RNA-seq differential expression analysis. bioconnector.org/workshops
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1 RNA-seq differential expression analysis bioconnector.org/workshops
2 Agenda Our data: source, pre-processing, structure Importing & exploring data Processing and analysis with DESeq2 - Structuring the count data and metadata - Running the analysis - Extracting results Data visualization Alternative approaches
3 What this class is not This is not an introductory R class. Pre-requisites: - Basic R skills: data frames, packages, importing data, saving results - Manipulating data with dplyr and %>% - Tidy data & advanced manipulation - Data Visualization with ggplot2 This is not a statistics course. This is not a comprehensive RNA-seq theory/practice course. Refer to the Conesa 2016 and Soneseson 2015 references on the workshop website. - We only discuss a simple 2-group design (treated vs. control). - Complex designs, multifactorial experiments, interactions, batch effects, etc. - Transcriptome assembly & reference-free approaches - Upstream analysis...
4 What this class is not This class does not cover upstream pre-processing. Sequence read QA/QC Our quantitation path: (Kallisto/Salmon + tximport): - "Alignment-free" transcript abundance estimation - Gene-level abundance summarization Alternative path 1 (STAR + featurecounts): - Spliced alignment to genome - Counting reads overlapping exons Alternative path 2 (Tophat+Cufflinks; HISAT+StringTie): - Spliced alignment to genome - Transcriptome assembly - Transcript abundance estimation
5 Course website: bioconnector.org Data Setup instructions Lessons dropdown: RNA-seq: airway? dropdown: FAQs, resources, etc.
6 Our data: Background Himes et al. "RNA-Seq Transcriptome Profiling Identifies CRISPLD2 as a Glucocorticoid Responsive Gene that Modulates Cytokine Function in Airway Smooth Muscle Cells." PLoS ONE Jun 13;9(6):e PMID: Glucocorticoids inhibit inflammatory processes, used to treat asthma because of antiinflammatory effects on airway smooth muscle (ASM) cells. RNA-seq to profile gene expression changes in 4 ASM cell lines treated w/ dexamethasone (synthetic glucocorticoid). Results: many differentially expressed genes. Focus on CRISPLD2 - Encodes a secreted protein involved in lung development - SNPs in CRISPLD2 in previous GWAS associated w/ inhaled corticosteroid resistance and bronchodilator response in asthma patients. - Confirmed the upregulated CRISPLD2 w/ qpcr and increased protein expression w/ Western blotting. They analyzed with Tophat and Cufflinks. We're taking a different approach with DESeq2. See recommended reading and resources page for more info.
7 Data pre-processing Analyzing RNA-seq data starts with sequencing reads. Many different approaches, see references on class website. Our workflow (previously done): - Reads downloaded from GEO (GSE:GSE52778) - Quantify transcript abundance (kallisto). - Summarize to gene-level abundance length-scaled counts (tximport). Our starting point is a count matrix: each cell indicates the number of reads originating from a particular gene (in rows) for each sample (in columns).
8 Data structure: counts + metadata countdata gene ctrl_1 ctrl_2 exp_1 exp_1 genea geneb genec gened genee genef countdata is the count matrix (number of reads coming from each gene for each sample) coldata id treatment sex... ctrl_1 control male... ctrl_2 control female... exp_1 treatment male... exp_2 treatment female... Sample names: ctrl_1, ctrl_2, exp_1, exp_2 coldata describes metadata about the columns of countdata First column of coldata must match column names of countdata (-1st)
9 Counting is (relatively) easy:
10 Problem: transcript length bias Trapnell, Cole, et al. "Differential analysis of gene regulation at transcript resolution with RNA-seq." Nature biotechnology 31.1 (2013):
11 Transcript quantification: kallisto Don't need a basepair-to-basepair alignment. Only need to know abundance. Kallisto determines which transcripts are compatible with the reads (and their abundance). Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016). Near-optimal probabilistic RNA-seq quantification. Nature biotechnology, 34(5),
12 Gene-level summarization: tximport Differential gene expression (c/f transcript): - More powerful - More accurate - More interpretable Gene-level summaries from transcript abundance estimates are more accurate than simple counts. Estimated abundance True abundance Soneson, C., Love, M. I., & Robinson, M. D. (2015). Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research.
13 Getting Started Go to bioconnector.org. Hit the data link on the top navbar. Download the following files, save them somewhere on your computer you can easily find. E.g., create a new folder on your desktop called airway and save it there, or move them to your project directory. - airway_scaledcounts.csv - airway_metadata.csv - annotables_grch38.csv Using project management: Open your.rproj file to start R running in the same folder as the data. File New file R script. Save this file as airway_analysis.r. Not using project management: Open RStudio. File New file R script. Save this file as airway_analysis.r in the same folder as the data. Quit RStudio, then double-click the R script to open R running in that folder.) Load the data: library(tidyverse) mycounts <- read_csv("airway_scaledcounts.csv") metadata <- read_csv("airway_metadata.csv")
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