Human Origins Array West Eurasia Results

Last updated: 2019-02-15

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```
Ignored files:
    Ignored:    .Rhistory
    Ignored:    Makefile
    Ignored:    analysis/flash_cache/
    Ignored:    data/.DS_Store
    Ignored:    data/raw/
    Ignored:    output/admixture/
    Ignored:    output/flash_backfit/
    Ignored:    output/flash_greedy/
    Ignored:    output/softImpute/

Unstaged changes:
    Modified:   analysis/hoa_global.Rmd
    Modified:   analysis/hoa_weurasia.Rmd
    Modified:   output/log/snake-run_flash_backfit-dataset=hoa_global_ld,prefix=HumanOriginsPublic2068_maf_geno_ldprune-57591216.out
```
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Expand here to see past versions:

File	Version	Author	Date	Message
Rmd	f5ef1af	jhmarcus	2019-02-15	added workflows for human origins datasets
html	f5ef1af	jhmarcus	2019-02-15	added workflows for human origins datasets

Imports

Lets import some needed packages

library(ggplot2)
library(tidyr)
library(dplyr)
library(RColorBrewer)
source("../code/viz.R")
getPalette = colorRampPalette(brewer.pal(12, "Set3"))

Human Origins West Eurasia (LD Pruned)

This is a subset of the Human Origins Array dataset with 770 sampled from across West Eurasia. I filtered out rare variants with global minor allele frequency less than 5%, and remove any variants with a missingness level greater than 1%. I then LD pruned the SNPs using standard parameters in plink, resulting in 139640 SNPs.

Greedy

Lets first read the greedy flashier fit

flash_fit = readRDS("../output/flash_greedy/hoa_weurasia_ld/HumanOriginsPublic2068_weurasia_maf_geno_ldprune.rds")
K = ncol(flash_fit$loadings$normalized.loadings[[1]]) 
n = nrow(flash_fit$loadings$normalized.loadings[[1]])
p = nrow(flash_fit$loadings$normalized.loadings[[2]])
print(K)

[1] 31

print(n)

[1] 770

print(p)

[1] 139640

Lets now plot the distribution of factors for each drift event

# read factors
delta_df = as.data.frame(flash_fit$loadings$normalized.loadings[[2]])
colnames(delta_df)[1:K] = 1:K 

# gather the data.frame for plotting
delta_gath_df = delta_df %>% 
                gather(K, value) %>%
                filter(K!=1)

# plot the factors
K_ = K
p_fct = ggplot(delta_gath_df, aes(x=value, fill=factor(K, 2:K_))) + 
        scale_fill_manual(values = getPalette(K_)) +
        geom_histogram() + 
        facet_wrap(~factor(K, levels=2:K_), scales = "free") + 
        labs(fill="K") + 
        scale_x_continuous(breaks = scales::pretty_breaks(n = 3)) +
        scale_y_continuous(breaks = scales::pretty_breaks(n = 3)) + 
        theme_bw()
p_fct

Expand here to see past versions of flash-greedy-ld-viz-factors-1.png:

Version	Author	Date
f5ef1af	jhmarcus	2019-02-15

This seems that greedyflashier found no contributions of factors after the 14th although it seems a little bit buggy b/c the 13th factor is all NA but the 14th factor has some non-zeros but it does indeed seem very sparse? Lets now take a look at the loadings:

# read the meta data
meta_df = read.table("../data/meta/HumanOriginsPublic2068_weurasia_maf_geno_ldprune.meta", sep=" ", header=T)

# setup loadings data.frame
l_df = as.data.frame(flash_fit$loadings$normalized.loadings[[1]])
l_df$iid = meta_df$iid # individual ids
l_df$clst = meta_df$clst # population labels

# join with the meta data
l_df = l_df %>% inner_join(meta_df, on="clst")
l_df = l_df %>% arrange(region, clst) # sort by region then by population
l_df$iid = factor(l_df$iid, levels = l_df$iid) # make sure the ids are sorted
colnames(l_df)[1:K] = 1:K

l_df = l_df %>% select_if(~sum(!is.na(.)) > 0)

# gather the data.frame for plotting
l_gath_df = l_df %>% 
            gather(K, value, -iid, -clst, -region, -country, -lat, -lon, -clst2) %>% 
            filter(K!=1) 

