Myeloid Leukemia

228 Kohlmann et al.
those patients after a training of classification engines (9,10). The gene lists
from supervised analyses can also further be interpreted in terms of biology.
For all gene expression profiles, master data tables have to be maintained. In
these tables, rows represent all genes for which data has been collected, and
columns represent microarray experiments from individual patients. Each cell
represents the measured fluorescence intensity from the corresponding target
probe set on the microarray. Before analyzing the data, it is a routine procedure
to normalize the data (11). This is a mandatory step in the data-mining process
in order to appropriately compare the measured gene expression levels. U133
set microarray signal intensity values can be normalized by scaling the raw
data intensities to a common target intensity using a recommended mask file
(U133A/B mask file; e.g., selected global target intensity value: 5000) (see
Note 9).
3.4.1. Identification of Differentially Expressed Genes
In microarray experiments, a common goal is to detect genes that show differential
expression across two or more biological conditions. Therefore, multiple
hypothesis testing algorithms are performed on all genes simultaneously
to determine whether each one is differentially expressed. The null hypothesis
is that there is no change in expression levels between various leukemia subclasses.
The alternative hypothesis is that there is significant differential gene
expression. The analyses can be performed either between two distinct classes
(pairwise comparisons; subtype A vs subtype B) or between one distinct class
and all other remaining classes in a one-vs-all (OVA) approach.
3.4.1.1. SIGNIFICANCE ANALYSIS OF MICROARRAYS
Supervised data analyses can be performed using the significance analysis
of microarrays (SAM) software. SAM is a statistical technique for finding significant
genes in large-scale microarray-based gene expression profiles, and
correlates gene expression data with an external variable, e.g., the leukemia
subclass or karyotype information. The SAM software is an add-in package for
Microsoft Excel and analyzes statistical significance of the changes in gene
expression from repeated permutations. It was proposed by Tusher and colleagues
(12). SAM identifies genes with statistically significant changes in
expression by assimilating a set of gene-specific t-tests. Each gene is assigned
a score on the basis of its change in gene expression relative to the standard
deviation of repeated measurements for that gene. Genes with scores greater
than an adjustable threshold are deemed potentially significant. The cutoff for
significance is determined by the tuning parameter delta, chosen by the user
based on the false discovery rate (FDR). The FDR, i.e., the percentage of genes
identified by chance, is estimated by analyzing repeated permutations of
the data.