Transcription of Practical Guide to Interpreting RNA-seq Data
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Practical Guide to Interpreting RNA-seq Data
Practical Guide to Interpreting RNA-seq DataSkyler Kuhn1,2 Mayank Tandon1,21. CCR Collaborative Bioinformatics Resource (CCBR), Center for Cancer Research, NCI2. Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer ResearchOverviewI. Experimental DesignHypothesis-drivenOverview of Best PracticeII. Quality-controlPre- and post- alignment QC metricsInterpretationIII. PipelineFastQ Files -> Counts matrixReproducibility 1IV. Downstream AnalysisPrincipal Components Analysis (PCA)Differential ExpressionPathway AnalysisV. Advanced VisualizationsGroup comparisonsAlternative Splicing EventsPathway Diagrams Design: Overview Hypothesis-drivenAddresses a well thought-out quantifiable questionConsiderations: Library Construction: mRNA versus total RNAS ingle-end versus Paired-end SequencingSequencing Depth: quantifying gene-level or transcript-level expressio
Practical Rules of Thumb Limma, DESeq2, and EdgeR will work be very similarly in most cases - Consensus or intersection of the three is sometimes used Limma works better with larger cohorts ( 7 or more samples per group) DESeq2 works better with small cohorts ( 3 or less per group) - May also be more sensitive for low depth data
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