Last updated: 2019-09-06

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/SnakefilefiltPAS
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Modified:   data/MetaDataSequencing.txt
    Deleted:    reads_graphs.Rmd

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These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
html 00fe2b4 brimittleman 2019-07-26 Build site.
Rmd cee6ce0 brimittleman 2019-07-26 get pvalues form <-16 tests
html 33a700a brimittleman 2019-07-25 Build site.
Rmd f3e3e16 brimittleman 2019-07-25 small edits from paper writing and add gwas analysis
html 272b0b4 brimittleman 2019-07-08 Build site.
Rmd b1e6dd1 brimittleman 2019-07-08 update ptt analysis

I am interesting in understanding the PTTqtls a bit more. I want to ask if pttQTLs are apaQTLs. I will ask if the PTTqtl genes are also apaQTL genes.

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()
library(workflowr)
This is workflowr version 1.4.0
Run ?workflowr for help getting started

PTT

totptt=read.table("../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", stringsAsFactors = F, header = T) %>% filter(-log10(bh)>1) %>% separate(pid,into = c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()

totpttQTL=read.table("../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", stringsAsFactors = F, header = T) %>% filter(-log10(bh)>1)
write.table(totpttQTL, file="../data/pttQTL/TotalPttQTL.txt", col.names = T, row.names = F, quote = F, sep="\t")

nucptt=read.table("../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", stringsAsFactors = F, header = T) %>% filter(-log10(bh)>1) %>% separate(pid,into = c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()


nucpttQTL=read.table("../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", stringsAsFactors = F, header = T) %>% filter(-log10(bh)>1)
write.table(nucpttQTL, file="../data/pttQTL/NuclearPttQTL.txt", col.names = T, row.names = F, quote = F, sep="\t")

APA

nucAPA=read.table("../data/apaQTLs/NuclearapaQTLGenes.txt", col.names = c("gene"),stringsAsFactors = F)
totAPA=read.table("../data/apaQTLs/TotalapaQTLGenes.txt", col.names = c("gene"),stringsAsFactors = F)
totptt %>% inner_join(totAPA,by="gene") %>% nrow()
[1] 20
totptt %>% anti_join(totAPA,by="gene") %>% nrow()
[1] 4
nucptt %>% inner_join(nucAPA,by="gene") %>% nrow()
[1] 54
nucptt %>% anti_join(nucAPA,by="gene") %>% nrow()
[1] 15

There are 8 total ptt genes that are not apaQTL genes and there are 15 nuclear ptt genes that are not apaQTL genes.

Look at the distribution. To do this I will pull out the ptt associations from the apa data.

I will right a script that takes a the fraction, pulls the ptt associations into 2 files, one for the associations and one for the non associations.

python pttQTLsinapaQTL.py Total
python pttQTLsinapaQTL.py Nuclear
names=c('pid' ,'nvar' ,'shape1' ,'shape2', 'dummy' ,'sid', 'dist', 'npval', 'slope', 'ppval' ,'bpval', 'bh')
totapaPTT=read.table("../data/pttQTL/Totalapa_TotalPttQTL.txt", col.names = names, stringsAsFactors = F)
totapaNotPTT=read.table("../data/pttQTL/Totalapa_NOT_TotalPttQTL.txt", col.names = names, stringsAsFactors = F)
qqplot(-log10(runif(nrow(totapaNotPTT))), -log10(totapaNotPTT$bpval),ylab="-log10 expression permuted pvalue", xlab="Uniform expectation", main="Total PTT genes in Total apaQTL")
points(sort(-log10(runif(nrow(totapaPTT)))), sort(-log10(totapaPTT$bpval)),col= alpha("Red"))
abline(0,1)
legend("topleft", legend=c("ptt gene", "All other genes"),col=c("red", "black"), pch=16,bty = 'n')

Version Author Date
272b0b4 brimittleman 2019-07-08
nucapaPTT=read.table("../data/pttQTL/Nuclearapa_NuclearPttQTL.txt", col.names = names, stringsAsFactors = F)
nucapaNotPTT=read.table("../data/pttQTL/Nuclearapa_NOT_NuclearPttQTL.txt", col.names = names, stringsAsFactors = F)
qqplot(-log10(runif(nrow(nucapaNotPTT))), -log10(nucapaNotPTT$bpval),ylab="-log10 expression permuted pvalue", xlab="Uniform expectation", main="Nuclear PTT genes in Nuclear apaQTL")
points(sort(-log10(runif(nrow(nucapaPTT)))), sort(-log10(nucapaPTT$bpval)),col= alpha("Red"))
abline(0,1)

legend("topleft", legend=c("ptt gene", "All other genes"),col=c("red", "black"), pch=16,bty = 'n')

Version Author Date
272b0b4 brimittleman 2019-07-08

These show the sharing is super high and we are getting at similar phenotypes in ptt and apa.

wilcox.test(nucapaPTT$bpval,nucapaNotPTT$bpval,alternative="less")

    Wilcoxon rank sum test with continuity correction

data:  nucapaPTT$bpval and nucapaNotPTT$bpval
W = 2372300, p-value < 2.2e-16
alternative hypothesis: true location shift is less than 0
wilcox.test(nucapaPTT$bpval,nucapaNotPTT$bpval,alternative="less")$p.value
[1] 9.869063e-41

