From reads to results: differen1al expression analysis with RNA seq. Alicia Oshlack Bioinforma1cs Division Walter and Eliza Hall Ins1tute
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1 From reads to results: differen1al expression analysis with RNA seq Alicia Oshlack Bioinforma1cs Division Walter and Eliza Hall Ins1tute
2 Purported benefits and opportuni1es of RNA seq All transcripts are sequenced not just ones for which probes are designed (cf microarrays) Annota1on of new exons, transcribed regions, genes or non coding RNAs No cross hybridiza1on Comparison of expression between genes Detec1on of low abundance transcripts Whole transcriptome sequencing The ability to look at alterna1ve splicing Allele specific expression RNA edi1ng
3 This talk Analysis of RNA seq data for the purpose of determining differen1al expression How much are expression levels changing between samples?
4 RNA seq Pepke et al, Nature Methods, 2009
5 Raw data Short sequence reads Quality scores
6 RNA seq analysis steps Raw sequence reads Map onto genome Summarize to genes/transcripts Sta1s1cal tes1ng: Determine differen1ally expressed genes Systems biology
7 Mapping: RNA seq
8 Sequencing transcripts not the genome Gene CDS CDS CDS CDS transcript CDS CDS CDS CDS
9 Specific issues for RNA seq Unlike ChIP seq or DNA seq, with RNA seq we really want to align to the transcriptome. The transcriptome is built from the genome, but exon boundary or junc1on reads will not align to the genome. The longer the reads, the more likely a read is to hit a junc1on. This can be avoided by using a clever reference.
10 The problem Hammer et al, Genome Research, 2010
11 Op1on 1: Don t worry about it! Map to genome. Captures any reads in unannotated exons. Can t map any reads that cross exon boundaries. Not dependent on any annota1on.
12 Op1on 2: Build a junc1on library Make a new reference by combining all known exons. Captures some junc1on reads. Biased towards well annotated genes. Mapping to the transcriptome is just a special junc1on library.
13 Op1on 3: De novo transcriptome Can be truly de novo by assembling transcripts without a reference, but this is not usually what we mean. More ocen we try and determine where splice junc1ons occur using the data itself. Computa1onally intensive, but unbiased by annota1on. Several socware packages to do this such as TopHat, SplitSeek,
14 A common approach Ini1ally map to genome Align unmapped reads to a junc1on library Align further unmapped reads to polya tail
15 RNA seq analysis steps Raw sequence reads Map onto genome Summarize to genes/transcripts Sta1s1cal tes1ng: Determine differen1ally expressed genes Systems biology
16 Summariza1on
17 CDS CDS CDS CDS Coding Sequence Exons Introns Splice Junc1ons Reads in exons Exons + junc1ons All reads start to end of transcript De novo methods
18 Table of counts gene Sample A Sample A Sample B Sample B A B C D E F G H I J K L
19 RNA seq analysis steps Raw sequence reads Map onto genome Summarize to genes/transcripts Sta1s1cal tes1ng: Determine differen1ally expressed genes Systems biology
20 Normaliza1on Removing technical sources of varia1on
21 Simple thought experiment Two samples A and B, sequenced to the same depth (same library size) Every gene that is expressed in A, is expressed in B at the same level Say there are a small number of genes that are expressed uniquely to sample B, but they are quite highly expressed (lots of reads) Many genes within the common set will appear differen1ally expressed (B < A)
22 Another way to view it Hypothe1cal example: Sequence 6 libraries to the same depth, with varying levels of uniqueto sample expression Differences in observed counts among the common genes Red=low, goldenyellow=high
23 Marioni et al RNA seq data 5 lanes of liver RNA, 5 lanes of kidney RNA Compare two single kidney libraries (technical replicates), acer adjus1ng for library size trimmed mean Distribu1on of log ra1os of counts M = library size 0 counts are omiped Red line = trimmed mean
24 Marioni et al RNA seq data 5 lanes of liver RNA, 5 lanes of kidney RNA Compare one liver to one kidney library, acer adjus1ng for library size trimmed mean Distribu1on of log ra1os of counts M = library size 0 counts are omiped Red line = trimmed mean
25 Should we expect the log ra1o to be 0 acer normaliza1on? I would argue: yes. Another way to look at it: M vs A plots trimmed mean
