Last updated: 2019-09-06
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Knit directory: apaQTL/analysis/
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html | 00fe2b4 | brimittleman | 2019-07-26 | Build site. |
Rmd | cee6ce0 | brimittleman | 2019-07-26 | get pvalues form <-16 tests |
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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
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