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library(mashr)
Loading required package: ashr
library(knitr)
library(kableExtra)
source('../code/generateDataV.R')
source('../code/summary.R')

We illustrate the problem about estimating the correlation matrix in mashr.

In my simple simulation, the current approach underestimates the null correlation. We want to find better positive definite estimator. We could try to estimate the pairwise correlation, ie. mle of \(\sum_{l,k} \pi_{lk} N_{2}(0, V + w_{l}U_{k})\) for any pair of conditions.

Problem

Simple simulation in \(R^2\) to illustrate the problem: \[ \hat{\beta}|\beta \sim N_{2}(\hat{\beta}; \beta, \left(\begin{matrix} 1 & 0.5 \\ 0.5 & 1 \end{matrix}\right)) \]

\[ \beta \sim \frac{1}{4}\delta_{0} + \frac{1}{4}N_{2}(0, \left(\begin{matrix} 1 & 0 \\ 0 & 0 \end{matrix}\right)) + \frac{1}{4}N_{2}(0, \left(\begin{matrix} 0 & 0 \\ 0 & 1 \end{matrix}\right)) + \frac{1}{4}N_{2}(0, \left(\begin{matrix} 1 & 1 \\ 1 & 1 \end{matrix}\right)) \]

\(\Rightarrow\) \[ \hat{\beta} \sim \frac{1}{4}N_{2}(0, \left( \begin{matrix} 1 & 0.5 \\ 0.5 & 1 \end{matrix} \right)) + \frac{1}{4}N_{2}(0, \left( \begin{matrix} 2 & 0.5 \\ 0.5 & 1 \end{matrix} \right)) + \frac{1}{4}N_{2}(0, \left( \begin{matrix} 1 & 0.5 \\ 0.5 & 2 \end{matrix} \right)) + \frac{1}{4}N_{2}(0, \left( \begin{matrix} 2 & 1.5 \\ 1.5 & 2 \end{matrix} \right)) \]

n = 4000

set.seed(1)
n = 4000; p = 2
Sigma = matrix(c(1,0.5,0.5,1),p,p)
U0 = matrix(0,2,2)
U1 = U0; U1[1,1] = 1
U2 = U0; U2[2,2] = 1
U3 = matrix(1,2,2)
Utrue = list(U0=U0, U1=U1, U2=U2, U3=U3)
data = generate_data(n, p, Sigma, Utrue)

Let’s check the result of mash under different correlation matrix:

  1. Identity \[ V.I = I_{2} \]
m.data = mash_set_data(data$Bhat, data$Shat)
U.c = cov_canonical(m.data)
m.I = mash(m.data, U.c, verbose= FALSE)
  1. The current approach: truncated empirical correlation, \(V.trun\)

The correlation of samples with maximum |z| scores less than 2.

Vhat = estimate_null_correlation(m.data, apply_lower_bound = FALSE)
Vhat
          [,1]      [,2]
[1,] 1.0000000 0.3439205
[2,] 0.3439205 1.0000000

It underestimates the correlation.

# Use underestimate cor
m.data.V = mash_set_data(data$Bhat, data$Shat, V=Vhat)
m.V = mash(m.data.V, U.c, verbose = FALSE)
  1. Overestimate correlation \[ V.o = \left( \begin{matrix} 1 & 0.65 \\ 0.65 & 1\end{matrix} \right) \]
# If we overestimate cor
V.o = matrix(c(1,0.65,0.65,1),2,2)
m.data.Vo = mash_set_data(data$Bhat, data$Shat, V=V.o)
m.Vo = mash(m.data.Vo, U.c, verbose=FALSE)
  1. mash.1by1

We run ash for each condition, and estimate correlation matrix based on the non-significant genes. The estimated cor is closer to the truth.

m.1by1 = mash_1by1(m.data)
strong = get_significant_results(m.1by1)
V.mash = cor(data$Bhat[-strong,])
V.mash
          [,1]      [,2]
[1,] 1.0000000 0.4597745
[2,] 0.4597745 1.0000000
m.data.1by1 = mash_set_data(data$Bhat, data$Shat, V=V.mash)
m.V1by1 = mash(m.data.1by1, U.c, verbose = FALSE)
  1. True correlation
# With correct cor
m.data.correct = mash_set_data(data$Bhat, data$Shat, V=Sigma)
m.correct = mash(m.data.correct, U.c, verbose = FALSE)

