342 Fendler and Atwal: Systematic deciphering of cancer genome networks Thus, we must also, in a systematic, well- statistic will be in the far tails of the t-distri- defined, mathematical way, amplify net- bution, returning a small p-value — a meas- works of interest using appropriate models. ure of how extreme an observation is [26]. Luckily, probabilistic network models are This problem is further compounded when optimized for specifically these tasks. The we test tens-of-thousands of genes where derived networks from modeling and ge- there is a greater chance of seeing a large nomic data may then improve understand- statistical fluctuation. We need to be even ing of gene interactions in cancer stricter in what we call a significant expres- progression and help link causative muta- sion change as opposed to a normal statisti- tions to disease. cal fluctuation. Typically, the p-value is corrected using, for example, a Bonferonni correction. This then boils down to filtering t o pic S out what gene is important, what gene is not, and what genes your analysis suggests are The Statistical Tools: Are All Network important but really are not. The last of these Identification Tools Created Equal? three are called “false positives.” These in- Armed with a massive amount of vestigations can be further improved upon probes on a single array or a complete ge- through the use of the false discovery rate nomic library from NGS technologies, the that determines how likely the “positive” whole genome can now be investigated in finding is a true positive (a real result) one experiment. While creating a revolution [27,28]. in cancer, genomic technologies still suffer Often, identification of gene candidates from difficulties in data analysis [23-25]. in cancer samples is insufficient to build a Core issues include noise and testing too cancer model, thus sometimes we must at- many hypotheses. Since most genes are not tempt to characterize the samples in some aberrantly expressed in a cell, gene expres- general way based on the genomic alter- sion fluctuates about its healthy homeosta- ations measured. One way to accomplish tic mean. Thus, each gene has a variable this task is through cluster analyses, such as range of expression values that may be any hierarchical clustering, employed by group- random value. If we are not careful, we can ing genes with similar expression [29], mistakenly associate a large deviation from which often leads to discovery of tumor sub- expression as significant, even though the types. These types of investigations define expression was just a fluctuation in the can- “distances” representing similarity between cer sample. Biologists attempt to mitigate gene expression profiles and grouping those this difficulty by increasing the number of similar ones together. The question here is, technical replicates, limiting technical er- what patterns of gene expression emerge rors, as well as increasing biological repli- and are they consistent across samples? cates reducing the impact of “passenger” These types of clustering analyses, referred genes — genes that are altered, but non-dri- to as unsupervised learning, often do not in- vers in the cancer. form why genes cluster; however, it can Despite these efforts, it is still difficult offer inferences into why. Other times, mak- to distinguish between a significant change ing predictions with expression data is used; and a normal statistical fluctuation. For ex- supervised learning, for example, utilizes ample, suppose a gene is suspected to be up- large sets of data to “train” a model, such as regulated. The expression mean is found Random Forest models [30], artificial neural from biological replicates of our cancer and networks [31], and support vector machines healthy replicates. These means can be com- [32], and then make predictions. pared using a t-test, which makes the as- While these analyses help identify in- sumption that the t-statistic follows the teresting genes, aggregate information, and t-distribution. If our measurement is signif- make predictions, they do not construct net- icantly differentially expressed, then the t- works of interacting genes. In this context,