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1 <tool id="ucscCancerBrowserStats" description="t-tests of Difference in genomic data" name="Difference between categories (t-test)" version="0.0.1">
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0
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2 <command interpreter="python">
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3 stats.py $genomicMatrix $clinicalFeatures $outFile -a="${category1}" -b="${category2}"
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4 </command>
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5 <inputs>
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6 <param format="tabular" name="genomicMatrix" type="data" label="Genomic Matrix"/>
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7 <param format="tabular" name="clinicalFeatures" type="data" label="Phenotype Matrix"/>
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8 <param type="text" name="category1" label="Category 1" optional="false"/>
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9 <param type="text" name="category2" label="Category 2" optional="false"/>
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10 </inputs>
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11 <outputs>
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12 <data format="tabular" name="outFile" />
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13 </outputs>
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14 <requirements>
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9
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15 <requirement type="package" version="1.0" >cancerBrowserStats</requirement>
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0
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16 </requirements>
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17 <tests>
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11
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18 <param name="genomicMatrix" value="sample.genomic.matrix.txt" />
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19 <param name="clinicalMatrix" value="sample.clinical.matrix.txt" />
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20 <param name="category1" value="A"/>
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21 <param name="category2" value="B"/>
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22 <output name="outFile" value="sample.stats.output.txt"/>
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23 </tests>
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24 <help>
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25
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26 This tool performs statistical tests found in the UCSC Cancer Genomics
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27 Browser. The input data is a genomic matrix (containing genomic data,
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28 with rows representing genes or probes and columns representing
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29 samples or patients), a clinical matrix of two (or more) columns
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30 assigning categorical values to the samples, and two categorical
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31 values of interest. The tool identifies the samples corresponding to
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32 each categorical value, then identifies the columns in the genomic
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33 matrix corresponding to those sets of samples, which identifies two
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34 groups of columns. For each row in the genomic matrix, it extracts
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35 the value for those two sets of columns, performs a t-test on the two
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36 sets of values, and returns the result for the row. Any values for
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37 any columns NOT pertaining to one of the categorical values of
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38 interest are ignored.
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39
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40 The user runs this tool with th following steps:
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41
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42
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43 1. Specify a genomic matrix. The expected format is with rows representing
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44 genes and columns representing samples, and the first line contains sample
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45 names.
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46
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47 2. Specify a clinical matrix. Here, rows indicate samples, columns
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48 indicate clinical features, and the header row contains feature names.
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49 The first column MUST indicate the sample names, and MUST correspond
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50 to the column names of the genomic matrix. The clinical feature of
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51 interest MUST be in the second column. Any other columns will be
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52 ignored.
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53
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54
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55 3. Indicate two clinical values that you want to use for defining the
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56 two groups. For example, the two groups could be "Red group" and
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57 "Green group", 0 and 1, or whatever.
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58
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59 The output indicates, for each row, the t-statistic reporting on the
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60 difference between the two groups of columns (as specified by the two
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61 clinical values), the p-value corresponding to that t-statistic, the
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62 median value for each group, and the difference between the medians. If it
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63 cannot calculate these values, it returns a vector of NAs.
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64
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65 For example, given the following genomic matrix for (1)::
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66
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67 Gene 1 2 3 4 5 6 7 8 9 10
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68 G1 2.0 2.2 3.2 1.1 5.1 8.1 3.2 1.1 8.1 0.2
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69 G2 0.1 8.2 9.1 4.2 6.1 4.9 3.9 2.3 1.1 0.2
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70
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71 and given the following clinical matrix for (2)::
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72
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73 sample_id Value
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74 1 A
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75 2 A
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76 3 B
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77 4 C
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78 5 B
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79 6 B
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80 7 A
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81 8 A
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82 9 B
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83 10 A
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84
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85 and given A for Category 1 and B for Category 2
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86
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87 the tool will assemble the following two groups of values::
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88
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89 G1 A:(2.0, 2.2, 3.2, 1.1, 0.2) B:(3.2, 5.1, 8.1, 8.1)
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90 G2 A:(0.1, 8.2, 3.9, 2.3, 0.2) B:(9.1, 6.1, 4.9, 1.1)
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91
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92 Note that the values for sample_id 4 do not appear, because it has a Value
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93 of C in the second column, which is neither A nor B.
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94
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95 And it will return the output::
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96
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97 Gene Statistic pValue Median1 Median2 Delta
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98 G1 -4.168999 0.004194 2.000000 6.600000 -4.600000
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99 G2 -1.198486 0.269724 2.300000 5.500000 -3.200000
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100
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101
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102 </help>
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103 </tool>
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