Difference between revisions of "ADMIXTURE"

From UFRC
Jump to navigation Jump to search
m (Text replacement - "#uppercase" to "uc")
Line 23: Line 23:
  
 
<!--Modules-->
 
<!--Modules-->
==Required Modules==
+
==Environment Modules==
 
+
Run <code>module spider {{#var:app}}</code> to find out what environment modules are available for this application.
===Serial===
 
* {{#var:app}}
 
<!--
 
===Parallel (OpenMP)===
 
* intel
 
* {{#var:app}}
 
===Parallel (MPI)===
 
* intel
 
* openmpi
 
* {{#var:app}}
 
 
 
 
==System Variables==
 
==System Variables==
 
* HPC_{{uc:{{#var:app}}}}_DIR - installation directory
 
* HPC_{{uc:{{#var:app}}}}_DIR - installation directory
Line 79: Line 68:
 
<!--Turn the Table of Contents and Edit paragraph links ON/OFF-->
 
<!--Turn the Table of Contents and Edit paragraph links ON/OFF-->
 
__NOTOC____NOEDITSECTION__
 
__NOTOC____NOEDITSECTION__
=Validation=
 
* Validate 4/5/2018
 

Revision as of 18:53, 10 June 2022

Description

ADMIXTURE website  

ADMIXTURE is a software tool for maximum likelihood estimation of individual ancestries from multilocus SNP genotype datasets. It uses the same statistical model as STRUCTURE but calculates estimates much more rapidly using a fast numerical optimization algorithm.

Specifically, ADMIXTURE uses a block relaxation approach to alternately update allele frequency and ancestry fraction parameters. Each block update is handled by solving a large number of independent convex optimization problems, which are tackled using a fast sequential quadratic programming algorithm. Convergence of the algorithm is accelerated using a novel quasi-Newton acceleration method. The algorithm outperforms EM algorithms and MCMC sampling methods by a wide margin.

Environment Modules

Run module spider ADMIXTURE to find out what environment modules are available for this application.

System Variables

  • HPC_ADMIXTURE_DIR - installation directory




Citation

If you publish research that uses ADMIXTURE you have to cite it as follows:

H. Zhou, D. H. Alexander, and K. Lange. A quasi-Newton method for accelerating the convergence of iterative optimization algorithms. Statistics and Computing, 2009.