RNA-seq differential expression analysis · Data pre-processing • Analyzing RNA-seq data starts...

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RNA-seq differential expression analysis bioconnector.org/workshops

Transcript of RNA-seq differential expression analysis · Data pre-processing • Analyzing RNA-seq data starts...

Page 1: RNA-seq differential expression analysis · Data pre-processing • Analyzing RNA-seq data starts with sequencing reads. • Many different approaches, see references on class website.

RNA-seq differential expression analysisbioconnector.org/workshops

http://bioconnector.org/workshops
Page 2: RNA-seq differential expression analysis · Data pre-processing • Analyzing RNA-seq data starts with sequencing reads. • Many different approaches, see references on class website.

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

Page 3: RNA-seq differential expression analysis · Data pre-processing • Analyzing RNA-seq data starts with sequencing reads. • Many different approaches, see references on class website.

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...

Page 4: RNA-seq differential expression analysis · Data pre-processing • Analyzing RNA-seq data starts with sequencing reads. • Many different approaches, see references on class website.

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

Page 5: RNA-seq differential expression analysis · Data pre-processing • Analyzing RNA-seq data starts with sequencing reads. • Many different approaches, see references on class website.

Course website: bioconnector.org• Data• Setup instructions• Lessons dropdown: RNA-seq: airway • ? dropdown: FAQs, resources, etc.

http://bioconnector.org
Page 6: RNA-seq differential expression analysis · Data pre-processing • Analyzing RNA-seq data starts with sequencing reads. • Many different approaches, see references on class website.

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. 2014 Jun 13;9(6):e99625. PMID: 24926665.

• Glucocorticoids inhibit inflammatory processes, used to treat asthma because of anti-inflammatory 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.

Page 7: RNA-seq differential expression analysis · Data pre-processing • Analyzing RNA-seq data starts with sequencing reads. • Many different approaches, see references on class website.

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).

Page 8: RNA-seq differential expression analysis · Data pre-processing • Analyzing RNA-seq data starts with sequencing reads. • Many different approaches, see references on class website.

Data structure: counts + metadata

gene ctrl_1 ctrl_2 exp_1 exp_1geneA 10 11 56 45geneB 0 0 128 54geneC 42 41 59 41geneD 103 122 1 23geneE 10 23 14 56geneF 0 1 2 0

… … … … …

id treatment sex ...ctrl_1 control male ...ctrl_2 control female ...exp_1 treatment male ...exp_2 treatment female ...

countData colData

Sample names:ctrl_1, ctrl_2, exp_1, exp_2

First column of colData must match column names of countData (-1st)

countData is the count matrix (number of reads coming from each gene for each sample)

colData describes metadata about the columns of countData

Page 9: RNA-seq differential expression analysis · Data pre-processing • Analyzing RNA-seq data starts with sequencing reads. • Many different approaches, see references on class website.

Counting is (relatively) easy:

Page 10: RNA-seq differential expression analysis · Data pre-processing • Analyzing RNA-seq data starts with sequencing reads. • Many different approaches, see references on class website.

Problem: transcript length bias

Trapnell, Cole, et al. "Differential analysis of gene regulation at transcript resolution with RNA-seq." Nature biotechnology 31.1 (2013): 46-53.

Page 11: RNA-seq differential expression analysis · Data pre-processing • Analyzing RNA-seq data starts with sequencing reads. • Many different approaches, see references on class website.

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), 525-527.

Page 12: RNA-seq differential expression analysis · Data pre-processing • Analyzing RNA-seq data starts with sequencing reads. • Many different approaches, see references on class website.

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.

True abundance

Estim

ated

abu

ndan

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Soneson, C., Love, M. I., & Robinson, M. D. (2015). Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research.

Page 13: RNA-seq differential expression analysis · Data pre-processing • Analyzing RNA-seq data starts with sequencing reads. • Many different approaches, see references on class website.

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(dplyr) library(readr) mycounts <- read_csv("airway_scaledcounts.csv") metadata <- read_csv("airway_metadata.csv")


Biases in RNA- Seq data October 30, 2013 NBIC Advanced RNA- Seq course

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