Last updated: 2019-04-19

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Rmd 1a21d43 jhmarcus 2019-04-17 updated simple tree doc

Take this with a grain of salt for now as I’m still debugging / following up with some issues with the simulation data the authors of badMIXTURE provided from the paper:

Here I perform simulations from Lawson et al. 2018. These simulations are specifically designed to illustrate challenges with interpreting admixture coefficients from PSD models as population genetic parameters. Specifically they ran ADMIXTURE (K=11) on three challenging simulation scenarios which are inspired by human demographic histories. They find ADMIXTURE generates the same coefficients under these three different scenarios. The figure below and found in the supplement of the badMIXTURE paper visually describes the simulation settings:

Here I attempt to replicate their findings by running ADMIXTURE on the same datasets simulated in the paper as well as running FLASH (Drift) to see if it can distinguish these models. Note, I downloaded the plink files from their simulations from here. I then filtered on any missingness and removed variants with minor allele frequency less than 5%. One odd feature of the data is there are 12 populations in the plink files but 13 in their supplementary figure. I will have to follow up to see if this is the exact simulation data they used. I’m following up with Daniel Lawson about this issue.

Imports

Lets import some needed packages:

library(ggplot2)
library(tidyr)
library(dplyr)
library(RColorBrewer)
library(knitr)
source("../code/viz.R")
source("../code/prep.R")

ADMIXTURE

Here the colors of the factors between the three ADMIXTURE runs changes (I need to work on factor color matching code) but sitting with the results one can see the similarities in the highlighted 4 populations.

Recent (Recent Admixture)

l_df = read.table("../output/admixture/recent_sim/Recent_admix_geno_maf.K11r1.Q", sep=" ", header=F)
K = ncol(l_df)
colnames(l_df) = 1:K
inds = read.table("../data/datasets/recent_sim/Recent_admix_geno_maf.fam", header=F, stringsAsFactors=F) %>% pull(V2)
pops = read.table("../data/datasets/recent_sim/Recent_admix_geno_maf.fam", header=F, stringsAsFactors=F) %>% pull(V1)
l_df$ID = inds
l_df$pop = factor(pops, levels=paste0("Pop", 1:13))
pops = paste0("Pop", 1:13) # all unique pop labels
l_df$ID = factor(l_df$ID, levels = l_df$ID) # make sure the ids are sorted

l_gath_df = l_df %>% gather(K, value, -ID, -pop)
l_gath_df47 = l_df %>% gather(K, value, -ID, -pop) %>% filter(pop %in% paste0("Pop", 4:7)) 

pall = structure_plot(gath_df=l_gath_df, colset="Set3", facet_levels=pops,
                      facet_grp="pop", label_size=5, fact_type="structure") +
       theme(plot.title = element_text(size=6))
p47 = structure_plot(gath_df=l_gath_df47, colset="Set3", facet_levels=pops,
                     facet_grp="pop", label_size=5, fact_type="structure") +
      theme(plot.title = element_text(size=6))
p = cowplot::plot_grid(pall, p47, nrow = 2, align = "v") 
Warning in align_plots(plotlist = plots, align = align, axis = axis):
Complex graphs cannot be vertically aligned unless axis parameter is set
properly. Placing graphs unaligned.
print(p)

Marginalisation (Recent Bottleneck)

l_df = read.table("../output/admixture/marginalisation_sim/Marginalisation_admix_geno_maf.K11r1.Q", sep=" ", header=F)
K = ncol(l_df)
colnames(l_df) = 1:K
inds = read.table("../data/datasets//marginalisation_sim/Marginalisation_admix_geno_maf.fam", header=F, stringsAsFactors=F) %>% 
       pull(V1)
pops = read.table("../data/datasets/marginalisation_sim/Marginalisation_admix_geno_maf.fam", header=F, stringsAsFactors=F) %>% 
       pull(V2)
l_df$ID = inds
l_df$pop = pops
pops = paste0("Pop", 1:13) # all unique pop labels
l_df$ID = factor(l_df$ID, levels = l_df$ID) # make sure the ids are sorted

l_gath_df = l_df %>% gather(K, value, -ID, -pop)
l_gath_df47 = l_df %>% gather(K, value, -ID, -pop) %>% filter(pop %in% paste0("Pop", 4:7)) 

pall = structure_plot(gath_df=l_gath_df, colset="Set3", facet_levels=pops,
                   facet_grp="pop", label_size=5, fact_type="structure") +
           theme(plot.title = element_text(size=6))
p47 = structure_plot(gath_df=l_gath_df47, colset="Set3", facet_levels=pops,
                     facet_grp="pop", label_size=5, fact_type="structure") +
      theme(plot.title = element_text(size=6))

p = cowplot::plot_grid(pall, p47, nrow = 2) 
print(p)

Remnants (Ghost Admixture)

There is something very funky about the Remnants simulation. See populations 8-13.

