Last updated: 2020-01-30

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Unstaged changes:
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
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    Modified:   analysis/pQTLexampleplot.Rmd
    Modified:   analysis/propeQTLs_explained.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/DistPAS2Sig.py
    Modified:   code/apaQTLsnake.err
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    Deleted:    reads_graphs.Rmd

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Rmd fd5ccd7 brimittleman 2020-01-31 add LD regress notes and first var in apa

A reviewer asked for what percent of eQTL variance can be explained by apaQTL variance. This is dificult to answer because apaQTL variance is related to a usage ratio. I will look at a set I can understand. I assume that an intronic PAS leads to degradation of the transcript. I will look if the difference in usage of these toward usage in UTR PAS. I will sum over intronic and utr sites. I can then look at the opposite direction increase in expression by genotype.

library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started
library(tidyverse)
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── Conflicts ─────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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The set I am looking at comes from the prop eQTL explained analysis.

nomnames=c("peakID", 'snp','dist', 'pval', 'slope')

nuclearapaUnexplained=read.table("../data/overlapeQTL_try2/apaNuclear_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp)  %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval))

nuclearapaexplained=read.table("../data/overlapeQTL_try2/apaNuclear_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>%  dplyr::slice(which.min(adjPval))  

eQTLeffect=read.table("../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.AllNomRes.GeneName_snploc.txt", stringsAsFactors = F, col.names = c("gene","snp","dist", "pval", "eQTL_es")) %>% select(gene, snp, eQTL_es)


alleQTLS_nuclear=bind_rows(nuclearapaUnexplained,nuclearapaexplained) %>% filter(loc=="intron") %>% inner_join(eQTLeffect, by=c("gene","snp"))

Subset positive effect size for apa and negative for eQTL.

SubsetDir= alleQTLS_nuclear %>% filter(slope > 0, eQTL_es<0) %>% mutate(DiffES=slope-eQTL_es) %>% arrange(desc(slope))

head(SubsetDir)
# A tibble: 6 x 15
# Groups:   gene, snp [6]
  chr   start end   gene  loc   strand PASnum snp     dist     pval slope
  <chr> <chr> <chr> <chr> <chr> <chr>  <chr>  <chr>  <int>    <dbl> <dbl>
1 4     3902… 3903… TMEM… intr… +      peak9… 4:39…    189 3.68e- 9 1.53 
2 16    8656… 8656… MTHF… intr… +      peak5… 16:8…  -1810 2.73e- 5 1.40 
3 1     2877… 2877… PHAC… intr… -      peak2… 1:28… -61170 5.77e-10 1.37 
4 10    1246… 1246… C10o… intr… +      peak1… 10:1…     18 1.28e- 8 1.26 
5 17    5628… 5628… MKS1  intr… +      peak5… 17:5…  11686 9.96e- 3 1.17 
6 7     6677… 6677… STAG… intr… -      peak1… 7:66…   6603 1.06e- 4 0.940
# … with 4 more variables: nPeaks <int>, adjPval <dbl>, eQTL_es <dbl>,
#   DiffES <dbl>

Look at examples:

sbatch run_qtlFacetBoxplots.sh Nuclear TMEM156 4 4:39030183 peak96746  
TMEMPheno=read.table("../data/ExampleQTLPlots/TMEM156_NuclearPeaksPheno.txt")

Pull in all phenos:

PhenoNum=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.CountsNumeric")
PhenoAnno=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz", header = T) 


PhenoBoth=as.data.frame(cbind(chrom=PhenoAnno$chrom,PhenoNum))
colnames(PhenoBoth)=colnames(PhenoAnno)

PhenoBoth_split= PhenoBoth %>% separate(chrom,into=c('chr', 'start', 'end', 'geneID') ,sep=":")%>% separate(geneID, into=c("gene", 'loc', 'strand', 'PAS'), sep="_") %>% dplyr::select(-chr, -start,-end,-strand)

Subset:

Pheno_TMEM=PhenoBoth_split %>% filter(gene=="TMEM156")

Look at the percent change between genotypes:

I need to get the genotypes:

genohead=as.data.frame(read.table("../data/ExampleQTLPlots/genotypeHeader.txt", stringsAsFactors = F, header = F)[,10:128] %>% t())
colnames(genohead)=c("header")
mkdir ../data/vareQTLvarAPAqtl
less /project2/gilad/briana/li_genotypes/genotypesYRI.gen.proc.5MAF.chr4.vcf.gz | grep 39030183 > ../data/vareQTLvarAPAqtl/TMEMGeno.txt

A/G

genotype=as.data.frame(read.table("../data/vareQTLvarAPAqtl/TMEMGeno.txt", stringsAsFactors = F, header = F) [,10:128] %>% t())
full_geno=bind_cols(Ind=genohead$header, dose=genotype$V1) %>% mutate(numdose=round(dose), genotype=ifelse(numdose==0, "TT", ifelse(numdose==1, "TA", "AA"))) %>% dplyr::select(Ind, genotype,numdose)


Pheno_TMEM_gather=Pheno_TMEM %>% gather(Ind, value, -PAS,-loc,-gene) %>% inner_join(full_geno,by="Ind")
Warning: Column `Ind` joining character vector and factor, coercing into
character vector
#intronic
Pheno_TMEM_peak96746= Pheno_TMEM_gather %>% filter(PAS=="peak96746") %>% group_by(genotype) %>% summarise(MeanUsage=mean(value)) %>% mutate(PAS="peak96746")


