Last updated: 2020-04-07

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Knit directory: Comparative_APA/analysis/

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Rmd d231c12 brimittleman 2020-04-07 diff in dom
html d81b148 brimittleman 2020-04-07 Build site.
Rmd a672b02 brimittleman 2020-04-07 numb dist for dom 
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I look at genes with the same dominant PAS but I want to look at the distribution of PAS in a gene. Are the number of PAS used for each gene conserved. I can use different usage filters then see if the distributions are the same or different when the gene has the same dominant site.

One question is whether we see examples of 1 dominant site in one species and the other has more than one site used at similar proportions.

I can start by looking at the genes with shared dominant PAS to see if the usages in each species are similar.

I will filter for genes with more than 1 PAS

PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)
PASpregene=PASMeta %>% group_by(gene) %>% summarize(nPAS=n())
PASmore2=PASpregene %>% filter(nPAS>1)
DiffDom=read.table("../data/DominantPAS_DF/Nuclear_DiffDom.txt",header = T,stringsAsFactors = F) %>% filter(gene %in% PASmore2$gene)
SameDom=read.table("../data/DominantPAS_DF/Nuclear_SameDom.txt",header = T,stringsAsFactors = F) %>% mutate(DiffinDom=Chimp-Human) %>% filter(gene %in% PASmore2$gene)

Plot this: - is more in human:

ggplot(SameDom,aes(x=DiffinDom))+ geom_histogram(bins=100)

Version Author Date
d81b148 brimittleman 2020-04-07

A bit shifted toward chimp.

summary(SameDom$DiffinDom)
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.7666667 -0.0008333  0.0733333  0.0781255  0.1591667  0.5408333

I can look at examples at each extreme.

Fore both species, i want to get the number of PAS used at certain cutoffs, 0%-50%

Cutoff=seq(0,.5,.1)
cutoffCol=c()
nPAS=seq(1,5,1)
ChimpGenes=c()
HumanGenes=c()
nPAScol=c()
for (i in Cutoff){
  for (n in nPAS){
    human= PASMeta %>% filter(gene %in% PASmore2$gene, Human >= i) %>% group_by(gene) %>% summarise(nPASingene=n()) %>% filter(nPASingene==n) %>% nrow() 
    HumanGenes=c(HumanGenes,human)
    chimp= PASMeta %>% filter(gene %in% PASmore2$gene,Chimp >= i) %>% group_by(gene) %>% summarise(nPASingene=n()) %>% filter(nPASingene==n) %>% nrow() 
    ChimpGenes=c(ChimpGenes,chimp)
    nPAScol=c(nPAScol,n)
    cutoffCol=c(cutoffCol,i)
  }
}

DFdata=as.data.frame(cbind(nPAScol,cutoffCol,ChimpGenes, HumanGenes))
DFdata_gather=DFdata %>% gather("Species", "NGene", -nPAScol, -cutoffCol)
DFdata_gather$nPAScol=as.factor(DFdata_gather$nPAScol)
DFdata_gather$cutoffCol=as.factor(DFdata_gather$cutoffCol)
ggplot(DFdata_gather,aes(x=cutoffCol, by=nPAScol, y=NGene,fill=nPAScol))+ geom_bar(position = "dodge", stat="identity") +facet_grid(~Species) + scale_fill_brewer(palette="Dark2", name="Number of PAS") + labs(title="Number of PAS per gene by usage",y="Number of Genes", x="Usage is at least")

Version Author Date
d81b148 brimittleman 2020-04-07

Do the same thing for same dominant:

PASMetaSame= PASMeta %>% filter(gene %in% SameDom$gene)
cutoffCol_same=c()
ChimpGenes_same=c()
HumanGenes_same=c()
nPAScol_same=c()
for (i in Cutoff){
  for (n in nPAS){
    human= PASMetaSame %>% filter( Human >= i) %>% group_by(gene) %>% summarise(nPASingene=n()) %>% filter(nPASingene==n) %>% nrow() 
    HumanGenes_same=c(HumanGenes_same,human)
    chimp= PASMetaSame %>% filter(Chimp >= i) %>% group_by(gene) %>% summarise(nPASingene=n()) %>% filter(nPASingene==n) %>% nrow() 
    ChimpGenes_same=c(ChimpGenes_same,chimp)
    nPAScol_same=c(nPAScol_same,n)
    cutoffCol_same=c(cutoffCol_same,i)
  }
}

