Last updated: 2022-11-04
Checks: 6 1
Knit directory: ChromatinSplicingQTLs/analysis/
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Yang asked for some specific plots to put in a grant or presentation or something. Here I will make those plots, possibly explore the data a little bit if something comes to mind. A lot of these plots were originally explored in this notebook but that notebook is getting unweildy and disorganized and overall a pain when I want to go back and re-run parts of it so I am re-plotting the highlights that Yang asked for here.
Load libraries and read in a bunch of files…
library(tidyverse)
library(data.table)
library(MASS)
library(RColorBrewer)
library(scales)
NMD.transcript.introns <- fread("../code/SplicingAnalysis/Annotations/NMD/NMD_trancsript_introns.bed.gz", col.names=c("chrom", "start", "stop", "name", "score", "strand")) %>%
mutate(stop=stop+1) %>%
unite(intron, chrom:stop, strand)
Non.NMD.transcript.introns <- fread("../code/SplicingAnalysis/Annotations/NMD/NonNMD_trancsript_introns.bed.gz", col.names=c("chrom", "start", "stop", "name", "score", "strand")) %>%
mutate(stop=stop+1) %>%
unite(intron, chrom:stop, strand)
NMD.specific.introns <- setdiff(NMD.transcript.introns$intron, Non.NMD.transcript.introns$intron)
Intron.Annotations.basic <- fread("../code/SplicingAnalysis/regtools_annotate_combined/basic.bed.gz") %>%
filter(known_junction ==1) %>%
unite(intron, chrom, start, end, strand)
Introns.Annotations.comprehensive <- fread("../code/SplicingAnalysis/regtools_annotate_combined/comprehensive.bed.gz") %>%
filter(known_junction ==1) %>%
unite(intron, chrom, start, end, strand)
Introns.Annotations.all <- fread("../code/SplicingAnalysis/regtools_annotate_combined/comprehensive.bed.gz") %>%
unite(intron, chrom, start, end, strand)
PhenotypeAliases <- read_tsv("../data/Phenotypes_recode_for_Plotting.txt")
PC.ShortAliases <- PhenotypeAliases %>%
dplyr::select(PC, ShorterAlias) %>% deframe()
PeaksToTSS <- Sys.glob("../code/Misc/PeaksClosestToTSS/*_assigned.tsv.gz") %>%
setNames(str_replace(., "../code/Misc/PeaksClosestToTSS/(.+?)_assigned.tsv.gz", "\\1")) %>%
lapply(read_tsv) %>%
bind_rows(.id="ChromatinMark") %>%
mutate(GenePeakPair = paste(gene, peak, sep = ";")) %>%
distinct(ChromatinMark, peak, gene, .keep_all=T)
TopSNPEffects.ByPairs <- fread("../code/pi1/PairwisePi1Traits.P.all.txt.gz")
coloc.tidy <- fread("../output/hyprcoloc_results/ForColoc/MolColocStandard/hyprcoloc.results.OnlyColocalized.Stats.txt.gz") %>%
separate(phenotype_full, into=c("PC", "P"), sep=";")
coloc.tidy.pairwise <- left_join(
coloc.tidy,
coloc.tidy %>%
dplyr::select(-iteration, -ColocPr, -RegionalPr, -TopSNPFinemapPr),
by=c("Locus"),
suffix=c("1", "2")
) %>%
filter(!(P1==P2 & PC1 == PC2)) %>%
filter(snp1 == snp2) %>%
