What are the challenges of analyzing microarray data?

There are several challenges when analyzing microarray data. One challenge is that different microarray platforms can yield varying results due to differences in design, probe annotation, and other factors. This makes it difficult to compare results across studies or platforms. Another challenge is annotation issues. The continuous evolution of genome annotations means that older annotations may become outdated, making the comparison of data across different studies or platforms harder. A third challenge is that microarrays often involve testing many hypotheses simultaneously, increasing the risk of false positives. Additionally, small or poorly estimated sample sizes can lead to unreliable results. Gene lists arising from microarray data can vary based on the composition of training and test sets. Another challenge is the number of transcripts from alternative splicing in the mammalian genome may not have been fully captured by earlier microarray designs, affecting cross-platform comparison accuracy. Lastly, when building models with many genes, there's a risk of data overfitting.