The results are summarized in table:

null.ind = which(apply(data$B,1,sum) == 0)
V.trun = c(get_loglik(m.V), length(get_significant_results(m.V)), sum(get_significant_results(m.V) %in% null.ind))
V.I = c(get_loglik(m.I), length(get_significant_results(m.I)), sum(get_significant_results(m.I) %in% null.ind))
V.over = c(get_loglik(m.Vo), length(get_significant_results(m.Vo)), sum(get_significant_results(m.Vo) %in% null.ind))
V.1by1 = c(get_loglik(m.V1by1), length(get_significant_results(m.V1by1)), sum(get_significant_results(m.V1by1) %in% null.ind))
V.correct = c(get_loglik(m.correct), length(get_significant_results(m.correct)), sum(get_significant_results(m.correct) %in% null.ind))
temp = cbind(V.I, V.trun, V.1by1, V.correct, V.over)
colnames(temp) = c('Identity','truncate', 'm.1by1', 'true', 'overestimate')
row.names(temp) = c('log likelihood', '# significance', '# False positive')
temp %>% kable() %>% kable_styling()
Identity truncate m.1by1 true overestimate
log likelihood -12390.14 -12307.65 -12304.13 -12302.62 -12301.81
# significance 166.00 30.00 25.00 25.00 70.00
# False positive 14.00 1.00 0.00 0.00 4.00

The ROC curve:

m.I.seq = ROC.table(data$B, m.I)
m.V.seq = ROC.table(data$B, m.V)
m.Vo.seq = ROC.table(data$B, m.Vo)
m.V1by1.seq = ROC.table(data$B, m.V1by1)
m.correct.seq = ROC.table(data$B, m.correct)

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Comparing accuracy

rrmse = rbind(RRMSE(data$B, data$Bhat, list(m.I = m.I, m.V = m.V, m.1by1 = m.V1by1, m.true = m.correct, m.over = m.Vo)))
colnames(rrmse) = c('Identity','V.trun','V.1by1','V.true','V.over')
row.names(rrmse) = 'RRMSE'
rrmse %>% kable() %>% kable_styling()
Identity V.trun V.1by1 V.true V.over
RRMSE 0.6522463 0.5925754 0.5811472 0.5817699 0.6052702 
barplot(rrmse, ylim=c(0,(1+max(rrmse))/2), las=2, cex.names = 0.7, main='RRMSE')

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3bfa4f5 zouyuxin 2018-08-13

Solution: MLE

K=1

Suppose a simple extreme case \[ \left(\begin{matrix} \hat{x} \\ \hat{y} \end{matrix} \right)| \left(\begin{matrix} x \\ y \end{matrix} \right) \sim N_{2}(\left(\begin{matrix} \hat{x} \\ \hat{y} \end{matrix} \right); \left(\begin{matrix} x \\ y \end{matrix} \right), \left( \begin{matrix} 1 & \rho \\ \rho & 1 \end{matrix}\right)) \] \[ \left(\begin{matrix} x \\ y \end{matrix} \right) \sim \delta_{0} \] \(\Rightarrow\) \[ \left(\begin{matrix} \hat{x} \\ \hat{y} \end{matrix} \right) \sim N_{2}(\left(\begin{matrix} \hat{x} \\ \hat{y} \end{matrix} \right); \left(\begin{matrix} 0 \\ 0 \end{matrix} \right), \left( \begin{matrix} 1 & \rho \\ \rho & 1 \end{matrix}\right)) \]

