Last updated: 2019-02-15
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Knit directory: threeprimeseq/analysis/
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File | Version | Author | Date | Message |
---|---|---|---|---|
html | de24d7d | Briana Mittleman | 2019-01-15 | Build site. |
Rmd | 57fbf88 | Briana Mittleman | 2019-01-15 | update dist to peak plots |
html | 2ec5ffd | Briana Mittleman | 2018-11-07 | Build site. |
Rmd | 962e39b | Briana Mittleman | 2018-11-07 | move chromhmm analysis to its own analysis |
html | a7ecc84 | Briana Mittleman | 2018-11-07 | Build site. |
Rmd | e43bd07 | Briana Mittleman | 2018-11-07 | group chromhmm by number |
html | b176cda | Briana Mittleman | 2018-11-06 | Build site. |
Rmd | 75467a1 | Briana Mittleman | 2018-11-06 | load in permutation results |
html | a5b4cf6 | Briana Mittleman | 2018-10-29 | Build site. |
Rmd | afb0ce9 | Briana Mittleman | 2018-10-29 | change plot colors |
html | 805dec6 | Briana Mittleman | 2018-10-26 | Build site. |
Rmd | 5cb6b0b | Briana Mittleman | 2018-10-26 | permutation code |
html | 96cfdcd | Briana Mittleman | 2018-10-24 | Build site. |
Rmd | 00b1020 | Briana Mittleman | 2018-10-24 | naive enrichment |
html | de860f0 | Briana Mittleman | 2018-10-24 | Build site. |
Rmd | 96a97f4 | Briana Mittleman | 2018-10-24 | add nuclear characterization |
This analysis is similar to the Characterize Total APAqtl analysis
I would like to study:
Library
library(workflowr)
This is workflowr version 1.2.0
Run ?workflowr for help getting started
library(reshape2)
library(tidyverse)
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library(VennDiagram)
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library(data.table)
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Permuted Results from APA:
I will add a column to this dataframe that will tell me if the association is significant at 10% FDR. This will help me plot based on significance later in the analysis. I am also going to seperate the PID into relevant pieces.
NuclearAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt", stringsAsFactors = F, header=T) %>% mutate(sig=ifelse(-log10(bh)>=1, 1,0 )) %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak"))
NuclearAPA$sig=as.factor(NuclearAPA$sig)
print(names(NuclearAPA))
[1] "chr" "start" "end" "gene" "strand" "peak" "nvar"
[8] "shape1" "shape2" "dummy" "sid" "dist" "npval" "slope"
[15] "ppval" "bpval" "bh" "sig"
I ran the QTL analysis based on the starting position of the gene.
ggplot(NuclearAPA, aes(x=dist, fill=sig, by=sig)) + geom_density(alpha=.5) + labs(title="Distance from snp to TSS", x="Base Pairs") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + scale_fill_brewer(palette="Paired")
Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.
Zoom in to 100,000.
ggplot(NuclearAPA, aes(x=dist, fill=sig, by=sig)) + geom_density(alpha=.5)+coord_cartesian(xlim = c(-150000, 150000)) + scale_fill_brewer(palette="Paired")
To perform this analysis I need to recover the peak positions.
The peak file I used for the QTL analysis is: /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed
peaks=read.table("../data/PeaksUsed/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed", col.names = c("chr", "peakStart", "peakEnd", "PeakNum", "PeakScore", "Strand", "Gene")) %>% mutate(peak=paste("peak", PeakNum,sep="")) %>% mutate(PeakCenter=peakStart+ (peakEnd- peakStart))
I want to join the peak start to the NuclearAPA file but the peak column. I will then create a column that is snppos-peakcenter.
NuclearAPA_peakdist= NuclearAPA %>% inner_join(peaks, by="peak") %>% separate(sid, into=c("snpCHR", "snpLOC"), by=":")
NuclearAPA_peakdist$snpLOC= as.numeric(NuclearAPA_peakdist$snpLOC)
NuclearAPA_peakdist= NuclearAPA_peakdist %>% mutate(DisttoPeak= snpLOC-PeakCenter)
Plot this by significance.
ggplot(NuclearAPA_peakdist, aes(x=DisttoPeak, fill=sig, by=sig)) + geom_density(alpha=.5) + labs(title="Distance from snp peak", x="log10 absolute value Distance to Peak") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + scale_fill_brewer(palette="Paired")
Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.
