Differential expression methods evaluation

Learning objectives

• describe common ways to assess methods’ performance
• explain false positive and false negative errors
• assess method given top DE genes and consistency matrix

---

Performance indicators

• known data: using data we know something about to get “positive controls”
• simulated data: null-data sets by re-sampling, modeling data sets based on various distributions
• comparing methods under different scenarios
• investigating results, i.e. distributions of the detected DEs

Type I and II errors

Fig: Sensitivity and specificity (adapated from Wikipedia)

Fig: Error types (adapated from Wikipedia)

Comparative study (1)

Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data (Wang et al, BMC Bioinformatics 2019)