RNA-Seq - DEG Analysis Methods

2 minute read

RNA-Seq Workflow

1. Read quality assessment, filtering and trimming
2. Map reads against reference genome
3. Perform read counting for required ranges (e.g. exonic gene ranges)
4. Normalization of read counts
5. Identification of differentially expressed genes (DEGs)
6. Clustering of gene expression profiles
7. Gene set enrichment analysis

Challenge Projects

1. Comparison of DEG analysis methods

• Run the workflow from start to finish (steps 1-7) on the full RNA-Seq data set from Howard et al. (2013).
• Challenge project tasks
  • Compare the DEG analysis method chosen for the paper presentation with at least 1-2 additional methods (e.g. one student compares edgeR vs. baySeq, and the other student DESeq2 vs. limma/voom). Assess the results as follows:
    • Analyze the the similarities and differences in the DEG lists obtained from the two methods using intersect matrices, venn diagrams and/or upset plots.
    • Assess the impact of the DEG method on the downstream gene set enrichment analysis?
    • Plot the performance of the DEG methods in thevform of ROC curves and record their AUC values. A consensus DEG set or the one from the Howard et al. (2013) paper could be used as the ‘pseudo’ ground truth result.

2. Comparison of DEG analysis methods

• Similar as above but with different combination of DEG methods and/or performance testing approach.

References

• Howard, B.E. et al., 2013. High-throughput RNA sequencing of pseudomonas-infected Arabidopsis reveals hidden transcriptome complexity and novel splice variants. PloS one, 8(10), p.e74183. PubMed
• Guo Y, Li C-I, Ye F, Shyr Y (2013) Evaluation of read count based RNAseq analysis methods. BMC Genomics 14 Suppl 8: S2 PubMed
• Hardcastle TJ, Kelly KA (2010) baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 11: 422 PubMed
• Liu R, Holik AZ, Su S, Jansz N, Chen K, Leong HS, Blewitt ME, Asselin-Labat M-L, Smyth GK, Ritchie ME (2015) Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic Acids Res. doi: 10.1093/nar/gkv412. PubMed
• Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15: 550 PubMed
• Zhou X, Lindsay H, Robinson MD (2014) Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic Acids Res 42: e91 PubMed