Pheno_TMEM_peak96738= Pheno_TMEM_gather %>% filter(PAS=="peak96738") %>% group_by(genotype) %>% summarise(MeanUsage=mean(value)) %>% mutate(PAS="peak96738")
#utr
Pheno_TMEM_peak96733= Pheno_TMEM_gather %>% filter(PAS=="peak96733") %>% group_by(genotype) %>% summarise(MeanUsage=mean(value)) %>% mutate(PAS="peak96733")

#end
Pheno_TMEM_peak96732= Pheno_TMEM_gather %>% filter(PAS=="peak96732") %>% group_by(genotype) %>% summarise(MeanUsage=mean(value)) %>% mutate(PAS="peak96732")
 
PhenoTMTMBoth= Pheno_TMEM_peak96746 %>% bind_rows(Pheno_TMEM_peak96733) %>% bind_rows(Pheno_TMEM_peak96738) %>% bind_rows(Pheno_TMEM_peak96732)

We assume 0 reads make it out of the intron. Sum over the end and UTR counts for each individual. Run DE with this?

AllCounts=read.table("../data/DiffIso/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.5percCov.bothfrac.fixed.forLC.fc",header = T) %>% select(contains("_N")) %>% rownames_to_column("chrom")

colnames(AllCounts)=colnames(PhenoAnno)

AllCounts= AllCounts %>% separate(chrom,into=c('chr', 'start', 'end', 'geneID') ,sep=":")%>% separate(geneID, into=c("gene", 'loc'), sep="_") 

#%>% dplyr::select(-chr, -start,-end,-strand)

Filter gene:

AllCounts_tmeme= AllCounts %>% filter(gene=="TMEM156", loc != "intron") %>% gather(Ind, count, -chr,-start,-end,-gene,-loc) %>% group_by(Ind) %>% summarise(APAcount=sum(count)) %>% inner_join(full_geno, by="Ind")
Warning: Column `Ind` joining character vector and factor, coercing into
character vector

I also need the expression values:

geneNames=read.table("../../genome_anotation_data/ensemble_to_genename.txt", sep="\t", col.names = c('gene_id', 'GeneName', 'source' ),stringsAsFactors = F, header = T)

less ../data/molPhenos/fastqtl_qqnorm_RNAseq_phase2.fixed.noChr.txt.gz | grep ENSG00000121895 > ../data/vareQTLvarAPAqtl/TMEM_RNA.txt
RNAhead=as.data.frame(read.table("../data/molPhenos/RNAhead.txt", stringsAsFactors = F, header = F)[,5:73] %>% t())

RNApheno=as.data.frame(read.table("../data/vareQTLvarAPAqtl/TMEM_RNA.txt", stringsAsFactors = F, header = F) [,5:73] %>% t())

full_pheno=bind_cols(Ind=RNAhead$V1, Expression=RNApheno$V1)

allRNA=full_geno %>% inner_join(full_pheno, by="Ind")
Warning: Column `Ind` joining factors with different levels, coercing to
character vector

Join these:

APAandRNA=allRNA %>% inner_join(AllCounts_tmeme,by = c("Ind", "genotype", "numdose"))
ggplot(APAandRNA,aes(x=genotype, y=Expression, by=genotype, fill=genotype))+ geom_boxplot() + labs(title="Normalized expression")

ggplot(APAandRNA,aes(x=genotype, y=APAcount, by=genotype, fill=genotype))+ geom_boxplot() + labs(title="UTR apa")

Test for effect on the residuals:

apa.LM=lm(APAandRNA$Expression ~ APAandRNA$APAcount)
boxplot(APAandRNA$numdose,APAandRNA$Expression)

summary(lm(resid(apa.LM) ~  APAandRNA$numdose))

Call:
lm(formula = resid(apa.LM) ~ APAandRNA$numdose)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.49609 -0.71353  0.06399  0.84476  2.23069 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)
(Intercept)         0.7467     0.5634   1.325    0.192
APAandRNA$numdose  -0.4443     0.3208  -1.385    0.173

Residual standard error: 1.121 on 45 degrees of freedom
Multiple R-squared:  0.04088,   Adjusted R-squared:  0.01957 
F-statistic: 1.918 on 1 and 45 DF,  p-value: 0.1729
boxplot(APAandRNA$numdose,resid(apa.LM))

Think about this more.


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_1.5.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 haven_1.1.2      lattice_0.20-38  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       utf8_1.1.4      
 [9] rlang_0.4.0      later_0.7.5      pillar_1.3.1     glue_1.3.0      
[13] withr_2.1.2      modelr_0.1.2     readxl_1.1.0     plyr_1.8.4      
[17] munsell_0.5.0    gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.12    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] fansi_0.4.0      broom_0.5.1      Rcpp_1.0.2       promises_1.0.1  
[29] scales_1.0.0     backports_1.1.2  jsonlite_1.6     fs_1.3.1        
[33] hms_0.4.2        digest_0.6.18    stringi_1.2.4    grid_3.5.1      
[37] rprojroot_1.3-2  cli_1.1.0        tools_3.5.1      magrittr_1.5    
[41] lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2 
[45] xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10  
[49] httr_1.3.1       rstudioapi_0.10  R6_2.3.0         nlme_3.1-137    
[53] git2r_0.26.1     compiler_3.5.1