Last updated: 2023-01-13

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Knit directory: ChromatinSplicingQTLs/analysis/

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Intro

That stacked bar graph of the number of eQTLs that coloc with an hQTL vs sQTL vs neither vs both…

Analysis

library(tidyverse)
library(data.table)

PhenotypeAliases <- read_tsv("../data/Phenotypes_recode_for_Plotting.txt")

PC.ShortAliases <- PhenotypeAliases %>%
  dplyr::select(PC, ShorterAlias) %>% deframe()

coloc.results <- read_tsv("../code/hyprcoloc/Results/ForColoc/MolColocStandard/results.txt.gz")

coloc.results.tidycolocalized <- read_tsv("../code/hyprcoloc/Results/ForColoc/MolColocStandard/tidy_results_OnlyColocalized.txt.gz") %>%
  separate(phenotype_full, into=c("PC", "P"), sep=";")

# Get all the phenotype classes
coloc.results.tidycolocalized$PC %>% unique()
 [1] "polyA.Splicing"                   "MetabolicLabelled.30min"         
 [3] "MetabolicLabelled.60min"          "H3K27AC"                         
 [5] "H3K4ME3"                          "H3K4ME1"                         
 [7] "chRNA.IER"                        "ProCap"                          
 [9] "polyA.Splicing.Subset_YRI"        "CTCF"                            
[11] "chRNA.Expression_ncRNA"           "chRNA.Splicing.Order"            
[13] "Expression.Splicing"              "Expression.Splicing.Subset_YRI"  
[15] "chRNA.Splicing"                   "MetabolicLabelled.30min.Splicing"
[17] "MetabolicLabelled.60min.Splicing" "chRNA.Expression.Splicing"       
[19] "H3K36ME3"                         "APA_Nuclear"                     
[21] "APA_Total"                        "polyA.IER"                       
[23] "MetabolicLabelled.30min.IER"      "MetabolicLabelled.60min.IER"     
[25] "polyA.IER.Subset_YRI"             "chRNA.Slopes"                    
eQTLs <- read_delim("../code/QTLs/QTLTools/Expression.Splicing.Subset_YRI/PermutationPassForColoc.txt.gz", delim=' ') %>%
  separate(phe_id, into=c("phe_id", "Locus"), sep=":") %>%
  filter(adj_beta_pval < 0.01)

bind_rows(
  coloc.results.tidycolocalized %>%
    group_by(Locus, snp) %>%
    filter(any(PC=="Expression.Splicing.Subset_YRI")) %>%
    summarise(
      ContainsChromatinEqtl = any(PC %in% c("H3K27AC", "H3K4ME1", "H3K4ME3")),
      ContainsSqtl = any(PC %in% c("polyA.Splicing_Subset.YRI", "chRNA.Splicing"))
      ) %>%
    right_join(eQTLs) %>%
    replace_na(list(ContainsChromatinEqtl=F, ContainsSqtl=F)) %>%
    mutate(SplicingDataset = "YRI Only"),
  coloc.results.tidycolocalized %>%
    group_by(Locus, snp) %>%
    filter(any(PC=="Expression.Splicing.Subset_YRI")) %>%
    summarise(
      ContainsChromatinEqtl = any(PC %in% c("H3K27AC", "H3K4ME1", "H3K4ME3")),
      ContainsSqtl = any(PC %in% c("polyA.Splicing", "chRNA.Splicing"))
      ) %>%
    right_join(eQTLs) %>%
    replace_na(list(ContainsChromatinEqtl=F, ContainsSqtl=F)) %>%
    mutate(SplicingDataset = "All geuvadis")
) %>%
  mutate(fill=paste(ContainsChromatinEqtl, ContainsSqtl)) %>%
  mutate(fill=recode(fill, !!!c("FALSE FALSE"="Neither", "FALSE TRUE"="sQTL only", "TRUE FALSE"="chromatinQTL only", "TRUE TRUE"="sQTL + chromatinQTL"))) %>%
  mutate(SplicingDataset = factor(SplicingDataset, levels=c("YRI Only","All geuvadis"))) %>%
  mutate(fill = factor(fill, levels=rev(c("sQTL + chromatinQTL","chromatinQTL only", "sQTL only", "Neither")))) %>%
  ggplot(aes(x=SplicingDataset, fill=fill)) +
  geom_bar() +
  scale_x_discrete(labels=c("YRI only\nn=85", "All geauvadis\nn=450")) +
  scale_fill_brewer(type="qual", palette="Set2") +
  labs(title="More chromatin localization with eQTLs than splicing", y="Number of colocalizing eQTLs", x="sQTL source dataset", fill=NULL) +
  theme_classic()

