Last updated: 2020-04-19
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Knit directory: Comparative_APA/analysis/
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | c837f7b | brimittleman | 2020-04-19 | add decile loc |
html | 51a499a | brimittleman | 2020-04-19 | Build site. |
Rmd | 1ae3d26 | brimittleman | 2020-04-18 | add prop utr |
I looked at the ortho UTR PAS where there are only 2 for the gene. This was too restrictive. I am going to look at the percent of the ortho UTR for genes with only UTR PAS that are in the otho UTR file. I will do this because the are easier to interpret.
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()
OverlapOrtho=read.table("../data/orthoUTR/FilteredPASOverlapOrthoUTR.text", header = T,stringsAsFactors = F)
Look for genes that only have UTR pas.
PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",header = T, stringsAsFactors = F)
PASutr= PASMeta %>% group_by(gene) %>%
summarise(locString = toString(loc)) %>%
filter(!grepl("intron", locString)) %>%
filter(!grepl("cds", locString)) %>%
filter(!grepl("utr5", locString)) %>%
filter(!grepl("end", locString)) %>%
mutate(numUTR= str_count(locString, pattern = "utr3"))
I need to make sure the number of UTR pas are in the ortho file:
OverlapOrtho_num= OverlapOrtho %>% group_by(gene) %>% summarise(nOrtho=n())
OrthoandMetha= OverlapOrtho_num %>% inner_join(PASutr, by="gene") %>% mutate(matched=ifelse(nOrtho==numUTR, "Yes","No"))
OrthoandMethayes= OrthoandMetha %>% filter(matched=="Yes")
OrthoandMetha %>% group_by(matched) %>% summarise(n())
# A tibble: 2 x 2
matched `n()`
<chr> <int>
1 No 275
2 Yes 1272
OrthoandMetha_sm=OrthoandMetha %>% select(gene, numUTR)
This means the analysis will look at 1272 genes. I will filter these genes in the orthofile.
OverlapOrtho_filt= OverlapOrtho %>% inner_join(OrthoandMetha_sm, by="gene")
nrow(OverlapOrtho_filt)
[1] 2398
Plot number of PAS:
OrthoandMethayes$numUTR=as.factor(OrthoandMethayes$numUTR)
ggplot(OrthoandMethayes,aes(x=numUTR)) + geom_bar(stat="count")
Version | Author | Date |
---|---|---|
51a499a | brimittleman | 2020-04-19 |
There are 1961 PAS in the set.
Are any of these differentially used:
PASMetaSmall=PASMeta %>% select(PAS, chr, start, end)
DiffIso=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(PASMetaSmall,by=c("chr", "start", "end")) %>% select(PAS, SigPAU2)
DiffIsoSig= DiffIso %>% filter(SigPAU2=="Yes")
DiffIsoSigGene=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt",header = T, stringsAsFactors = F)
OverlapOrtho_filt %>% inner_join(DiffIsoSig, by="PAS") %>% nrow()
[1] 104
OverlapOrtho_filt %>% inner_join(DiffIsoSig, by="PAS") %>% select(gene) %>% unique() %>% nrow()
[1] 73
70 significant in 42 genes.
Seems worth it.
I will look at proportion of the UTR.