dplyr::select(ColocalizedTopSNP = snp1, GeneLocus=Locus, everything(), -snp2) %>%
unite(TraitPair, P1, PC1, P2, PC2, remove=F)
Now let’s start to plot some stuff:
coloc.tidy.pairwise %>%
filter(
(PC1 %in% c("Expression.Splicing.Subset_YRI")) &
(PC2 %in% c("polyA.Splicing"))
) %>%
ggplot(aes(y=beta1, x=beta2)) +
geom_point(alpha=0.5, color='black') +
geom_vline(xintercept=0) +
geom_hline(yintercept=0) +
geom_text(
data = . %>%
summarise(cor=cor.test(beta1,beta2)[["estimate"]], pval=cor.test(beta1,beta2)[["p.value"]]) %>%
mutate(R = signif(cor, 3), P=format.pval(pval, 3)) %>%
mutate(label = str_glue("R:{R}\nP:{P}")),
aes(x=-Inf, y=-Inf, label=label),
hjust=-0.1, vjust=-0.1, color='black'
) +
theme_bw() +
labs(title="All colocalized eQTLs and sQTLs", x="sQTL beta\n(standardized units)", y="eQTL beta\n(standardized units)")
P1Category <- "polyA.Splicing"
sQTL.eQTL.dat.ColocalizedAndUncolocalized <- TopSNPEffects.ByPairs %>%
filter(
(PC1 %in% c(P1Category)) &
(PC2 %in% c("Expression.Splicing.Subset_YRI")) &
(FDR.x < 0.1)) %>%
mutate(intron = str_replace(P1, "^(.+):(.+?):(.+?):clu.+?_([+-])$", "chr\\1_\\2_\\3_\\4")) %>%
mutate(IntronAnnotation = case_when(
intron %in% NMD.specific.introns ~ "Annotated NMD",
intron %in% Intron.Annotations.basic$intron ~ "Annotated basic",
intron %in% Introns.Annotations.comprehensive$intron ~ "Annotated Not basic",
TRUE ~ "Unannotated"
)) %>%
dplyr::select(beta1=beta.x, beta2=trait.x.beta.in.y, P1, PC1, P2, PC2, IntronAnnotation) %>%
unite(TraitPair, P1, PC1, P2, PC2, remove=F) %>%
filter(!TraitPair %in% coloc.tidy.pairwise$TraitPair) %>%
mutate(Colocalized = "Not colocalized") %>%
bind_rows(
coloc.tidy.pairwise %>%
filter(
(PC2 %in% c("Expression.Splicing.Subset_YRI")) &
(PC1 %in% c(P1Category))
) %>%
mutate(intron = str_replace(P1, "^(.+):(.+?):(.+?):clu.+?_([+-])$", "chr\\1_\\2_\\3_\\4")) %>%
mutate(IntronAnnotation = case_when(
intron %in% NMD.specific.introns ~ "Annotated NMD",
intron %in% Intron.Annotations.basic$intron ~ "Annotated basic",
intron %in% Introns.Annotations.comprehensive$intron ~ "Annotated Not basic",
TRUE ~ "Unannotated"
)) %>%
dplyr::select(beta1, beta2, P1, PC1, P2, PC2, IntronAnnotation) %>%
mutate(Colocalized = "Colocalized")
) %>%
mutate(Is.Concordant = sign(beta1)==sign(beta2)) %>%
mutate(ConsistentWithNMD = case_when(
!IntronAnnotation == "Annotated basic" & Is.Concordant ~ "Inconsistent with NMD",
!IntronAnnotation == "Annotated basic" & !Is.Concordant ~ "Consistent with NMD",
IntronAnnotation == "Annotated basic" ~ NA_character_
))
P.sQTL.eQTLs <- ggplot(sQTL.eQTL.dat.ColocalizedAndUncolocalized, aes(x=beta1, y=beta2, color=IntronAnnotation)) +
stat_ellipse(
data = mvrnorm(1000, mu=c(0,0), matrix(c(1,0.8,0.8,1),2,2)) %>%
as.data.frame() %>%