Look at the summarys based on significance:
NuclearAPA_peakdist_sig=NuclearAPA_peakdist %>% filter(sig==1)
NuclearAPA_peakdist_notsig=NuclearAPA_peakdist %>% filter(sig==0)
summary(NuclearAPA_peakdist_sig$DisttoPeak)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-1003786 -17579 -91 -8818 6588 891734
summary(NuclearAPA_peakdist_notsig$DisttoPeak)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-70147526 -265059 -2067 7263 255169 125172864
ggplot(NuclearAPA_peakdist, aes(y=DisttoPeak,x=sig, fill=sig, by=sig)) + geom_boxplot() + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + scale_fill_brewer(palette="Paired")
Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.
Look like there are some outliers that are really far. I will remove variants greater than 1*10^6th away
NuclearAPA_peakdist_filt=NuclearAPA_peakdist %>% filter(abs(DisttoPeak) <= 1*(10^6))
ggplot(NuclearAPA_peakdist_filt, aes(y=DisttoPeak,x=sig, fill=sig, by=sig)) + geom_boxplot() + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + facet_grid(.~strand) + scale_fill_brewer(palette="Paired")
Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.
ggplot(NuclearAPA_peakdist_filt, aes(x=DisttoPeak, fill=sig, by=sig)) + geom_density() + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + facet_grid(.~strand)+ scale_fill_brewer(palette="Paired")
Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.
I am going to plot a violin plot for just the significant ones.
ggplot(NuclearAPA_peakdist_sig, aes(x=log10(abs(DisttoPeak)+1))) + geom_density(fill="deepskyblue3")+ labs(title="Nuclear: Distance from QTL to PAS Peak", x="Distance from SNP to PAS")
Within 1000 bases of the peak center.
NuclearAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 1000) %>% nrow()
[1] 192
NuclearAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 10000) %>% nrow()
[1] 420
NuclearAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 100000) %>% nrow()
[1] 726
192 QTLs are within 1000 bp, 420 are within 10000, and 726 are within 100,000bp
Next I want to pull in the expression values and compare the expression of genes with a nuclear APA qtl in comparison to genes without one. I will also need to pull in the gene names file to add in the gene names from the ensg ID.
Remove the # from the file.
expression=read.table("../data/mol_pheno/fastqtl_qqnorm_RNAseq_phase2.fixed.noChr.txt", header = T,stringsAsFactors = F)
expression_mean=apply(expression[,5:73],1,mean,na.rm=TRUE)
expression_var=apply(expression[,5:73],1,var,na.rm=TRUE)
expression$exp.mean= expression_mean
expression$exp.var=expression_var
expression= expression %>% separate(ID, into=c("Gene.stable.ID", "ver"), sep ="[.]")
Now I can pull in the names and join the dataframes.
geneNames=read.table("../data/ensemble_to_genename.txt", sep="\t", header=T,stringsAsFactors = F)
expression=expression %>% inner_join(geneNames,by="Gene.stable.ID")
expression=expression %>% select(Chr, start, end, Gene.name, exp.mean,exp.var) %>% rename("gene"=Gene.name)
Now I can join this with the qtls.
NuclearAPA_wExp=NuclearAPA %>% inner_join(expression, by="gene")
gene_wQTL_N= NuclearAPA_wExp %>% group_by(gene) %>% summarise(sig_gene=sum(as.numeric(as.character(sig)))) %>% ungroup() %>% inner_join(expression, by="gene") %>% mutate(sigGeneFactor=ifelse(sig_gene>=1, 1,0))
gene_wQTL_N$sigGeneFactor= as.factor(as.character(gene_wQTL_N$sigGeneFactor))
summary(gene_wQTL_N$sigGeneFactor)
0 1
4589 607
There are 607 genes with a QTL
ggplot(gene_wQTL_N, aes(x=exp.mean, by=sigGeneFactor, fill=sigGeneFactor)) + geom_density(alpha=.3) +labs(title="Mean in RNA expression by genes with significant QTL", x="Mean in normalized expression") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))+ scale_fill_brewer(palette="Paired")
Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.
