Last updated: 2019-07-09

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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
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/chromHHMQTL.Rmd
    Modified:   analysis/nonNormQTL.Rmd
    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':

    ggsave

Run 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_4pc
sbatch apaQTL_Nominal_4pc.sh
sbatch apaQTL_permuted.4pc.sh

Concatinate 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] 440

apaQTL 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)

Version Author Date
6dae9b1 brimittleman 2019-06-13
1a29304 brimittleman 2019-06-11
84c56ab brimittleman 2019-05-09

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] 771

apaQTL 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)

Version Author Date
6dae9b1 brimittleman 2019-06-13
1a29304 brimittleman 2019-06-11
84c56ab brimittleman 2019-05-09

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)

Version Author Date
6dae9b1 brimittleman 2019-06-13
1a29304 brimittleman 2019-06-11
84c56ab brimittleman 2019-05-09
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