dplyr::select(beta1=V1, beta2=V2),
type = "norm", linetype = 2, color="green", alpha=0.3) +
stat_ellipse(
data = mvrnorm(1000, mu=c(0,0), matrix(c(1,-0.8,-0.8,1),2,2)) %>%
as.data.frame() %>%
dplyr::select(beta1=V1, beta2=V2),
type = "norm", linetype = 2, color="red", alpha=0.3) +
geom_point(alpha=0.1) +
geom_vline(xintercept=0) +
geom_hline(yintercept=0) +
# geom_smooth(method='lm') +
geom_text(
data = . %>%
group_by(IntronAnnotation, Colocalized) %>%
summarise(cor=cor.test(beta1,beta2)[["estimate"]], pval=cor.test(beta1,beta2)[["p.value"]]) %>%
mutate(R = signif(cor, 3), P=format.pval(pval, 3)) %>%
mutate(label = str_glue("R:{R}\nP:{P}")),
aes(x=-Inf, y=-Inf, label=label),
hjust=-.1, vjust=-0.1, color='black', size=3
) +
geom_text(
data = . %>%
group_by(IntronAnnotation, ConsistentWithNMD, Colocalized) %>%
distinct(P2, .keep_all=T) %>%
ungroup() %>%
count(IntronAnnotation, ConsistentWithNMD, Colocalized) %>%
filter(ConsistentWithNMD=="Consistent with NMD"),
aes(x=-Inf, y=Inf, label=n),
hjust=-0.2, vjust=1.5, color='red'
) +
geom_text(
data = . %>%
group_by(IntronAnnotation, ConsistentWithNMD, Colocalized) %>%
distinct(P2, .keep_all=T) %>%
ungroup() %>%
count(IntronAnnotation, ConsistentWithNMD, Colocalized) %>%
filter(ConsistentWithNMD=="Inconsistent with NMD"),
aes(x=Inf, y=Inf, label=n),
hjust=1.2, vjust=1.5, color='green'
) +
facet_grid(Colocalized ~ IntronAnnotation) +
theme_bw() +
labs(title="genetic effects of NMD introns and host gene expression", x="chRNA sQTL beta\n(standardized units)", y="eQTL beta\n(standardized units)", caption="Colocalized sQTL/eQTLs betas at top co-finemapped SNP\nUncolocalized betas relative to top chRNA sQTL SNP") +
guides(colour = guide_legend(override.aes = list(alpha = 1)))
P.sQTL.eQTLs
If you sum up all the red and the green counts in the colocalized sub-plots, and then only take distinct genes, you get the following:
CountsOfNMD.Consitent.eGene.sQTL.Effects <- sQTL.eQTL.dat.ColocalizedAndUncolocalized %>%
filter(Colocalized == "Colocalized") %>%
distinct(P2, .keep_all=T) %>%
count(ConsistentWithNMD) %>%
drop_na()
CountsOfNMD.Consitent.eGene.sQTL.Effects
ConsistentWithNMD n
1: Consistent with NMD 373
2: Inconsistent with NMD 49
(CountsOfNMD.Consitent.eGene.sQTL.Effects$n[1] - CountsOfNMD.Consitent.eGene.sQTL.Effects$n[2])/3970
[1] 0.08161209
For reference, let’s make the same plots and do the same calculation for H3K27AC, H3K4ME3, and H3K4ME1:
P1Category <- c("H3K4ME3", "H3K4ME1", "H3K27AC")
hQTL.eQTL.dat.ColocalizedAndUncolocalized <- TopSNPEffects.ByPairs %>%
filter(
(PC1 %in% c(P1Category)) &
(PC2 %in% c("Expression.Splicing.Subset_YRI")) &
(FDR.x < 0.1)) %>%
dplyr::select(beta1=beta.x, beta2=trait.x.beta.in.y, P1, PC1, P2, PC2) %>%
unite(TraitPair, P1, PC1, P2, PC2, remove=F) %>%
