Remap qtls with 4 pcs
Briana Mittleman
5/8/2019
Last updated: 2019-07-09
Checks: 6 1
Knit directory: apaQTL/analysis/
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Unstaged changes:
Modified: analysis/DiffIsoAnalysis.Rmd
Modified: analysis/NuclearSpecAPAqtl.Rmd
Modified: analysis/NuclearSpecIncludeNotTested.Rmd
Modified: analysis/PrematureTermQTL.Rmd
Modified: analysis/QTLlocation.Rmd
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Modified: analysis/nucSpecinEQTLs.Rmd
Modified: analysis/overlapapaqtlsandeqtls.Rmd
Modified: analysis/propeQTLs_explained.Rmd
Modified: analysis/pttgeneinAPA.Rmd
Modified: code/BothFracDTPlotGeneRegions.sh
Modified: code/Snakefile
Deleted: code/Upstream10Bases_general.py
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
Deleted: code/test.txt
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
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 |
|---|---|---|---|---|
| Rmd | 30a2657 | brimittleman | 2019-07-09 | change plot lables |
| html | 3cddcab | brimittleman | 2019-07-03 | Build site. |
| Rmd | e1201a2 | brimittleman | 2019-07-03 | add prop qtl of tested |
| html | 6dae9b1 | brimittleman | 2019-06-13 | Build site. |
| html | 1a29304 | brimittleman | 2019-06-11 | Build site. |
| Rmd | 8cbea22 | brimittleman | 2019-06-11 | change genotypes |
| html | 84c56ab | brimittleman | 2019-05-09 | Build site. |
| Rmd | d63b37f | brimittleman | 2019-05-09 | results |
| html | 144c00b | brimittleman | 2019-05-08 | Build site. |
| Rmd | 5e39f1c | brimittleman | 2019-05-08 | choose pcs and start qtl rerun |
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(cowplot)
Attaching package: 'cowplot'The following object is masked from 'package:ggplot2':
ggsaveRun QTL script
head -n 5 APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.4PCs
head -n 5 APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.4PCs
add batch to these:
metadata=read.table("../data/MetaDataSequencing.txt",stringsAsFactors = F,header = T) %>% filter(fraction=="total") %>% dplyr::select(line,batch)
pc_tot=as.data.frame(read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.4PCs",header=T) %>% dplyr::select(-id) %>% t() )%>% rownames_to_column(var="line")
pc_tot_batch=as.data.frame(pc_tot %>% full_join(metadata,by="line") %>% t()) %>% mutate()
colnames(pc_tot_batch)=unname(unlist(pc_tot_batch[1,]))
pc_tot_batch= pc_tot_batch[2:nrow(pc_tot_batch),] %>% mutate(id=c("1", "2", "3", "4", "batch")) %>% dplyr::select(id, contains("NA"))
write.table(pc_tot_batch,file="../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.4PCswithBatch", col.names = T, row.names = F, quote=F,sep="\t")pc_nuc=as.data.frame(read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.4PCs",header=T) %>% dplyr::select(-id) %>% t() )%>% rownames_to_column(var="line")
pc_nuc_batch=as.data.frame(pc_nuc %>% full_join(metadata,by="line") %>% t()) %>% mutate()
colnames(pc_nuc_batch)=unname(unlist(pc_nuc_batch[1,]))
pc_nuc_batch= pc_nuc_batch[2:nrow(pc_nuc_batch),] %>% mutate(id=c("1", "2", "3", "4", "batch")) %>% dplyr::select(id, contains("NA"))
write.table(pc_nuc_batch,file="../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.4PCswithBatch", col.names = T, row.names = F, quote=F,sep = "\t")mkdir ../data/apaQTLNominal_4pc
mkdir ../data/apaQTLPermuted_4pcsbatch apaQTL_Nominal_4pc.sh
sbatch apaQTL_permuted.4pc.shConcatinate results in permuted directory:
cat ../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_chr* > ../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Total_permRes.txt
cat ../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_chr* > ../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear_permRes.txt
Run correction script
Rscript apaQTLCorrectPvalMakeQQ_4pc.R Evaluate results:
Total
totRes=read.table("../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Total_permResBH.txt", stringsAsFactors = F, header = T) %>% separate(pid, into=c("Chr", "Start", "End", "PeakID"), sep=":") %>% separate(PeakID, into=c("Gene", "Loc", "Strand","Peak"), sep="_")Total Apa QTLs
TotQTLs= totRes %>% filter(-log10(bh)>=1)
nrow(TotQTLs)[1] 440apaQTL genes:
TotQTLs_gene=TotQTLs %>% group_by(Gene) %>% summarise(nQTL=n())
