view ttest/stats.xml @ 11:cd4c13ae11ce

modify

<tool id="ucscCancerBrowserStats" description="t-tests of Difference in genomic data" name="Difference between categories (t-test)" version="0.0.1">
  <command interpreter="python">
    stats.py  $genomicMatrix $clinicalFeatures $outFile -a="${category1}" -b="${category2}"
  </command>
  <inputs>
    <param format="tabular" name="genomicMatrix" type="data" label="Genomic Matrix"/>
    <param format="tabular" name="clinicalFeatures" type="data" label="Phenotype Matrix"/>
    <param type="text" name="category1" label="Category 1" optional="false"/>
    <param type="text" name="category2" label="Category 2" optional="false"/>
  </inputs>
  <outputs>
    <data format="tabular" name="outFile" />
  </outputs>
  <requirements>
    <requirement type="package" version="1.0" >cancerBrowserStats</requirement>
  </requirements>
  <tests>
    <param name="genomicMatrix" value="sample.genomic.matrix.txt" />
    <param name="clinicalMatrix" value="sample.clinical.matrix.txt" />
    <param name="category1" value="A"/>
    <param name="category2" value="B"/>
    <output name="outFile" value="sample.stats.output.txt"/>
  </tests>
  <help>

This tool performs statistical tests found in the UCSC Cancer Genomics
Browser.  The input data is a genomic matrix (containing genomic data,
with rows representing genes or probes and columns representing
samples or patients), a clinical matrix of two (or more) columns
assigning categorical values to the samples, and two categorical
values of interest.  The tool identifies the samples corresponding to
each categorical value, then identifies the columns in the genomic
matrix corresponding to those sets of samples, which identifies two
groups of columns.  For each row in the genomic matrix, it extracts
the value for those two sets of columns, performs a t-test on the two
sets of values, and returns the result for the row.  Any values for
any columns NOT pertaining to one of the categorical values of
interest are ignored.

The user runs this tool with th following steps:


1. Specify a genomic matrix.  The expected format is with rows representing 
genes and columns representing samples, and the first line contains sample 
names.

2. Specify a clinical matrix.  Here, rows indicate samples, columns
indicate clinical features, and the header row contains feature names.
The first column MUST indicate the sample names, and MUST correspond
to the column names of the genomic matrix.  The clinical feature of
interest MUST be in the second column.  Any other columns will be
ignored.


3. Indicate two clinical values that you want to use for defining the
two groups.  For example, the two groups could be "Red group" and
"Green group", 0 and 1, or whatever.

The output indicates, for each row, the t-statistic reporting on the
difference between the two groups of columns (as specified by the two
clinical values), the p-value corresponding to that t-statistic, the
median value for each group, and the difference between the medians.  If it 
cannot calculate these values, it returns a vector of NAs.

For example, given the following genomic matrix for (1)::

    Gene  1    2    3    4    5    6    7    8    9    10
    G1    2.0  2.2  3.2  1.1  5.1  8.1  3.2  1.1  8.1  0.2
    G2    0.1  8.2  9.1  4.2  6.1  4.9  3.9  2.3  1.1  0.2

and given the following clinical matrix for (2)::

    sample_id Value
    1         A
    2         A
    3         B
    4         C
    5         B
    6         B
    7         A
    8         A
    9         B
    10        A
    
and given A for Category 1 and B for Category 2

the tool will assemble the following two groups of values::

    G1 A:(2.0, 2.2, 3.2, 1.1, 0.2) B:(3.2, 5.1, 8.1, 8.1)
    G2 A:(0.1, 8.2, 3.9, 2.3, 0.2) B:(9.1, 6.1, 4.9, 1.1) 

Note that the values for sample_id 4 do not appear, because it has a Value
of C in the second column, which is neither A nor B.

And it will return the output::

    Gene Statistic  pValue    Median1   Median2   Delta
    G1   -4.168999  0.004194  2.000000  6.600000  -4.600000
    G2   -1.198486  0.269724  2.300000  5.500000  -3.200000


  </help>
</tool>