Overlapping= read.table("../data/orthoUTR/PASOverlapinDistal3UTR_bothWritten.bed", col.names = c("chrpas", "startpas", "endpas","PAS", "humanusage", "strandpas", "chrutr", "startutr", "endutr","geneUTR", "score","strand"),stringsAsFactors = F) %>% filter(PAS %in% OverlapOrtho_filt$PAS)
Overlapping_pos= Overlapping %>% filter(strand=="+") %>% mutate(length=endutr-startutr, center=endpas-100, cent2start=center-startutr, prop=cent2start/length)
Overlapping_neg= Overlapping %>% filter(strand=="-") %>% mutate(length=endutr-startutr, center=endpas-100, cent2start=endutr- center, prop=cent2start/length)
Overlapping_both=Overlapping_pos %>% bind_rows(Overlapping_neg) %>% mutate(SigPAS=ifelse(PAS %in% DiffIsoSig$PAS, "Yes","No"), sigGene=ifelse(geneUTR %in% DiffIsoSigGene$gene, "Yes","No")) %>% rename(gene=geneUTR) %>% inner_join(OrthoandMetha_sm, by="gene")
plot:
ggplot(Overlapping_both,aes(x=prop, by=SigPAS, fill=SigPAS)) + geom_density(alpha=.4) + scale_fill_brewer(palette = "Dark2") + labs(title="UTR location for differentially used PAS",x="Proportion of 3' ortho exon UTR")
Version | Author | Date |
---|---|---|
51a499a | brimittleman | 2020-04-19 |
ggplot(Overlapping_both,aes(x=prop, by=SigPAS, col=SigPAS)) + stat_ecdf()+scale_color_brewer(palette = "Dark2") + labs(title="UTR location for differentially used PAS",x="Proportion of 3' ortho exon UTR")
Version | Author | Date |
---|---|---|
51a499a | brimittleman | 2020-04-19 |
Overlapping_both_yes= Overlapping_both %>% filter(SigPAS=="Yes")
nrow(Overlapping_both_yes)
[1] 104
Overlapping_both_no= Overlapping_both %>% filter(SigPAS=="No")
nrow(Overlapping_both_no)
[1] 2294
wilcox.test(Overlapping_both_yes$prop,Overlapping_both_no$prop)
Wilcoxon rank sum test with continuity correction
data: Overlapping_both_yes$prop and Overlapping_both_no$prop
W = 114140, p-value = 0.4563
alternative hypothesis: true location shift is not equal to 0
Not a significant difference in the distribution.
Remove genes with 1 PAS:
Overlapping_both2more=Overlapping_both %>% filter(numUTR>1)
ggplot(Overlapping_both2more,aes(x=prop, by=SigPAS, fill=SigPAS)) + geom_density(alpha=.4) + scale_fill_brewer(palette = "Dark2") + labs(title="UTR location for differentially used PAS \n greater than 1 PAS",x="Proportion of 3' ortho exon UTR")
I want to change this to look at enrichment by decile. I will seperate all of these into decile and ask if there is an erichment for any location.
quantile(Overlapping_both$prop, seq(0,1, by=.1))
0% 10% 20% 30% 40% 50%
-1.90625000 0.07546672 0.20337713 0.37202002 0.54706861 0.70125598
60% 70% 80% 90% 100%
0.80891937 0.87249379 0.92417320 0.95787417 3.15217391
Overlapping_both_dec= Overlapping_both %>% mutate(decile_rank = ntile(Overlapping_both$prop,10), signum=ifelse(SigPAS=="Yes", 1, 0))
Overlapping_both_decG= Overlapping_both_dec%>% group_by(decile_rank) %>% summarise(Sig=sum(signum), numAll=n())
Overlapping_both_decG$decile_rank=as.factor(Overlapping_both_decG$decile_rank)
ggplot(Overlapping_both_decG, aes(x=decile_rank, y=Sig)) +geom_bar(stat="identity") + labs(x="Decile for proportion of 3' UTR", y= "Number of Significant PAS", title="Significant PAS by location in ortho 3' UTR")
Expected value if this were uniform. It would be the number of significant /10 in each decile.
