Processes | Expression | Feature Flagger

Feature Flagger
Genomic data sets often contain values or observations that deviate substantially from expected values. These deviations can often be ascribed to a variety of technical or other factors , that have little to do with biological cause and effects. In these situations, it is often better to exclude outliers from an analysis than it is to include them, particularly where the cause of the anomalies can be explained by technical problems.
The Feature Flagger process screens array data and flags features that have unusually low signals, as judged by a sufficiently large deviation from the specified group median . Flagged features can then be examined more closely to determine whether they warrant further investigation.
What do I need?
Two data sets are required to run this process.
The first data set, the Input Data Set , contains all of the numeric data to be analyzed. This data set must be in the tall format where each sample corresponds to one row and each column corresponds to a separate experimental condition or array.
The drosophilaaging.sas7bdat data set, shown below, is a normalized data set derived from the Drosophila Aging experiment described in Sample Case Studies . It has 49 columns and 100 rows corresponding to 49 arrays and 100 individual probes , respectively.
The second data set is the Experimental Design Data Set (EDDS) . This required data set tells how the experiment was performed, providing information about the columns in the input data set. Note that one column in the EDDS must be named ColumnName and the values contained in this column must exactly match the column names in the input data set.
The drosophilaaging_exp.sas7bdat EDDS is shown below. Note that the ColumnName column lists the column names in the input data set. The Array column corresponds to an index variable . Note the variables describing experimental conditions.
For detailed information about the files and data sets used or created by JMP Life Sciences software, see Files and Data Sets .
The output generated by this process is summarized in a Results window. Refer to the Feature Flagger output documentation for detailed descriptions and guides to interpreting your results.