Myeloid Leukemia

Classification of AML by DNA-Oligonucleotide Microarrays 229
3.4.1.2. STATISTICAL SIGNIFICANCE
Microarrays can measure the expression of thousands of genes to identify
expression changes between different biological states. Methods based on conventional
t-tests provide the probability (P) that a difference in gene expression
occurred by chance. Although p < 0.01 is significant in the context of
experiments designed to evaluate small numbers, a microarray experiment for
more 10,000 genes would identify 100 genes by chance. Thus, methods are
needed to determine the significance of these changes while accounting for the
enormous number of genes. Commonly, to address the multiple testing problem,
false-discovery rates (FDR) of genes are calculated according to a statistical
method adapted specifically for microarrays (13). As it automatically takes
into account the fact that thousands of genes are simultaneously being tested,
the FDR is a widely accepted method to measure statistical significance in
genome-wide studies. A measure of statistical significance called the q-value
is associated with each tested feature. Similarly to the p-value, the q-value
gives each measured gene its own individual measure of significance. Whereas
the p-value is a measure of significance in terms of the false-positive rate, the
q-value is a measure in terms of the false-discovery rate (FDR). In a microarray
data set, the q-value of a particular feature is the expected proportion of false
positives incurred when calling that feature significant. The q-values can be
used as an exploratory guide for which features to investigate further, e.g.,
through the use of pathway applications or classification engines.
3.4.2. Estimation of Prediction Performance
The generalization performance of the different algorithms can be estimated
by performing cross-validation methods (CV). These methods are based on the
idea that the most unbiased test of the predictive error is by applying it to data
that were not used in the building of the initial predictive model. A common
application is to partition a dataset into two parts—to fit the model on the first
part, and to assess the predictive capability of that model on the second part.
Depending on the CV method, the complete data set is split into different proportions
of a training set and a test set. Each approach is performed to determine
the accuracy, i.e., the probability of correct classification of a previously
unknown sample.
3.4.2.1. LEAVE-ONE-OUT CROSS-VALIDATION
The leave-one-out cross-validation (LOOCV) method is one of several
approaches to estimating how well a model that was trained on training data is
going to perform on future as-yet-unseen data. LOOCV implies that one sample
is excluded from the complete data set n and the remaining samples are used
for training. This training and prediction process is repeated n times to include