The
Upper Quartile Scaling
normalization
process applies a scaling
factor
based on upper quartile to scale each column. The within column upper quartile is calculated by excluding the rows of all 0 (or
missing
) values. Each upper quartile is further standardized by dividing by the geometric
mean
among all upper quartiles across columns to be the upper quartile scaling factors.
For additional details, please refer to
Bullard et al.
(BMC Bioinformatics 2010).
Two data sets are
required
by this process:
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•
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A SAS
Input Data Set
. The
sam_mus_gse18905_chr1_6s.sas7bdat
data set serves as an example, and is shown below. This is a
tall
data set, with 12 columns and 3,421 rows.
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|
•
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An
Experimental Design Data Set (EDDS)
. 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
edf_mus_gse18905_chr1_6s.sas7bdat
data set serves as an example, and is shown below.
|
The
sam_mus_gse18905_ch1_6s.sas7bdat
and
edf_mus_gse18905_chr1_6s.sas7bdat
data sets were downloaded from
GEO
.
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
Upper Quartile Scaling
output documentation for detailed descriptions of the output of this process.