1.1 Basic concepts

• Organisms are assayed on multiple features
• Variability in feature measures exhibits structure
• Clustering:
  • For some grouping, between-group variation is larger than within-group variation
  • Our goals are to find, evaluate, and interpret such groupings
• Classification:
  • Organisms are sorted into classes and labeled
  • Rules for classification are maps from features to labels
  • Our goals are to find, evaluate, and interpret such rules

1.2 Yeast cell cycle: phenotypic transitions (Lee, Rinaldi et al. Science 2002)

• How do you expect gene expression time series to cluster?

1.3 Expression clusters

• Spellman et al MBC ’98; dendrogram on left: bottom half labeled MCM

1.4 Species and organ of origin: microarrays and orthologues (McCall et al., NAR 2012)

1.5 Question

• Multivariate analysis of the yeast cell cycle uses the gene expression trajectory over time as the data vector

• Multivariate analysis of tissue of origin data uses a snapshot of the transcriptome as the data vector

• Should the same methods be used for visualization and interpretation? Why or why not?
  • roles of convenience and agnosticism
  • roles of biological knowledge and potential for corroboration

1.6 Species, organ of origin, and batch: RNA-seq and orthologues (Lin et al., PNAS 2014)