Is there something interesting about the non overlap genes:

pttOnlyTot=totptt %>% anti_join(totAPA,by="gene")


totapaPTT_pttstatus=totapaPTT %>% separate(pid, into=c("chr", "start", "end", "geneID"),sep=":")  %>% separate(geneID, into=c("gene", "pos", "strand", "pas"),sep="_") %>% mutate(APAQTL=ifelse(gene %in% pttOnlyTot$gene, "No", "Yes"))
ggplot(totapaPTT_pttstatus,aes(x=APAQTL, y=bpval)) + geom_boxplot()

Version Author Date
33a700a brimittleman 2019-07-25
pttOnlyTot$gene
[1] "C10orf88" "PHACTR4"  "USP37"    "ZNF718"  
pttOnlyNuc=nucptt %>% anti_join(nucAPA,by="gene")


nucapaPTT_pttstatus=nucapaPTT %>% separate(pid, into=c("chr", "start", "end", "geneID"),sep=":")  %>% separate(geneID, into=c("gene", "pos", "strand", "pas"),sep="_") %>% mutate(APAQTL=ifelse(gene %in% pttOnlyTot$gene, "No", "Yes"))


ggplot(nucapaPTT_pttstatus,aes(x=APAQTL, y=bpval)) + geom_boxplot()

Version Author Date
33a700a brimittleman 2019-07-25
pttOnlyNuc$gene
 [1] "ATP5C1"   "ARHGAP5"  "AP4E1"    "USP14"    "PRKCZ"    "MAP3K7CL"
 [7] "BCL2L13"  "IRAK2"    "FYCO1"    "IL17RB"   "SEC24B"   "HSPA9"   
[13] "PREP"     "ERLIN2"   "HACD4"   

Plot these:

nucpttQTL %>% separate(pid, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene","loc"),sep="_") %>%  anti_join(nucAPA,by="gene") %>% select(gene, chr,sid)
       gene chr         sid
1    ATP5C1  10   rs2802460
2    ATP5C1  10   rs2802460
3   ARHGAP5  14 rs114000106
4   ARHGAP5  14 rs114000106
5     AP4E1  15  rs56056949
6     AP4E1  15  rs56056949
7     USP14  18  rs11876726
8     USP14  18  rs11876726
9     PRKCZ   1  rs13302944
10 MAP3K7CL  21   rs9808644
11  BCL2L13  22   rs9604775
12    IRAK2   3   rs6776059
13    FYCO1   3  rs59842697
14    FYCO1   3  rs59842697
15   IL17RB   3   rs2289205
16   IL17RB   3   rs2289205
17   SEC24B   4   rs1509314
18    HSPA9   5  rs41294546
19    HSPA9   5  rs41294546
20     PREP   6  rs76627946
21     PREP   6  rs76627946
22   ERLIN2   8  rs77049216
23    HACD4   9  rs57721805
24    HACD4   9  rs57721805

Examples done need to rerun

These are from exploratory analysis

sbatch  run_qtlFacetBoxplots.sh Nuclear PPFIA1 11 rs188630869 none
sbatch  run_qtlFacetBoxplots.sh Nuclear ZDHHC17 12  rs74792673 none
sbatch  run_qtlFacetBoxplots.sh Nuclear ZNF844  19  rs116146966 none
sbatch  run_qtlFacetBoxplots.sh Nuclear PRKCZ   1   rs13302944 none


sbatch  run_qtlFacetBoxplots.sh Nuclear POU2F1  1   rs6668193 none
sbatch  run_qtlFacetBoxplots.sh Nuclear MAP3K7CL    21  rs9808644 none
sbatch  run_qtlFacetBoxplots.sh Nuclear BCL2L13 22  rs8141618 none
sbatch  run_qtlFacetBoxplots.sh Nuclear LINC00342   2   rs193252777 none
sbatch  run_qtlFacetBoxplots.sh Nuclear DZIP3   3   rs112409302 none

sbatch  run_qtlFacetBoxplots.sh Nuclear SUB1    5   rs76941541 none
sbatch  run_qtlFacetBoxplots.sh Nuclear BLVRA   7   rs1181594 none
sbatch  run_qtlFacetBoxplots.sh Nuclear NAPEPLD 7   rs56047178 none
sbatch  run_qtlFacetBoxplots.sh Nuclear WNT2    7   rs76105832 none
sbatch  run_qtlFacetBoxplots.sh Nuclear HACD4   9   rs57721805 none
totpttQTL %>% separate(pid, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene","loc"),sep="_") %>%  anti_join(totAPA,by="gene") %>% select(gene, chr,sid)
      gene chr        sid
1 C10orf88  10  rs7904973
2 C10orf88  10  rs7904973
3  PHACTR4   1 rs76130516
4    USP37   2 rs79468589
5   ZNF718   4  rs6814287
6   ZNF718   4 rs28531086
sbatch  run_qtlFacetBoxplots.sh Total C10orf88  10  rs7091776 none
sbatch  run_qtlFacetBoxplots.sh Total UROS  10  rs11244646 none
sbatch  run_qtlFacetBoxplots.sh Total LRRC57    15  rs61489160 none
sbatch  run_qtlFacetBoxplots.sh Total ELMOD3    2   rs908302 none
sbatch  run_qtlFacetBoxplots.sh Total USP37 2   rs79468589 none
sbatch  run_qtlFacetBoxplots.sh Total PLSCR1    3   rs59690244 none
sbatch  run_qtlFacetBoxplots.sh Total ZNF718    4   rs6814287 none
sbatch  run_qtlFacetBoxplots.sh Total ZSCAN12   6   rs3799500 none

Moved these reslts to a ptt dir in the example qtl dir.


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] workflowr_1.4.0 forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     git2r_0.25.2     highr_0.7        tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[49] broom_0.5.1      crayon_1.3.4