Last updated: 2019-03-05

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Knit directory: drift-workflow/analysis/

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File Version Author Date Message
Rmd fb4c571 jhmarcus 2019-03-05 fixed md formatting;
html fb4c571 jhmarcus 2019-03-05 fixed md formatting;
Rmd 17bb442 jhmarcus 2019-03-05 addded miss per pop and contributer
html 17bb442 jhmarcus 2019-03-05 addded miss per pop and contributer
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
17bb442 jhmarcus 2019-03-05
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
17bb442 jhmarcus 2019-03-05
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

Version Author Date
17bb442 jhmarcus 2019-03-05

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

Version Author Date
17bb442 jhmarcus 2019-03-05

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