comparison ttest/stats.xml @ 12:fd8529cd1564 default tip

better t-test
author jingchunzhu
date Mon, 28 Sep 2015 12:36:12 -0700
parents cd4c13ae11ce
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11:cd4c13ae11ce 12:fd8529cd1564
1 <tool id="ucscCancerBrowserStats" description="t-tests of Difference in genomic data" name="Difference between categories (t-test)" version="0.0.1"> 1 <tool id="ucscCancerBrowserStats" description="t-tests of difference in genomic data" name="Difference between categories (t-test)" version="0.0.1">
2 <command interpreter="python"> 2 <command interpreter="python">
3 stats.py $genomicMatrix $clinicalFeatures $outFile -a="${category1}" -b="${category2}" 3 stats.py $genomicMatrix $clinicalFeatures $outFile -a="${category1}" -b="${category2}"
4 </command> 4 </command>
5 <inputs> 5 <inputs>
6 <param format="tabular" name="genomicMatrix" type="data" label="Genomic Matrix"/> 6 <param format="tabular" name="genomicMatrix" type="data" label="Genomic Matrix"/>
21 <param name="category2" value="B"/> 21 <param name="category2" value="B"/>
22 <output name="outFile" value="sample.stats.output.txt"/> 22 <output name="outFile" value="sample.stats.output.txt"/>
23 </tests> 23 </tests>
24 <help> 24 <help>
25 25
26 This tool performs statistical tests found in the UCSC Cancer Genomics 26 This tool performs t-test on genomic data between two groups of samples, which can be used to identify for example, differentially expressed genes or probes. The genomic data is in the format of UCSC Xena genomic matrix (a tab-deliminated matrix) with rows representing genes or probes and columns representing samples. The phenotype matrix assigns samples into groups. The tool compares two groups of samples, and computes the t-statistics, p value, and delta of medians for each probe/gene between the two groups. The result can be downloaded to programs such as EXCEL for sorting based on the t-statistics.
27 Browser. The input data is a genomic matrix (containing genomic data,
28 with rows representing genes or probes and columns representing
29 samples or patients), a clinical matrix of two (or more) columns
30 assigning categorical values to the samples, and two categorical
31 values of interest. The tool identifies the samples corresponding to
32 each categorical value, then identifies the columns in the genomic
33 matrix corresponding to those sets of samples, which identifies two
34 groups of columns. For each row in the genomic matrix, it extracts
35 the value for those two sets of columns, performs a t-test on the two
36 sets of values, and returns the result for the row. Any values for
37 any columns NOT pertaining to one of the categorical values of
38 interest are ignored.
39 27
40 The user runs this tool with th following steps: 28 The user runs this tool with the following steps:
29
30 1. Specify a genomic matrix. The expected format is with rows representing genes and columns representing samples, and the first line contains sample names. Matrix can be obtained from UCSC Xena bulk download. See below for an example.
41 31
42 32
43 1. Specify a genomic matrix. The expected format is with rows representing 33 2. Specify a phenotype matrix. Here, rows indicate samples, columns indicate phenotypes or annotations. Matrix can be obtained from UCSC Xena heatmap download. See below for an example.
44 genes and columns representing samples, and the first line contains sample
45 names.
46
47 2. Specify a clinical matrix. Here, rows indicate samples, columns
48 indicate clinical features, and the header row contains feature names.
49 The first column MUST indicate the sample names, and MUST correspond
50 to the column names of the genomic matrix. The clinical feature of
51 interest MUST be in the second column. Any other columns will be
52 ignored.
53 34
54 35
55 3. Indicate two clinical values that you want to use for defining the 36 3. Specify the two categorical values that you want to use for defining the two groups. For example, the two groups could be A and B, 0 and 1, etc.
56 two groups. For example, the two groups could be "Red group" and
57 "Green group", 0 and 1, or whatever.
58 37
59 The output indicates, for each row, the t-statistic reporting on the
60 difference between the two groups of columns (as specified by the two
61 clinical values), the p-value corresponding to that t-statistic, the
62 median value for each group, and the difference between the medians. If it
63 cannot calculate these values, it returns a vector of NAs.
64 38
65 For example, given the following genomic matrix for (1):: 39 4. The output is, for each probe/gene (in each row), the t-statistics, the p-value, the median value for each group, and the difference between the medians. If it cannot calculate these values, it returns a vector of NAs.
66 40
67 Gene 1 2 3 4 5 6 7 8 9 10 41
42 **Input genomic matrix**::
43
44 Gene s1 s2 s3 s4 s5 s6 s7 s8 s9 s10
68 G1 2.0 2.2 3.2 1.1 5.1 8.1 3.2 1.1 8.1 0.2 45 G1 2.0 2.2 3.2 1.1 5.1 8.1 3.2 1.1 8.1 0.2
69 G2 0.1 8.2 9.1 4.2 6.1 4.9 3.9 2.3 1.1 0.2 46 G2 0.1 8.2 9.1 4.2 6.1 4.9 3.9 2.3 1.1 0.2
70 47
71 and given the following clinical matrix for (2):: 48 **Input phenotyp matrix**::
72 49
73 sample_id Value 50 sample_id Value
74 1 A 51 s1 A
75 2 A 52 s2 A
76 3 B 53 s3 B
77 4 C 54 s4 C
78 5 B 55 s5 B
79 6 B 56 s6 B
80 7 A 57 s7 A
81 8 A 58 s8 A
82 9 B 59 s9 B
83 10 A 60 s10 A
84 61
85 and given A for Category 1 and B for Category 2 62 **Category 1 : A**
86 63
87 the tool will assemble the following two groups of values:: 64 **Category 2 : B**
88 65
89 G1 A:(2.0, 2.2, 3.2, 1.1, 0.2) B:(3.2, 5.1, 8.1, 8.1) 66 **Output**::
90 G2 A:(0.1, 8.2, 3.9, 2.3, 0.2) B:(9.1, 6.1, 4.9, 1.1)
91
92 Note that the values for sample_id 4 do not appear, because it has a Value
93 of C in the second column, which is neither A nor B.
94
95 And it will return the output::
96 67
97 Gene Statistic pValue Median1 Median2 Delta 68 Gene Statistic pValue Median1 Median2 Delta
98 G1 -4.168999 0.004194 2.000000 6.600000 -4.600000 69 G1 -4.168999 0.004194 2.000000 6.600000 -4.600000
99 G2 -1.198486 0.269724 2.300000 5.500000 -3.200000 70 G2 -1.198486 0.269724 2.300000 5.500000 -3.200000
100 71