I can do a similar analysis but test the variance in the gene expression.
ggplot(gene_wQTL_N, aes(x=exp.var, by=sigGeneFactor, fill=sigGeneFactor)) + geom_density(alpha=.3) + labs(title="Varriance in RNA expression by genes with significant QTL", x="Variance in normalized expression") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))+ scale_fill_brewer(palette="Paired")
Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.
I can also look at peak coverage for peaks with QLTs and those without. I will first look at this on peak level then mvoe to gene level. The peak scores come from the coverage in the peaks.
The NuclearAPA_peakdist data frame has the information I need for this.
ggplot(NuclearAPA_peakdist, aes(x=PeakScore,fill=sig,by=sig)) + geom_density(alpha=.5)+ scale_x_log10() + labs(title="Peak score by significance") + scale_fill_brewer(palette="Paired")
This is expected. It makes sense that we have more power to detect qtls in higher expressed peaks. This leads me to believe that filtering out low peaks may add power but will not mitigate the effect. ##Where are the SNPs
I created the significant SNP files in the Characterize Total APAqtl analysis analysis.
chromHmm=read.table("../data/ChromHmmOverlap/chromHMM_regions.txt", col.names = c("number", "name"), stringsAsFactors = F)
NuclearOverlapHMM=read.table("../data/ChromHmmOverlap/Nuc_overlapHMM.bed", col.names=c("chrom", "start", "end", "sid", "significance", "strand", "number"))
NuclearOverlapHMM$number=as.integer(NuclearOverlapHMM$number)
NuclearOverlapHMM_names=NuclearOverlapHMM %>% left_join(chromHmm, by="number")
ggplot(NuclearOverlapHMM_names, aes(x=name)) + geom_bar() + labs(title="ChromHMM labels for Nuclear APAQtls" , y="Number of SNPs", x="Region")+theme(axis.text.x = element_text(angle = 90, hjust = 1))
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.14.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] bindrcpp_0.2.2 cowplot_0.9.3 ggpubr_0.1.8
[4] magrittr_1.5 data.table_1.11.8 VennDiagram_1.6.20
[7] futile.logger_1.4.3 forcats_0.3.0 stringr_1.4.0
[10] dplyr_0.7.6 purrr_0.2.5 readr_1.1.1
[13] tidyr_0.8.1 tibble_1.4.2 ggplot2_3.0.0
[16] tidyverse_1.2.1 reshape2_1.4.3 workflowr_1.2.0
loaded via a namespace (and not attached):
[1] tidyselect_0.2.4 haven_1.1.2 lattice_0.20-35
[4] colorspace_1.3-2 htmltools_0.3.6 yaml_2.2.0
[7] rlang_0.2.2 pillar_1.3.0 glue_1.3.0
[10] withr_2.1.2 RColorBrewer_1.1-2 modelr_0.1.2
[13] lambda.r_1.2.3 readxl_1.1.0 bindr_0.1.1
[16] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0
[19] cellranger_1.1.0 rvest_0.3.2 evaluate_0.13
[22] labeling_0.3 knitr_1.20 broom_0.5.0
[25] Rcpp_0.12.19 formatR_1.5 scales_1.0.0
[28] backports_1.1.2 jsonlite_1.6 fs_1.2.6
[31] hms_0.4.2 digest_0.6.17 stringi_1.2.4
[34] rprojroot_1.3-2 cli_1.0.1 tools_3.5.1
[37] lazyeval_0.2.1 futile.options_1.0.1 crayon_1.3.4
[40] whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[43] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.11
[46] httr_1.3.1 rstudioapi_0.9.0 R6_2.3.0
[49] nlme_3.1-137 git2r_0.24.0 compiler_3.5.1