Last updated: 2019-12-19

Checks: 6 1

Knit directory: drift-workflow/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.4.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(20190211) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .RData
    Ignored:    .Rhistory
    Ignored:    analysis/.Rhistory
    Ignored:    analysis/figure/
    Ignored:    analysis/flash_cache/
    Ignored:    data.tar.gz
    Ignored:    data/datasets/
    Ignored:    data/raw/
    Ignored:    docs/figure/.DS_Store
    Ignored:    docs/figure/hoa_global.Rmd/.DS_Store
    Ignored:    output.tar.gz
    Ignored:    output/

Untracked files:
    Untracked:  analysis/cor-vb-approx.Rmd
    Untracked:  code/ebfa.R

Unstaged changes:
    Modified:   analysis/index.Rmd
    Modified:   analysis/simple_2pop_tree_simulation_bimodal.Rmd
    Modified:   code/ebnm_functions.R

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd e3936bc jhmarcus 2019-12-01 pushing 2 pop analysis
html e3936bc jhmarcus 2019-12-01 pushing 2 pop analysis

Here we simulate Gaussian data under a simple 2 population tree i.e. a split and explore the interpretability of flashier and drift fits to the data. The data generated under this tree can be represented as a probabilistic matrix factorization model with 1 shared factor and 2 population specific factors … this is what we’d like to recover.

Import

Import the required packages and load helper scripts for this analysis:

library(ggplot2)
library(dplyr)
library(tidyr)
library(flashier)
library(drift.alpha)

source("../code/ebnm_functions.R")

Simulate

Here I simulate the data generated from a simple 2 population tree. I set the residual std. dev. to 1 and prior variances to 1. There are 50 individuals per population and 10000 simulated SNPs:

##### sim ##### 
set.seed(235)
n_per_pop <- 50
pops <- c(rep("Pop1", n_per_pop), rep("Pop2", n_per_pop))
sigma_e <- 1.0
sigma_b <- c(1.0, 1.0, 1.0)
p = 10000
sim_res <- drift.alpha::two_pop_tree_sim(n_per_pop, p, sigma_e, sigma_b)
K <- 3
Y <- sim_res$Y
n <- nrow(Y)
p <- ncol(Y)

##### viz ##### 
plot_loadings(sim_res$L, pops)

Greedy

Run the greedy algorithm which seems to recover the tree:

##### fit ##### 
flash_greedy_res <- flash.init(Y, var.type=0) %>%
                    flash.add.greedy(Kmax=K,
                                    prior.family=list(c(drift.alpha:::prior.bimodal(), 
                                                        prior.normal()), 
                                                      c(drift.alpha:::prior.bimodal(), 
                                                        prior.normal()),
                                                      c(drift.alpha:::prior.bimodal(), 
                                                        prior.normal()),
                                                      c(drift.alpha:::prior.bimodal(), 
                                                        prior.normal()),
                                                      c(drift.alpha:::prior.bimodal(), 
                                                        prior.normal()),
                                                      c(drift.alpha:::prior.bimodal(), 
                                                        prior.normal())))
Adding factor 1 to flash object...
Adding factor 2 to flash object...
Adding factor 3 to flash object...
Wrapping up...
Done.
##### viz ##### 
pm <- flash_greedy_res$loadings.pm[[1]] 
plot_loadings(pm, pops)

Version Author Date
e3936bc jhmarcus 2019-12-01

Initialize from greedy

Initialize the backfitting algorithm with the greedy solution which recovers a sparser representation of the tree i.e. it zeros out the shared factor:

##### fit ##### 
flash_backfit_res <- flash_greedy_res %>% flash.backfit()
Backfitting 3 factors (tolerance: 1.49e-02)...
  Difference between iterations is within 1.0e+02...
  Difference between iterations is within 1.0e+01...
  Difference between iterations is within 1.0e+00...
  Difference between iterations is within 1.0e-01...
  Difference between iterations is within 1.0e-02...
Wrapping up...
Done.
##### viz ##### 
pm <- flash_backfit_res$loadings.pm[[1]] 
elbo_gr_bf <- flash_backfit_res$elbo
plot_loadings(flash_backfit_res$loadings.pm[[1]], pops)

Version Author Date
e3936bc jhmarcus 2019-12-01

Initialize from truth and fix the priors

Initialize from the truth and fix the prior which as expected recovers the tree representation we want:

