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AIMsetfinder is a collection of Rscripts to identify sets of Ancestry Informative Markers (AIMs), that minimize the logloss error of a naive Bayes classifier.
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README.md

AIMsetfinder

Peter Pfaffelhuber, Franziska Grundner-Culemann, Veronika Lipphardt, Franz Baumdicker

Overview:

AIMsetfinder is a collection of Rscripts to identify sets of Ancestry Informative Markers (AIMs), that minimize the logloss error of a naive Bayes classifier.

It takes as input:

  • SNP data (e.g. 1000 Genomes SNP data or user's own data in vcf.gz format)
  • biogeographic information (or alternatively any discrete phenotype)

to select a set of specified size of optimal AIMs to classify the samples.

The output is:

  • a vcf.gz file with the selected AIMs
  • a list of SNP identifiers

which can be used in ancestry inferrence methods.

  • Furthermore the posterior probabilites of a naive classifier based on these AIMs are given for the input data.

Table of contents

Quick start

git clone https://github.com/fbaumdicker/AIMsetfinder.git
cd AIMsetfinder

Install dependencies and then run the test: Rscript pipeline_example.r

Installing dependencies

For data analysis as well as for our simulation studies, we rely on R scripts.

dependencies

  • For multicore-computing, we require the R-package parallel.
  • Since, both data from the 1000 genomes project, which is analysed here, and the coalescent simulations, come in vcf-format, we require the R-package vcfR.
  • For some steps in the analysis, we use vcftools \cite{Danecek2011} and bcftools \cite{Li2011}, both can be installed using
sudo apt-get install vcftools bcftools

data resources (optional)

In addition, data from the 1000 genomes project (phase 3) was downloaded, as well as information on the sampling locations. For this, we used

wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/ALL.*
wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/integrated_call_samples_v3.20130502.ALL.panel

The latter file was renamed {\tt 1000G_SampleListWithLocations.txt}, and the first row (the header) was removed.

for simulations (optional)

The simulation studies are performed using msprime. Most easily installed via pip3 install msprime. Msprime is a fast coalescent simulator. In particular, structured populations (with varying population sizes etc) can be simulated. We are using the python-interface of msprime. See msprime documentation for more information.

Overview of dependencies:

How to run

To run the test set: Rscript example_pipeline.r

In data/sim/ooa/, you will find the file ooa_chromosome_1_example.vcf.gz, a small set of 240 simulated individuals that is used in this tutorial.

Directory structure and analysis output

The analysis generates the following files:

./AIMs                list of identifiers of the chosen AIMs
./AIMs.vcf.gz         corresponding states for all individuals
./predictions.csv     table of posterior probabilites for all classes/BGAs as predicted by naive Bayes using AIMs
./classifications.tab classification into the class with the largest probability as in predictions.csv 

In which step different files are produced is described in more details in readme.pdf.

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