrittr_1.5    cowplot_0.9.4   forcats_0.3.0  
 [5] stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1    
 [9] tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1
[13] workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   reshape2_1.4.3     haven_1.1.2       
 [4] lattice_0.20-38    colorspace_1.3-2   generics_0.0.2    
 [7] htmltools_0.3.6    yaml_2.2.0         utf8_1.1.4        
[10] rlang_0.4.0        later_0.7.5        pillar_1.3.1      
[13] glue_1.3.0         withr_2.1.2        RColorBrewer_1.1-2
[16] modelr_0.1.2       readxl_1.1.0       plyr_1.8.4        
[19] munsell_0.5.0      gtable_0.2.0       cellranger_1.1.0  
[22] rvest_0.3.2        evaluate_0.12      labeling_0.3      
[25] knitr_1.20         httpuv_1.4.5       fansi_0.4.0       
[28] broom_0.5.1        Rcpp_1.0.4.6       promises_1.0.1    
[31] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[34] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[37] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[40] cli_1.1.0          tools_3.5.1        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