decile=seq(1,10, 1)
pval=c()
enrich=c()
expected=sum(Overlapping_both_decG$Sig)/10
for (i in decile){
x=Overlapping_both_decG[[i,2]]
m=240
n=2398-240
N=2398
k=sum(Overlapping_both_decG$Sig)
en=(x/k)/(m/N)
p=phyper(x, m, n, k,lower.tail=F)
pval=c(pval,round(p,2))
enrich=c(enrich,en)
}
pval
[1] 0.05 0.15 0.24 0.91 0.34 0.91 0.73 0.60 0.24 0.24
enrich
[1] 1.4411058 1.2489583 1.1528846 0.5764423 1.0568109 0.5764423 0.7685897
[8] 0.8646635 1.1528846 1.1528846
Overlapping_both_decGpval=as.data.frame(cbind(Overlapping_both_decG, pval,enrich))
ggplot(Overlapping_both_decGpval, aes(x=decile_rank, y=Sig)) +geom_bar(stat="identity") + labs(x="Decile for proportion of 3' UTR", y= "Number of Significant PAS", title="Significant PAS by location in ortho 3' UTR") + geom_hline(yintercept =expected) + geom_text(aes(label=paste("P=",pval)), position=position_dodge(width=0.9), vjust=-0.25)
Same analysis minus 1 gene:
Overlapping_both2more_dec= Overlapping_both2more %>% mutate(decile_rank = ntile(Overlapping_both2more$prop,10), signum=ifelse(SigPAS=="Yes", 1, 0))
Overlapping_both2more_decG= Overlapping_both2more_dec%>% group_by(decile_rank) %>% summarise(Sig=sum(signum), numAll=n())
Overlapping_both2more_decG$decile_rank=as.factor(Overlapping_both2more_decG$decile_rank)
pval2=c()
enrich2=c()
expected=sum(Overlapping_both2more_decG$Sig)/10
for (i in decile){
x=Overlapping_both2more_decG[[i,2]]
m=Overlapping_both2more_decG[[i,3]]
n=sum(Overlapping_both2more_decG$numAll) - m
N=sum(Overlapping_both2more_decG$numAll)
k=sum(Overlapping_both2more_decG$Sig)
en=(x/k)/(m/N)
p=phyper(x, m, n, k,lower.tail=F)
pval2=c(pval2,round(p,2))
enrich2=c(enrich2,en)
}
pval2
[1] 0.15 0.15 0.48 0.91 0.83 0.48 0.23 0.09 0.47 0.60
enrich2
[1] 1.2452229 1.2532051 0.9578638 0.5784024 0.6748028 0.9578638 1.1568047
[8] 1.3410093 0.9640039 0.8676036
Overlapping_both2more_decGpval=as.data.frame(cbind(Overlapping_both2more_decG, pval2,enrich2))
ggplot(Overlapping_both2more_decGpval, aes(x=decile_rank, y=Sig)) +geom_bar(stat="identity") + labs(x="Decile for proportion of 3' UTR", y= "Number of Significant PAS", title="Significant PAS by location in ortho 3' UTR\n all PAS") + geom_hline(yintercept =expected) + geom_text(aes(label=paste("P=",pval2)), position=position_dodge(width=0.9), vjust=-0.25)
AllOverlapping= read.table("../data/orthoUTR/PASOverlapinDistal3UTR_bothWritten.bed", col.names = c("chrpas", "startpas", "endpas","PAS", "humanusage", "strandpas", "chrutr", "startutr", "endutr","geneUTR", "score","strand"),stringsAsFactors = F) %>% rename(gene="geneUTR")
AllOverlapping_pos= AllOverlapping %>% filter(strand=="+") %>% mutate(length=endutr-startutr, center=endpas-100, cent2start=center-startutr, prop=cent2start/length)
AllOverlapping_neg= AllOverlapping %>% filter(strand=="-") %>% mutate(length=endutr-startutr, center=endpas-100, cent2start=endutr- center, prop=cent2start/length)
AllOverlapping_both=AllOverlapping_pos %>% bind_rows(AllOverlapping_neg) %>% mutate(SigPAS=ifelse(PAS %in% DiffIsoSig$PAS, "Yes","No")) %>% inner_join(OrthoandMetha_sm, by="gene")
Look how many of these are diff used:
AllOverlapping_both %>% group_by(SigPAS) %>% summarise(n())
# A tibble: 2 x 2
SigPAS `n()`
<chr> <int>
1 No 2301
2 Yes 105