l_df = read.table("../output/admixture/remnants_sim/Remnants_admix_geno_maf.K11r1.Q", sep=" ", header=F)
K = ncol(l_df)
colnames(l_df) = 1:K
inds = read.table("../data/datasets/remnants_sim/Remnants_admix_geno_maf.fam", header=F, stringsAsFactors=F) %>% pull(V1)
pops = read.table("../data/datasets/remnants_sim/Remnants_admix_geno_maf.fam", header=F, stringsAsFactors=F) %>% pull(V2)
l_df$ID = inds
l_df$pop = pops
pops = paste0("Pop", 1:13)# all unique pop labels
l_df$ID = factor(l_df$ID, levels = l_df$ID) # make sure the ids are sorted

l_gath_df = l_df %>% gather(K, value, -ID, -pop)
l_gath_df47 = l_df %>% gather(K, value, -ID, -pop) %>% filter(pop %in% paste0("Pop", 4:7)) 

pall = structure_plot(gath_df=l_gath_df, colset="Set3", facet_levels=pops,
                   facet_grp="pop", label_size=5, fact_type="structure") +
           theme(plot.title = element_text(size=6))
p47 = structure_plot(gath_df=l_gath_df47, colset="Set3", facet_levels=pops,
                     facet_grp="pop", label_size=5, fact_type="structure") +
      theme(plot.title = element_text(size=6))
p = cowplot::plot_grid(pall, p47, nrow = 2) 
print(p)

FLASH-Greedy

Recent (Recent Admixture)

flash_fit = readRDS("../output/flash_greedy/recent_sim/Recent_admix_geno_maf.rds")
plot_pve(flash_fit)

print(flash_fit$pve)
 [1] 4.644314e-01 3.672962e-02 3.293419e-02 1.533617e-02 3.460167e-03
 [6] 4.380101e-03 2.193011e-03 2.220532e-03 1.516786e-03 7.452720e-04
[11] 3.638727e-04 2.204384e-04 4.979738e-04 5.088945e-04 5.083526e-04
[16] 2.872698e-04 2.060834e-04 3.908611e-04 3.955174e-04 2.987267e-04
[21] 1.552774e-04 2.467179e-04 1.647525e-04 1.561975e-04 9.676400e-05
[26] 8.065071e-05 1.010647e-04 7.148456e-05 2.887337e-04 2.635893e-04
[31] 1.846785e-04

It looks like the pve drops off at around 10 factors so lets go with visualizing the top 10:

l_df = as.data.frame(flash_fit$loadings$normalized.loadings[[1]])
colnames(l_df)[1:31] = 1:31
inds = read.table("../data/datasets/recent_sim/Recent_admix_geno_maf.fam", header=F, stringsAsFactors=F) %>% pull(V2)
pops = read.table("../data/datasets/recent_sim/Recent_admix_geno_maf.fam", header=F, stringsAsFactors=F) %>% pull(V1)
l_df$ID = inds
l_df$pop = pops
pops = paste0("Pop", 1:13)

factors_incl = paste0(2:10)
l_gath_df = l_df %>%          
            select_if(~sum(!is.na(.)) > 0) %>%
            gather(K, value, -ID, -pop) %>%
            filter(K %in% factors_incl)

l_gath_df47 = l_df %>%          
              filter(pop %in% paste0("Pop", 4:7)) %>%
              select_if(~sum(!is.na(.)) > 0) %>%
              gather(K, value, -ID, -pop) %>%
              filter(K %in% factors_incl)

pall = structure_plot(gath_df=l_gath_df, colset="Set3", facet_levels=pops,
                   facet_grp="pop", label_size=5, fact_type="nonnegative") +
           theme(plot.title = element_text(size=6))

p47 = structure_plot(gath_df=l_gath_df47, colset="Set3", facet_levels=pops,
                     facet_grp="pop", label_size=5, fact_type="nonnegative") +
      theme(plot.title = element_text(size=6))

p = cowplot::plot_grid(pall, p47, nrow = 2) 
print(p)

Looks pretty clean!

Marginalisation (Recent Bottleneck)

flash_fit = readRDS("../output/flash_greedy/marginalisation_sim/Marginalisation_admix_geno_maf.rds")
plot_pve(flash_fit)

print(flash_fit$pve)
 [1] 4.650849e-01 3.675585e-02 3.233046e-02 1.511297e-02 4.308358e-03
 [6] 2.896828e-03 2.464024e-03 2.282352e-03 6.153068e-04 5.829713e-04
[11] 4.275390e-04 5.578858e-04 5.114740e-04 3.479125e-04 1.310698e-04
[16] 6.477571e-04 3.644369e-04 8.658475e-05 2.993560e-04 4.205884e-04
[21] 4.013920e-04 2.405113e-04 1.489529e-04 3.378401e-04 1.160602e-04
[26] 2.484946e-04 1.684698e-04 1.800022e-04 2.718841e-04 1.748917e-04
[31] 2.117316e-04

