Local PCA/population structure (lostruct)

To install the package, make sure you have devtools (by doing install.packages("devtools")), and then running

install.packages("data.table")
devtools::install_github("petrelharp/local_pca/lostruct")
library(lostruct)

Note: the library is called lostruct.

Using the R package

The example scripts in the directories above mostly work without the R package. To start using the code on your own data, have a look at these files:

• A quick example : in four lines of code, reads in chromosome 22 from a TPED, and does local PCA.

• Setting up the medicago data : after documenting where the data are from, does local PCA on a small subset of the whole dataset, to establish how the functions work.

• Script for medicago analysis : an Rscript to run the same analysis on medicago data, varying various parameters by command-line options: run Rscript run_on_medicago.R --help for a list.

• Report summarizing an analysis : an Rmarkdown file that can be compiled with templater to produce visualizations of the results of the above.

Prerequisites

• To use the functions to read in windows out of BCF file, you will need bcftools.
• To compile the example report, you probably want templater.

Standalone code

Also included is code we used to analyze the datasets in the paper (before the R package was written). The general order to see the code in each directory is

1. recode : turn bases into numbers
2. PCA : find local PCs
3. distance : compute distance matrix between windows from local PCs
4. MDS : visualize the result

There are standalone examples for each of the three datasets studied:

POPRES (Homo sapiens, SNP chip data from a few worldwide populations)

Chromosome 1 is the example given. See also popres_example.R for an example of some steps using the package.

• POPRES_SNPdata_recode12.R : recodes the TPED as numeric
• POPRES_cov.R : computes covariance matrix for the entire chromsome 1
• POPRES_PCA_win100.R : computes local PCs
• POPRES_jackknife_var.R : estimates SE of local PCs
• POPRES_distance.R : computes distance matrix from local PCs
• POPRES_MDS.R : finds and plots MDS visualization of distance matrix

DPGP (Drosophila melanogaster population genome project)

Chromosome 3L is the example given .

• DPGP_recode_and_cov.R : recodes data as numeric, removes individuals with more than 8% missing data, sites with more than 20% missing data, and computes whole-chromosome covariance matrix
• DPGP_PCA_plot.R : plots PCs for entire 3L
• DPGP_PCA_win103.R : computes local PCs along 3L in windows of 1000 SNPs
• DPGP_var_between_win.R : computes variance of PCs between windows
• DPGP_jackknife_var.R : does jackknife estimate of SE for PCs on windows of 1000 SNPs
• DPGP_distance.R : computes distance matrix from local PCs
• DPGP_MDS_1d.R : computes and plots MDS plots from distance matrix
• DPGP_get_extreme_points.R : identifies extreme points (with interaction)
• DPGP_combine_extremes_and_get_cov.R : combines each of three sets of extreme windows and computes covariances for each

Medicago (Medicago truncatula hapmap)

For Medicago, it calculates the pairwise distance for all 8 chromosome together and then apply MDS and use subset of the whole MDS result for each chromosome.

• Medicago_VCF_recode.py : recodes VCF file as numeric
• Medicago_recode_and_cov.R : computes covariance matrix for (entire) chromosome 1
• Medicago_PCA_win104.R : computes local PCs for chromosome 1
• Medicago_distance_all_chr.R : computes a distance matrix from PC information
• Medicago_MDS.R : computes and plots MDS plots from the distance matrix

A note on implementation:

This method works through the genome doing something (PCA on the covariance matrix) one window at a time. Because of this, it can be frustratingly slow to first load the entire dataset into memory. There are several methods implemented here to avoid this; for instance, vcf_windower() which is used to compute PCs for the medicago data. The interface is via a function that takes an integer, n, and returns a data frame of the genomic data in the nth window.