Machine learning methods (including clustering, dimensionality reduction, classification and regression modeling, resampling techniques, etc.), ANOVA modeling, and empirical Bayes analysis. 12 hour minimum ($876 internal, $1116 external) per project.
Unsupervised methods provide exploratory data analysis useful for getting a big picture view: can provide valuable QC information and can help to both assess expected trends and identify unexpected patterns in your data.
RNAseq experiments yield simultaneous measurements of many intrinsically similar variables (gene expression levels) but with often limited sample sizes. Empirical Bayes methods provide a statistical approach designed just for such situations which "borrow strength" across genes to increase statistical power and decrease false discovery.
Deliverables:
Tables of model parameters, p-values, and FDR q-values (in tab-delimited and excel format)
Boxplots (stratified by sample group) and pairs plots of top genes provided in png and pdf format
Many methods available for classification and regression as appropriate to your analysis. Model performance may be assessed using standard metrics evaluated under cross-validation or using independent test sets if available. Analysis will be conducted using R and/or Python scripts.