Last updated: 2020-07-14

Checks: 7 0

Knit directory: drift-workflow/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.6.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

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.

Seed: set.seed(20190211)

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.

Session information: recorded

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

Cache: none

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

File paths: relative

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

Repository version: 8bf63c8

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 8bf63c8. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

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:    .snakemake/
    Ignored:    analysis/r1africa1nfb_cache/
    Ignored:    analysis/test.png
    Ignored:    data/datasets/
    Ignored:    data/raw/
    Ignored:    data/simulations/
    Ignored:    data/sites/
    Ignored:    notebooks/.ipynb_checkpoints/
    Ignored:    output/
    Ignored:    sandbox/.ipynb_checkpoints/

Unstaged changes:
    Modified:   analysis/1kg_phase3.Rmd
    Modified:   code/run_flash_drift_rand.R
    Modified:   code/structure_plot.R
    Modified:   snakefiles/flash/Snakefile

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 repository in which changes were made to the R Markdown (analysis/figure-simple-sim.Rmd) and HTML (docs/figure-simple-sim.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 8bf63c8 Joseph Marcus 2020-07-14 wflow_publish(“figure-simple-sim.Rmd”) 
suppressMessages({
  library(flashier)
  library(drift.alpha)
  library(tidyverse)
  library(alstructure)
  library(lfa)
  source("../code/structure_plot.R")
})

Simulaton

Simulate non-negative genotype data roughly between 0 and 2 using a Gaussian factor analysis model under with three pops A, B, C and C is 50% admixture between A and B:

# specify simulation
n <- 150
p <- 5000
K <- 3
subpops <- rep(c("A", "B", "C"), each=50)
colors <- c("#66c2a5", "#fc8d62", "#8da0cb")

# simulate "genotype data"
a <- 1 + rnorm(p, mean=0, sd=.1)
b <- rnorm(p, mean=0, sd=.2)
c <- rnorm(p, mean=0, sd=.2)
FF <- cbind(a, b, c)
L <- matrix(NA, nrow=n, ncol=3)
L[, 1] <- 1
L[, 2] <- 0
L[, 3] <- 0
L[1:50, 2] <- 1
L[101:150, 2] <- .5
L[51:100, 3]  <- 1
L[101:150, 3] <- .5
sd_e <- .1
E <- matrix(rnorm(n*p, mean=0, sd=.05), ncol=p)
Y <-  L %*% t(FF) + E

PCA

Run PCA:

Z <- scale(Y)
pc_res <- lfa:::trunc.svd(Z, d=10)
PC <- pc_res$u
print(pc_res$d / sum(pc_res$d))
 [1] 0.58313401 0.04751677 0.04682370 0.04669639 0.04637075 0.04626028
 [7] 0.04614121 0.04592766 0.04569024 0.04543900
p_pca <- qplot(1:n, PC[,1], color=subpops) + 
  xlab("") + ylab("PC1 (.598)") + 
  theme_classic() +
  theme(axis.text.y = element_text(size = 12),
        axis.title.y=element_text(size=12),
        legend.title=element_blank()) + 
  theme(legend.position = c(0.2, 0.6)) +
  ggtitle("(A) PCA") + 
  theme(plot.title = element_text(hjust=0.5))
p_pca

ALStructure

Run an ADMIXTURE model using ALStructure:

al_res <- alstructure(t(Y), 2)
Q <- t(al_res$Q_hat)
p_admix <- create_structure_plot(Q, subpops, colors[c(1, 3)], c("A", "B", "C")) + 
  ylab("Admixture fraction") + 
  ggtitle("(B) ALStructure") +
  theme(plot.title = element_text(hjust=0.5))
p_admix

Drift (random init)

