Last updated: 2022-06-08
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Knit directory: ChromatinSplicingQTLs/analysis/
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 9c30891 | Benjmain Fair | 2022-06-08 | update hhyprcoloc output |
In a previous notebook I explored the genewise hyprcoloc output, and noted that 30min and 60min 4sU colocalize (with all default parameters/thresholds) 80% of the time that they are tested. I expect this to be closer to 100%, and we should get similarly high colocalization with eQTL from polyA RNA-seq. Perhaps just by filtering for colocalizations above some threshold we can get more believable results. I could/should technically re-run hyprcoloc with different parameters, but before I do that, to understand the results better, let’s see how these colocalization rates change after filter for different posterior probabilities for colocalization.
library(tidyverse)
── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.2.1 ✔ purrr 0.3.3
✔ tibble 3.0.4 ✔ dplyr 1.0.2
✔ tidyr 1.1.2 ✔ stringr 1.4.0
✔ readr 1.4.0 ✔ forcats 0.5.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
library(viridis)
Loading required package: viridisLite
library(gplots)
Attaching package: 'gplots'
The following object is masked from 'package:stats':
lowess
library(data.table)
Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':
between, first, last
The following object is masked from 'package:purrr':
transpose
library(qvalue)
# library(purrr)
sample_n_of <- function(data, size, ...) {
dots <- quos(...)
group_ids <- data %>%
group_by(!!! dots) %>%
group_indices()
sampled_groups <- sample(unique(group_ids), size)
data %>%
filter(group_ids %in% sampled_groups)
}
dat <- Sys.glob("../code/hyprcoloc/Results/ForColoc/MolColocTest*_*/results.txt.gz") %>%
setNames(str_replace(., "../code/hyprcoloc/Results/ForColoc/MolColocTest(.*?)_(.+?)/results.txt.gz", "\\1_0.\\2")) %>%
lapply(read_tsv) %>%
bind_rows(.id="Threshold")
── Column specification ────────────────────────────────────────────────────────
cols(
GeneLocus = col_character(),
HyprcolocIteration = col_double(),
PosteriorColocalizationPr = col_double(),
RegionalAssociationPr = col_double(),
TopCandidateSNP = col_character(),
ProportionPosteriorPrExplainedByTopSNP = col_double(),
Trait = col_character()
)
── Column specification ────────────────────────────────────────────────────────
cols(
GeneLocus = col_character(),
HyprcolocIteration = col_double(),
PosteriorColocalizationPr = col_double(),
RegionalAssociationPr = col_double(),
TopCandidateSNP = col_character(),
ProportionPosteriorPrExplainedByTopSNP = col_double(),
Trait = col_character()
)
── Column specification ────────────────────────────────────────────────────────
cols(
GeneLocus = col_character(),
HyprcolocIteration = col_double(),
PosteriorColocalizationPr = col_double(),
RegionalAssociationPr = col_double(),
TopCandidateSNP = col_character(),
ProportionPosteriorPrExplainedByTopSNP = col_double(),
Trait = col_character()
)
── Column specification ────────────────────────────────────────────────────────
cols(
GeneLocus = col_character(),
HyprcolocIteration = col_double(),
PosteriorColocalizationPr = col_double(),
RegionalAssociationPr = col_double(),
TopCandidateSNP = col_character(),
ProportionPosteriorPrExplainedByTopSNP = col_double(),
Trait = col_character()
)
── Column specification ────────────────────────────────────────────────────────
cols(
GeneLocus = col_character(),
HyprcolocIteration = col_double(),
PosteriorColocalizationPr = col_double(),
RegionalAssociationPr = col_double(),
TopCandidateSNP = col_character(),
ProportionPosteriorPrExplainedByTopSNP = col_double(),
Trait = col_character()
)
── Column specification ────────────────────────────────────────────────────────
cols(
GeneLocus = col_character(),
HyprcolocIteration = col_double(),
PosteriorColocalizationPr = col_double(),
RegionalAssociationPr = col_double(),
TopCandidateSNP = col_character(),
ProportionPosteriorPrExplainedByTopSNP = col_double(),
Trait = col_character()
)
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)