#### viz #####
pops = unique(l_df$clst)

# West Eurasia
west_eurasia_pops = get_pops(meta_df, "WestEurasia")
p_west_eurasia = positive_structure_plot(l_gath_df, west_eurasia_pops, K, label_size=5)
p_west_eurasia

Expand here to see past versions of flash-greedy-ld-viz-loadings-1.png:

Version	Author	Date
f5ef1af	jhmarcus	2019-02-15

It does seem cool that many groups can be made up of a sparse set of colors but it looks like many groups can be quite noisy as well.

Backfitting

flash_fit = readRDS("../output/flash_backfit/hoa_weurasia_ld/HumanOriginsPublic2068_weurasia_maf_geno_ldprune.rds")
K = ncol(flash_fit$loadings$normalized.loadings[[1]]) 
n = nrow(flash_fit$loadings$normalized.loadings[[1]])
p = nrow(flash_fit$loadings$normalized.loadings[[2]])
print(K)

[1] 31

print(n)

[1] 770

print(p)

[1] 139640

Lets now plot the distribution of factors for each drift event

# read factors
delta_df = as.data.frame(flash_fit$loadings$normalized.loadings[[2]])
colnames(delta_df)[1:K] = 1:K 

# gather the data.frame for plotting
delta_gath_df = delta_df %>% 
                gather(K, value) %>%
                filter(K!=1)

# plot the factors
K_ = K
p_fct = ggplot(delta_gath_df, aes(x=value, fill=factor(K, 2:K_))) + 
        scale_fill_manual(values = getPalette(K_)) +
        geom_histogram() + 
        facet_wrap(~factor(K, levels=2:K_), scales = "free") + 
        labs(fill="K") + 
        scale_x_continuous(breaks = scales::pretty_breaks(n = 3)) +
        scale_y_continuous(breaks = scales::pretty_breaks(n = 3)) + 
        theme_bw()
p_fct

The factors look a bit sparser? Lets now look at the loadings:

# read the meta data
meta_df = read.table("../data/meta/HumanOriginsPublic2068_weurasia_maf_geno_ldprune.meta", sep=" ", header=T)

# setup loadings data.frame
l_df = as.data.frame(flash_fit$loadings$normalized.loadings[[1]])
l_df$iid = meta_df$iid # individual ids
l_df$clst = meta_df$clst # population labels

# join with the meta data
l_df = l_df %>% inner_join(meta_df, on="clst")
l_df = l_df %>% arrange(region, clst) # sort by region then by population
l_df$iid = factor(l_df$iid, levels = l_df$iid) # make sure the ids are sorted
colnames(l_df)[1:K] = 1:K

l_df = l_df %>% select_if(~sum(!is.na(.)) > 0)

# gather the data.frame for plotting
l_gath_df = l_df %>% 
            gather(K, value, -iid, -clst, -region, -country, -lat, -lon, -clst2) %>% 
            filter(K!=1) 

#### viz #####
pops = unique(l_df$clst)

# West Eurasia
west_eurasia_pops = get_pops(meta_df, "WestEurasia")
p_west_eurasia = positive_structure_plot(l_gath_df, west_eurasia_pops, K, label_size=5)
p_west_eurasia

At first glance it doesn’t seem the backfitting changed much of the result.

Session information

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] bindrcpp_0.2.2     RColorBrewer_1.1-2 dplyr_0.7.6       
[4] tidyr_0.8.1        ggplot2_3.0.0     

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0        compiler_3.5.1    pillar_1.3.0     
 [4] git2r_0.23.0      plyr_1.8.4        workflowr_1.1.1  
 [7] bindr_0.1.1       R.methodsS3_1.7.1 R.utils_2.7.0    
[10] tools_3.5.1       digest_0.6.18     evaluate_0.12    
[13] tibble_1.4.2      gtable_0.2.0      pkgconfig_2.0.1  
[16] rlang_0.3.1       yaml_2.2.0        xfun_0.4         
[19] flashier_0.1.0    withr_2.1.2       stringr_1.3.1    
[22] knitr_1.21        rprojroot_1.3-2   grid_3.5.1       
[25] tidyselect_0.2.4  glue_1.3.0        R6_2.3.0         
[28] rmarkdown_1.11    reshape2_1.4.3    purrr_0.2.5      
[31] magrittr_1.5      whisker_0.3-2     backports_1.1.2  
[34] scales_0.5.0      htmltools_0.3.6   assertthat_0.2.0 
[37] colorspace_1.3-2  labeling_0.3      stringi_1.2.4    
[40] lazyeval_0.2.1    munsell_0.5.0     crayon_1.3.4     
[43] R.oo_1.22.0

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