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Python Lab Pandas Library - II

Python Lab Pandas Library - II. Proteomics Informatics, Spring 2014 Week 8 18 th Mar, 2014 Himanshu.Grover@nyumc.org. From last w eek…. Today. Differential Gene Expression example leukemia dataset ( Golub et. a l. ) http://www.biolab.si/supp/bi-cancer/projections /. HW-3a.

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Python Lab Pandas Library - II

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  1. Python LabPandas Library - II Proteomics Informatics, Spring 2014 Week 8 18th Mar, 2014 Himanshu.Grover@nyumc.org

  2. From last week…

  3. Today • Differential Gene Expression example • leukemia dataset (Golub et. al. ) • http://www.biolab.si/supp/bi-cancer/projections/

  4. HW-3a • For top-5 differentially expressed genes (according to p-value) • compute pairwise correlation matrix (it will actually be a data frame) • Check out the documentation of <DataFrame>.corr() • (exprDF.corr? in our example) • Hint: • Use exprDF.ix[:, <columns>] to get dataframe with only top-5 genes • Apply corr to this • Save it to another variable • Write it to a file: • Check out <DataFrame>.to_csv function

  5. HW-3b • For top-5 genes output summary statistics to a file • Hint: • Check out <DataFrame>.describe function • This outputs a dataframe. Save it to a variable and then to a file • Similar ideas from previous part

  6. HW-3c • Add a new function for preprocessing all gene expression values before applying t-test • For every gene (x), perform: • (x-mean(x))/std(x, ddof=1) • Return this value • Use apply() as discussed in the case of applying t-test • Note that this is similar to the “perform_tTest_two()” case, since this operation on each column returns back the whole column (Series) of values back.

  7. References • Book: Python for Data Analysis

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