Last updated: 2020-04-06

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
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    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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Rmd 71101c1 brimittleman 2020-04-06 add dom pattern and de

I want to look at genes with the same or different dominant PAS. I can ask if they are more or less likely to be differential expressed.

library(workflowr)
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First load the dominance structure. I want same and different PAS genes.

NuclearDiffDom=read.table("../data/DominantPAS_DF/Nuclear_DiffDom.txt",header = T,stringsAsFactors = F) %>% mutate(Dominance="Different") %>% select(gene, Dominance)
NuclearSameDom=read.table("../data/DominantPAS_DF/Nuclear_SameDom.txt",header = T,stringsAsFactors = F)  %>% mutate(Dominance="Same")%>% select(gene, Dominance)

Domiance=NuclearDiffDom %>% bind_rows(NuclearSameDom)

Call DE at 5% fdr for now.

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) %>% rename("gene"=Gene.name) %>% mutate(DE=ifelse(adj.P.Val<=.05, "Yes","No")) %>% select(gene,DE)

Join both:
Look at genes we have data for both:

DomandDE=Domiance %>% inner_join(DE,by="gene")
nrow(DomandDE)
[1] 8345

Group and summarize

DomandDE %>% group_by(DE, Dominance) %>% summarise(nSet=n())
# A tibble: 4 x 3
# Groups:   DE [2]
  DE    Dominance  nSet
  <chr> <chr>     <int>
1 No    Different  1542
2 No    Same       3660
3 Yes   Different   961
4 Yes   Same       2182

Enrichment for DE and same:

x=nrow(DomandDE %>% filter(Dominance=="Same", DE=="Yes"))
m=nrow(DomandDE %>% filter(DE=="Yes"))
n=nrow(DomandDE %>% filter(DE=="No"))
k=nrow(DomandDE %>% filter(Dominance=="Same"))
N=nrow(DomandDE)
phyper(x,m,n,k,lower.tail=F)
[1] 0.80981
enrich=(x/k)/(m/N)
enrich
[1] 0.9916882

Opposite direction. Different dominance and DE

x=nrow(DomandDE %>% filter(Dominance=="Different", DE=="Yes"))
m=nrow(DomandDE %>% filter(DE=="Yes"))
n=nrow(DomandDE %>% filter(DE=="No"))
k=nrow(DomandDE %>% filter(Dominance=="Different"))
N=nrow(DomandDE)
phyper(x,m,n,k,lower.tail=F)
[1] 0.1771116
enrich=(x/k)/(m/N)
enrich
[1] 1.0194

Ok so this is not significant but differentially used are. Maybe when I look to see the differential when one pas is intronic and one is 3’UTR

Compare these to the other ones with a different dominant gene first.

NuclearDiffDom_pattern=read.table("../data/DominantPAS_DF/Nuclear_DiffDom.txt",header = T,stringsAsFactors = F) %>% mutate(Dominance="Different", HumanIntChimpUTR=ifelse(ChimpLoc=="utr3" & HumanLoc=="intron", "Yes", "No"), HumanUTRChimpInt=ifelse(ChimpLoc=="intron" & HumanLoc=="utr3", "Yes", "No"), Pattern=ifelse(HumanIntChimpUTR=="Yes" | HumanUTRChimpInt=="Yes", "Yes","No")) %>% select(gene,HumanIntChimpUTR,HumanUTRChimpInt,Pattern )

PatternandDE= NuclearDiffDom_pattern %>% inner_join(DE,by="gene")
nrow(PatternandDE)
[1] 2503

First do Human Intronic chimp UTR

x=nrow(PatternandDE %>% filter(HumanIntChimpUTR=="Yes", DE=="Yes"))
m=nrow(PatternandDE %>% filter(DE=="Yes"))
n=nrow(PatternandDE %>% filter(DE=="No"))
k=nrow(PatternandDE %>% filter(HumanIntChimpUTR=="No"))
N=nrow(PatternandDE)
phyper(x,m,n,k,lower.tail=F)
[1] 1
enrich=(x/k)/(m/N)
enrich
[1] 0.5574375

Opposite direction:

x=nrow(PatternandDE %>% filter(HumanUTRChimpInt=="Yes", DE=="Yes"))
m=nrow(PatternandDE %>% filter(DE=="Yes"))
n=nrow(PatternandDE %>% filter(DE=="No"))
k=nrow(PatternandDE %>% filter(HumanUTRChimpInt=="No"))
N=nrow(PatternandDE)
phyper(x,m,n,k,lower.tail=F)
[1] 1
enrich=(x/k)/(m/N)
enrich
[1] 0.01262316

