Last updated: 2020-06-29
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Knit directory: Comparative_APA/analysis/
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Unstaged changes:
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Rmd | 117fd63 | brimittleman | 2020-01-21 | redo differential analysis with double filt |
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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library(ggpubr)
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library(reshape2)
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I want to look more at the genes we found with dAPA.
Question 1:
Do genes with differential APA have different numbers of PAS in each species?
DiffUsage=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherPAS_2_Nuclear.txt", header = T, stringsAsFactors = F)
PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS, chr, start,end, gene, loc)
DiffUsagePAS=DiffUsage %>% inner_join(PASMeta, by=c("gene","chr", "start", "end"))
Number of PAS in each species:
PAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", stringsAsFactors = F, header = T)
PAS_sm=PAS %>% dplyr::select(gene, Chimp, Human)
PAS_m= melt(PAS_sm, id.var="gene", variable.name="species", value.name="meanUsage") %>% filter(meanUsage >=0.1) %>% group_by(species, gene) %>% summarise(nPAS=n())
Filter these by those with dAPA:
PAS_m_dAPA= PAS_m %>% mutate(dAPA=ifelse(gene %in% DiffUsagePAS$gene, "Y", "N"))
ggplot(PAS_m_dAPA,aes(by=dAPA, y=nPAS,x=species, fill=dAPA)) + geom_boxplot() + stat_compare_means(method = "t.test") + scale_fill_brewer(palette = "Dark2") + labs(y="Number of PAS detected usage", title="Number of PAS detected by gene with differential usage")
Question 2: Where are the differentially used PAS?
ggplot(DiffUsagePAS,aes(x=loc, fill=loc)) + geom_bar(stat="count") + theme(text= element_text(size=16), legend.position = "none") + scale_fill_brewer(palette = "Dark2") + labs(x="Genic Region", y="Number of dAPA PAS", title="Location of dAPA PAS")
for supplement
pdf("../output/supplement/Fig2_figSup1.pdf", height=4, width=4)
ggplot(DiffUsagePAS,aes(x=loc, fill=loc)) + geom_bar(stat="count") + scale_fill_brewer(palette = "RdYlBu") + labs(x="Genic Region", y="Number of PAS", title="Location of differentially used PAS") + theme_classic() + theme(axis.text.x = element_text(size=10, angle=90),plot.title = element_text(hjust = 0.5, face="bold"),axis.text.y = element_text(size=10),text=element_text(size=10), legend.position = "none",plot.margin = unit(c(0,0,0,0), "cm"))+scale_x_discrete(labels=c("Coding","5'kb downstream","Intronic", "3' UTR", "5' UTR"))
dev.off()
png
2
Seperate by location:
#negative deltaPAU is used more in chimp
DiffUsagePAS_dir= DiffUsagePAS %>% mutate(direction=ifelse(deltaPAU >=0, "Human", "Chimp"))
ggplot(DiffUsagePAS_dir,aes(x=loc, fill=loc)) + geom_bar(stat="count") + facet_grid(~direction)+ scale_fill_brewer(palette = "Dark2") + labs(x="Genic Region", y="Number of dAPA PAS", title="Location of dAPA PAS")+ theme(text= element_text(size=16), legend.position = "none")
This is opposite of the results using just the dominant PAS. I probably shouldn’t put too much into that.
Question 3: Does locaiton of the PAS effect the absolute value of the effect size
ggplot(DiffUsagePAS_dir,aes(x=loc, y=abs(deltaPAU), fill=loc)) + geom_violin()
Explore conservation:
https://www.ultraconserved.org
https://useast.ensembl.org/info/genome/compara/conservation_and_constrained.html
phylo p from genomebrowser
mkdir ../data/PhyloP
mkdir ../data/DNDS
PhyloP: Column #1 contains a one-based position coordinate. Column #2 contains a score showing the posterior probability that the phylogenetic hidden Markov model (HMM) of phastCons is in its most conserved state at that base position.