filter(!TraitPair %in% coloc.tidy.pairwise$TraitPair) %>%
mutate(Colocalized = "Not colocalized") %>%
bind_rows(
coloc.tidy.pairwise %>%
filter(
(PC2 %in% c("Expression.Splicing.Subset_YRI")) &
(PC1 %in% c(P1Category))
) %>%
dplyr::select(beta1, beta2, P1, PC1, P2, PC1) %>%
mutate(Colocalized = "Colocalized")
) %>%
mutate(Is.Concordant = sign(beta1)==sign(beta2)) %>%
mutate(ConsistentWithNMD = if_else(Is.Concordant, "Consistent with enhancer/promoter activation", "Inconsistent with enhancer/promoter activation"))
P.hQTL.eQTLs <- ggplot(hQTL.eQTL.dat.ColocalizedAndUncolocalized, aes(x=beta1, y=beta2, color=PC1)) +
stat_ellipse(
data = mvrnorm(1000, mu=c(0,0), matrix(c(1,0.8,0.8,1),2,2)) %>%
as.data.frame() %>%
dplyr::select(beta1=V1, beta2=V2),
type = "norm", linetype = 2, color="green", alpha=0.3) +
stat_ellipse(
data = mvrnorm(1000, mu=c(0,0), matrix(c(1,-0.8,-0.8,1),2,2)) %>%
as.data.frame() %>%
dplyr::select(beta1=V1, beta2=V2),
type = "norm", linetype = 2, color="red", alpha=0.3) +
geom_point(alpha=0.05) +
geom_vline(xintercept=0) +
geom_hline(yintercept=0) +
# geom_smooth(method='lm') +
geom_text(
data = . %>%
group_by(PC1, Colocalized) %>%
summarise(cor=cor.test(beta1,beta2)[["estimate"]], pval=cor.test(beta1,beta2)[["p.value"]]) %>%
mutate(R = signif(cor, 3), P=format.pval(pval, 3)) %>%
mutate(label = str_glue("R:{R}\nP:{P}")),
aes(x=-Inf, y=-Inf, label=label),
hjust=-.1, vjust=-0.1, color='black', size=3
) +
geom_text(
data = . %>%
group_by(PC1, ConsistentWithNMD, Colocalized) %>%
distinct(P2, .keep_all=T) %>%
ungroup() %>%
count(PC1, ConsistentWithNMD, Colocalized) %>%
filter(ConsistentWithNMD=="Consistent with enhancer/promoter activation"),
aes(x=Inf, y=Inf, label=n),
hjust=1.2, vjust=1.5, color='green'
) +
geom_text(
data = . %>%
group_by(PC1, ConsistentWithNMD, Colocalized) %>%
distinct(P2, .keep_all=T) %>%
ungroup() %>%
count(PC1, ConsistentWithNMD, Colocalized) %>%
filter(ConsistentWithNMD=="Inconsistent with enhancer/promoter activation"),
aes(x=-Inf, y=Inf, label=n),
hjust=-0.2, vjust=1.5, color='red'
) +
facet_grid(Colocalized ~ PC1) +
theme_bw() +
labs(title="genetic effects of activating chromatin marks on cis gene expression", x="xQTL beta\n(standardized units)", y="eQTL beta\n(standardized units)", caption="Colocalized xQTL/eQTLs betas at top co-finemapped SNP\nUncolocalized betas relative to top xQTL SNP", color="xQTL") +
guides(colour = guide_legend(override.aes = list(alpha = 1)))
P.hQTL.eQTLs
CountsOfNMD.Consitent.eGene.hQTL.Effects <- hQTL.eQTL.dat.ColocalizedAndUncolocalized %>%
filter(Colocalized == "Colocalized") %>%
distinct(P2, .keep_all=T) %>%
count(ConsistentWithNMD) %>%
dplyr::select(ConsistentWithPromoterEnhancerActivation=ConsistentWithNMD,n) %>%
drop_na()
CountsOfNMD.Consitent.eGene.hQTL.Effects