ggplot(AllOverlapping_both,aes(x=prop, by=SigPAS, fill=SigPAS)) + geom_density(alpha=.4) + scale_fill_brewer(palette = "Dark2") + labs(title="UTR location for differentially used PAS \n All genes",x="Proportion of 3' ortho exon UTR")
ggplot(AllOverlapping_both,aes(x=prop, by=SigPAS, col=SigPAS)) + stat_ecdf()+scale_color_brewer(palette = "Dark2") + labs(title="UTR location for differentially used PAS \n all genes",x="Proportion of 3' ortho exon UTR")
AllOverlapping_both_yes= AllOverlapping_both %>% filter(SigPAS=="Yes")
nrow(AllOverlapping_both_yes)
[1] 105
AllOverlapping_both_no= AllOverlapping_both %>% filter(SigPAS=="No")
nrow(AllOverlapping_both_no)
[1] 2301
wilcox.test(AllOverlapping_both_yes$prop,AllOverlapping_both_no$prop)
Wilcoxon rank sum test with continuity correction
data: AllOverlapping_both_yes$prop and AllOverlapping_both_no$prop
W = 116000, p-value = 0.4905
alternative hypothesis: true location shift is not equal to 0
Remove genes with greater than 1:
AllOverlapping_both2more=AllOverlapping_both %>% filter(numUTR>1)
ggplot(AllOverlapping_both2more,aes(x=prop, by=SigPAS, fill=SigPAS)) + geom_density(alpha=.4) + scale_fill_brewer(palette = "Dark2") + labs(title="UTR location for differentially used PAS \n All genes with > 1 PAS in set",x="Proportion of 3' ortho exon UTR")
AllOverlapping_both2more_yes= AllOverlapping_both2more %>% filter(SigPAS=="Yes")
nrow(AllOverlapping_both2more_yes)
[1] 104
AllOverlapping_both2more_no= AllOverlapping_both2more %>% filter(SigPAS=="No")
nrow(AllOverlapping_both2more_no)
[1] 1465
wilcox.test(AllOverlapping_both2more_yes$prop,AllOverlapping_both2more_no$prop)
Wilcoxon rank sum test with continuity correction
data: AllOverlapping_both2more_yes$prop and AllOverlapping_both2more_no$prop
W = 75027, p-value = 0.7963
alternative hypothesis: true location shift is not equal to 0
AllOverlapping_both_dec= AllOverlapping_both %>% mutate(decile_rank = ntile(AllOverlapping_both$prop,10), signum=ifelse(SigPAS=="Yes", 1, 0))
AllOverlapping_both_decG= AllOverlapping_both_dec%>% group_by(decile_rank) %>% summarise(Sig=sum(signum), numAll=n())
AllOverlapping_both_decG$decile_rank=as.factor(AllOverlapping_both_decG$decile_rank)
pvalAll=c()
enrichAll=c()
expected=sum(AllOverlapping_both_decG$Sig)/10
for (i in decile){
x=AllOverlapping_both_decG[[i,2]]
m=AllOverlapping_both_decG[[i,3]]
n=sum(AllOverlapping_both_decG$numAll) - m
N=sum(AllOverlapping_both_decG$numAll)
k=sum(AllOverlapping_both_decG$Sig)
en=(x/k)/(m/N)
p=phyper(x, m, n, k,lower.tail=F)
pvalAll=c(pvalAll,round(p,2))
enrichAll=c(enrichAll,en)
}
pvalAll
[1] 0.05 0.16 0.24 0.92 0.35 0.92 0.62 0.61 0.25 0.24
enrichAll
[1] 1.4262004 1.2360403 1.1457143 0.5704801 1.0502381 0.5704801 0.8557202
[8] 0.8592857 1.1409603 1.1457143
AllOverlapping_both_decGpval=as.data.frame(cbind(AllOverlapping_both_decG, pvalAll,enrichAll))
ggplot(AllOverlapping_both_decGpval, aes(x=decile_rank, y=Sig)) +geom_bar(stat="identity") + labs(x="Decile for proportion of 3' UTR", y= "Number of Significant PAS", title="Significant PAS by location in ortho 3' UTR\n all PAS") + geom_hline(yintercept =expected) + geom_text(aes(label=paste("P=",pvalAll)), position=position_dodge(width=0.9), vjust=-0.25)
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 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 magrittr_1.5 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