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comparison pca.xml @ 22:95a05c1ef5d5

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update to devshed revision aaece207bd01
author Richard Burhans <burhans@bx.psu.edu>
date Mon, 11 Mar 2013 11:28:06 -0400
parents 8ae67e9fb6ff
children 8997f2ca8c7a
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21:d6b961721037 22:95a05c1ef5d5
1 <tool id="gd_pca" name="PCA" version="1.0.0"> 1 <tool id="gd_pca" name="PCA" version="1.0.0">
2 <description>: Principal Component Analysis of genotype data</description> 2 <description>: Principal Components Analysis of genotype data</description>
3 3
4 <command interpreter="python"> 4 <command interpreter="python">
5 pca.py "$input" "$input.extra_files_path" "$output" "$output.files_path" 5 pca.py "$input" "$input.extra_files_path" "$output" "$output.files_path"
6 </command> 6 </command>
7 7
54 54
55 **What it does** 55 **What it does**
56 56
57 The user selects a gd_ped dataset generated by the Prepare Input tool. 57 The user selects a gd_ped dataset generated by the Prepare Input tool.
58 The PCA tool runs a 58 The PCA tool runs a
59 Principal Component Analysis on the input genotype data and constructs 59 Principal Components Analysis on the input genotype data and constructs
60 a plot of the top two principal components. It also reports the 60 a plot of the top two principal components. It also reports the
61 following estimates of the statistical significance of the analysis. 61 following estimates of the statistical significance of the analysis.
62 62
63 1. Average divergence between each pair of populations. Specifically, 63 1. Average divergence between each pair of populations. Specifically,
64 from the covariance matrix X whose eigenvectors were computed, we can 64 from the covariance matrix X whose eigenvectors were computed, we can