Last updated: 2022-06-08

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Rmd 9c30891 Benjmain Fair 2022-06-08 update hhyprcoloc output

Introduction

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.

Analysis

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