ConsistentWithPromoterEnhancerActivation n
1: Consistent with enhancer/promoter activation 658
2: Inconsistent with enhancer/promoter activation 102
(CountsOfNMD.Consitent.eGene.hQTL.Effects$n[1] - CountsOfNMD.Consitent.eGene.hQTL.Effects$n[2])/3970
[1] 0.1400504
ggsave("../code/scratch/eQTL.sQTLs.Betas.pdf", P.sQTL.eQTLs, width=8)
ggsave("../code/scratch/eQTL.hQTLs.Betas.pdf", P.hQTL.eQTLs, width=8)
I wonder if this plot might be more informative (in terms of counting the non-colocalized effects, and also not seeing a trend) if i broke up the non-colocalized facets into those with nominal eQTL P < 0.01 and those > 0.01
eQTL.P.Threshold <- 0.1
P1Category <- "polyA.Splicing"
sQTL.eQTL.dat.ColocalizedAndUncolocalized <- TopSNPEffects.ByPairs %>%
filter(
(PC1 %in% c(P1Category)) &
(PC2 %in% c("Expression.Splicing.Subset_YRI")) &
(FDR.x < 0.1)) %>%
mutate(intron = str_replace(P1, "^(.+):(.+?):(.+?):clu.+?_([+-])$", "chr\\1_\\2_\\3_\\4")) %>%
mutate(IntronAnnotation = case_when(
intron %in% NMD.specific.introns ~ "Annotated NMD",
intron %in% Intron.Annotations.basic$intron ~ "Annotated basic",
intron %in% Introns.Annotations.comprehensive$intron ~ "Annotated Not basic",
TRUE ~ "Unannotated"
)) %>%
dplyr::select(beta1=beta.x, beta2=trait.x.beta.in.y, P1, PC1, P2, PC2, IntronAnnotation, eQTL.P = trait.x.p.in.y) %>%
unite(TraitPair, P1, PC1, P2, PC2, remove=F) %>%
filter(!TraitPair %in% coloc.tidy.pairwise$TraitPair) %>%
mutate(Colocalized = if_else(
eQTL.P < 0.05,
paste0("Not colocalized, eQTL P<", eQTL.P.Threshold),
paste0("Not colocalized, eQTL P>", eQTL.P.Threshold)
)) %>%
bind_rows(
coloc.tidy.pairwise %>%
filter(
(PC2 %in% c("Expression.Splicing.Subset_YRI")) &
(PC1 %in% c(P1Category))
) %>%
mutate(intron = str_replace(P1, "^(.+):(.+?):(.+?):clu.+?_([+-])$", "chr\\1_\\2_\\3_\\4")) %>%
mutate(IntronAnnotation = case_when(
intron %in% NMD.specific.introns ~ "Annotated NMD",
intron %in% Intron.Annotations.basic$intron ~ "Annotated basic",
intron %in% Introns.Annotations.comprehensive$intron ~ "Annotated Not basic",
TRUE ~ "Unannotated"
)) %>%
dplyr::select(beta1, beta2, P1, PC1, P2, PC2, IntronAnnotation) %>%
mutate(Colocalized = "Colocalized")
) %>%
mutate(Is.Concordant = sign(beta1)==sign(beta2)) %>%
mutate(ConsistentWithNMD = case_when(
!IntronAnnotation == "Annotated basic" & Is.Concordant ~ "Inconsistent with NMD",
!IntronAnnotation == "Annotated basic" & !Is.Concordant ~ "Consistent with NMD",
# IntronAnnotation == "Annotated basic" ~ NA_character_,
IntronAnnotation == "Annotated basic" & Is.Concordant ~ "Inconsistent with NMD",
IntronAnnotation == "Annotated basic" & !Is.Concordant ~ "Consistent with NMD"
)) %>%
filter(!is.na(Colocalized))
ggplot(sQTL.eQTL.dat.ColocalizedAndUncolocalized, aes(x=beta1, y=beta2, color=IntronAnnotation)) +
