Last updated: 2020-06-29

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File Version Author Date Message
html 3bd7c49 Joseph Marcus 2020-06-29 Build site.
Rmd 488e33c Joseph Marcus 2020-06-29 workflowr::wflow_publish(“r1africa1nfb.Rmd”)
Rmd 9fe7c59 Joseph Marcus 2020-06-25 wolves and africa

Here I analyze a genotyping array dataset (r1africa1nfb) of humans from Africa. This dataset was originally organized by Peter et al. 2020 which merged previously published datasets. r1africa1nfb has 749 individuals and 20724 SNPs.

Imports

Import the required libraries and scripts:

suppressMessages({
  library(lfa)
  library(flashier)
  library(drift.alpha)
  library(ggplot2)
  library(RColorBrewer)
  library(viridis)
  library(reshape2)
  library(tidyverse)
  library(alstructure)
  source("../code/structure_plot.R")
})

Data

Here I read the data, remove SNPs that are too rare or common, and mean-impute missing data:

data_path <- "../data/datasets/r1africa1nfb/r1africa1nfb"
G <- t(lfa::read.bed(data_path))
[1] "reading in 749 individuals"
[1] "reading in 20984 snps"
[1] "snp major mode"
[1] "reading snp 20000"
colnames(G) <- NULL
rownames(G) <- NULL
n <- nrow(G)
daf <- colSums(G, na.rm=T) / (2 * n)
colors <- brewer.pal(8, "Set2")

# filter out too rare and too common SNPs
Y <- G[,((daf>=.05) & (daf <=.95))]

# mean impute
mu <- colMeans(Y, na.rm = TRUE)
for(j in 1:ncol(Y)){
  Y[is.na(Y[,j]), j] <- mu[j]
}

coords <- read.table("../data/datasets/r1africa1nfb/r1africa1nfb.coord", header=F)
colnames(coords) <- c("Long", "Lat")

p <- ncol(Y)
print(n)
[1] 749
print(p)
[1] 20724

ADMIXTURE

Here I display ADMIXTURE plots from K=2 to K=8. I ran 5 replicates of ADMIXTURE for each K and then plot the one the achieves the highest likelihood among the replicates:

Version Author Date
fdef1bb Joseph Marcus 2020-06-29

flash-greedy

Here I run the EBMF greedy algorithm:

Kmax <- 8
greedy <- init_from_data(Y, Kmax=Kmax)

# prepare
sd <- sqrt(greedy$prior_s2)
L <- greedy$EL
LDsqrt <- L %*% diag(sd)
s2 <- greedy$resid_s2
Kmax <- ncol(LDsqrt)
df <- cbind(coords, LDsqrt)
gath_df <- df %>% 
  gather(K, value, -Lat, -Long) %>% 
  filter(K!=1) 

# plot
jit <- 2
buf <- 5
p <- ggplot() + 
     geom_path(data=map_data("world"), 
               aes(long, lat, group=group), 
               color="gray", size=0.25) + 
     geom_jitter(data=gath_df, 
                 aes(Long, Lat, color=value), 
                 width=jit, height=jit, shape=21) +      
     scale_color_viridis() + 
     coord_map() +
     facet_wrap(.~factor(K, levels = paste0(2:Kmax)), ncol=3, nrow=3) + 
     theme_void() +
     xlim(min(df$Long)-buf, max(df$Long)+buf) + 
     ylim(min(df$Lat)-buf, max(df$Lat)+buf) 
p

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

drift (no-extrapolation)

Here I run drift with no extrapolation and initialized from the greedy fit:

dr_slow <- drift(greedy, 
                 verbose=FALSE, 
                 extrapolate=FALSE, 
                 maxiter=2000, 
                 tol=1e-4)

# prepare
sd <- sqrt(dr_slow$prior_s2)
L <- dr_slow$EL
LDsqrt <- L %*% diag(sd)
s2 <- dr_slow$resid_s2
Kmax <- ncol(LDsqrt)
df <- cbind(coords, LDsqrt)
gath_df <- df %>% 
  gather(K, value, -Lat, -Long) %>% 
  filter(K!=1) 