DFdata_same=as.data.frame(cbind(nPAScol_same,cutoffCol_same,ChimpGenes_same, HumanGenes_same))
DFdata_gather_same=DFdata_same %>% gather("Species", "NGene", -nPAScol_same, -cutoffCol_same)
DFdata_gather_same$nPAScol_same=as.factor(DFdata_gather_same$nPAScol_same)
DFdata_gather_same$cutoffCol_same=as.factor(DFdata_gather_same$cutoffCol_same)
ggplot(DFdata_gather_same,aes(x=cutoffCol_same, by=nPAScol_same, y=NGene,fill=nPAScol_same))+ geom_bar(position = "dodge", stat="identity") +facet_grid(~Species) + scale_fill_brewer(palette="Dark2", name="Number of PAS") + labs(title="Number of PAS per gene by usage \n same domiant",y="Number of Genes", x="Usage is at least")

Version Author Date
d81b148 brimittleman 2020-04-07

Different dominannt:

PASMetaDiff= PASMeta %>% filter(gene %in% DiffDom$gene)
cutoffCol_Diff=c()
ChimpGenes_Diff=c()
HumanGenes_Diff=c()
nPAScol_Diff=c()
for (i in Cutoff){
  for (n in nPAS){
    human= PASMetaDiff %>% filter( Human >= i) %>% group_by(gene) %>% summarise(nPASingene=n()) %>% filter(nPASingene==n) %>% nrow() 
    HumanGenes_Diff=c(HumanGenes_Diff,human)
    chimp= PASMetaDiff %>% filter(Chimp >= i) %>% group_by(gene) %>% summarise(nPASingene=n()) %>% filter(nPASingene==n) %>% nrow() 
    ChimpGenes_Diff=c(ChimpGenes_Diff,chimp)
    nPAScol_Diff=c(nPAScol_Diff,n)
    cutoffCol_Diff=c(cutoffCol_Diff,i)
  }
}

DFdata_Diff=as.data.frame(cbind(nPAScol_Diff,cutoffCol_Diff,ChimpGenes_Diff, HumanGenes_Diff))
DFdata_gather_Diff=DFdata_Diff %>% gather("Species", "NGene", -nPAScol_Diff, -cutoffCol_Diff)
DFdata_gather_Diff$nPAScol_Diff=as.factor(DFdata_gather_Diff$nPAScol_Diff)
DFdata_gather_Diff$cutoffCol_Diff=as.factor(DFdata_gather_Diff$cutoffCol_Diff)
ggplot(DFdata_gather_Diff,aes(x=cutoffCol_Diff, by=nPAScol_Diff, y=NGene,fill=nPAScol_Diff))+ geom_bar(position = "dodge", stat="identity") +facet_grid(~Species) + scale_fill_brewer(palette="Dark2", name="Number of PAS") + labs(title="Number of PAS per gene by usage \n different domiant",y="Number of Genes", x="Usage is at least")

Version Author Date
d81b148 brimittleman 2020-04-07

Number of PAS in same vs diffferent:

NpasSame=PASMetaSame%>% group_by(gene) %>% summarise(nPAS=n()) %>% group_by(nPAS) %>% summarise(Same=n(),PropSame=Same/nrow(PASMetaSame)) %>% select(-Same)
NpasDiff=PASMetaDiff %>% group_by(gene) %>% summarise(nPAS=n()) %>% group_by(nPAS) %>% summarise(Different=n(), PropDiff=Different/nrow(PASMetaDiff)) %>% select(-Different)

NpasBoth= NpasSame %>% inner_join(NpasDiff) %>% gather("GeneSet", "Prop", -nPAS)
Joining, by = "nPAS"
NpasBoth$nPAS=as.factor(NpasBoth$nPAS)

ggplot(NpasBoth,aes(fill=GeneSet, y=Prop, x=nPAS))+ geom_bar(position = "dodge", stat="identity") + scale_fill_brewer(palette="Dark2", labels=c("Different Dominant", "Same Dominant"),name="") + labs(y="Proportion of Genes", x="Number of PAS", title="Number of PAS distribution by Dominance structure")

Version Author Date
d81b148 brimittleman 2020-04-07

When the dominant are diff:

PASMeta_humanDom_diff=PASMeta %>% filter(PAS%in%DiffDom$HumanPAS) %>% mutate(Diff=Human-Chimp)

ggplot(PASMeta_humanDom_diff,aes(x=Human, y=Chimp))+geom_point() + geom_abline(slope=1, intercept = 0,col="red") +labs(title="PAS usage for different dominant, condition on Human")

ggplot(PASMeta_humanDom_diff,aes(x=Diff))+geom_histogram(bins=100) + labs(title="Difference in Human - Chimp for Human Dominant")