This also seems to drop off at 10 factors so lets visualize that:

l_df = as.data.frame(flash_fit$loadings$normalized.loadings[[1]])
colnames(l_df)[1:31] = 1:31
inds = read.table("../data/datasets/marginalisation_sim/Marginalisation_admix_geno_maf.fam", header=F, stringsAsFactors=F) %>% pull(V1)
pops = read.table("../data/datasets/marginalisation_sim/Marginalisation_admix_geno_maf.fam", header=F, stringsAsFactors=F) %>% pull(V2)
l_df$ID = inds
l_df$pop = pops
pops = paste0("Pop", 1:13)

factors_incl = paste0(2:10)
l_gath_df = l_df %>%          
            select_if(~sum(!is.na(.)) > 0) %>%
            gather(K, value, -ID, -pop) %>%
            filter(K %in% factors_incl)

l_gath_df47 = l_df %>%          
              filter(pop %in% paste0("Pop", 4:7)) %>%
              select_if(~sum(!is.na(.)) > 0) %>%
              gather(K, value, -ID, -pop) %>%
              filter(K %in% factors_incl)

pall = structure_plot(gath_df=l_gath_df, colset="Set3", facet_levels=pops,
                   facet_grp="pop", label_size=5, fact_type="nonnegative") +
           theme(plot.title = element_text(size=6))

p47 = structure_plot(gath_df=l_gath_df47, colset="Set3", facet_levels=pops,
                     facet_grp="pop", label_size=5, fact_type="nonnegative") +
      theme(plot.title = element_text(size=6))

p = cowplot::plot_grid(pall, p47, nrow = 2) 
print(p)

Remnants (Ghost Admixture)

flash_fit = readRDS("../output/flash_greedy/remnants_sim/Remnants_admix_geno_maf.rds")
plot_pve(flash_fit)

print(flash_fit$pve)
 [1] 4.650896e-01 3.224806e-02 3.719610e-02 1.446442e-02 3.490551e-03
 [6] 2.377310e-03 1.795288e-03 2.196230e-03 2.981648e-03 8.615182e-04
[11] 5.692530e-04 4.063218e-04 5.629490e-04 2.410158e-04 2.481862e-04
[16] 4.541307e-04 2.923784e-04 3.260667e-04 2.663500e-04 6.961251e-05
[21] 3.122211e-04 2.113871e-04 2.954215e-04 3.431343e-04 7.240811e-05
[26] 1.982314e-04 1.477614e-04 1.569933e-04 7.707317e-05 8.636696e-05
[31] 1.029189e-04

Yet again seems to drop off at 10 factors:

flash_fit = readRDS("../output/flash_greedy/remnants_sim/Remnants_admix_geno_maf.rds")

l_df = as.data.frame(flash_fit$loadings$normalized.loadings[[1]])
colnames(l_df)[1:31] = 1:31
inds = read.table("../data/datasets/remnants_sim/Remnants_admix_geno_maf.fam", header=F, stringsAsFactors=F) %>% pull(V1)
pops = read.table("../data/datasets/remnants_sim/Remnants_admix_geno_maf.fam", header=F, stringsAsFactors=F) %>% pull(V2)
l_df$ID = inds
l_df$pop = pops
pops = paste0("Pop", 1:13)

factors_incl = paste0(2:10)
l_gath_df = l_df %>%          
            select_if(~sum(!is.na(.)) > 0) %>%
            gather(K, value, -ID, -pop) %>%
            filter(K %in% factors_incl)

l_gath_df47 = l_df %>%          
              filter(pop %in% paste0("Pop", 4:7)) %>%
              select_if(~sum(!is.na(.)) > 0) %>%
              gather(K, value, -ID, -pop) %>%
              filter(K %in% factors_incl)

pall = structure_plot(gath_df=l_gath_df, colset="Set3", facet_levels=pops,
                   facet_grp="pop", label_size=5, fact_type="nonnegative") +
           theme(plot.title = element_text(size=6))

p47 = structure_plot(gath_df=l_gath_df47, colset="Set3", facet_levels=pops,
                     facet_grp="pop", label_size=5, fact_type="nonnegative") +
      theme(plot.title = element_text(size=6))

p = cowplot::plot_grid(pall, p47, nrow = 2) 
print(p)


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: macOS  10.14.2

Matrix products: default
BLAS/LAPACK: /Users/jhmarcus/miniconda3/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] knitr_1.21         RColorBrewer_1.1-2 dplyr_0.8.0.1     
[4] tidyr_0.8.2        ggplot2_3.1.0     

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       compiler_3.5.1   pillar_1.3.1     git2r_0.23.0    
 [5] plyr_1.8.4       workflowr_1.2.0  tools_3.5.1      digest_0.6.18   
 [9] evaluate_0.12    tibble_2.0.1     gtable_0.2.0     pkgconfig_2.0.2 
[13] rlang_0.3.1      yaml_2.2.0       xfun_0.4         flashier_0.1.1  
[17] withr_2.1.2      stringr_1.4.0    fs_1.2.6         rprojroot_1.3-2 
[21] grid_3.5.1       tidyselect_0.2.5 cowplot_0.9.4    glue_1.3.0      
[25] R6_2.4.0         rmarkdown_1.11   reshape2_1.4.3   purrr_0.3.0     
[29] magrittr_1.5     whisker_0.3-2    backports_1.1.3  scales_1.0.0    
[33] htmltools_0.3.6  assertthat_0.2.0 colorspace_1.4-0 labeling_0.3    
[37] stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0    crayon_1.3.4