Empirical evaluation of data transformations and ranking statistics for microarray analysis

摘要: There are many options in handling microarray data that can affect study conclusions, sometimes drastically. Working with a two-color platform, this uses ten spike-in experiments to evaluate the relative effectiveness of some these for experimental goal detecting differential expression. We consider two transformations, background subtraction and intensity normalization, as well six different statistics differentially expressed genes. Findings support use an intensity-based normalization procedure also indicate local be detrimental effectively verify robust outperform t-statistics identifying genes when there few replicates. Finally, we find choice image analysis software substantially influence conclusions.