Last updated: 2020-04-06
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Knit directory: Comparative_APA/analysis/
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Unstaged changes:
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File | Version | Author | Date | Message |
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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)
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()
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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
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