The
Control Set Normalization
process normalizes data by subtraction of (the
mean
or
median
value of) control sample measurements or arrays, from experimental samples. The subtraction can be done either within subject (for example, replicate arrays per unique subjects) or across a set or sets of experimental subjects and control subjects.
Two data sets are required for this process. The first, the
Input Data Set
, contains all of the numeric data to be analyzed. The
drosophilaaging.sas7bdat
data set from the
Drosophila
aging experiment of Jin, et al. (2001) that is described in
Drosophila Aging Experimental Data
serves as an example, and is shown below. It has 48 data columns and 100 rows. Note that this is a
tall
data set; each
probe
corresponds to one row whereas each column corresponds to a separate experimental condition.
The second data set is the
Experimental Design Data Set (EDDS)
. The
drosophilaaging_exp.sas7bdat
EDDS serves as an example, and is shown below. 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 raw data from this experiment were evaluated using
Distribution Analysis
. The
Overlay Plot
shown below illustrates the raw univariate
distributions
of all 48 channels from the 24 arrays.
Visually, the estimated distributions significantly vary among all the 48 channels here. This inherent variability among arrays and dye indicates that
normalization
across arrays and channels is essential for effective analysis of these data.
For detailed information about the files and data sets used or created by JMP Life Sciences software, see
Files and Data Sets
.
Refer to the
Control Set Normalization
output documentation for detailed descriptions of the output of this process.