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Introduction to RNA-Seq Mary Piper Bioinformatics Consultant and - PowerPoint PPT Presentation

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DataCamp RNA-Seq Differential Expression Analysis RNA - SEQ DIFFERENTIAL EXPRESSION ANALYSIS Introduction to RNA-Seq Mary Piper Bioinformatics Consultant and Trainer DataCamp RNA-Seq Differential Expression Analysis DataCamp RNA-Seq

  1. DataCamp RNA-Seq Differential Expression Analysis RNA - SEQ DIFFERENTIAL EXPRESSION ANALYSIS Introduction to RNA-Seq Mary Piper Bioinformatics Consultant and Trainer

  2. DataCamp RNA-Seq Differential Expression Analysis

  3. DataCamp RNA-Seq Differential Expression Analysis

  4. DataCamp RNA-Seq Differential Expression Analysis

  5. DataCamp RNA-Seq Differential Expression Analysis

  6. DataCamp RNA-Seq Differential Expression Analysis

  7. DataCamp RNA-Seq Differential Expression Analysis

  8. DataCamp RNA-Seq Differential Expression Analysis RNA-Seq questions What genes are differentially expressed between sample groups? Are there any trends in gene expression over time or across conditions. Which groups of genes change similarly over time or across conditions. What processes or pathways are important for my condition of interest?

  9. DataCamp RNA-Seq Differential Expression Analysis RNA - SEQ DIFFERENTIAL EXPRESSION ANALYSIS Let's practice!

  10. DataCamp RNA-Seq Differential Expression Analysis RNA - SEQ DIFFERENTIAL EXPRESSION ANALYSIS RNA-Seq Workflow Mary Piper Bioinformatics Consultant and Trainer

  11. DataCamp RNA-Seq Differential Expression Analysis RNA-Seq Workflow: RNA-Seq Experimental Design Technical replicates: Generally low technical variation, so unnecessary. Biological replicates: Crucial to the success of RNA-Seq differential expression analyses. The more replicates the better, but at the very least have 3. Batch effects: Avoid as much as possible and note down all experimental variables.

  12. DataCamp RNA-Seq Differential Expression Analysis

  13. DataCamp RNA-Seq Differential Expression Analysis

  14. DataCamp RNA-Seq Differential Expression Analysis

  15. DataCamp RNA-Seq Differential Expression Analysis RNA-Seq Workflow: Quality control

  16. DataCamp RNA-Seq Differential Expression Analysis

  17. DataCamp RNA-Seq Differential Expression Analysis RNA-Seq Workflow: Alignment

  18. DataCamp RNA-Seq Differential Expression Analysis

  19. DataCamp RNA-Seq Differential Expression Analysis RNA-Seq Workflow: Count matrix wt_rawcounts <- read.csv("fibrosis_wt_rawcounts.csv")

  20. DataCamp RNA-Seq Differential Expression Analysis

  21. DataCamp RNA-Seq Differential Expression Analysis

  22. DataCamp RNA-Seq Differential Expression Analysis

  23. DataCamp RNA-Seq Differential Expression Analysis RNA - SEQ DIFFERENTIAL EXPRESSION ANALYSIS Back to you!

  24. DataCamp RNA-Seq Differential Expression Analysis RNA - SEQ DIFFERENTIAL EXPRESSION ANALYSIS Differential Gene Expression Overview Mary Piper Bioinformatics Consultant and Trainer

  25. DataCamp RNA-Seq Differential Expression Analysis

  26. DataCamp RNA-Seq Differential Expression Analysis

  27. DataCamp RNA-Seq Differential Expression Analysis

  28. DataCamp RNA-Seq Differential Expression Analysis Introduction to dataset: Smoc2

  29. DataCamp RNA-Seq Differential Expression Analysis

  30. DataCamp RNA-Seq Differential Expression Analysis RNA-Seq count distribution ggplot(raw_counts) + geom_histogram(aes(x = wt_normal1), stat = "bin", bins = 200) + xlab("Raw expression counts") + ylab("Number of genes")

  31. DataCamp RNA-Seq Differential Expression Analysis Preparation for differential expression analysis: raw counts wt_rawcounts <- read.csv("fibrosis_wt_rawcounts.csv")

  32. DataCamp RNA-Seq Differential Expression Analysis Preparation for differential expression analysis: metadata # Create vectors containing metadata for the samples genotype <- c("wt", "wt", "wt", "wt", "wt", "wt", "wt") condition <- c("normal", "fibrosis", "normal", "fibrosis", "normal", "fibrosis", "fibrosis") # Combine vectors into a data frame wt_metadata <- data.frame(genotype, wildtype) # Create the row names with the associated sample names rownames(wt_metadata) <- c("wt_normal3", "wt_fibrosis3", "wt_normal1", "wt_fibrosis2", "wt_normal2", "wt_fibrosis4", "wt_fibrosis1")

  33. DataCamp RNA-Seq Differential Expression Analysis Preparation for differential expression analysis: metadata

  34. DataCamp RNA-Seq Differential Expression Analysis RNA - SEQ DIFFERENTIAL EXPRESSION ANALYSIS Let's practice!

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