I want to get the average score for each of the tested PAS. I can use pybigwig.
python extractPhyloReg.py
phylores=read.table("../data/PhyloP/PAS_phyloP.txt", col.names = c("chr","start","end", "phyloP"), stringsAsFactors = F) %>% drop_na()
NucReswPhy=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(phylores, by=c("chr","start","end"))
Plot:
ggplot(NucReswPhy,aes(y=phyloP, x=SigPAU2,fill=SigPAU2)) + geom_boxplot() + stat_compare_means(method.args = list(alternative = "l"))+ scale_fill_brewer(palette = "Dark2", name="Signficant")
ggplot(NucReswPhy,aes(x=phyloP, by=SigPAU2, fill=SigPAU2)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2", name="Signficant PAS") + labs(title="Mean PhyloP scores for tested PAS")
The significant PAS have on average lower phyloP scores.
Positive scores — Measure conservation, which is slower evolution than expected, at sites that are predicted to be conserved. Negative scores — Measure acceleration, which is faster evolution than expected, at sites that are predicted to be fast-evolving.
I can look at those with negative values:
x=nrow(NucReswPhy %>% filter(SigPAU2=="Yes", phyloP<0))
m= nrow(NucReswPhy %>% filter(phyloP<0))
n=nrow(NucReswPhy %>% filter(phyloP>=0))
k=nrow(NucReswPhy %>% filter(SigPAU2=="Yes"))
#actual:
x
[1] 604
#pval
phyper(x-1,m,n,k,lower.tail=F)
[1] 0.01503101
This means these regions are more likely to be fast evolving.
Look at this by location: (is it driven by region)
NucReswPhy_meta= NucReswPhy %>% inner_join(PASMeta, by=c("chr", "start", "end", "gene"))
ggplot(NucReswPhy_meta,aes(x=phyloP, by=SigPAU2, fill=SigPAU2)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2") + facet_grid(~loc)
NucReswPhy_meta_group=NucReswPhy_meta %>% group_by(loc,SigPAU2) %>% summarise(n=n(),meanPhylo=mean(phyloP))
NucReswPhy_meta_group
# A tibble: 10 x 4
# Groups: loc [5]
loc SigPAU2 n meanPhylo
<chr> <chr> <int> <dbl>
1 cds No 7048 2.15
2 cds Yes 262 2.14
3 end No 3574 0.372
4 end Yes 172 0.324
5 intron No 13484 0.0622
6 intron Yes 544 0.0833
7 utr3 No 15408 1.04
8 utr3 Yes 1280 0.904
9 utr5 No 1158 0.300
10 utr5 Yes 81 0.261
Compare this to the genes that are expressed for this I will need to make a bedfile with the genes. I will pull them in as well as the gene annotation:
DAPAGeneSig=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", stringsAsFactors = F, header = T)
DAPAGene=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", stringsAsFactors = F, header = T) %>% dplyr::select(gene) %>% unique() %>% mutate(Sig=ifelse(gene %in% DAPAGeneSig$gene,"Yes","No"))
To be safe ill use the longest transcripts from table browser refseq.
This will be easiest in a python dictionary.
python parseHg38.py
sort -k1,1 -k2,2n ../../genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_GenesParsed.bed > ../../genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_GenesParsed_sort.bed
genes=read.table("../../genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_GenesParsed_sort.bed", col.names = c("chr", "start", "end","name","score","strand"),stringsAsFactors = F) %>% filter(name %in% DAPAGene$gene ) %>% rename("gene"=name)
genesWithSig= genes %>% inner_join(DAPAGene, by="gene")
write.table(genes, "../data/PhyloP/NuclearSigGenes.bed", col.names = F, row.names = F, quote=F, sep="\t")
Get the mean phyloP scores.
python extractPhyloRegGene.py
phyloresG=read.table("../data/PhyloP/PAS_phyloP_genes.txt", col.names = c("chr","start","end", "phyloP"), stringsAsFactors = F) %>% drop_na()
GenesPhy=genesWithSig %>% inner_join(phyloresG, by=c("chr","start","end"))
ggplot(GenesPhy,aes(x=phyloP, by=Sig, fill=Sig)) + geom_density(alpha=.5)+ scale_fill_brewer(palette = "Dark2", name="Gene with \nSignificant PAS") + labs(title="Mean PhyloP scores for tested Genes") + annotate("text",label="Wilcoxan, p=2.6.4e -12",x=4,y=1.5)
ggplot(GenesPhy,aes(y=phyloP, x=Sig,fill=Sig)) + geom_boxplot() + stat_compare_means()+ scale_fill_brewer(palette = "Dark2")
These are also genes with a shift. See if more likely to have - value.