stat_ellipse(
data = mvrnorm(1000, mu=c(0,0), matrix(c(1,0.8,0.8,1),2,2)) %>%
as.data.frame() %>%
dplyr::select(beta1=V1, beta2=V2),
type = "norm", linetype = 2, color="green", alpha=0.3) +
stat_ellipse(
data = mvrnorm(1000, mu=c(0,0), matrix(c(1,-0.8,-0.8,1),2,2)) %>%
as.data.frame() %>%
dplyr::select(beta1=V1, beta2=V2),
type = "norm", linetype = 2, color="red", alpha=0.3) +
geom_point(alpha=0.1) +
geom_vline(xintercept=0) +
geom_hline(yintercept=0) +
geom_text(
data = . %>%
group_by(IntronAnnotation, Colocalized) %>%
summarise(cor=cor.test(beta1,beta2)[["estimate"]], pval=cor.test(beta1,beta2)[["p.value"]]) %>%
mutate(R = signif(cor, 3), P=format.pval(pval, 3)) %>%
mutate(label = str_glue("R:{R}\nP:{P}")),
aes(x=-Inf, y=-Inf, label=label),
hjust=-.1, vjust=-0.1, color='black', size=3
) +
geom_text(
data = . %>%
group_by(IntronAnnotation, ConsistentWithNMD, Colocalized) %>%
distinct(P2, .keep_all=T) %>%
ungroup() %>%
count(IntronAnnotation, ConsistentWithNMD, Colocalized) %>%
filter(ConsistentWithNMD=="Consistent with NMD"),
aes(x=-Inf, y=Inf, label=n),
hjust=-0.2, vjust=1.5, color='red'
) +
geom_text(
data = . %>%
group_by(IntronAnnotation, ConsistentWithNMD, Colocalized) %>%
distinct(P2, .keep_all=T) %>%
ungroup() %>%
count(IntronAnnotation, ConsistentWithNMD, Colocalized) %>%
filter(ConsistentWithNMD=="Inconsistent with NMD"),
aes(x=Inf, y=Inf, label=n),
hjust=1.2, vjust=1.5, color='green'
) +
facet_grid(Colocalized ~ IntronAnnotation) +
theme_bw() +
labs(title="genetic effects of NMD introns and host gene expression", x="chRNA sQTL beta\n(standardized units)", y="eQTL beta\n(standardized units)", caption="Colocalized sQTL/eQTLs betas at top co-finemapped SNP\nUncolocalized betas relative to top chRNA sQTL SNP") +
guides(colour = guide_legend(override.aes = list(alpha = 1)))
Let’s revisit making QQ plots to reproduce Carlos’ observation that polyA-specific eQTLs are more likely to be sQTLs…
brewer.pal(n=6, name="Paired")
[1] "#A6CEE3" "#1F78B4" "#B2DF8A" "#33A02C" "#FB9A99" "#E31A1C"
ColorKey <- c("chRNA.Expression.Splicing TRUE"="#1F78B4",
"chRNA.Expression.Splicing FALSE"="#A6CEE3",
"H3K36ME3 TRUE"="#33A02C",
"H3K36ME3 FALSE"="#B2DF8A",
"H3K27AC TRUE"="#E31A1C",
"H3K27AC FALSE"="#FB9A99")
LabelKey <- c("chRNA.Expression.Splicing"="chRNA",
"H3K36ME3"="H3K36ME3",
"H3K27AC"="H3K27AC@TSS")
sGenes <- paste0("../code/QTLs/QTLTools/", c("polyA.Splicing.Subset_YRI", "polyA.Splicing","chRNA.Splicing"),"/GroupedPermutationPassForColoc.FDR_Added.txt.gz") %>%
setNames(str_replace(., "../code/QTLs/QTLTools/(.+?)/GroupedPermutationPassForColoc.FDR_Added.txt.gz", "\\1")) %>%
lapply(fread) %>%
bind_rows(.id="Dataset")
Dat.to.plot <- TopSNPEffects.ByPairs %>%
filter(
PC1 == "Expression.Splicing.Subset_YRI" &
