Human Origins Array Global Results

Last updated: 2019-02-15

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```
Ignored files:
    Ignored:    .Rhistory
    Ignored:    Makefile
    Ignored:    analysis/.Rhistory
    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
```
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Expand here to see past versions:

File	Version	Author	Date	Message
Rmd	b4749ac	jhmarcus	2019-02-15	fixed some typos
html	b4749ac	jhmarcus	2019-02-15	fixed some typos
Rmd	7a2b6c7	jhmarcus	2019-02-15	added backfit
html	7a2b6c7	jhmarcus	2019-02-15	added backfit
Rmd	f5ef1af	jhmarcus	2019-02-15	added workflows for human origins datasets
html	f5ef1af	jhmarcus	2019-02-15	added workflows for human origins datasets
Rmd	4afc77e	jhmarcus	2019-02-15	init hoa global analysis

Imports

Lets import some needed packages:

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

# this is the color palette we will use over and over 
getPalette = colorRampPalette(brewer.pal(12, "Set3"))

Human Origins Global (LD Pruned)

This is the full Human Origins dataset 2068 sampled from around the world. 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 167178 SNPs.

Greedy

Lets first read the greedy flashier fit

flash_fit = readRDS("../output/flash_greedy/hoa_global_ld/HumanOriginsPublic2068_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] 2068

print(p)

[1] 167178

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

We can see the later factors tend to get sparser but they still seem to contribute! Lets now take a look at the loadings:

############### data ############### 

# read the meta data
meta_df = read.table("../data/meta/HumanOriginsPublic2068_maf_geno_ldprune.meta", sep=" ", header=T)

# setup loadings data.frame
l_df = as.data.frame(flash_fit$loadings$normalized.loadings[[1]])
l_df$iid = as.vector(meta_df$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

# 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)

# Africa
africa_pops = get_pops(meta_df, "Africa")
p_africa = positive_structure_plot(l_gath_df %>% filter(region == "Africa"), africa_pops, K, label_size=5)

# America
america_pops = get_pops(meta_df, "America")
p_america = positive_structure_plot(l_gath_df %>% filter(region == "America"), america_pops, K, label_size=5) 

# Central Asia Siberia
central_asia_siberia_pops = get_pops(meta_df, "CentralAsiaSiberia")
p_central_asia_siberia = positive_structure_plot(l_gath_df %>% filter(region == "CentralAsiaSiberia"), central_asia_siberia_pops, K, label_size=5)

# East Asia
east_asia_pops = get_pops(meta_df, "EastAsia")
p_east_asia = positive_structure_plot(l_gath_df %>% filter(region == "EastAsia"), east_asia_pops, K, label_size=5)

# South Asia
south_asia_pops = get_pops(meta_df, "SouthAsia")
p_south_asia= positive_structure_plot(l_gath_df %>% filter(region == "SouthAsia"), south_asia_pops, K, label_size=5)

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

# Oceania
oceania_pops = get_pops(meta_df, "Oceania")
p_oceania = positive_structure_plot(l_gath_df %>% filter(region == "Oceania"), oceania_pops, K, label_size=5) 

p = cowplot::plot_grid(p_africa, p_west_eurasia, p_central_asia_siberia, p_america, p_east_asia, p_south_asia, p_oceania, 
                       rel_heights = c(1.2, 1.3, 1, 1, 1, 1, 1.1),
                       nrow = 7, align = "v") 
p

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

Version	Author	Date
f5ef1af	jhmarcus	2019-02-15

Backfitting

Lets first read the backfit flashier fit

flash_fit = readRDS("../output/flash_backfit/hoa_global_ld/HumanOriginsPublic2068_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] 2068

print(p)

[1] 167178

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-backfit-ld-viz-factors-1.png:

Version	Author	Date
7a2b6c7	jhmarcus	2019-02-15

Some of the drift event histograms look quite odd i.e. see 4 and 6. Lets now take a look at the loadings:

############### data ############### 

# read the meta data
meta_df = read.table("../data/meta/HumanOriginsPublic2068_maf_geno_ldprune.meta", sep=" ", header=T)

# setup loadings data.frame
l_df = as.data.frame(flash_fit$loadings$normalized.loadings[[1]])
l_df$iid = as.vector(meta_df$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

# 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)

# Africa
africa_pops = get_pops(meta_df, "Africa")
p_africa = positive_structure_plot(l_gath_df %>% filter(region == "Africa"), africa_pops, K, label_size=5)

# America
america_pops = get_pops(meta_df, "America")
p_america = positive_structure_plot(l_gath_df %>% filter(region == "America"), america_pops, K, label_size=5) 

# Central Asia Siberia
central_asia_siberia_pops = get_pops(meta_df, "CentralAsiaSiberia")
p_central_asia_siberia = positive_structure_plot(l_gath_df %>% filter(region == "CentralAsiaSiberia"), central_asia_siberia_pops, K, label_size=5)

# East Asia
east_asia_pops = get_pops(meta_df, "EastAsia")
p_east_asia = positive_structure_plot(l_gath_df %>% filter(region == "EastAsia"), east_asia_pops, K, label_size=5)

# South Asia
south_asia_pops = get_pops(meta_df, "SouthAsia")
p_south_asia= positive_structure_plot(l_gath_df %>% filter(region == "SouthAsia"), south_asia_pops, K, label_size=5)

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

# Oceania
oceania_pops = get_pops(meta_df, "Oceania")
p_oceania = positive_structure_plot(l_gath_df %>% filter(region == "Oceania"), oceania_pops, K, label_size=5) 

p = cowplot::plot_grid(p_africa, p_west_eurasia, p_central_asia_siberia, p_america, p_east_asia, p_south_asia, p_oceania, 
                       rel_heights = c(1.2, 1.3, 1, 1, 1, 1, 1.1),
                       nrow = 7, align = "v") 
p

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

Version	Author	Date
7a2b6c7	jhmarcus	2019-02-15

Its hard to visually tell the difference with so many events.

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        cowplot_0.9.4     rprojroot_1.3-2  
[25] grid_3.5.1        tidyselect_0.2.4  glue_1.3.0       
[28] R6_2.3.0          rmarkdown_1.11    reshape2_1.4.3   
[31] purrr_0.2.5       magrittr_1.5      whisker_0.3-2    
[34] backports_1.1.2   scales_0.5.0      htmltools_0.3.6  
[37] assertthat_0.2.0  colorspace_1.3-2  labeling_0.3     
[40] stringi_1.2.4     lazyeval_0.2.1    munsell_0.5.0    
[43] crayon_1.3.4      R.oo_1.22.0

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