summary(TotQTLs_gene$nQTL) Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 1.000 1.000 1.254 1.000 4.000 hist(TotQTLs_gene$nQTL)
Location distribution for peaks:
TotQTLs_loc= TotQTLs %>% group_by(Loc) %>% summarise(nLoc=n()) %>% mutate(PropLoc=nLoc/nrow(TotQTLs)) %>% mutate(fraction="Total")
totQTLloc=ggplot(TotQTLs_loc, aes(x=Loc, y=PropLoc, fill=Loc)) + geom_bar(stat = "Identity") + labs(x="Location of Significant Peak", y="Proportion of QTLs", title="Total QTL peak distribution")+ theme(axis.text.x = element_text(angle = 90, hjust = 1))Nucelar:
nucRes=read.table("../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear_permResBH.txt", stringsAsFactors = F, header = T) %>% separate(pid, into=c("Chr", "Start", "End", "PeakID"), sep=":") %>% separate(PeakID, into=c("Gene", "Loc", "Strand","Peak"), sep="_")Nuclear Apa QTLs
NucQTLs= nucRes %>% filter(-log10(bh)>=1)
nrow(NucQTLs)[1] 771apaQTL genes:
NucQTLs_gene= NucQTLs %>% group_by(Gene) %>% summarise(nQTL=n())
summary(NucQTLs_gene$nQTL) Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 1.000 1.000 1.266 1.000 4.000 hist(NucQTLs_gene$nQTL)
Location distribution for peaks:
NucQTLs_loc= NucQTLs %>% group_by(Loc) %>% summarise(nLoc=n()) %>% mutate(PropLoc=nLoc/nrow(NucQTLs)) %>% mutate(fraction="Nuclear")
nucQTLloc=ggplot(NucQTLs_loc, aes(x=Loc, y=PropLoc, fill=Loc)) + geom_bar(stat = "Identity") + labs(x="Location of Significant Peak", y="Proportion of QTLs", title="Nuclear QTL peak distribution")+theme(axis.text.x = element_text(angle = 90, hjust = 1))plot_grid(totQTLloc, nucQTLloc)
write.table(TotQTLs, file="../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt", col.names = T, row.names = F, quote=F)
write.table(NucQTLs, file="../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt", col.names = T, row.names = F, quote=F)Replot these as proportion of tested.
TotTested=read.table("../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Total_permResBH.txt", header = T,stringsAsFactors = F) %>% separate(pid, into=c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID,into=c("gene", "Loc", "strand", "PAS"), sep="_") %>% group_by(Loc) %>% summarise(nTested=n()) %>% mutate(fraction="Total")
NucTested=read.table("../data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear_permResBH.txt", header = T,stringsAsFactors = F) %>% separate(pid, into=c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID,into=c("gene", "Loc", "strand", "PAS"), sep="_") %>% group_by(Loc) %>% summarise(nTested=n()) %>% mutate(fraction="Nuclear")
TotalBoth=bind_rows(TotQTLs_loc) %>% full_join(TotTested, by=c("Loc","fraction"))
NuclearBoth=bind_rows(NucQTLs_loc) %>% full_join(NucTested, by=c("Loc","fraction"))
allboth=bind_rows(NuclearBoth,TotalBoth) %>% mutate(PropQTLofTested=nLoc/nTested)ggplot(allboth,aes(x=Loc, y=PropQTLofTested, fill=Loc)) + geom_bar(stat="identity") +facet_grid(~fraction) + scale_fill_brewer(palette = "Dark2", labels=c("Coding", "5KB downstream", "Intronic", "3' UTR","5' UTR")) + labs(title="Proportion of Tested PAS with a QTL", y="Proportion PAS tested",x="") + geom_text(aes(label=nLoc),nudge_y = .001) + theme(legend.position = "top", legend.title = element_blank(),axis.text.x = element_text(angle = 45, hjust = 1)) + scale_x_discrete(labels=c("Coding", "5KB downstream", "Intronic", "3' UTR","5' UTR"))
| Version | Author | Date |
|---|---|---|
| 3cddcab | brimittleman | 2019-07-03 |
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] cowplot_0.9.4 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 RColorBrewer_1.1-2 cellranger_1.1.0
[4] plyr_1.8.4 compiler_3.5.1 pillar_1.3.1
[7] git2r_0.25.2 highr_0.7 workflowr_1.4.0
[10] tools_3.5.1 digest_0.6.18 lubridate_1.7.4
[13] jsonlite_1.6 evaluate_0.12 nlme_3.1-137
[16] gtable_0.2.0 lattice_0.20-38 pkgconfig_2.0.2
[19] rlang_0.4.0 cli_1.1.0 rstudioapi_0.10
[22] yaml_2.2.0 haven_1.1.2 withr_2.1.2
[25] xml2_1.2.0 httr_1.3.1 knitr_1.20
[28] hms_0.4.2 generics_0.0.2 fs_1.3.1
[31] rprojroot_1.3-2 grid_3.5.1 tidyselect_0.2.5
[34] glue_1.3.0 R6_2.3.0 readxl_1.1.0
[37] rmarkdown_1.10 reshape2_1.4.3 modelr_0.1.2
[40] magrittr_1.5 whisker_0.3-2 backports_1.1.2
[43] scales_1.0.0 htmltools_0.3.6 rvest_0.3.2
[46] assertthat_0.2.0 colorspace_1.3-2 labeling_0.3
[49] stringi_1.2.4 lazyeval_0.2.1 munsell_0.5.0
[52] broom_0.5.1 crayon_1.3.4