PC2 %in% c("H3K27AC", "chRNA.Expression.Splicing", "H3K36ME3")
) %>%
filter(
PC2 %in% c("chRNA.Expression.Splicing", "H3K36ME3") |
paste(P1, P2, sep=";") %in% PeaksToTSS$GenePeakPair
) %>%
group_by(GeneLocus, PC2) %>%
arrange(FDR.y) %>%
distinct(PC2, .keep_all=T) %>%
ungroup() %>%
# mutate(Is_xQTL = FDR.y<0.05) %>%
mutate(Is_xQTL = p_permutation.y<0.05) %>%
mutate(FacetLabel = recode(PC2, !!!LabelKey)) %>%
mutate(Group = paste(PC2, Is_xQTL)) %>%
add_count(Group, FacetLabel)
Dat.to.plot %>%
ggplot(aes(x=-log10(p_permutation.x), y=-log10(p_permutation.y), color=Group)) +
geom_label(
data = . %>%
distinct(Group, FacetLabel, n) %>%
group_by(FacetLabel) %>%
mutate(rn = row_number()),
aes(label=paste0("n=", n), vjust=rn+1, color=Group),
x=-Inf, y=Inf, hjust=-0.5) +
geom_point(alpha=0.1) +
scale_color_manual(values=ColorKey) +
facet_wrap(~FacetLabel) +
theme_bw() +
theme(legend.position = "none") +
labs(caption="Number of eGenes that are xQTLs, 10% FDR", x="-log10(q) (eQTL)", y="-log10(q) (xQTL)")
Dat.to.plot %>%
inner_join(
sGenes %>%
dplyr::select(GeneLocus=grp_id, sGene.P = adj_beta_pval, Dataset)
) %>%
group_by(Dataset, Group, FacetLabel) %>%
mutate(ExpectedP = percent_rank(sGene.P)) %>%
ungroup() %>%
ggplot(aes(x=-log10(ExpectedP), y=-log10(sGene.P), color=Group)) +
geom_abline() +
geom_point() +
scale_color_manual(values=c(ColorKey)) +
facet_grid(Dataset ~ FacetLabel, scales="free_y") +
theme_bw() +
theme(legend.position = "none") +
labs(title="QQ plot of sQTL P-values", color="xQTL SNP", y="Observed -log10(P)", x="Theoretical -log10(P)", caption=str_wrap("", 30))
Hmmm, again I cannot reproduce the results… Maybe I have to group by intron type to see a difference… Most sQTLs are in annotated basic introns, which do not effect expression. So all those sQTLs could swamp and difference that distinguished polyA-specific eQTLs.
Let’s try to reproduce the results more to exactly how Carlos did it…
test.SNPs <- paste0("../code/QTLs/QTLTools/", c("chRNA.Expression.Splicing", "Expression.Splicing.Subset_YRI"), "/NominalPassForColoc.RandomSamplePvals.txt.gz") %>%
setNames(str_replace(., "../code/QTLs/QTLTools/(.+?)/NominalPassForColoc.RandomSamplePvals.txt.gz", "\\1")) %>%
lapply(read_tsv, col_names=c("trait.x.p.in.y")) %>%
bind_rows(.id="PC2") %>%
mutate(PC1 = "TestSNPs") %>%
group_by(PC2) %>%
sample_n(5000) %>%
ungroup()
show_col(hue_pal()(4))
hue_pal()(4)
[1] "#F8766D" "#7CAE00" "#00BFC4" "#C77CFF"
Colors <- c("H3K27AC"="#808080", "TestSNPs"="#000000", "Annotated NMD"="#F8766D", "Annotated Not basic"="#7CAE00", "Unannotated"="#C77CFF", "Annotated basic"="#00BFC4")
TopSNPEffects.ByPairs %>%
filter(
(PC2 %in% c("chRNA.Expression.Splicing", "Expression.Splicing.Subset_YRI")) &