26 Should we expect the log ra1o to be 0 acer normaliza1on? Housekeeping genes shown in blue. Trimmed mean Median of 545 housekeeping genes [from Eisenberg and Levanon (2003), Trends in Gene1cs]
27 Shic in log ra1os is caused by RNA composi1on Sequencing real estate is fixed. Underlying RNA composi1on can be very different e.g. several liverspecific genes An adjustment at the analysis stage should be made
28 The adjustment to data analysis is straighsorward Assump1on: core set of genes that do not change in expression. Pick a reference sample, compute TMM rela1ve to reference TMM (Trimmed Mean of M values) M=log ra1o Adjustment to sta1s1cal analysis: Use addi1onal offset (GLM) Use effec1ve library size (Fisher s exact test)
29 Differen1al expression Sta1s1cal tes1ng for differences in expression level Several methods available edger next talk List of DE genes
30 RNA seq analysis steps Raw sequence reads Map onto genome Summarize to genes/transcripts Sta1s1cal tes1ng: Determine differen1ally expressed genes Systems biology
31 Going beyond gene lists Gene length bias and GO analysis
32 Sequencing the whole transcript RNA fragmenta1on
33 Length bias in RNA seq Equal number of transcripts 6X length 6X number of fragments More power to detect DE at a given threshold For genes of the same expression level longer transcripts will have more reads Therefore there is more informa1on for longer transcripts than shorter ones Longer genes have higher power to detect DE This length bias should not be present for microarrays
34 Propor1on of DE genes
35 Gene Ontology analysis Gene Ontology categorises genes into func1onal groups We wish to know if certain gene ontologies have more DE genes than expected If a category has lots of long genes we expect it to have more DE genes Can t use simple sta1s1cal tests
36 Aim Develop a computa1onal method for gene category tes1ng that can account for gene level biases in differen1al expression detec1on Category tes1ng refers to tes1ng if a set of genes has an over representa1on of DE genes
37 Three step procedure 1. Determine which genes are differen1ally expressed 2. Define a probability weigh1ng func1on 3. Generate many random samples to produce a null distribu1on in order to calculate significance of a category
38 Probability weigh1ng func1on Fit a func1on to the binary data series 1=DE, 0=!DE We chose a spline with a monotonicity requirement
39 Random sampling Select a random set of genes the same size as the set of DE genes However the probability of selec1ng a gene is weighted by the value of the probability weigh1ng func1on (based on the length or read count of the gene) Then count how many genes in the DE set have the GO category of interest Repeat this many 1mes Calculate a p value for the category
40 Results Categories with short genes get a higher rank in GOseq Categories with long genes get a lower rank
41 Even for a small number of categories there are a significant number of discrepancies between the methods. Over all 20% different
42 The GOseq func1on A func1on has been wripen in R to perform the category tests Input: gene iden1fiers and which genes are differen1ally expressed Output: GO categories with p values Can also use your own categories
43 Systems Biology: Integra1ng data Gene expression ChIP seq Transcrip1on factors Histone modifica1ons Epigene1c data Genome sequencing Copy number SNPs
44 Overview of analysis Mapping Millions of short reads Burrows Wheeler Transform Bow e, BWA, SOAP2 Hash-tables PerM, SHRiMP, BFAST, ELAND Unmapped reads Map to Junc on library created from annota on. Summarization Reads aligned to reference Map to de novo junc on library. SplitSeek, Tophat, SOAPals By Coding Sequence. By Exon. By Gene span. Junc on reads. Table of counts Normalization Between sample. TMM, upper quar le DE testing Poisson test. DEGseg Within sample. RPKM, quan le Nega ve binomial test. edger, bayseq, DEseq List of Differen ally Expressed genes Systems Biology Test for enriched categories/pathways. GOseq Infer networks and integrate with other data Biological insight
45 The future Capacity is increasing Analysis methodology is cri1cal Integra1on of different types of data Opportuni1es to use this data in new and imagina1ve ways
46 Acknowledgements Mark Robinson Maphew Young Maphew Wakefield Gordon Smyth Terry Speed Maphew Ritchie Natalie Thorne Davis McCarthy
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