\[ f(\hat{x},\hat{y}) = \prod_{i=1}^{n} \frac{1}{2\pi\sqrt{1-\rho^2}} \exp \{-\frac{1}{2(1-\rho^2)}\left[ \hat{x}_{i}^2 + \hat{y}_{i}^2 - 2\rho \hat{x}_{i}\hat{y}_{i}\right] \} \] The MLE of \(\rho\): \[ \begin{align*} l(\rho) &= -\frac{n}{2}\log(1-\rho^2) - \frac{1}{2(1-\rho^2)}\left( \sum_{i=1}^{n} x_{i}^2 + y_{i}^2 - 2\rho x_{i}y_{i} \right) \\ l(\rho)' &= \frac{n\rho}{1-\rho^2} - \frac{\rho}{(1-\rho^2)^2} \sum_{i=1}^{n} (x_{i}^2 + y_{i}^2) + \frac{\rho^2 + 1}{(1-\rho^2)^2} \sum_{i=1}^{n} x_{i}y_{i} = 0 \\ &= \rho^{3} - \rho^{2}\frac{1}{n}\sum_{i=1}^{n} x_{i}y_{i} - \left( 1- \frac{1}{n} \sum_{i=1}^{n} x_{i}^{2} + y_{i}^{2} \right) \rho - \frac{1}{n}\sum_{i=1}^{n} x_{i}y_{i} = 0 \\ l(\rho)'' &= \frac{n(\rho^2+1)}{(1-\rho^2)^2} - \frac{1}{2}\left( \frac{8\rho^2}{(1-\rho^2)^{3}} + \frac{2}{(1-\rho^2)^2} \right)\sum_{i=1}^{n}(x_{i}^2 + y_{i}^2) + \{ \left( \frac{8\rho^2}{(1-\rho^2)^{3}} + \frac{2}{(1-\rho^2)^2} \right)\rho + \frac{4\rho}{(1-\rho^2)^2} \}\sum_{i=1}^{n}x_{i}y_{i} \end{align*} \]

The log likelihood is not a concave function in general. The score function has either 1 or 3 real solutions.

Kendall and Stuart (1979) noted that at least one of the roots is real and lies in the interval [−1, 1]. However, it is possible that all three roots are real and in the admissible interval, in which case the likelihood can be evaluated at each root to determine the true maximum likelihood estimate.

I simulate the data with \(\rho=0.6\) and plot the loglikelihood function:

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Version Author Date
568fbe6 zouyuxin 2018-08-13

\(l(\rho)'\) has one real solution

polyroot(c(- sum(data$Bhat[,1]*data$Bhat[,2]),  - (n - sum(data$Bhat[,1]^2 + data$Bhat[,2]^2)), - sum(data$Bhat[,1]*data$Bhat[,2]), n))
[1] 0.6193031+0.000000i 0.0058209+1.009339i 0.0058209-1.009339i

In general

The general derivation is in estimate correlation mle

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6

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] kableExtra_0.9.0 knitr_1.20       mashr_0.2-15     ashr_2.2-14     

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.19      highr_0.7         pillar_1.3.0     
 [4] compiler_3.5.1    git2r_0.23.0      plyr_1.8.4       
 [7] workflowr_1.1.1   R.methodsS3_1.7.1 R.utils_2.6.0    
[10] iterators_1.0.10  tools_3.5.1       digest_0.6.15    
[13] viridisLite_0.3.0 tibble_1.4.2      evaluate_0.11    
[16] lattice_0.20-35   pkgconfig_2.0.2   rlang_0.2.2      
[19] Matrix_1.2-14     foreach_1.4.4     rstudioapi_0.7   
[22] yaml_2.2.0        parallel_3.5.1    mvtnorm_1.0-8    
[25] xml2_1.2.0        httr_1.3.1        stringr_1.3.1    
[28] REBayes_1.3       hms_0.4.2         rprojroot_1.3-2  
[31] grid_3.5.1        R6_2.2.2          rmarkdown_1.10   
[34] rmeta_3.0         readr_1.1.1       magrittr_1.5     
[37] whisker_0.3-2     scales_1.0.0      backports_1.1.2  
[40] codetools_0.2-15  htmltools_0.3.6   MASS_7.3-50      
[43] rvest_0.3.2       abind_1.4-5       assertthat_0.2.0 
[46] colorspace_1.3-2  stringi_1.2.4     Rmosek_8.0.69    
[49] munsell_0.5.0     doParallel_1.0.14 pscl_1.5.2       
[52] truncnorm_1.0-8   SQUAREM_2017.10-1 crayon_1.3.4     
[55] R.oo_1.22.0

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