# (PC1 %in% c("polyA.Splicing.Subset_YRI", "H3K27AC")) &
(PC1 %in% c("polyA.Splicing", "H3K27AC")) &
(FDR.x < 0.1)) %>%
bind_rows(test.SNPs) %>%
mutate(intron = case_when(
PC1 %in% c("polyA.Splicing.Subset_YRI", "chRNA.Splicing", "polyA.Splicing") ~ str_replace(P1, "^(.+):(.+?):(.+?):clu.+?_([+-])$", "chr\\1_\\2_\\3_\\4"),
TRUE ~ NA_character_)) %>%
mutate(IntronAnnotation = case_when(
intron %in% NMD.specific.introns ~ "Annotated NMD",
intron %in% Intron.Annotations.basic$intron ~ "Annotated basic",
intron %in% Introns.Annotations.comprehensive$intron ~ "Annotated Not basic",
!is.na(intron) ~ "Unannotated",
TRUE ~ ""
)) %>%
# filter(!IntronAnnotation=="Annotated basic") %>%
filter(PC2 == "Expression.Splicing.Subset_YRI") %>%
mutate(SnpSet = case_when(
str_detect(PC1, "Splicing") ~ IntronAnnotation,
TRUE ~ PC1
)) %>%
group_by(SnpSet, PC2) %>%
mutate(ExpectedP = percent_rank(trait.x.p.in.y)) %>%
ungroup() %>%
# mutate(SnpSetColor = recode(SnpSet, !!!Colors)) %>%
ggplot(aes(x=-log10(ExpectedP), y=-log10(trait.x.p.in.y), color=SnpSet)) +
geom_abline() +
geom_point() +
# scale_color_identity() +
scale_color_manual(values=Colors) +
theme_bw() +
labs(title="QQ plot of eQTL P-values", color="xQTL SNP", y="-log10(P)")
ggsave("../code/scratch/QQ.eQTLs.By.xQTL.pdf", height=4, width=5)
Let’s make the “polarized” version of this QQ plot… If the eQTL and xQTL betas have the same sign, plot them upright, if opposite site, mirror the QQ plot.
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
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 LC_TIME=C
[4] LC_COLLATE=C LC_MONETARY=C LC_MESSAGES=C
[7] LC_PAPER=C LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] scales_1.1.0 RColorBrewer_1.1-2 MASS_7.3-51.4 data.table_1.14.2
[5] forcats_0.4.0 stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4
[9] readr_1.3.1 tidyr_1.2.0 tibble_3.1.7 ggplot2_3.3.6
[13] tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 lubridate_1.7.4 assertthat_0.2.1 rprojroot_2.0.2
[5] digest_0.6.20 utf8_1.1.4 R6_2.4.0 cellranger_1.1.0
[9] backports_1.4.1 reprex_0.3.0 evaluate_0.15 highr_0.9
[13] httr_1.4.4 pillar_1.7.0 rlang_1.0.5 readxl_1.3.1
[17] rstudioapi_0.14 R.oo_1.22.0 R.utils_2.9.0 rmarkdown_1.13
[21] labeling_0.3 munsell_0.5.0 broom_1.0.0 compiler_3.6.1
[25] httpuv_1.5.1 modelr_0.1.8 xfun_0.31 pkgconfig_2.0.2
[29] htmltools_0.5.3 tidyselect_1.1.2 workflowr_1.6.2 fansi_0.4.0
[33] crayon_1.3.4 dbplyr_1.4.2 withr_2.5.0 later_0.8.0
[37] R.methodsS3_1.7.1 grid_3.6.1 jsonlite_1.6 gtable_0.3.0
[41] lifecycle_1.0.1 DBI_1.1.0 git2r_0.26.1 magrittr_1.5
[45] cli_3.3.0 stringi_1.4.3 farver_2.1.0 fs_1.5.2
[49] promises_1.0.1 xml2_1.3.2 ellipsis_0.3.2 generics_0.1.3
[53] vctrs_0.4.1 tools_3.6.1 glue_1.6.2 hms_0.5.3
[57] fastmap_1.1.0 yaml_2.2.0 colorspace_1.4-1 rvest_0.3.5
[61] knitr_1.39 haven_2.3.1