# plot
p <- ggplot() + 
     geom_path(data=map_data("world"), 
               aes(long, lat, group=group), 
               color="gray", size=0.25) + 
     geom_jitter(data=gath_df, 
                 aes(Long, Lat, color=value), 
                 width=jit, height=jit, shape=21) +      
     scale_color_viridis() + 
     coord_map() +
     facet_wrap(.~factor(K, levels = paste0(2:Kmax)), ncol=3, nrow=3) + 
     theme_void() +
     xlim(min(df$Long)-buf, max(df$Long)+buf) + 
     ylim(min(df$Lat)-buf, max(df$Lat)+buf) 
p

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

drift (extrapolation)

Here I run drift with extrapolation and initialized from the same greedy fit:

dr_fast <- drift(greedy, 
                 verbose=FALSE, 
                 extrapolate=TRUE, 
                 maxiter=2000, 
                 tol=1e-4)

# prepare
sd <- sqrt(dr_fast$prior_s2)
L <- dr_fast$EL
LDsqrt <- L %*% diag(sd)
s2 <- dr_fast$resid_s2
Kmax <- ncol(LDsqrt)
df <- cbind(coords, LDsqrt)
gath_df <- df %>% 
  gather(K, value, -Lat, -Long) %>% 
  filter(K!=1) 

# plot
p <- ggplot() + 
     geom_path(data=map_data("world"), 
               aes(long, lat, group=group), 
               color="gray", size=0.25) + 
     geom_jitter(data=gath_df, 
                 aes(Long, Lat, color=value), 
                 width=jit, height=jit, shape=21) +      
     scale_color_viridis() + 
     coord_map() +
     facet_wrap(.~factor(K, levels = paste0(2:Kmax)), ncol=3, nrow=3) + 
     theme_void() +
     xlim(min(df$Long)-buf, max(df$Long)+buf) + 
     ylim(min(df$Lat)-buf, max(df$Lat)+buf) 
p

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

Compare no-extrapolation vs extrapolation ELBOs:

d <- dr_slow$iterations %>%
     mutate(extrapolate = "FALSE") %>%
     bind_rows(dr_fast$iterations %>% mutate(extrapolate = "TRUE"))
ggplot(d, aes(x = iter, y = elbo, col = extrapolate)) + geom_line()

Version Author Date
3bd7c49 Joseph Marcus 2020-06-29

It seems the extrapolation and no-extrapolation algorithms find similar solutions with similar quality but extrapolation finds it faster.


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] alstructure_0.1.0  forcats_0.5.0      stringr_1.4.0     
 [4] dplyr_0.8.5        purrr_0.3.4        readr_1.3.1       
 [7] tidyr_1.0.2        tibble_3.0.1       tidyverse_1.3.0   
[10] reshape2_1.4.3     viridis_0.5.1      viridisLite_0.3.0 
[13] RColorBrewer_1.1-2 ggplot2_3.3.0      drift.alpha_0.0.9 
[16] flashier_0.2.4     lfa_1.9.0         

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] htmltools_0.3.6  httpuv_1.4.5     Matrix_1.2-15    plyr_1.8.4      
[21] pkgconfig_2.0.3  invgamma_1.1     broom_0.5.6      haven_2.2.0     
[25] corpcor_1.6.9    scales_1.1.0     whisker_0.3-2    later_0.7.5     
[29] git2r_0.26.1     farver_2.0.3     generics_0.0.2   ellipsis_0.3.0  
[33] withr_2.2.0      ashr_2.2-50      cli_2.0.2        magrittr_1.5    
[37] crayon_1.3.4     readxl_1.3.1     evaluate_0.14    fansi_0.4.1     
[41] fs_1.3.1         nlme_3.1-137     xml2_1.3.2       truncnorm_1.0-8 
[45] tools_3.5.1      hms_0.5.3        lifecycle_0.2.0  munsell_0.5.0   
[49] reprex_0.3.0     irlba_2.3.3      compiler_3.5.1   rlang_0.4.5     
[53] grid_3.5.1       rstudioapi_0.11  labeling_0.3     rmarkdown_1.10  
[57] gtable_0.3.0     DBI_1.0.0        R6_2.4.1         gridExtra_2.3   
[61] lubridate_1.7.4  knitr_1.20       workflowr_1.6.1  rprojroot_1.3-2 
[65] stringi_1.4.6    parallel_3.5.1   SQUAREM_2020.2   Rcpp_1.0.4.6    
[69] vctrs_0.2.4      dbplyr_1.4.3     tidyselect_1.0.0