fastStructure is a fast algorithm for inferring population structure from large SNP genotype data.
It is based on a variational Bayesian framework for posterior inference and is written in Python3.x (thanks to the work of @jashapiro).
Here, we summarize how to setup this software package, compile the C and Cython scripts and run
the algorithm on a test simulated genotype dataset.
This repo has two components: a library of C and Cython scripts in vars and a set of Cython and pure Python scripts to load the data and run the algorithm.
Most python distributions have the first three modules packaged in them. It's also straightforward to install these dependencies:
- using package managers for MACOSX and several Linux distributions,
- from platform-specific binary packages, and
- directly from source.
Below are detailed instructions for installing these dependencies, provided by Thierry Gosselin from Université Laval, Québec. These instructions were tested on Mac OS 10.8 (Mountain Lion), 10.9 (Mavericks) and 10.10 (Yosemite). (Note that the latest versions of Cython and GSL can be different from those below.)
Most Linux distributions already come with wget; this is, however, not true for the Mac OS.
Git is already pre-installed on Mac OS and is pretty easly to install on CentOS, Ubuntu, etc. if it is not already available on those systems using yum or apt.
cd tmp
wget http://gnu.mirror.vexxhost.com/gsl/gsl-latest.tar.gz
tar -zxvf gsl-latest.tar.gz -C /install/directory
cd /install/directory/gsl-1.16
./configure --prefix=/install/directory/gsl-1.16
make
make installIt is easiest to install these 3 packages once you have cloned this repository using the below command:
cd fastStructure/
pip install -r requirements.txtfastStructure is not currently available on PyPi so in order to install/use it, you must clone this repository.
To obtain the source code from github, clone this repo into the desired directory:
git clone https://github.com/rajanil/fastStructureTo retrieve the latest code updates, you can do the following:
cd fastStructure
git fetch
git merge origin/masterBefore building python extensions, it is important to identify the path to the library
files libgsl.so and libgslcblas.so, and header file
gsl/gsl_sf_psi.h that are part of your GSL installation. For a default
installation of GSL, the libraries (.so files) are usually found in /usr/local/lib
and the header files (.h files) in /usr/local/include. In this case, you can add these
lines to your .bash_profile or .profile file on your home directory.
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
export CFLAGS="-I/usr/local/include"
export LDFLAGS="-L/usr/local/lib"Then, run source ~/.bash_profile to set these environment variables.
To build library extensions, run the following:
cd fastStructure/vars/
python setup.py build_ext --inplaceTo compile the main cython scripts, run the following:
cd fastStructure/
python setup.py build_ext --inplaceEach setup will create some .c and .so (shared object) files.
The setup may give some warnings, which are OK. If you get errors that indicate the
build failed, this might be because the wrong compiler is being used or
environment variables (like LD_LIBRARY_PATH) are set incorrectly. To use a specific
gcc compiler, try the following:
CC=</path/to/compiler> python setup.py build_ext --inplaceThe main script you will need to execute is structure.py. To see command-line
options that need to be passed to the script, you can do the following:
user@host [~] python structure.py
Here is how you can use this script
Usage: python structure.py
-K <int> (number of populations)
--input=<file> (/path/to/input/file)
--output=<file> (/path/to/output/file)
--tol=<float> (convergence criterion; default: 10e-6)
--prior={simple,logistic} (choice of prior; default: simple)
--cv=<int> (number of test sets for cross-validation, 0 implies no CV step; default: 0)
--format={bed,str} (format of input file; default: bed)
--full (to output all variational parameters; optional)
--seed=<int> (manually specify seed for random number generator; optional)fastStructure performs inference for the simplest, independent-loci, admixture model, with two choices of priors
that can be specified using the --prior flag. Thus, unlike Structure, fastStructure does not require the
mainparams and extraparam files. The inference algorithm used by fastStructure is fundamentally different from
that of Structure and requires the setting of far fewer options. All options for fastStructure can be passed
via the flags listed above.
The key options to pass to the scripts are the input file, the output file and the number of populations.
Assuming the input file is named genotypes.bed (with corresponding genotypes.fam and genotypes.bim),
the output file is named genotypes_output and the number of populations you would like is 3,
you can run the algorithm as follows:
user@host [~] python structure.py -K 3 --input=genotypes --output=genotypes_outputThis generates a genotypes_output.3.log file that tracks how the algorithm proceeds, and files
genotypes_output.3.meanQ and genotypes_output.3.meanP containing the posterior mean of
admixture proportions and allele frequencies, respectively. The orders of samples and
SNPs in the output files
match those in the .fam file and .bim file, respectively. Note that input file names should
not include suffixes (e.g., .bed) and are relative to the main project directory (unless a full
path is provided).
The current implementation can import data from plink bed format and the original Structure format. If the data are in plink format, ensure that bed, bim and fam files for the dataset are all present in the same path.
While the original Structure program allowed for a more flexible input format, fastStructure expects a more specific Structure-like input format. Specifically, rows in the data file correspond to samples, with two rows per sample (note that only diploids are handled by this software), and columns correspond to SNPs. The first 6 columns of the file will be ignored; these typically would include IDs, metadata, etc. This software only handles bi-allelic loci. The two alleles at each locus can be encoded as desired; however, missing data should be encoded as -9.
A test simulated dataset is provided in tests/data/testdata.bed with genotypes sampled for
200 individuals at 500 SNP loci. The output files in tests/ were generated using the run_tests.sh script in the tests folder.
To rerun the tests, follow the below instructions:
user@host [~] cd fastStructure/tests/
user@host [~] chmod +x run_tests.sh
user@host [~] ./run_tests.shExecuting the code with the provided test data should generate a log file identical to the ones in tests/,
as a final check that the source code has been downloaded and compiled correctly. The algorithm scales
linearly with number of samples, number of loci and value of K; the expected runtime for a new dataset can be
computed from the runtime in the above log file.
In order to choose the appropriate number of model components that explain structure in the dataset, we recommend running the algorithm for multiple choices of K. We have provided a utility tool to parse through the output of these runs and provide a reasonable range of values for the model complexity appropriate for this dataset.
Assuming the algorithm was run on the test dataset for choices of K ranging from 1 to 10, and
the output flag was --output=tests/testoutput_simple, you can obtain the model complexity
by doing the following:
user@host [~] python chooseK.py --input=tests/testoutput_simple
Model complexity that maximizes marginal likelihood = 2
Model components used to explain structure in data = 4In order to visualize the expected admixture proportions inferred by fastStructure, we have provided a simple tool to generate Distruct plots using the mean of the variational posterior distribution over admixture proportions. The samples in the plot will be grouped according to population labels inferred by fastStructure. However, if the user would like to group the samples according to some other categorical label (e.g., geographic location), these labels can be provided as a separate file using the flag --popfile. The order of labels in this file (one label per row) should match the order of samples in the input data files.
user@host [~] python distruct.py
Here is how you can use this script
Usage: python distruct.py
-K <int> (number of populations)
--input=<file> (/path/to/input/file; same as output flag passed to structure.py)
--output=<file> (/path/to/output/file)
--popfile=<file> (file with known categorical labels; optional)
--title=<figure title> (a title for the figure; optional)Assuming the algorithm was run on the test dataset for K=5, and
the output flag was --output=tests/testoutput_simple, you can generate a Distruct plot
by doing the following:
user@host [~] python distruct.py -K 5 --input=tests/testoutput_simple --output=tests/testoutput_simple_distruct.svgAnil Raj, Matthew Stephens, and Jonathan K. Pritchard. fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets, (Genetics) June 2014 197:573-589 [Genetics, Biorxiv]