── Column specification ────────────────────────────────────────────────────────
cols(
chrom = col_character(),
TSS_start = col_double(),
gene = col_character(),
strand = col_character(),
peak = col_character(),
distance = col_double()
)
── Column specification ────────────────────────────────────────────────────────
cols(
chrom = col_character(),
TSS_start = col_double(),
gene = col_character(),
strand = col_character(),
peak = col_character(),
distance = col_double()
)
── Column specification ────────────────────────────────────────────────────────
cols(
chrom = col_character(),
TSS_start = col_double(),
gene = col_character(),
strand = col_character(),
peak = col_character(),
distance = col_double()
)
dat %>%
distinct(Threshold, GeneLocus, TopCandidateSNP, .keep_all = T) %>%
pull(PosteriorColocalizationPr) %>% hist()
dat %>%
separate(Trait, into=c("PC", "P"), sep=";") %>%
pull(PC) %>% unique() %>% sort()
[1] "chRNA.Expression_cheRNA" "chRNA.Expression_eRNA"
[3] "chRNA.Expression_lncRNA" "chRNA.Expression_snoRNA"
[5] "chRNA.Expression.Splicing" "chRNA.IER"
[7] "chRNA.IR" "chRNA.IRjunctions"
[9] "chRNA.Slopes" "chRNA.Splicing"
[11] "CTCF" "Expression.Splicing"
[13] "Expression.Splicing.Subset_YRI" "H3K27AC"
[15] "H3K36ME3" "H3K4ME1"
[17] "H3K4ME3" "MetabolicLabelled.30min"
[19] "MetabolicLabelled.30min.IER" "MetabolicLabelled.30min.IR"
[21] "MetabolicLabelled.30min.IRjunctions" "MetabolicLabelled.30min.Splicing"
[23] "MetabolicLabelled.60min" "MetabolicLabelled.60min.IER"
[25] "MetabolicLabelled.60min.IR" "MetabolicLabelled.60min.IRjunctions"
[27] "MetabolicLabelled.60min.Splicing" "polyA.IER"
[29] "polyA.IR" "polyA.IR.Subset_YRI"
[31] "polyA.IRjunctions" "polyA.Splicing"
[33] "polyA.Splicing.Subset_YRI" "ProCap"
dat %>%
separate(Trait, into=c("PC", "P"), sep=";") %>%
count(Threshold, PC) %>%
ggplot(aes(x=PC, y=n)) +
geom_col() +
facet_wrap(~Threshold) +
theme_bw() +
labs(title = "Number of Loci:molQTL pairs attempted to colocalize in total") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
Carlos wants to know: - how many sQTL and irQTLs coloc with polyA eQTLs? And how many coloc with polyA eQTL but not polyA sQTL?
dat.forcarlos <- dat %>%
filter(!str_detect(Threshold, "eQTL")) %>%
separate(Trait, into=c("PC", "P"), sep=";", remove = F) %>%
# pull(PC) %>% unique()
filter(PC %in% c("Expression.Splicing.Subset_YRI", "polyA.Splicing.Subset_YRI", "polyA.IER", "chRNA.Expression.Splicing", "chRNA.Splicing", "chRNA.IER"))
dat.forcarlos %>%
count(PC, Threshold) %>%
ggplot(aes(x=PC, y=n)) +
geom_col() +
facet_wrap(~Threshold) +
labs(title="Number features attempted for coloc at different P thresholds", y="NumFeatures", x="PhenotypeClass") +
theme(axis.text.x=element_text(angle=45, hjust=1))
dat.forcarlos %>%
filter(!is.na(TopCandidateSNP)) %>%
group_by(GeneLocus, TopCandidateSNP, Threshold) %>%
mutate(Contains_eQTL = any(PC == "Expression.Splicing.Subset_YRI")) %>%
mutate(Contains_sQTL = any(PC %in% c("polyA.Splicing.Subset_YRI", "polyA.IER", "chRNA.Splicing", "chRNA.IER"))) %>%
mutate(Contains_chRNA_specific_sQTL = any(PC %in% c("chRNA.Splicing", "chRNA.IER"))
& !any(PC %in% c("polyA.Splicing.Subset_YRI", "polyA.IER"))) %>%
ungroup() %>%
distinct(Threshold, GeneLocus, TopCandidateSNP, .keep_all=T) %>%
filter(Contains_eQTL) %>%
group_by(Threshold) %>%
summarise(
Num_eQTL = sum(Contains_eQTL),
Num_sQTL_coloc_eQTL = sum(Contains_sQTL),
Num_chRNA_specific_sQTL_coloc_eQTL = sum(Contains_chRNA_specific_sQTL)
) %>%
gather(key="eQTL_type", value="count", -Threshold) %>%
ggplot(aes(x=eQTL_type, y=count)) +
geom_col() +
geom_text(aes(label=count), color="black", angle=90, hjust=1) +
facet_wrap(~Threshold, scales="free_y") +
labs(title="Num eQTL coloc with sQTL") +
theme_classic() +
theme(axis.text.x=element_text(angle=45, hjust=1))
`summarise()` ungrouping output (override with `.groups` argument)
Below is some code for making files, and then some code for running my script to plot some colocalizations. I will plot 5 loci where metabolic labelled samples did not coloc, 5 where they did, 5 where promoterQTL/eQTL coloc, 5 where non-promoter QTL coloc, 5 where sQTL/eQTL coloc, and 5 where sQTL/eQTL don’t coloc.
