1 Data import and preparations | 2 How many genes are expressed? | 3 Cluster stability analysis | 4 Lineage Markers | 5 Differentially expressed genes from E3.25 to E3.5 | 6 Principal Component Analysis | 7 Further analyses of FGF4-KO | 8 Jensen-Shannon Divergence analysis | 9 Classification of temporal profiles | 10 qPCR data analysis | A Influence of cell position on gene expression | B Correlation between Fgf ligands and Fgf receptors | C Session info | D The data import script readdata.R

1 Data import and preparations | 2 How many genes are expressed? | 3 Cluster stability analysis | 4 Lineage Markers | 5 Differentially expressed genes from E3.25 to E3.5 | 6 Principal Component Analysis | 7 Further analyses of FGF4-KO | 8 Jensen-Shannon Divergence analysis | 9 Classification of temporal profiles | 10 qPCR data analysis | A Influence of cell position on gene expression | B Correlation between Fgf ligands and Fgf receptors | C Session info | D The data import script readdata.R

Example data | Using ggplot2

Introduction | Setup and Notation | Likelihood for Incremental Normalization | Profile Likelihood | Summary | References

Getting started | Limitations | Other determinants of variance | Numerical stability and convergence | Running VSN on data from a single two-colour array | Running VSN on data from multiple arrays ("single colour normalisation") | Running VSN on Affymetrix genechip data | Print-tip groups | Normalisation against an existing reference dataset | The calibration parameters | The calibration parameters and the additive-multiplicative error model | More on calibration | Variance stabilisation without calibration | Quality assessment | References

Demo | Session Info