Last updated: 2020-01-26

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
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    Modified:   analysis/speciesSpecific_DF.Rmd

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Rmd 10d6e05 brimittleman 2020-01-26 add gene level phyloP
html 26e6362 brimittleman 2020-01-24 Build site.
Rmd d867ef6 brimittleman 2020-01-24 by loc
html 5910b06 brimittleman 2020-01-24 Build site.
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Rmd 117fd63 brimittleman 2020-01-22 redo differential analysis with double filt 
library(tidyverse)
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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_10perc_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_10perc_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 at 10% usage", title="Number of PAS detected by gene with differential usage")

Version Author Date
5800231 brimittleman 2020-01-22

Question 2: Where are the differentially used PAS?

ggplot(DiffUsagePAS,aes(x=loc, fill=loc)) + geom_bar(stat="count")

Version Author Date
5800231 brimittleman 2020-01-22

Seperate by location:

#negative deltaPAU is used more in human 
DiffUsagePAS_dir= DiffUsagePAS %>% mutate(direction=ifelse(deltaPAU >=0, "Chimp", "Human"))

ggplot(DiffUsagePAS_dir,aes(x=loc, fill=loc)) + geom_bar(stat="count")  + facet_grid(~direction)

Version Author Date
5800231 brimittleman 2020-01-22

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()

Version Author Date
5800231 brimittleman 2020-01-22

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"))

40756 have results of the 40776 Plot:

ggplot(NucReswPhy,aes(y=phyloP, x=SigPAU2,fill=SigPAU2)) + geom_boxplot() + stat_compare_means()+ scale_fill_brewer(palette = "Dark2")

Version Author Date
5910b06 brimittleman 2020-01-24 
ggplot(NucReswPhy,aes(x=phyloP, by=SigPAU2, fill=SigPAU2)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2")

Version Author Date
5910b06 brimittleman 2020-01-24

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"))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 707
#actual:
x
[1] 788
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.0001570509

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)

Version Author Date
26e6362 brimittleman 2020-01-24 
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       7141    2.16  
 2 cds    Yes       333    2.16  
 3 end    No       3564    0.450 
 4 end    Yes       247    0.403 
 5 intron No      10478    0.0630
 6 intron Yes       737    0.0702
 7 utr3   No      15351    1.04  
 8 utr3   Yes      1659    0.933 
 9 utr5   No       1151    0.300 
10 utr5   Yes        95    0.230

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"))

I lose 2237 from NAs in the values.

ggplot(GenesPhy,aes(x=phyloP, by=Sig, fill=Sig)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2")

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] 202
#actual:
x
[1] 246
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 7.212381e-05

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   0.03571   0.19797   0.34906   0.45455 147.00000 
NucResDNDS_fix %>% group_by(SigPASinGene) %>% summarise(n=n())
# A tibble: 2 x 2
  SigPASinGene     n
  <chr>        <int>
1 no            5481
2 yes           1827

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=1827 \n No=5481", 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] 115
#actual:
x
[1] 115
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.6666078

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)

Use gorilla:

Top results:

RNA binding

translation factor activity, RNA binding

protein-containing complex

eukaryotic translation initiation factor

cellular protein-containing complex assembly

intracellular transport

establishment of localization in cell   

protein targeting to membrane

nuclear-transcribed mRNA catabolic process, nonsense-mediated decay 
     
translational initiation     

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_1.5.0    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_1.0.2         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