Last updated: 2022-07-21

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Rmd 79d5612 Yuxin Zou 2022-07-21 wflow_publish("analysis/ukb_bloodcells_20220619_ukb_canonicalvsrandom.Rmd") 
library(mvsusieR)
Loading required package: mashr
Warning: package 'mashr' was built under R version 4.1.2
Loading required package: ashr
Loading required package: susieR
library(reshape2)
library(ggplot2)
plot_sharing = function(X, col = 'black', to_cor=FALSE, title="", remove_names=F) {
  clrs <- colorRampPalette(rev(c("#D73027","#FC8D59","#FEE090","#FFFFBF",
                                 "#E0F3F8","#91BFDB","#4575B4")))(128)
  if (to_cor) lat <- cov2cor(X)
  else lat = X/max(diag(X))
  lat[lower.tri(lat)] <- NA
  n <- nrow(lat)
  if (remove_names) {
    colnames(lat) = paste('t',1:n, sep = '')
    rownames(lat) = paste('t',1:n, sep = '')
  }
  melted_cormat <- melt(lat[n:1,], na.rm = TRUE)
  melted_cormat$Var2 = as.factor(melted_cormat$Var2)
  melted_cormat$Var1 = as.factor(melted_cormat$Var1)
  p = ggplot(data = melted_cormat, aes(Var2, Var1, fill = value))+
    geom_tile(color = "white")+ggtitle(title) + 
    scale_fill_gradientn(colors = clrs, limit = c(-1,1), space = "Lab") +
    theme_minimal()+ 
    coord_fixed() +
    theme(
      axis.title.x = element_blank(),
      axis.title.y = element_blank(),
      axis.text.x = element_text(color=col, size=8,angle=45,hjust=1),
      axis.text.y = element_text(color=rev(col), size=8),
      title =element_text(size=10),
      # panel.grid.major = element_blank(),
      panel.border = element_blank(),
      panel.background = element_blank(),
      axis.ticks = element_blank(),
      legend.justification = c(1, 0),
      legend.position = c(0.6, 0),
      legend.direction = "horizontal")+
    guides(fill = guide_colorbar(title="", barwidth = 7, barheight = 1,
                                 title.position = "top", title.hjust = 0.5))
  if(remove_names){
    p = p + scale_x_discrete(labels= 1:n) + scale_y_discrete(labels= n:1)
  }
  
  return(p)
}

In simulation with UKB blood cell traits priors, the random effect prior performs slightly better than canonical prior.

Load simulated data

dat = readRDS('data/analysis_20220619/data_ukb_609_ukb_bloodcells_mixture_2.rds')
meta = dat$meta

There are 3 causal variants.

Load the model using canonical prior

m_can = readRDS('data/analysis_20220619/data_ukb_9_ukb_bloodcells_mixture_1_mnm_rss_naive_corZ_1.rds')
susie_plot(m_can$result, y='PIP', b = meta$true_coef)

Load the model using random effects prior

m_ran = readRDS('data/analysis_20220619/data_ukb_9_ukb_bloodcells_mixture_1_mnm_rss_identity_corZ_1.rds')
susie_plot(m_ran$result, y='PIP', b = meta$true_coef)

The variant 1375 has PIP 0.78 using canonical prior, PIP 0.99 using random effects prior. The true effect of variant 1375 is

rename = list('WBC_count' = 'WBC#',
              'RBC_count' = 'RBC#',
              'Haemoglobin' = 'HGB',
              'MCV' = 'MCV',
              'RDW' = 'RDW',
              "Platelet_count" = 'PLT#',
              "Plateletcrit" = 'PCT',
              "PDW" = 'PDW',
              "Lymphocyte_perc" = 'LYMPH%',
              "Monocyte_perc" = 'MONO%',
              "Neutrophill_perc" = 'NEUT%',
              "Eosinophill_perc" = 'EO%',
              "Basophill_perc" = 'BASO%',
              "Reticulocyte_perc" = 'RET%',
              "MSCV" = 'MSCV', 
              "HLR_perc" = 'HLR%')
trait_names = sapply(colnames(meta$residual_variance), function(x) rename[[x]])
bloodcells_col = cbind(trait_names, 
                       c('Compound white cell', 'Mature red cell', 'Mature red cell', 
                         'Mature red cell', 'Mature red cell', 'Platelet', 'Platelet',
                         'Platelet', 'Compound white cell', 'Compound white cell', 
                         'Compound white cell', 'Compound white cell', 'Compound white cell',
                         'Immature red cell', 'Mature red cell','Immature red cell'),
                       c('#33cccc', 'red', 'red', 'red', 'red',
                         '#cc66ff', '#cc66ff', '#cc66ff',
                         '#33cccc', '#33cccc', '#33cccc', '#33cccc', '#33cccc',
                         'pink', 'red', 'pink'))
trait_new_order = c("RBC#", "HGB", "MCV", "RDW", "MSCV", "RET%", "HLR%", "PLT#", "PCT", "PDW", 
                    "WBC#", "LYMPH%", "MONO%", "NEUT%", "EO%", "BASO%")
traits_index = match(trait_new_order, trait_names)

