I'm performing some meta-analysis on gene-expression data from microarrays, and am looking through some of the techniques used to do this. One thing that crops up often is the use of latent variable models.
Either they are used on a per gene basis to calculate the probability of differential expression, such as Choi et al., or in a dimension reduction scheme, to highlight groups of "signature" genes as in Martoglio et al..
Both of these latent-variable based approaches are appealing to me, probably because of my Machine Learning background, as the model that the authors are using to define differential expression in both cases makes more sense to me than the more traditional statistical methods (*x*-tests, ranking).
However, I'm trying to embark on a pragmatism-not-idealism approach to work (and actually get something done), and I know that latent variable models can be a lot of effort sometimes. My questions are, therefore:
- Does anyone have any "good" experiences analysing gene-expression using latent-variable modelling approaches for differential analaysis in microarrays? For example a latent-variable model out-performed a more standard approach like SAM, or it did better at meta-analysis than RankProd.
- Does anyone have a feel for how easy latent variable models are when trying to explain to your biologist collaborators? Is the richer model worth the effort of trying to explain it?
- Is there a 'standard' R package that is used more than others for this kind of analysis? Typically, when meta-analysis shows people mention RankProd. Is there an equivalent package that the community recommends for latent-variable based approaches?