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annotate pca.xml @ 29:fb944979bf35

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Update to Miller Lab devshed revision 5f0be4d1db30
author Richard Burhans <burhans@bx.psu.edu>
date Thu, 25 Jul 2013 12:01:47 -0400
parents 8997f2ca8c7a
children a631c2f6d913
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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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3
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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 <!--
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17 <tests>
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18 <test>
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19 <param name="input" value="fake" ftype="gd_ped" >
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20 <metadata name="base_name" value="admix" />
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21 <composite_data value="test_out/prepare_population_structure/prepare_population_structure.html" />
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22 <composite_data value="test_out/prepare_population_structure/admix.ped" />
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23 <composite_data value="test_out/prepare_population_structure/admix.map" />
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24 <edit_attributes type="name" value="fake" />
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25 </param>
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27 <output name="output" file="test_out/pca/pca.html" ftype="html" compare="diff" lines_diff="2">
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28 <extra_files type="file" name="admix.geno" value="test_out/pca/admix.geno" />
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29 <extra_files type="file" name="admix.gd_indivs" value="test_out/pca/admix.gd_indivs" />
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30 <extra_files type="file" name="admix.gd_snp" value="test_out/pca/admix.gd_snp" />
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31 <extra_files type="file" name="coordinates.txt" value="test_out/pca/coordinates.txt" />
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32 <extra_files type="file" name="explained.txt" value="test_out/pca/explained.txt" />
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33 <extra_files type="file" name="par.admix" value="test_out/pca/par.admix" compare="diff" lines_diff="10" />
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34 <extra_files type="file" name="PCA.pdf" value="test_out/pca/PCA.pdf" compare="sim_size" delta = "1000" />
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35 </output>
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36
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37 </test>
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38 </tests>
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39 -->
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40
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41 <help>
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42
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43 **Dataset formats**
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44
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45 The input dataset is in gd_ped_ format.
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46 The output dataset is html_ with links to a pdf for a graphical output and
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47 text files. (`Dataset missing?`_)
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48
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49 .. _gd_ped: ./static/formatHelp.html#gd_ped
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50 .. _html: ./static/formalHelp.html#html
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51 .. _Dataset missing?: ./static/formatHelp.html
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52
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53 -----
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54
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55 **What it does**
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56
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57 The user selects a gd_ped dataset generated by the Prepare Input tool.
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58 The PCA tool runs a
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59 Principal Components Analysis on the input genotype data and constructs
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60 a plot of the top two principal components. It also reports the
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61 following estimates of the statistical significance of the analysis.
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62
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63 1. Average divergence between each pair of populations. Specifically,
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64 from the covariance matrix X whose eigenvectors were computed, we can
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65 compute a "distance", d, for each pair of individuals (i,j): d(i,j) =
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66 X(i,i) + X(j,j) - 2X(i,j). For each pair of populations (a,b) now
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67 define an average distance: D(a,b) = \sum d(i,j) (in pop a, in pop b)
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68 / (\|pop a\| * \|pop b\|). We then normalize D so that the diagonal
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69 has mean 1 and report it.
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70
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71 2. Anova statistics for population differences along each
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72 eigenvector. For each eigenvector, a P-value for statistical
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73 significance of differences between each pair of populations along
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74 that eigenvector is printed. +++ is used to highlight P-values less
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75 than 1e-06. \*\*\* is used to highlight P-values between 1e-06 and
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76 1e-03. If there are more than 2 populations, then an overall P-value
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77 is also printed for that eigenvector, as are the populations with
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78 minimum (minv) and maximum (maxv) eigenvector coordinate. [If there is
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79 only 1 population, no Anova statistics are printed.]
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80
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81 3. Statistical significance of differences between populations. For
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82 each pair of populations, the above Anova statistics are summed across
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83 eigenvectors. The result is approximately chisq with d.o.f. equal to
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84 the number of eigenvectors. The chisq statistic and its p-value are
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85 printed. [If there is only 1 population, no statistics are printed.]
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86
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87 We post-process the output of the PCA tool to estimate "admixture
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88 fractions". For this, we take three populations at a time and
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89 determine each one's average point in the PCA plot (by separately
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90 averaging first and second coordinates). For each combination of two
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91 center points, modeling two ancestral populations, we try to model the
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92 third central point as having a certain fraction, r, of its SNP
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93 genotypes from the second ancestral population and the remainder from
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94 the first ancestral population, where we estimate r. The output file
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95 "coordinates.txt" then contains pairs of lines like
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96
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97 projection along chord Population1 -> Population2
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98 Population3: 0.12345
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99
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100 where the number (in this case 0.1245) is the estimation of r.
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101 Computations with simulated data suggests that the true r is
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102 systematically underestimated, perhaps giving roughly 0.6 times r.
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103
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104 -----
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105
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106 **Acknowledgments**
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107
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108 We use the programs "smartpca" and "ploteig" downloaded from
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109
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110 http://genepath.med.harvard.edu/~reich/Software.htm
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111
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112 and described in the paper "Population structure and eigenanalysis"
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113 by Nick Patterson, Alkes L. Price, and David Reich, PLoS Genetics, 2 (2006), e190.
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114
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115 </help>
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116 </tool>