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Machine learning methods (including clustering, dimensionality reduction= , classification and regression modeling, resampling techniques, etc.), ANO= VA modeling, and empirical Bayes analysis. 12 hour minimum ($876= internal, $1116 external) per project.
Unsupervised methods provide exploratory data analysis useful for gettin= g a big picture view: can provide valuable QC information and can help to b= oth assess expected trends and identify unexpected patterns in your data.= p>
RNAseq experiments yield simultaneous measurements of many intrinsically= similar variables (gene expression levels) but with often limited sample s= izes. Empirical Bayes methods provide a statistical approach designed just = for such situations which "borrow strength" across genes to increase statis= tical power and decrease false discovery.
Deliverables:
Tables of model parameters, p-values, and FDR q-values (in tab-delim= ited and excel format)
Boxplots (stratified by sample group) and pairs plots of top genes p= rovided 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 availabl= e. Analysis will be conducted using R and/or Python scripts.