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Update to Miller Lab devshed revision 3c4110ffacc3
author Richard Burhans <burhans@bx.psu.edu>
date Fri, 20 Sep 2013 13:25:27 -0400
parents 8997f2ca8c7a
children 03c22b722882
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1 <tool id="gd_pca" name="PCA" version="1.0.0">
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2 <description>: Principal Components Analysis of genotype data</description>
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4 <command interpreter="python">
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5 pca.py '$input' '$input.extra_files_path' '$output' '$output.files_path'
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6 </command>
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7
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8 <inputs>
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9 <param name="input" type="data" format="gd_ped" label="Dataset" />
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10 </inputs>
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11
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12 <outputs>
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13 <data name="output" format="html" />
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14 </outputs>
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16 <requirements>
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17 <requirement type="package" version="5.0.1">eigensoft</requirement>
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18 <requirement type="package" version="0.1">gd_c_tools</requirement>
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19 <requirement type="package" version="3.2.1">beautifulsoup</requirement>
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20 </requirements>
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21
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22 <!--
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23 <tests>
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24 <test>
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25 <param name="input" value="fake" ftype="gd_ped" >
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26 <metadata name="base_name" value="admix" />
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27 <composite_data value="test_out/prepare_population_structure/prepare_population_structure.html" />
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28 <composite_data value="test_out/prepare_population_structure/admix.ped" />
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29 <composite_data value="test_out/prepare_population_structure/admix.map" />
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30 <edit_attributes type="name" value="fake" />
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31 </param>
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32
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33 <output name="output" file="test_out/pca/pca.html" ftype="html" compare="diff" lines_diff="2">
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34 <extra_files type="file" name="admix.geno" value="test_out/pca/admix.geno" />
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35 <extra_files type="file" name="admix.gd_indivs" value="test_out/pca/admix.gd_indivs" />
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36 <extra_files type="file" name="admix.gd_snp" value="test_out/pca/admix.gd_snp" />
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37 <extra_files type="file" name="coordinates.txt" value="test_out/pca/coordinates.txt" />
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38 <extra_files type="file" name="explained.txt" value="test_out/pca/explained.txt" />
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39 <extra_files type="file" name="par.admix" value="test_out/pca/par.admix" compare="diff" lines_diff="10" />
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40 <extra_files type="file" name="PCA.pdf" value="test_out/pca/PCA.pdf" compare="sim_size" delta = "1000" />
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41 </output>
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42
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43 </test>
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44 </tests>
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45 -->
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46
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47 <help>
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48
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49 **Dataset formats**
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50
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51 The input dataset is in gd_ped_ format.
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52 The output dataset is html_ with links to a pdf for a graphical output and
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53 text files. (`Dataset missing?`_)
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54
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55 .. _gd_ped: ./static/formatHelp.html#gd_ped
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56 .. _html: ./static/formalHelp.html#html
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57 .. _Dataset missing?: ./static/formatHelp.html
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59 -----
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60
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61 **What it does**
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62
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63 The user selects a gd_ped dataset generated by the Prepare Input tool.
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64 The PCA tool runs a
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65 Principal Components Analysis on the input genotype data and constructs
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66 a plot of the top two principal components. It also reports the
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67 following estimates of the statistical significance of the analysis.
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68
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69 1. Average divergence between each pair of populations. Specifically,
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70 from the covariance matrix X whose eigenvectors were computed, we can
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71 compute a "distance", d, for each pair of individuals (i,j): d(i,j) =
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72 X(i,i) + X(j,j) - 2X(i,j). For each pair of populations (a,b) now
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73 define an average distance: D(a,b) = \sum d(i,j) (in pop a, in pop b)
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74 / (\|pop a\| * \|pop b\|). We then normalize D so that the diagonal
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75 has mean 1 and report it.
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76
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77 2. Anova statistics for population differences along each
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78 eigenvector. For each eigenvector, a P-value for statistical
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79 significance of differences between each pair of populations along
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80 that eigenvector is printed. +++ is used to highlight P-values less
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81 than 1e-06. \*\*\* is used to highlight P-values between 1e-06 and
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82 1e-03. If there are more than 2 populations, then an overall P-value
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83 is also printed for that eigenvector, as are the populations with
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84 minimum (minv) and maximum (maxv) eigenvector coordinate. [If there is
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85 only 1 population, no Anova statistics are printed.]
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86
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87 3. Statistical significance of differences between populations. For
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88 each pair of populations, the above Anova statistics are summed across
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89 eigenvectors. The result is approximately chisq with d.o.f. equal to
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90 the number of eigenvectors. The chisq statistic and its p-value are
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91 printed. [If there is only 1 population, no statistics are printed.]
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92
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93 We post-process the output of the PCA tool to estimate "admixture
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94 fractions". For this, we take three populations at a time and
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95 determine each one's average point in the PCA plot (by separately
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96 averaging first and second coordinates). For each combination of two
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97 center points, modeling two ancestral populations, we try to model the
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98 third central point as having a certain fraction, r, of its SNP
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99 genotypes from the second ancestral population and the remainder from
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100 the first ancestral population, where we estimate r. The output file
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101 "coordinates.txt" then contains pairs of lines like
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102
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103 projection along chord Population1 -> Population2
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104 Population3: 0.12345
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105
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106 where the number (in this case 0.1245) is the estimation of r.
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107 Computations with simulated data suggests that the true r is
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108 systematically underestimated, perhaps giving roughly 0.6 times r.
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109
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110 -----
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111
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112 **Acknowledgments**
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113
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114 We use the programs "smartpca" and "ploteig" downloaded from
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115
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116 http://genepath.med.harvard.edu/~reich/Software.htm
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117
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118 and described in the paper "Population structure and eigenanalysis"
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119 by Nick Patterson, Alkes L. Price, and David Reich, PLoS Genetics, 2 (2006), e190.
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120
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121 </help>
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122 </tool>