The *t *test is a simple, statistically based method for detecting differentially expressed genes (see Box for details of how it is calculated). In replicated experiments, the error variance (see Box ) can be estimated for each gene from the log ratios, and a standard *t *test can be conducted for each gene []; the resulting *t *statistic can be used to determine which genes are significantly differentially expressed (see below). This gene-specific *t *test is not affected by heterogeneity in variance across genes because it only uses information from one gene at a time. It may, however, have low power because the sample size - the number of RNA samples measured for each condition - is small. In addition, the variances estimated from each gene are not stable: for example, if the estimated variance for one gene is small, by chance, the *t *value can be large even when the corresponding fold change is small. It is possible to compute a global *t *test, using an estimate of error variance that is pooled across all genes, if it is assumed that the variance is homogeneous between different genes [,]. This is effectively a fold-change test because the global *t *test ranks genes in an order that is the same as fold change; that is, it does not adjust for individual gene variability. It may therefore suffer from the same biases as a fold-change test if the error variance is not truly constant for all genes.

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