dat.ToPlotColocs <- dat %>%
filter(Threshold == "_0.001") %>%
separate(Trait, into=c("PC", "P"), sep=";", remove = F) %>%
left_join(PeaksToTSS %>% select(ChromatinMark, peak, gene), by=c("PC"="ChromatinMark", "P"="peak")) %>%
mutate( PC = case_when(
gene == GeneLocus ~ paste(PC, "AtPromoter" ,sep="_"),
!is.na(gene) ~ paste(PC, "AtDistalPromoter" ,sep="_"),
TRUE ~ PC
) )
# both metabolic coloc
Targets <- dat.ToPlotColocs %>%
filter(!is.na(TopCandidateSNP)) %>%
group_by(GeneLocus, TopCandidateSNP) %>%
filter(any(str_detect(PC, "MetabolicLabelled.30min")) & any(str_detect(PC, "MetabolicLabelled.60min"))) %>%
ungroup() %>%
pull(GeneLocus) %>% unique()
dat.ToPlotColocs %>%
filter(Threshold == "_0.001") %>%
filter(PC %in% c("MetabolicLabelled.30min", "MetabolicLabelled.60min", "Expression.Splicing.Subset_YRI", "H3K27AC", "H3K27AC_AtPromoter", "H3K27AC_AtDistalPromoter")) %>%
filter(GeneLocus %in% Targets) %>%
sample_n_of(10, GeneLocus) %>%
select(-Threshold) %>%
write_tsv("scratch/ColocExamples/GroupMetabolicColoc.tsv")
# metabolic don't coloc
Targets <- dat.ToPlotColocs %>%
filter(PC %in% c("MetabolicLabelled.30min", "MetabolicLabelled.60min")) %>%
group_by(GeneLocus) %>%
filter(any(str_detect(PC, "MetabolicLabelled.30min")) & any(str_detect(PC, "MetabolicLabelled.60min"))) %>%
ungroup() %>%
group_by(GeneLocus, TopCandidateSNP) %>%
filter(
(is.na(TopCandidateSNP) & PC %in% c("MetabolicLabelled.30min", "MetabolicLabelled.60min")) | (sum(str_detect(PC, "MetabolicLabelled")) == 1)
) %>%
ungroup() %>%
pull(GeneLocus) %>% unique()
Targets <- dat.ToPlotColocs %>%
filter(GeneLocus %in% TestedTargets) %>%
filter(is.na(TopCandidateSNP))
dat.ToPlotColocs %>%
filter(Threshold == "_0.001") %>%
filter(PC %in% c("MetabolicLabelled.30min", "MetabolicLabelled.60min", "Expression.Splicing.Subset_YRI", "H3K27AC", "H3K27AC_AtPromoter", "H3K27AC_AtDistalPromoter")) %>%
filter(GeneLocus %in% Targets) %>%
sample_n_of(10, GeneLocus) %>%
select(-Threshold) %>%
write_tsv("scratch/ColocExamples/GroupMetabolicNotColoc.tsv")
# Promoter and eqtl coloc TODO
Targets <- dat.ToPlotColocs %>%
filter(PC %in% c("MetabolicLabelled.30min", "MetabolicLabelled.60min", "Expression.Splicing.Subset_YRI", "H3K27AC", "H3K27AC_AtPromoter", "H3K27AC_AtDistalPromoter")) %>%
filter(!is.na(TopCandidateSNP)) %>%
group_by(GeneLocus, TopCandidateSNP) %>%
filter(any(PC == "Expression.Splicing.Subset_YRI") & any(PC == "H3K27AC_AtPromoter")) %>%
ungroup() %>%
pull(GeneLocus) %>% unique()
dat.ToPlotColocs %>%
filter(Threshold == "_0.001") %>%
filter(PC %in% c("MetabolicLabelled.30min", "MetabolicLabelled.60min", "Expression.Splicing.Subset_YRI", "H3K27AC", "H3K27AC_AtPromoter", "H3K27AC_AtDistalPromoter")) %>%