Run drift with random init

set.seed(2000)
EL <- matrix(runif(n * K), ncol = K)  
EL[, 1] <- 1
EF <- t(solve(crossprod(EL), crossprod(EL, Y))) 
dr <- drift(init_from_EL(Y, EL, EF), miniter=20, maxiter=20, 
            extrapolate=FALSE, verbose=TRUE)
   1 :      479635.883 
   2 :      786240.483 
   3 :     1130811.680 
   4 :     1137561.450 
   5 :     1137891.162 
   6 :     1138051.844 
   7 :     1138138.160 
   8 :     1138185.703 
   9 :     1138204.738 
  10 :     1138214.899 
  11 :     1138220.815 
  12 :     1138224.497 
  13 :     1138226.935 
  14 :     1138228.658 
  15 :     1138229.948 
  16 :     1138230.963 
  17 :     1138231.788 
  18 :     1138232.476 
  19 :     1138233.061 
  20 :     1138233.563 
dr <- drift(dr, miniter=2, maxiter=1000, tol=1e-4, 
            extrapolate=TRUE, verbose=TRUE)
   1 :     1138233.998 
   2 :     1138234.558 
   3 :     1138235.139 
   4 :     1138235.708 
   5 :     1138236.255 
   6 :     1138236.783 
   7 :     1138237.308 
   8 :     1138237.852 
   9 :     1138238.239 
  10 :     1138238.921 
  11 :     1138239.657 
  12 :     1138240.300 
  13 :     1138240.803 
  14 :     1138240.923 
  15 :     1138241.167 
  16 :     1138241.550 
  17 :     1138242.012 
  18 :     1138242.471 
  19 :     1138242.649 
  20 :     1138242.872 
  21 :     1138243.154 
  22 :     1138243.428 
  23 :     1138243.668 
  24 :     1138243.816 
  25 :     1138243.949 
  26 :     1138244.146 
  27 :     1138244.361 
  28 :     1138244.543 
  29 :     1138244.588 
  30 :     1138244.710 
  31 :     1138244.863 
  32 :     1138245.002 
  33 :     1138245.100 
  34 :     1138245.108 
  35 :     1138245.186 
  36 :     1138245.296 
  37 :     1138245.405 
  38 :     1138245.474 
  39 :     1138245.492 
  40 :     1138245.519 
  41 :     1138245.554 
  42 :     1138245.601 
  43 :     1138245.670 
  44 :     1138245.781 
  45 :     1138245.936 
  46 :     1138245.959 
  47 :     1138245.984 
  48 :     1138246.004 
  49 :     1138246.014 
  50 :     1138246.018 
  51 :     1138246.023 
  52 :     1138246.030 
  53 :     1138246.038 
  54 :     1138246.049 
  55 :     1138246.067 
  56 :     1138246.094 
  57 :     1138246.107 
  58 :     1138246.117 
  59 :     1138246.128 
  60 :     1138246.134 
  61 :     1138246.135 
  62 :     1138246.135 
  63 :     1138246.136 
  64 :     1138246.136 
  65 :     1138246.136 
p_drift <- create_structure_plot(dr$EL, subpops, colors[c(2,3,1)], c("A", "B", "C")) + 
  ggtitle("(C) Drift") + theme(plot.title = element_text(hjust=0.5))
p_drift

Figure

Make the figure:

p_grid <- cowplot::plot_grid(p_pca, p_admix, p_drift, nrow=3)
p_grid + ggsave("../output/figures/simple-sim.pdf", width=7, height=6)


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

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              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] lfa_1.9.0         alstructure_0.1.0 forcats_0.5.0    
 [4] stringr_1.4.0     dplyr_0.8.5       purrr_0.3.4      
 [7] readr_1.3.1       tidyr_1.0.2       tibble_3.0.1     
[10] ggplot2_3.3.0     tidyverse_1.3.0   drift.alpha_0.0.9
[13] flashier_0.2.4   

loaded via a namespace (and not attached):
 [1] httr_1.4.1       jsonlite_1.6     modelr_0.1.6     assertthat_0.2.1
 [5] mixsqp_0.3-43    cellranger_1.1.0 yaml_2.2.0       ebnm_0.1-24     
 [9] pillar_1.4.3     backports_1.1.6  lattice_0.20-38  glue_1.4.0      
[13] digest_0.6.25    promises_1.0.1   rvest_0.3.5      colorspace_1.4-1
[17] cowplot_0.9.4    htmltools_0.3.6  httpuv_1.4.5     Matrix_1.2-15   
[21] plyr_1.8.4       pkgconfig_2.0.3  invgamma_1.1     broom_0.5.6     
[25] haven_2.2.0      corpcor_1.6.9    scales_1.1.0     whisker_0.3-2   
[29] later_0.7.5      git2r_0.26.1     farver_2.0.3     generics_0.0.2  
[33] ellipsis_0.3.0   withr_2.2.0      ashr_2.2-50      cli_2.0.2       
[37] magrittr_1.5     crayon_1.3.4     readxl_1.3.1     evaluate_0.14   
[41] fs_1.3.1         fansi_0.4.1      nlme_3.1-137     xml2_1.3.2      
[45] truncnorm_1.0-8  tools_3.5.1      hms_0.5.3        lifecycle_0.2.0 
[49] munsell_0.5.0    reprex_0.3.0     irlba_2.3.3      compiler_3.5.1  
[53] rlang_0.4.5      grid_3.5.1       rstudioapi_0.11  labeling_0.3    
[57] rmarkdown_1.10   gtable_0.3.0     DBI_1.0.0        reshape2_1.4.3  
[61] R6_2.4.1         lubridate_1.7.4  knitr_1.20       workflowr_1.6.1 
[65] rprojroot_1.3-2  stringi_1.4.6    parallel_3.5.1   SQUAREM_2020.2  
[69] Rcpp_1.0.4.6     vctrs_0.2.4      dbplyr_1.4.3     tidyselect_1.0.0