Last updated: 2019-03-05

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File Version Author Date Message
Rmd e2a3aba jhmarcus 2019-03-05 updated plink filtering command
html e2a3aba jhmarcus 2019-03-05 updated plink filtering command
Rmd 9a69c08 jhmarcus 2019-03-04 updated data hoa
html 9a69c08 jhmarcus 2019-03-04 updated data hoa
Rmd f8154d8 jhmarcus 2019-03-04 added to data rmd
Rmd a1580ed jhmarcus 2019-03-04 added data exploration
html a1580ed jhmarcus 2019-03-04 added data exploration

Here I explore basic properties of the Human Origins Array dataset. I downloaded the data from:

https://reich.hms.harvard.edu/sites/reich.hms.harvard.edu/files/inline-files/NearEastPublic.tar.gz

I subsequently converted the eigenstrat files to plink format using the following parameter file and convertf command:

genotypename:   HumanOriginsPublic2068.geno
snpname:    HumanOriginsPublic2068.snp
indivname:  HumanOriginsPublic2068.ind
outputformat:   PACKEDPED
genotypeoutname:    HumanOriginsPublic2068.bed
snpoutname: HumanOriginsPublic2068.bim
indivoutname:   HumanOriginsPublic2068.fam
familynames:    NO
convertf -p eig2plink.par

I then removed the sex chromosomes using the the following plink command:

plink --bfile HumanOriginsPublic2068 --make-bed --autosome --out HumanOriginsPublic2068_auto

Imports

Lets import some needed packages:

library(ggplot2)
library(tidyr)
library(dplyr)
library(lfa)

Read Genotypes

Here I read the full genotype matrix of the Human Origins dataset:

Y = t(lfa:::read.bed("../data/raw/NearEastPublic/HumanOriginsPublic2068_auto"))
[1] "reading in 2068 individuals"
[1] "reading in 616938 snps"
[1] "snp major mode"
[1] "reading snp 20000"
[1] "reading snp 40000"
[1] "reading snp 60000"
[1] "reading snp 80000"
[1] "reading snp 100000"
[1] "reading snp 120000"
[1] "reading snp 140000"
[1] "reading snp 160000"
[1] "reading snp 180000"
[1] "reading snp 200000"
[1] "reading snp 220000"
[1] "reading snp 240000"
[1] "reading snp 260000"
[1] "reading snp 280000"
[1] "reading snp 300000"
[1] "reading snp 320000"
[1] "reading snp 340000"
[1] "reading snp 360000"
[1] "reading snp 380000"
[1] "reading snp 400000"
[1] "reading snp 420000"
[1] "reading snp 440000"
[1] "reading snp 460000"
[1] "reading snp 480000"
[1] "reading snp 500000"
[1] "reading snp 520000"
[1] "reading snp 540000"
[1] "reading snp 560000"
[1] "reading snp 580000"
[1] "reading snp 600000"
# number of individuals
n = nrow(Y)

# number of SNPs
p = ncol(Y)

Missingness per SNP

Here I compute the missingness per SNP:

n_miss_snp = colSums(is.na(Y))
p_snpmss = qplot(n_miss_snp / n, bins=100) + theme_bw() + 
           scale_x_continuous(breaks = pretty(n_miss_snp / n, n = 10)) +
           xlab("Missingness Fraction") +
           ylab("Count")

p_snpmss

Version Author Date
9a69c08 jhmarcus 2019-03-04
a1580ed jhmarcus 2019-03-04

There are very few SNPs with high levels of missing data so we can use a very stringent missingness threshold without losing much information.

Missingness per Individual

Here I compute the missingness per individual:

n_miss_ind = rowSums(is.na(Y))
p_indmss = qplot(n_miss_ind / p) + theme_bw() +
           xlab("Missingness Fraction") +
           ylab("Count")
p_indmss

Version Author Date
9a69c08 jhmarcus 2019-03-04
a1580ed jhmarcus 2019-03-04

It seems like a few individuals are missing about 20000 of their SNPs which is a bit worrisome maybe they should be removed from the analysis? For now I will in include them and see if they pop up as any outliers in the PCs.

Missingness per Population

Here I compute the missingness fraction per population:

# meta data
meta_df = read.table("../data/meta/HumanOriginsPublic2068.meta", sep="\t", header=T)
meta_df$miss_frac = n_miss_ind / p

# average missingness per pop for sorting
avg_miss_df = meta_df %>% 
              group_by(Simple.Population.ID) %>% 
              summarise(avg_miss=mean(miss_frac)) %>%
              arrange(desc(avg_miss)) 

# distribution of missingness per pop
p_popmss = ggplot(meta_df, aes(x=factor(Simple.Population.ID, 
                                        levels=avg_miss_df$Simple.Population.ID), 
                               y=miss_frac)) + 
           geom_boxplot() +
           theme_classic() +
           theme(axis.text.x = element_text(angle = 90, hjust = 1, size=6)) +
           xlab("Population") +
           ylab("Missingness Fraction")
p_popmss

Missingness per Contributer

Here I compute the average missingness fraction per contributor:

# average missingness per contributer for sorting
avg_miss_df = meta_df %>% 
              group_by(Contributor) %>% 
              summarise(avg_miss=mean(miss_frac)) %>%
              arrange(desc(avg_miss)) 

# distribution of missingness per contributer
p_conmss = ggplot(meta_df, aes(x=factor(Contributor, 
                                        levels=avg_miss_df$Contributor), 
                               y=miss_frac)) + 
           geom_boxplot() +
           theme_classic() +
           theme(axis.text.x = element_text(angle = 90, hjust = 1, size=6)) +
           xlab("Contributer") +
           ylab("Missingness Fraction")
p_conmss

It seems like there is some variation in the amount of missingness per pop and contributor (there might be some confounding there) but the total amount of missingness is so low I think it can be ignored?

Filter

Given the above results here are the plink commands I ran to filter the data:

plink --bfile HumanOriginsPublic2068 --geno .005 --maf .05 --make-bed --autosome --out HumanOriginsPublic2068_auto_maf05_geno005```
These filtering steps take us from 616938 to 343758 SNPs … which still likely contains a lot of information about population structure. 

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] lfa_1.12.0    dplyr_0.8.0.1 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         withr_2.1.2     
[17] stringr_1.4.0    knitr_1.21       fs_1.2.6         rprojroot_1.3-2 
[21] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.4.0        
[25] rmarkdown_1.11   purrr_0.3.0      corpcor_1.6.9    magrittr_1.5    
[29] whisker_0.3-2    backports_1.1.3  scales_1.0.0     htmltools_0.3.6 
[33] assertthat_0.2.0 colorspace_1.4-0 labeling_0.3     stringi_1.2.4   
[37] lazyeval_0.2.1   munsell_0.5.0    crayon_1.3.4