x=nrow(GenesPhy %>% filter(Sig=="Yes", phyloP<0))
m= nrow(GenesPhy %>% filter(phyloP<0))
n=nrow(GenesPhy %>% filter(phyloP>=0))
k=nrow(GenesPhy %>% filter(Sig=="Yes"))
#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 156
#actual:
x
[1] 183
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.005601686
Enriched for genes with - scores.
DN (non synonymous) /DS (synonymous): from ensamble site - ratio of substitution rate (quick and dirty way to look at evo), ration >1 usually evidence for positive selection. values are in ../data/DNDS/HumanChimp_DNDS.csv
Remove NA values
DNDS= read.csv("../data/DNDS/HumanChimp_DNDS.csv", header = T,stringsAsFactors = F) %>% drop_na() %>% group_by(Gene.name) %>% slice(1) %>% ungroup() %>% mutate(DNDSratio= dN.with.Chimpanzee/dS.with.Chimpanzee) %>% dplyr::select(Gene.name, dN.with.Chimpanzee,dS.with.Chimpanzee,DNDSratio) %>% rename("gene"=Gene.name)
Join with all results then subset based on significance:
I will get all genes,
NucResGenes=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt",header = T)
NucResAll=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% dplyr::select(gene) %>% unique() %>% mutate(SigPASinGene=ifelse(gene %in% NucResGenes$gene, "yes", "no"))
NucResDNDS= NucResAll %>% inner_join(DNDS,by="gene")
We do not have information for 1236 of the genes. I will assess results on the 7308 with data. There are also genes with ratio problems due to zero in the ds column. If it is infinity, i can make it 1 for now because there are only fixed non syn mutations fixing. If both are 0 I will make it 0.
NucResDNDS_fix=NucResDNDS %>% mutate(DNDSratio = replace_na(DNDSratio,0))
NucResDNDS_fix[NucResDNDS_fix == Inf] <- 1
summary(NucResDNDS_fix$DNDSratio)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00000 0.03313 0.19685 0.34812 0.45372 147.00000
NucResDNDS_fix %>% group_by(SigPASinGene) %>% summarise(n=n())
# A tibble: 2 x 2
SigPASinGene n
<chr> <int>
1 no 5865
2 yes 1451
Plot this.
ggplot(NucResDNDS_fix,aes(y=log10(DNDSratio+1), x=SigPASinGene, fill=SigPASinGene))+ geom_boxplot() + stat_compare_means( label.y.npc = "middle") + scale_fill_brewer(palette = "Dark2") + labs(x="dAPA in Nuclear") + annotate("text", label="Yes=1451 \n No=5865", y=1.8,x=2)
I can ask if they are more likely to be above 1. I can do this with a hypergeo.
x=nrow(NucResDNDS_fix %>% filter(SigPASinGene=="yes", DNDSratio>=1))
m= nrow(NucResDNDS_fix %>% filter(DNDSratio>=1))
n=nrow(NucResDNDS_fix %>% filter(DNDSratio<1))
k=nrow(NucResDNDS_fix %>% filter(SigPASinGene=="yes"))
#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 94
#actual:
x
[1] 102
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.1502389
No enrichment for positive selected genes.
Gene ontology: Need a ranked list of genes. I can do this for the differential apa genes by pvalue.
http://cbl-gorilla.cs.technion.ac.il
NucRes=read.table("../data/DiffIso_Nuclear/SignifianceEitherPAS_2_Nuclear.txt",header = T,stringsAsFactors = F) %>% arrange(p.adjust) %>% dplyr::select(gene) %>% unique()
write.table(NucRes,"../data/DiffIso_Nuclear/SignifianceGenes_orderPval.txt",col.names = F, row.names = F, quote = F)
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] reshape2_1.4.3 ggpubr_0.2 magrittr_1.5 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
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 workflowr_1.6.0 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