mashr_application1

Format data to mashr format

Run mashr

Get sharing

Estimate mixture proportion

Effect Size Comparison

Posterior Mean VS. Original Effect
Scaled by standard deviation: Posterior Mean VS. Original Effect

Last updated: 2020-01-04

Checks: 6 1

Knit directory: mash-single-cell-rnaseq/

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Remarks:

Used data from “Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response” https://doi.org/10.1038/s41588-018-0046-7. eQTLs from 4 conditions downloaded from https://zenodo.org/record/1158560#.Xgjl5BdKhp8.
For data preprocessing, we first filter with the criteria p-value <0.05. Then, take the top signals (the most significant SNP for each gene) in naive condition, and filter gene-SNP pairs in other conditions based on top signals in naive condition. The resulting “top_snps.RData” contains 15678 significant Gene-SNP pairs in 4 conditions.
The 4 conditions are: naive/ IF/ IF+SL/ SL. (IF, SL are two drug treatments)
Mashr re-estimate the effect size of SNPs, incorporating information across conditions.
Ways to assess the model fit. Log-likelihood and others?

library(ashr)
library(mashr)

Format data to mashr format

load("/Users/nicholeyang/Desktop/Rotation/mash-single-cell-rnaseq/data/top_snps.RData")

dt_beta <- top_snps[,c("beta_naive", "beta_IF", "beta_IFSL", "beta_SL")]
dt_pval <- top_snps[,c("p_nominal_naive", "p_nominal_IF", "p_nominal_IFSL", "p_nominal_SL")]

dt_beta = as.matrix(dt_beta)
dt_pval = as.matrix(dt_pval)

head(dt_beta)

  beta_naive    beta_IF  beta_IFSL    beta_SL
1 -0.3370090 -0.4037950 -0.0941426 -0.2029850
2 -0.0743763 -0.0419751 -0.0325720 -0.0214099
3 -0.0786190 -0.0507808 -0.0591935 -0.0275128
4  0.0826863 -0.0162367 -0.0407589  0.0786256
5 -0.4299870 -0.3545930 -0.2523280 -0.2987360
6 -0.3760470 -0.0694328  0.0700316 -0.0902658

head(dt_pval)

  p_nominal_naive p_nominal_IF p_nominal_IFSL p_nominal_SL
1      0.00061327   0.00140049      0.5007650    0.0545107
2      0.00121529   0.08250130      0.1340060    0.3738330
3      0.00117232   0.04579800      0.0610855    0.5071570
4      0.00256488   0.68539900      0.5733360    0.0466930
5      0.03554660   0.03811650      0.0227163    0.0728410
6      0.01376920   0.37466200      0.2537470    0.5845360

#str(dt_beta)
#str(dt_pval)

Run mashr

dt_mash = mash_set_data(dt_beta, Shat = NULL, pval = dt_pval)
head(dt_mash$Bhat)

  beta_naive    beta_IF  beta_IFSL    beta_SL
1 -0.3370090 -0.4037950 -0.0941426 -0.2029850
2 -0.0743763 -0.0419751 -0.0325720 -0.0214099
3 -0.0786190 -0.0507808 -0.0591935 -0.0275128
4  0.0826863 -0.0162367 -0.0407589  0.0786256
5 -0.4299870 -0.3545930 -0.2523280 -0.2987360
6 -0.3760470 -0.0694328  0.0700316 -0.0902658

head(dt_mash$Shat)

  beta_naive    beta_IF  beta_IFSL    beta_SL
1 0.09837735 0.12640121 0.13982546 0.10556982
2 0.02298923 0.02417428 0.02173655 0.02407456
3 0.02422377 0.02542536 0.03160567 0.04148048
4 0.02741970 0.04008005 0.07237725 0.03952874
5 0.20455348 0.17100359 0.11075895 0.16653569
6 0.15266426 0.07820994 0.06136150 0.16508883

# Step2: set up covariance matrix 
U.c = cov_canonical(dt_mash)  
print(names(U.c))

[1] "identity"      "beta_naive"    "beta_IF"       "beta_IFSL"    
[5] "beta_SL"       "equal_effects" "simple_het_1"  "simple_het_2" 
[9] "simple_het_3"

# Step3: fit model 
m.c = mash(dt_mash, U.c)

 - Computing 15678 x 253 likelihood matrix.
 - Likelihood calculations took 2.49 seconds.
 - Fitting model with 253 mixture components.
 - Model fitting took 14.10 seconds.
 - Computing posterior matrices.
 - Computation allocated took 12.65 seconds.

