The
Distribution Analysis
process displays univariate
distribution
results for
variables
in a SAS data set. It also optionally computes and overlays density estimates for the variables. Examination of the results of this process enables you to assess the quality of your data and evaluate the effectiveness of different quality control and
normalization
methods at preparing your data for analysis.
One
Input Data Set
is
required
for this process. This data set
contains all of the numeric data to be analyzed. It 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_norm.sas7bdat
data set is a normalized data set derived from the
Drosophila
Aging Experiment described in
Drosophila Aging Experimental Data
, and serves as an example. It has 49 columns and 100 rows.
An
Experimental Design Data Set (EDDS)
is
optional
. This 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 serves as an example, and is shown below. Note that the
ColumnName
column lists the column names in the input data set. This column is a concatenation of several other columns detailing the experimental design.
The
drosophilaaging_norm.sas7bdat
and
drosophilaaging_exp.sas7bdat
data sets are included in the
Sample Data
folder that comes with JMP Genomics.
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
Distribution Analysis
output documentation for detailed descriptions of the output of this process.