filter(GeneLocus %in% Targets) %>%
sample_n_of(10, GeneLocus) %>%
select(-Threshold) %>%
write_tsv("scratch/ColocExamples/GroupPromoterEqtlColoc.tsv")
# Promoter and eqtl don't coloc
Targets <- dat.ToPlotColocs %>%
filter(PC %in% c("MetabolicLabelled.30min", "MetabolicLabelled.60min", "Expression.Splicing.Subset_YRI", "H3K27AC", "H3K27AC_AtPromoter", "H3K27AC_AtDistalPromoter")) %>%
filter(!is.na(TopCandidateSNP)) %>%
group_by(GeneLocus, TopCandidateSNP) %>%
filter(any(PC == "Expression.Splicing.Subset_YRI") & any(PC == "H3K27AC_AtPromoter")) %>%
ungroup() %>%
pull(GeneLocus) %>% unique()
dat.ToPlotColocs %>%
filter(Threshold == "_0.001") %>%
filter(PC %in% c("MetabolicLabelled.30min", "MetabolicLabelled.60min", "Expression.Splicing.Subset_YRI", "H3K27AC", "H3K27AC_AtPromoter", "H3K27AC_AtDistalPromoter")) %>%
filter(GeneLocus %in% Targets) %>%
sample_n_of(10, GeneLocus) %>%
select(-Threshold) %>%
write_tsv("scratch/ColocExamples/GroupPromoterEqtlColoc.tsv")
dat %>%
count(Threshold, GeneLocus) %>%
ggplot(aes(x=n, color=Threshold)) +
stat_ecdf() +
coord_cartesian(xlim=c(0,10)) +
theme_bw()
dat.tidy
dat %>%
filter(Threshold == "_0.01") %>%
select(-Threshold) %>%
# add_count(GeneLocus, TopCandidateSNP) %
) %>%
write_tsv("../code/scratch/TestHyprcolocPlots.tsv")
conda activate r_essentials
echo "hello world"
sessionInfo()
R version 3.4.3 (2017-11-30)
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] qvalue_2.10.0 data.table_1.12.8 gplots_3.0.1 viridis_0.5.1
[5] viridisLite_0.3.0 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2
[9] purrr_0.3.3 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[13] ggplot2_3.2.1 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.3 lubridate_1.7.9.2 gtools_3.5.0 assertthat_0.2.1
[5] rprojroot_1.3-2 digest_0.6.27 plyr_1.8.4 R6_2.4.1
[9] cellranger_1.1.0 backports_1.1.5 evaluate_0.14 httr_1.4.2
[13] pillar_1.4.7 rlang_0.4.9 lazyeval_0.2.2 readxl_1.3.1
[17] rstudioapi_0.10 gdata_2.18.0 whisker_0.3-2 rmarkdown_2.6
[21] labeling_0.3 splines_3.4.3 munsell_0.5.0 broom_0.7.3
[25] compiler_3.4.3 httpuv_1.5.2 modelr_0.1.8 xfun_0.20
[29] pkgconfig_2.0.3 htmltools_0.4.0 tidyselect_1.1.0 gridExtra_2.3
[33] workflowr_1.5.0 fansi_0.4.0 crayon_1.3.4 withr_2.1.2
[37] later_1.0.0 bitops_1.0-6 grid_3.4.3 jsonlite_1.6
[41] gtable_0.3.0 lifecycle_0.2.0 git2r_0.26.1 magrittr_1.5
[45] scales_1.1.0 KernSmooth_2.23-15 cli_2.0.0 stringi_1.4.3
[49] farver_2.0.1 reshape2_1.4.3 fs_1.3.1 promises_1.1.0
[53] xml2_1.2.0 ellipsis_0.3.0 generics_0.1.0 vctrs_0.3.6
[57] tools_3.4.3 glue_1.4.2 hms_0.5.3 yaml_2.2.0
[61] colorspace_2.0-0 caTools_1.17.1 rvest_0.3.6 knitr_1.26
[65] haven_2.3.1