# Step4: extract posterior summarize 

head(get_lfsr(m.c))   # local false sign rates

    beta_naive     beta_IF   beta_IFSL     beta_SL
1 1.461406e-05 0.001824021 0.004385345 0.002141932
2 1.876247e-04 0.099080350 0.099233110 0.099949419
3 3.520635e-05 0.016831278 0.016881393 0.018060472
4 2.629536e-03 0.495295005 0.494371262 0.468454622
5 4.657397e-04 0.001811972 0.001688937 0.001921296
6 7.303887e-02 0.855488753 0.873244307 0.858295958

head(get_pm(m.c))

   beta_naive      beta_IF    beta_IFSL      beta_SL
1 -0.24998712 -0.250345427 -0.243688943 -0.245792041
2 -0.04582012 -0.038363102 -0.038302041 -0.038231226
3 -0.05954638 -0.058071103 -0.058122522 -0.057942265
4  0.06609349  0.024188691  0.024158574  0.027910831
5 -0.26644629 -0.265655730 -0.264519032 -0.265028508
6 -0.19970374 -0.005753663 -0.002717302 -0.005782514

head(get_psd(m.c))

  beta_naive    beta_IF  beta_IFSL    beta_SL
1 0.05799366 0.06082965 0.06221880 0.05897826
2 0.01624149 0.01686986 0.01685286 0.01691490
3 0.01488694 0.01620381 0.01625526 0.01650750
4 0.02791045 0.02987130 0.03139002 0.03048489
5 0.07604640 0.07563894 0.07428845 0.07540395
6 0.13864721 0.02489209 0.02284952 0.02834736

head(get_significant_results(m.c))

 661 2075 2694 3191 3304 3305 
 661 2068 2687 3184 3297 3298

print(length(get_significant_results(m.c)))

[1] 15450

Get sharing

print(get_pairwise_sharing(m.c, factor=0))

           beta_naive   beta_IF beta_IFSL   beta_SL
beta_naive  1.0000000 0.9922977 0.9803883 0.9858900
beta_IF     0.9922977 1.0000000 0.9977311 0.9984487
beta_IFSL   0.9803883 0.9977311 1.0000000 0.9989194
beta_SL     0.9858900 0.9984487 0.9989194 1.0000000

print(get_loglik(m.c))

[1] 39976.75

Estimate mixture proportion

print(get_estimated_pi(m.c))

         null      identity    beta_naive       beta_IF     beta_IFSL 
 0.0006367433  0.0000000000  0.2126963603  0.0000000000  0.0000000000 
      beta_SL equal_effects  simple_het_1  simple_het_2  simple_het_3 
 0.0000000000  0.7166936937  0.0000000000  0.0000000000  0.0699732027

barplot(get_estimated_pi(m.c),las = 2)

Version	Author	Date
59bbc5e	Nicholeyang0215	2019-11-21

mash_plot_meta(m.c,get_significant_results(m.c)[1])

Version	Author	Date
f74b3e4	Nicholeyang0215	2020-01-04

Effect Size Comparison

Posterior Mean VS. Original Effect

Condtion 1-4 are: naive/ IF/ IF+SL/ SL. (IF, SL are two drug treatments)

par(mfrow = c(2,2))

for (i in c(1:4)){
  
  plot(dt_mash$Bhat[,i], get_pm(m.c)[,i], pch = 20, ylab = "Posterior Mean", xlab = "Original Effect", main = paste('Condition_',i, sep = ""))
  abline(coef = c(0,1), col = "red")
  
}

Version	Author	Date
f74b3e4	Nicholeyang0215	2020-01-04

Scaled by standard deviation: Posterior Mean VS. Original Effect

par(mfrow = c(2,2))
for ( i in c(1:4)){
  
  plot(dt_mash$Bhat[,i]/dt_mash$Shat[,i], get_pm(m.c)[,i]/get_psd(m.c)[,i], pch = 20, ylab = "Posterior Mean", xlab = "Original Effect", main = paste('Condition_',i, sep = ""))
  abline(coef = c(0,1), col = "red")
  
}

Version	Author	Date
f74b3e4	Nicholeyang0215	2020-01-04

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.15

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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] mashr_0.2.21.0641 ashr_2.2-38      

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2        plyr_1.8.4        compiler_3.5.1   
 [4] later_1.0.0       git2r_0.26.1      highr_0.7        
 [7] workflowr_1.5.0   iterators_1.0.12  tools_3.5.1      
[10] digest_0.6.18     evaluate_0.13     lattice_0.20-38  
[13] rlang_0.4.0       Matrix_1.2-15     foreach_1.4.7    
[16] yaml_2.2.0        parallel_3.5.1    mvtnorm_1.0-11   
[19] xfun_0.4          stringr_1.4.0     knitr_1.21       
[22] fs_1.3.1          rprojroot_1.3-2   grid_3.5.1       
[25] glue_1.3.0        R6_2.4.0          rmarkdown_1.11   
[28] mixsqp_0.1-97     rmeta_3.0         magrittr_1.5     
[31] whisker_0.3-2     backports_1.1.3   promises_1.1.0   
[34] codetools_0.2-16  htmltools_0.4.0   MASS_7.3-51.1    
[37] assertthat_0.2.1  abind_1.4-5       httpuv_1.5.2     
[40] stringi_1.3.1     doParallel_1.0.15 pscl_1.5.2       
[43] truncnorm_1.0-8   SQUAREM_2017.10-1