colnames(meta$true_coef) = colnames(meta$residual_variance)
barplot(meta$true_coef[1375,traits_index], las = 2, cex.names=0.6)

It is simulated from this covaraince structure

plot_sharing(meta$trueU$`1375`[traits_index, traits_index], col=bloodcells_col[traits_index,3]) 
Warning: Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.

The posterior weights for each component in canonical prior is

w = m_can$result$mixture_weights[2, 1375,]
names(w) = c('null', names(meta$prior$naive$xUlist))
round(w, 4)
        null  singleton_1  singleton_2  singleton_3  singleton_4  singleton_5 
      0.0000       0.0000       0.0000       0.0000       0.0000       0.0000 
 singleton_6  singleton_7  singleton_8  singleton_9 singleton_10 singleton_11 
      0.0015       0.0000       0.0000       0.0000       0.0001       0.0001 
singleton_12 singleton_13 singleton_14 singleton_15 singleton_16     shared_1 
      0.0236       0.0000       0.0000       0.0000       0.0000       0.5148 
    shared_2     shared_3     shared_4     shared_5 
      0.2985       0.1380       0.0233       0.0000 

sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggplot2_3.3.6       reshape2_1.4.4      mvsusieR_0.0.3.0518
[4] susieR_0.12.07      mashr_0.2.57        ashr_2.2-54        
[7] workflowr_1.7.0    

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8.3       invgamma_1.1       mvtnorm_1.1-3      lattice_0.20-44   
 [5] prettyunits_1.1.1  getPass_0.2-2      ps_1.7.0           assertthat_0.2.1  
 [9] rprojroot_2.0.3    digest_0.6.29      utf8_1.2.2         truncnorm_1.0-8   
[13] R6_2.5.1           plyr_1.8.7         evaluate_0.15      highr_0.9         
[17] httr_1.4.3         pillar_1.7.0       progress_1.2.2     rlang_1.0.2       
[21] rstudioapi_0.13    irlba_2.3.5        whisker_0.4        callr_3.7.0       
[25] jquerylib_0.1.4    Matrix_1.3-3       rmarkdown_2.14     labeling_0.4.2    
[29] stringr_1.4.0      munsell_0.5.0      mixsqp_0.3-43      compiler_4.1.0    
[33] httpuv_1.6.5       xfun_0.30          pkgconfig_2.0.3    SQUAREM_2021.1    
[37] htmltools_0.5.2    tidyselect_1.1.2   tibble_3.1.7       matrixStats_0.62.0
[41] reshape_0.8.9      fansi_1.0.3        withr_2.5.0        crayon_1.5.1      
[45] dplyr_1.0.9        later_1.3.0        grid_4.1.0         DBI_1.1.2         
[49] jsonlite_1.8.0     gtable_0.3.0       lifecycle_1.0.1    git2r_0.30.1      
[53] magrittr_2.0.3     scales_1.2.0       cli_3.3.0          stringi_1.7.6     
[57] farver_2.1.0       fs_1.5.2           promises_1.2.0.1   bslib_0.3.1       
[61] ellipsis_0.3.2     vctrs_0.4.1        generics_0.1.2     cowplot_1.1.1     
[65] rmeta_3.0          tools_4.1.0        glue_1.6.2         softImpute_1.4-1  
[69] purrr_0.3.4        hms_1.1.1          processx_3.5.3     abind_1.4-5       
[73] fastmap_1.1.0      yaml_2.3.5         colorspace_2.0-3   knitr_1.39        
[77] sass_0.4.1