PASMeta_ChimpDom_diff=PASMeta %>% filter(PAS%in%DiffDom$ChimpPAS) %>% mutate(Diff=Human-Chimp)

ggplot(PASMeta_ChimpDom_diff,aes(x=Human, y=Chimp))+geom_point() + geom_abline(slope=1, intercept = 0,col="red") +labs(title="PAS usage for different dominant, condition on Chimp")

ggplot(PASMeta_ChimpDom_diff,aes(x=Diff))+geom_histogram(bins=100) + labs(title="Difference in Human - Chimp for Chimp Dominant")

ggplot(PASMeta_humanDom_diff,aes(x=Diff))+geom_histogram(bins=100, fill="#D95F02",alpha=.5) + labs(title="Human Usage - Chimp Chimp \n Colored by dominant") + geom_histogram(data=PASMeta_ChimpDom_diff,aes(x=Diff), bins = 100, fill="#1B9E77", alpha=.5) + geom_vline(xintercept = mean(PASMeta_ChimpDom_diff$Diff), col="#1B9E77")+ geom_vline(xintercept = mean(PASMeta_humanDom_diff$Diff), col="#D95F02") + geom_histogram(bins=100, data=SameDom, aes(x=DiffinDom), alpha=.3)+ geom_vline(xintercept = mean(SameDom$DiffinDom))

mean(PASMeta_humanDom_diff$Diff)
[1] 0.106281
mean(PASMeta_ChimpDom_diff$Diff)
[1] -0.164446

Do this same analysis but by number of PAS in the gene.

PASMeta_humanDom_diffsmall= PASMeta_humanDom_diff %>% select(gene, Diff) %>% mutate(Species="Human")%>% full_join(PASpregene, by="gene")

PASMeta_ChimpDom_diffsmall= PASMeta_ChimpDom_diff %>% select(gene, Diff) %>% mutate(Species="Chimp")%>% full_join(PASpregene, by="gene")

SameDomSmall=SameDom %>% rename(Diff=DiffinDom) %>% select(gene, Diff) %>% mutate(Species="Both")%>% full_join(PASpregene, by="gene")



Alldiff=PASMeta_humanDom_diffsmall %>% bind_rows(PASMeta_ChimpDom_diffsmall) %>% bind_rows(SameDomSmall) %>% full_join(PASpregene, by="gene")



ggplot(PASMeta_humanDom_diffsmall,aes(x=Diff)) +geom_histogram(bins=100, fill="#D95F02",alpha=.5) + geom_histogram(data=PASMeta_ChimpDom_diffsmall,aes(x=Diff), bins = 100, fill="#1B9E77", alpha=.5) + geom_histogram(bins=100, data=SameDomSmall, aes(x=Diff), alpha=.3)+ facet_grid(~nPAS)
Warning: Removed 6796 rows containing non-finite values (stat_bin).

Warning: Removed 6796 rows containing non-finite values (stat_bin).
Warning: Removed 4143 rows containing non-finite values (stat_bin).

I want something a bit different dominant PAS in human - human value for dominant PAS in chimp

DiffDomfromH= DiffDom %>% select(Human, ChimpPAS) %>% rename(PAS=ChimpPAS, humanDom=Human) %>% inner_join(PASMeta, by="PAS")%>% mutate(Diff=humanDom-Human,Dom="Human") %>% select(gene,Dom, Diff) %>% inner_join(PASpregene, by="gene")%>% filter(nPAS<6)

DiffDomfromC= DiffDom %>% select(Chimp, HumanPAS) %>% rename(PAS=HumanPAS, ChimpDom=Chimp) %>% inner_join(PASMeta, by="PAS")%>% mutate(Diff=ChimpDom-Chimp,Dom="Chimp")%>% select(gene,Dom, Diff) %>% inner_join(PASpregene, by="gene") %>% filter(nPAS<6)


ggplot(DiffDomfromH,aes(x=Diff))+ geom_histogram(bins=50, fill="#D95F02",alpha=.5) + geom_histogram(data=DiffDomfromC, bins=50,fill="#1B9E77", alpha=.5 ) + facet_grid(~nPAS) + labs(x="Difference in Mean usage", title="Dominant PAS in species - same species value for \nthe 'dominant' in other species")

ggplot(DiffDomfromH,aes(x=Diff))+ geom_histogram(bins=50, fill="#D95F02",alpha=.5) + geom_histogram(data=DiffDomfromC, bins=50,fill="#1B9E77", alpha=.5 ) + labs(x="Difference in Mean usage", title="Dominant PAS in species - same species value for \nthe 'dominant' in other species")


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   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         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       broom_0.5.1        Rcpp_1.0.2        
[28] promises_1.0.1     scales_1.0.0       backports_1.1.2   
[31] jsonlite_1.6       fs_1.3.1           hms_0.4.2         
[34] digest_0.6.18      stringi_1.2.4      grid_3.5.1        
[37] rprojroot_1.3-2    cli_1.1.0          tools_3.5.1       
[40] magrittr_1.5       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