Either pattern:

x=nrow(PatternandDE %>% filter(Pattern=="Yes", DE=="Yes"))
m=nrow(PatternandDE %>% filter(DE=="Yes"))
n=nrow(PatternandDE %>% filter(DE=="No"))
k=nrow(PatternandDE %>% filter(Pattern=="No"))
N=nrow(PatternandDE)
phyper(x,m,n,k,lower.tail=F)
[1] 1
enrich=(x/k)/(m/N)
enrich
[1] 0.5863967

Not significant. I need to use the full set to have power:

NuclearDiffDom_patternonly=NuclearDiffDom_pattern %>% select(gene,Pattern )
NuclearSameDom_pattern=read.table("../data/DominantPAS_DF/Nuclear_SameDom.txt",header = T,stringsAsFactors = F)  %>% mutate(Dominance="Same", Pattern ="No")%>% select(gene, Pattern)
BothPattern=bind_rows(NuclearDiffDom_patternonly,NuclearSameDom_pattern)

PatternbothandDE= BothPattern %>% inner_join(DE,by="gene")
nrow(PatternbothandDE)
[1] 8345
x=nrow(PatternbothandDE %>% filter(Pattern=="Yes", DE=="Yes"))
m=nrow(PatternbothandDE %>% filter(DE=="Yes"))
n=nrow(PatternbothandDE %>% filter(DE=="No"))
k=nrow(PatternbothandDE %>% filter(Pattern=="No"))
N=nrow(PatternbothandDE)
phyper(x,m,n,k,lower.tail=F)
[1] 1
enrich=(x/k)/(m/N)
enrich
[1] 0.1284556

No significance here.

Look at effect sizes. Are DE with different dominant stronger than DE without same dominant

DEfull= 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) %>% rename("gene"=Gene.name) %>% mutate(DE=ifelse(adj.P.Val<=.05, "Yes","No")) %>% inner_join(Domiance,by="gene")

DEfullsig= DEfull %>% filter(DE=="Yes") %>% rename(dom=Dominance)
DEfullsig$dom=as.factor(DEfullsig$dom)

Plot by dominance:

ggplot(DEfullsig,aes(x=dom,y=abs(logFC),fill=dom)) + geom_boxplot() + stat_compare_means()+ scale_fill_brewer(palette = "Dark2") + labs(x="Dominance Structure", y="abs(eQTL log effect size)", title="Larger effect sizes in genes with different domianant PAS") + theme(legend.position = "none")

DEfullsigdiff=DEfullsig %>% filter(dom=="Different")
DEfullsigSame=DEfullsig %>% filter(dom=="Same")

summary(abs(DEfullsigdiff$logFC))
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1986  0.4856  0.7628  1.0428  1.2471  7.0644 
summary(abs(DEfullsigSame$logFC))
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1711  0.4669  0.6714  0.9208  1.0618  7.1145 
wilcox.test(abs(DEfullsigdiff$logFC),abs(DEfullsigSame$logFC),alternative = "greater")

    Wilcoxon rank sum test with continuity correction

data:  abs(DEfullsigdiff$logFC) and abs(DEfullsigSame$logFC)
W = 1137400, p-value = 7.412e-05
alternative hypothesis: true location shift is greater than 0

I could make a qq plot to look at the pvalues.

DEfull_dom= DEfull %>% filter(Dominance=="Same")
DEfull_not= DEfull %>% filter(Dominance=="Different")

wilcox.test(DEfull_dom$adj.P.Val,DEfull_not$adj.P.Val)

    Wilcoxon rank sum test with continuity correction

data:  DEfull_dom$adj.P.Val and DEfull_not$adj.P.Val
W = 7414100, p-value = 0.3077
alternative hypothesis: true location shift is not equal to 0
qqplot(-log10(runif(nrow(DEfull_dom))), -log10(DEfull_dom$adj.P.Val), xlab="-log10(Uniform)", ylab="-log10(pval)", main="")
points(sort(-log10(runif(nrow(DEfull_not)))), sort(-log10(DEfull_not$adj.P.Val)),col= alpha("Red"))
abline(0,1)

legend("topleft", legend=c("Different Dominant","Same Dominant"),col=c("black", "red"), pch=16,bty = 'n')

No difference here and qq plot is not a good way to look at it.


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    forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   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        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