##### fit ##### 
flash_fix_res <- flash.init(Y, var.type=0) %>%
                 flash.init.factors(EF=list(u=sim_res$L, d=rep(1, K), v=sim_res$F), 
                                    prior.family=list(c(prior.fixtwopm(pi = c(0.0, 1.0)), 
                                                        prior.fixnormal()), 
                                                      c(prior.fixtwopm(pi = c(.5, .5)), 
                                                        prior.fixnormal()),
                                                      c(prior.fixtwopm(pi = c(.5, .5)), 
                                                        prior.fixnormal()))) %>%
                 flash.backfit()
Backfitting 3 factors (tolerance: 1.49e-02)...
  Difference between iterations is within 1.0e+02...
  Difference between iterations is within 1.0e+01...
Wrapping up...
Done.
##### viz ##### 
pm <- flash_fix_res$loadings.pm[[1]] 
elbo_init_true <- flash_fix_res$elbo
plot_loadings(flash_fix_res$loadings.pm[[1]], pops)

Version Author Date
e3936bc jhmarcus 2019-12-01

Compare the final elbos between the solution where we initialize from the truth and fix the prior to the solution where we initialize from the greedy fit and don’t fix the prior:

print(elbo_init_true)
[1] -1482301
print(elbo_gr_bf)
[1] -1465622
print(elbo_init_true>elbo_gr_bf)
[1] FALSE

The solution where we initialize with greedy fit has a higher ELBO then when we initialize from the truth and fix the prior.

drift

Lets now try our new drift algorithm which uses the same bimodal mixture prior for the loadings and Gaussian prior for the factors but a new variational approximation for the factors which accounts for correlations in the posterior i.e. full mvn for the variational approximation:

init <- drift.alpha:::init_from_flash(flash_greedy_res)
drift_res <- drift.alpha:::drift(init)
   1 :    -1465398.387 
   2 :    -1464851.616 
   3 :    -1464736.843 
   4 :    -1464704.063 
   5 :    -1464692.839 
   6 :    -1464688.561 
   7 :    -1464686.825 
   8 :    -1464686.094 
   9 :    -1464685.780 
  10 :    -1464685.642 
  11 :    -1464685.582 
  12 :    -1464685.555 
  13 :    -1464685.543 
  14 :    -1464685.537 
plot_loadings(drift_res$EL, pops)

Very exciting! It seems to maintain the tree structure that the greedy solution finds.

print(drift_res$prior_s2)
[1] 1.0185987 0.9678496 1.0085159
print(drift_res$resid_s2)
[1] 1.001795

drift recovers the correct prior and residual variances.

drift_res$CovF
           [,1]       [,2]       [,3]
[1,]  0.3371207 -0.3302804 -0.3305514
[2,] -0.3302804  0.3432082  0.3238444
[3,] -0.3305514  0.3238444  0.3437556
cov2cor(drift_res$CovF)
           [,1]       [,2]       [,3]
[1,]  1.0000000 -0.9709821 -0.9710049
[2,] -0.9709821  1.0000000  0.9428283
[3,] -0.9710049  0.9428283  1.0000000

We can see drift is estimating very strong correlations in the posterior. Lets try initializing from the flash backfit solution:

