The two Affymetrix Latin-Square spike-in data sets (HG-U113A and HG-U95Av2) each contain several spiked-in transcripts in known locations on a set of chips.
Validation using both spike-in data and real experimental data proves the method is effective at isolating differentially expressed genes statistically, thereby eliminating the need for arbitrary restrictions such as fold change.
More empirical alternatives include the use of spike-in data and re-sampling.
Results are comparable to those obtained with simulations and spike-in data.
Spike-in data confirm observations and conclusions made on the simulations.
Choe et al. generated a spike-in data set with a defined background, in contrast to the Affymetrix spike-in data sets, which use HeLa RNA as background [2].
The comparison process relies on four steps: gene list analysis, simulations, spike-in data and re-sampling.
To verify these results, we measured each program's correlation and slope in the Affymetrix experimental spike-in data set.
The three other approaches (simulations, spike-in data and re-sampling) propose a direct comparison of power values.
The VAS and SMRI data were used for variation and precision assessment; the AAS and spike-in data were used for accuracy and overall performance evaluation.
As discussed later, this additional normalization introduces a variety of problems so, except for spike-in data, all comparisons will be made using RPM rather than RPKM.
