Last updated: 2020-01-30
Checks: 6 1
Knit directory: hgen471/
This reproducible R Markdown analysis was created with workflowr (version 1.5.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish
to commit the R Markdown file and build the HTML.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20200105)
was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish
or wflow_git_commit
). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .DS_Store
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: analysis/.Rhistory
Unstaged changes:
Modified: analysis/L6-download-data.Rmd
Modified: analysis/L6-population-structure.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote
), click on the hyperlinks in the table below to view them.
File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | e73a329 | Hae Kyung Im | 2020-01-27 | L6 examples |
html | e73a329 | Hae Kyung Im | 2020-01-27 | L6 examples |
library(tidyverse)
work.dir ="~/Downloads/hapmap/"
## qqunif function
source("https://gist.githubusercontent.com/hakyim/38431b74c6c0bf90c12f/raw/21fbae9a48dc475f42fa60f0ef5509d071dea873/qqunif")
Download plink for mac
## Download plink from https://www.cog-genomics.org/plink2
## wget http://s3.amazonaws.com/plink1-assets/plink_mac_20200121.zip
## TODO: move plink executable to ~/bin/
## add to the path if necessary
## export PATH="$PATH:~/bin/"
Download HapMap data
refdir=~/Downloads/hapmap
mkdir $refdir
cd $refdir
mkdir output
## Download plink format hapmap 3 genotype data
## http://www.sanger.ac.uk/resources/downloads/human/hapmap3.html
wget -r ftp://ftp.ncbi.nlm.nih.gov/hapmap/genotypes/2010-05_phaseIII/plink_format/ (This also downloads tar-ed individual population ped/map files)
##wget ftp://ftp.ncbi.nlm.nih.gov/hapmap/genotypes/2010-05_phaseIII/plink_format/hapmap3_r3_b36_fwd.consensus.qc.poly.map
##wget ftp://ftp.ncbi.nlm.nih.gov/hapmap/genotypes/2010-05_phaseIII/plink_format/hapmap3_r3_b36_fwd.consensus.qc.poly.ped
# FINISHED --2018-01-26 10:46:48--
# Total wall clock time: 3m 56s
# Downloaded: 4 files, 2.5G in 3m 56s (10.9 MB/s)
## remove annoying DS_Store files in OSX
## find . -name '.DS_Store' |xargs rm
## Mac OSX needs command line toos + homebrew installed.
## then run
## brew update
## brew install wget
## cd and use tabs to get to subfolder where gz files are
mv ftp.ncbi.nlm.nih.gov/hapmap/genotypes/2010-05_phaseIII/plink_format/* $refdir
rm -rf ftp.ncbi*
gunzip *.gz
Download population data
## get reported population data
wget ftp://ftp.ncbi.nlm.nih.gov/hapmap/phase_3/relationships_w_pops_051208.txt
Create small bed/bim/fam file
## create binary plink file for chr 22 (to have small example)
plink --file hapmap3_r3_b36_fwd.consensus.qc.poly --make-bed --chr 22 --out hapmapch22
## get superpopulation data
wget https://www.dropbox.com/s/yozrzsdrwqej63q/phase3_corrected.psam?dl=1
mv phase3_corrected.psam\?dl\=1 phase3_corrected.psam
## problems with zsh in Catalina to download dropbox link
## zsh: no matches found:
## https://www.dropbox.com/s/yozrzsdrwqej63q/phase3_corrected.psam?dl=1
## problems downloading data from dropbox
## https://www.bartbusschots.ie/s/2019/06/12/bash-to-zsh-file-globbing-and-no-matches-found-errors/
## setopt NULL_GLOB
## This doesn't work
## This worked: pasted the dropbox link on browser url and downloaded fine
Calculate Fst among populations
plink --bfile hapmapch22 --fst --within relationships_w_pops_051208.txt --mwithin 5 --out output/hapmapch22-all
Calculate Fst among sub-populations
## create list of EUR, AFR,
reldata = read_tsv(paste0(work.dir, "relationships_w_pops_051208.txt"))
samdata = read_tsv(paste0(work.dir,"phase3_corrected.psam"),guess_max = 2500) ## guess_max set to large number so that R reader consideres non numeric PAT and MAT values
names(samdata)[names(samdata)=="$IID"] = "IID"
superpop = samdata %>% select(SuperPop,Population) %>% unique()
eur = reldata %>% inner_join(superpop, by=c("population"="Population")) %>% filter(SuperPop=="EUR")
afr = reldata %>% inner_join(superpop, by=c("population"="Population")) %>% filter(SuperPop=="AFR")
eas = reldata %>% inner_join(superpop, by=c("population"="Population")) %>% filter(SuperPop=="EAS")
reldata %>% inner_join(superpop, by=c("population"="Population")) %>% count(population, SuperPop) %>% arrange(SuperPop)
Write list of EUR, EAS, AFR individuals
write_tsv(eur, paste0(work.dir,"EUR.sam"))
write_tsv(afr, paste0(work.dir,"AFR.sam"))
write_tsv(eas, paste0(work.dir,"EAS.sam"))
## plink --bfile hapmapch22 --fst --keep EUR.sam --within relationships_w_pops_051208.txt --mwithin 5 --out hapmapch22-EUR
plink --bfile hapmapch22 --fst --keep AFR.sam --within relationships_w_pops_051208.txt --mwithin 5 --out output/hapmapch22-AFR
plink --bfile hapmapch22 --fst --keep EUR.sam --within relationships_w_pops_051208.txt --mwithin 5 --out output/hapmapch22-EUR
plink --bfile hapmapch22 --fst --keep EAS.sam --within relationships_w_pops_051208.txt --mwithin 5 --out output/hapmapch22-EAS
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.2
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.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
loaded via a namespace (and not attached):
[1] workflowr_1.5.0 Rcpp_1.0.3 rprojroot_1.3-2 digest_0.6.22
[5] later_1.0.0 R6_2.4.1 backports_1.1.5 git2r_0.26.1
[9] magrittr_1.5 evaluate_0.14 stringi_1.4.3 rlang_0.4.1
[13] fs_1.3.1 promises_1.1.0 whisker_0.4 rmarkdown_1.17
[17] tools_3.6.1 stringr_1.4.0 glue_1.3.1 httpuv_1.5.2
[21] xfun_0.11 yaml_2.2.0 compiler_3.6.1 htmltools_0.4.0
[25] knitr_1.26