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

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introduction to rna seq

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

Sep 07, 2022 603 likes •961 views

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

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DataCamp RNA-Seq Differential Expression Analysis

Introduction to RNA-Seq

RNA-SEQ DIFFERENTIAL EXPRESSION ANALYSIS

Mary Piper

Bioinformatics Consultant and Trainer

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DataCamp RNA-Seq Differential Expression Analysis

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DataCamp RNA-Seq Differential Expression Analysis

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DataCamp RNA-Seq Differential Expression Analysis

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DataCamp RNA-Seq Differential Expression Analysis

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DataCamp RNA-Seq Differential Expression Analysis

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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?

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DataCamp RNA-Seq Differential Expression Analysis

Let's practice!

RNA-SEQ DIFFERENTIAL EXPRESSION ANALYSIS

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DataCamp RNA-Seq Differential Expression Analysis

RNA-Seq Workflow

RNA-SEQ DIFFERENTIAL EXPRESSION ANALYSIS

Mary Piper

Bioinformatics Consultant and Trainer

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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.

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DataCamp RNA-Seq Differential Expression Analysis

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RNA-Seq Workflow: Quality control

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RNA-Seq Workflow: Alignment

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RNA-Seq Workflow: Count matrix

wt_rawcounts <- read.csv("fibrosis_wt_rawcounts.csv")

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DataCamp RNA-Seq Differential Expression Analysis

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DataCamp RNA-Seq Differential Expression Analysis

Back to you!

RNA-SEQ DIFFERENTIAL EXPRESSION ANALYSIS

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DataCamp RNA-Seq Differential Expression Analysis

Differential Gene Expression Overview

RNA-SEQ DIFFERENTIAL EXPRESSION ANALYSIS

Mary Piper

Bioinformatics Consultant and Trainer

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DataCamp RNA-Seq Differential Expression Analysis

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DataCamp RNA-Seq Differential Expression Analysis

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Introduction to dataset: Smoc2

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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")

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Preparation for differential expression analysis: raw counts

wt_rawcounts <- read.csv("fibrosis_wt_rawcounts.csv")

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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")

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DataCamp RNA-Seq Differential Expression Analysis

Preparation for differential expression analysis: metadata

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Let's practice!

RNA-SEQ DIFFERENTIAL EXPRESSION ANALYSIS