ggsave("../../../carlos_and_ben_shared/rough_figs/OriginalSubplots/NumberOfColocalizing_eQTLs_sQTLs_hQTLs.pdf", height=4, width=4)

same plot but now consider all eQTLs from all geuvadis. but still the two axis groups as sQTLs as called from either YRI only or all geuvadis

eQTLs <- read_delim("../code/QTLs/QTLTools/Expression.Splicing/PermutationPassForColoc.txt.gz", delim=' ') %>%
  separate(phe_id, into=c("phe_id", "Locus"), sep=":") %>%
  filter(adj_beta_pval < 0.01)

bind_rows(
  coloc.results.tidycolocalized %>%
    group_by(Locus, snp) %>%
    filter(any(PC=="Expression.Splicing")) %>%
    summarise(
      ContainsChromatinEqtl = any(PC %in% c("H3K27AC", "H3K4ME1", "H3K4ME3")),
      ContainsSqtl = any(PC %in% c("polyA.Splicing_Subset.YRI", "chRNA.Splicing"))
      ) %>%
    right_join(eQTLs) %>%
    replace_na(list(ContainsChromatinEqtl=F, ContainsSqtl=F)) %>%
    mutate(SplicingDataset = "YRI Only"),
  coloc.results.tidycolocalized %>%
    group_by(Locus, snp) %>%
    filter(any(PC=="Expression.Splicing")) %>%
    summarise(
      ContainsChromatinEqtl = any(PC %in% c("H3K27AC", "H3K4ME1", "H3K4ME3")),
      ContainsSqtl = any(PC %in% c("polyA.Splicing", "chRNA.Splicing"))
      ) %>%
    right_join(eQTLs) %>%
    replace_na(list(ContainsChromatinEqtl=F, ContainsSqtl=F)) %>%
    mutate(SplicingDataset = "All geuvadis")
) %>%
  mutate(fill=paste(ContainsChromatinEqtl, ContainsSqtl)) %>%
  mutate(fill=recode(fill, !!!c("FALSE FALSE"="Neither", "FALSE TRUE"="sQTL only", "TRUE FALSE"="chromatinQTL only", "TRUE TRUE"="sQTL + chromatinQTL"))) %>%
  mutate(SplicingDataset = factor(SplicingDataset, levels=c("YRI Only","All geuvadis"))) %>%
  mutate(fill = factor(fill, levels=rev(c("sQTL + chromatinQTL","chromatinQTL only", "sQTL only", "Neither")))) %>%
  ggplot(aes(x=SplicingDataset, fill=fill)) +
  geom_bar() +
  scale_x_discrete(labels=c("YRI only\nn=85", "All geauvadis\nn=450")) +
  scale_fill_brewer(type="qual", palette="Set2") +
  labs(title="More chromatin localization with eQTLs than splicing", y="Number of colocalizing eQTLs", x="sQTL source dataset", fill=NULL) +
  theme_classic()


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] data.table_1.14.2 forcats_0.4.0     stringr_1.4.0     dplyr_1.0.9      
 [5] purrr_0.3.4       readr_1.3.1       tidyr_1.2.0       tibble_3.1.7     
 [9] ggplot2_3.3.6     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    rmarkdown_1.13     labeling_0.3       munsell_0.5.0     
[21] broom_1.0.0        compiler_3.6.1     httpuv_1.5.1       modelr_0.1.8      
[25] xfun_0.31          pkgconfig_2.0.2    htmltools_0.5.3    tidyselect_1.1.2  
[29] workflowr_1.6.2    fansi_0.4.0        crayon_1.3.4       dbplyr_1.4.2      
[33] withr_2.5.0        later_0.8.0        grid_3.6.1         jsonlite_1.6      
[37] gtable_0.3.0       lifecycle_1.0.1    DBI_1.1.0          git2r_0.26.1      
[41] magrittr_1.5       scales_1.1.0       cli_3.3.0          stringi_1.4.3     
[45] farver_2.1.0       fs_1.5.2           promises_1.0.1     xml2_1.3.2        
[49] ellipsis_0.3.2     generics_0.1.3     vctrs_0.4.1        RColorBrewer_1.1-2
[53] tools_3.6.1        glue_1.6.2         hms_0.5.3          fastmap_1.1.0     
[57] yaml_2.2.0         colorspace_1.4-1   rvest_0.3.5        knitr_1.39        
[61] haven_2.3.1