init <- drift.alpha:::init_from_flash(flash_backfit_res)
drift_res <- drift.alpha:::drift(init)
   1 :    -1465184.207 
   2 :    -1465104.297 
   3 :    -1465098.698 
   4 :    -1465094.188 
   5 :    -1465090.174 
   6 :    -1465083.404 
   7 :    -1465076.797 
   8 :    -1465070.348 
   9 :    -1465064.063 
  10 :    -1465057.963 
  11 :    -1465052.095 
  12 :    -1465046.547 
  13 :    -1465041.447 
  14 :    -1465036.954 
  15 :    -1465033.212 
  16 :    -1465030.290 
  17 :    -1465026.778 
  18 :    -1465021.477 
  19 :    -1465016.299 
  20 :    -1465011.244 
  21 :    -1465006.319 
  22 :    -1465001.538 
  23 :    -1464996.929 
  24 :    -1464992.535 
  25 :    -1464988.423 
  26 :    -1464984.677 
  27 :    -1464981.389 
  28 :    -1464978.638 
  29 :    -1464976.461 
  30 :    -1464974.634 
  31 :    -1464970.995 
  32 :    -1464967.332 
  33 :    -1464963.789 
  34 :    -1464960.362 
  35 :    -1464957.048 
  36 :    -1464953.850 
  37 :    -1464950.773 
  38 :    -1464947.831 
  39 :    -1464945.047 
  40 :    -1464942.455 
  41 :    -1464940.096 
  42 :    -1464938.018 
  43 :    -1464936.260 
  44 :    -1464934.847 
  45 :    -1464933.779 
  46 :    -1464932.980 
  47 :    -1464931.239 
  48 :    -1464928.862 
  49 :    -1464926.553 
  50 :    -1464924.310 
  51 :    -1464922.131 
  52 :    -1464920.015 
  53 :    -1464917.962 
  54 :    -1464915.972 
  55 :    -1464914.050 
  56 :    -1464912.200 
  57 :    -1464910.428 
  58 :    -1464908.745 
  59 :    -1464907.164 
  60 :    -1464905.702 
  61 :    -1464904.382 
  62 :    -1464903.231 
  63 :    -1464902.273 
  64 :    -1464901.365 
  65 :    -1464900.729 
  66 :    -1464900.173 
  67 :    -1464898.656 
  68 :    -1464896.876 
  69 :    -1464895.141 
  70 :    -1464893.448 
  71 :    -1464891.799 
  72 :    -1464890.191 
  73 :    -1464888.625 
  74 :    -1464887.102 
  75 :    -1464885.623 
  76 :    -1464884.190 
  77 :    -1464882.808 
  78 :    -1464881.480 
  79 :    -1464880.214 
  80 :    -1464879.018 
  81 :    -1464877.902 
  82 :    -1464876.875 
  83 :    -1464875.946 
  84 :    -1464875.123 
  85 :    -1464874.410 
  86 :    -1464873.807 
  87 :    -1464873.310 
  88 :    -1464872.911 
  89 :    -1464872.600 
  90 :    -1464872.363 
  91 :    -1464872.186 
  92 :    -1464872.003 
  93 :    -1464871.295 
  94 :    -1464870.266 
  95 :    -1464869.264 
  96 :    -1464868.287 
  97 :    -1464867.334 
  98 :    -1464866.406 
  99 :    -1464865.502 
 100 :    -1464864.621 
plot_loadings(drift_res$EL, pops)

This keeps the same representation but with a larger elbo.

print(drift_res$elbo)
[1] -1464865
print(flash_backfit_res$elbo)
[1] -1465622

sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: macOS Mojave 10.14.2

Matrix products: default
BLAS/LAPACK: /Users/jhmarcus/miniconda3/envs/flash_e/lib/R/lib/libRblas.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] ebnm_0.1-24       ashr_2.2-38       drift.alpha_0.0.1 flashier_0.2.4   
[5] tidyr_0.8.3       dplyr_0.8.0.1     ggplot2_3.1.1    

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2        compiler_3.6.1    pillar_1.3.1     
 [4] git2r_0.26.1      plyr_1.8.4        workflowr_1.4.0  
 [7] iterators_1.0.12  tools_3.6.1       digest_0.6.18    
[10] lattice_0.20-38   evaluate_0.13     tibble_2.1.1     
[13] gtable_0.3.0      pkgconfig_2.0.2   rlang_0.4.0      
[16] foreach_1.4.7     Matrix_1.2-17     parallel_3.6.1   
[19] yaml_2.2.0        xfun_0.6          withr_2.1.2      
[22] stringr_1.4.0     knitr_1.22        fs_1.2.7         
[25] rprojroot_1.3-2   grid_3.6.1        tidyselect_0.2.5 
[28] glue_1.3.1        R6_2.4.0          rmarkdown_1.12   
[31] mixsqp_0.2-4      purrr_0.3.2       magrittr_1.5     
[34] whisker_0.3-2     MASS_7.3-51.4     codetools_0.2-16 
[37] backports_1.1.4   scales_1.0.0      htmltools_0.3.6  
[40] assertthat_0.2.1  colorspace_1.4-1  labeling_0.3     
[43] stringi_1.4.3     pscl_1.5.2        doParallel_1.0.15
[46] lazyeval_0.2.2    munsell_0.5.0     truncnorm_1.0-8  
[49] SQUAREM_2017.10-1 crayon_1.3.4