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Simon Andrews @simon_andrews v2.3 RNA-Seq Analysis Simon Andrews @simon_andrews v2.3.

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Presentation on theme: "Simon Andrews @simon_andrews v2.3 RNA-Seq Analysis Simon Andrews @simon_andrews v2.3."— Presentation transcript:

1 Simon Andrews @simon_andrews v2.3
RNA-Seq Analysis Simon Andrews @simon_andrews v2.3

2 RNA-Seq Libraries rRNA depleted mRNA Fragment Random prime + RT
2nd strand synthesis (+ U) A-tailing Adapter Ligation (U strand degradation) Sequencing NNNN u u u u u A T u A T A T

3 RNA-Seq Analysis QC (Trimming) Mapping Mapped QC Statistical Analysis
Quantitation Mapped QC

4 QC: Raw Data Sequence call quality

5 QC: Raw Data Sequence bias

6 QC: Raw Data Duplication level

7 Mapping Exon 1 Exon 2 Exon 3 Genome Simple mapping within exons Mapping between exons Spliced mapping Can simplify by aligning first to a transcriptome and then translate back to genomic coordinates. Can map unmatched reads to the whole genome.

8 Spliced Mapping Software
Tophat ( Star (

9 TopHat pipeline Reference FastQ files Indexed Genome
Reference GTF Models Indexed Transcriptome Reads Maps to transcriptome? Yes Translate coords and report No Maps to genome? Yes Report No Split map to genome Yes Build consensus and report No Discard

10 Post Mapping QC Mapping statistics
Proportion of reads which are in transcripts Proportion of reads in transcripts in exons Strand specificity Consistency of coverage SeqMonk (RNA-Seq QC plot) RNASeqQC (easiest through GenePattern)

11 SeqMonk Mapping QC (good)

12 SeqMonk Mapping QC (bad)

13 Quantitation Splice form 1 Exon 1 Exon 2 Exon 3 Splice form 2 Exon 1
Definitely splice form 1 Definitely splice form 2 Ambiguous

14 Options for handling splice variants
Ignore them – combine exons and analyse at gene level Simple, powerful, inaccurate in some cases DE-Seq, SeqMonk Assign ambiguous reads based on unique ones – quantitate transcripts and optionally merge to gene level Potentially cleaner more powerful signal High degree of uncertainty Cufflinks, bitSeq, RSEM

15 Read counting Simple (exon or transcript) Complex (re-assignment)
HTSeq (htseq-count) BEDTools (multicov) featureCounts SeqMonk (graphical) Complex (re-assignment) Cufflinks, bitSeq, RSEM

16 Count Corrections Size of library Length of transcript Other factors

17 RPKM / FPKM / TPM RPKM (Reads per kilobase of transcript per million reads of library) Corrects for total library coverage Corrects for gene length Comparable between different genes within the same dataset FPKM (Fragments per kilobase of transcript per million reads of library) Only relevant for paired end libraries Pairs are not independent observations RPKM/2 TPM (transcripts per million) Normalises to transcript copies instead of reads Corrects for cases where the average transcript length differs between samples

18 Normalisation

19 Normalisation

20 Filtering Genes Remove things which are uninteresting or shouldn’t be measured Reduces noise – easier to achieve significance Non-coding (miRNA, snoRNA etc) in RNA-Seq Known mis-spliced forms (exon skipping etc) Mitochonidrial genes X/Y chr genes in mixed sex populations Unknown/Unannotated genes

21 Filtering Mouse mRNAs

22 Visualising Expression
Comparing the same gene in different samples Normalised log2 RPM values Comparing different genes in the same sample Normalised log2 RPKM values

23 Linear Log2 CD74 Eef1a1 Actb Lars2 Eef2

24 Differential Expression
Microarrays traditionally used continuous statistical tests (t-test ANOVA etc) RNA-Seq differs in that it is count based data, so continuous tests fail at low counts Most differential tests use count based distribution tests, usually based on a negative binomial distribution

25 Negative binomial tests (DE-Seq)
Are the counts we see for gene X in condition 1 consistent with those for gene X in condition 2? Initially modelled using simple Poisson distribution using mean expression as the only parameter Doesn’t model real data very well

26 Poisson vs Negative binomial

27 Parameters Size factors Variance – required for NB distribution
Estimator of library sampling depth More stable measure than total coverage Based on median ratio between conditions Variance – required for NB distribution Custom variance distribution fitted to real data Insufficient observation to allow direct measure Smooth distribution assumed to allow fitting

28 Dispersion shrinkage Plot observed per gene dispersion
Calculate average dispersion for genes with similar observation Individual dispersions regressed towards the mean. Weighted by Distance from mean Number of observations Points more than 2SD above the mean are not regressed

29 Other filters Cook’s Distance – Identifies high variance
Effect on mean of removal of one replicate For n<3 test not performed For n = 3-6 failures are removed For n>6 outliers removed to make trimmed mean Disable with cooksCutoff=FALSE Hit count optimisation Low intensity reads are removed Limits multiple-testing to give max significant hits Disable with independentFiltering=FALSE

30 Replicates Compared to arrays, RNA-Seq is a very clean technical measure of expression Generally don’t run technical replicates Some statistics can be run on single replicates, but they can only tell you about technical noise (how likely is it that this change is due to a technical issue) Assessing biological variation requires biological replicates

31 Replicates Traditional statistics require min 3x3
DESeq can operate at 2x2, but this is minimum, not recommended True number of replicates required will depend on your biology and requirements 4x4 design is fairly common Always expect at least one sample to fail Randomise samples during sample prep

32 The problem of power… Gene A (1kb) Gene B (8kb) In a library Gene B is much better observed for the same copy number Power to detect DE is proportional to length

33 5x5 Replicates 5,000 out of 22,000 genes (23%) identified as DE using DESeq (p<0.05)

34 Intensity difference test
Different approach to differential expression Doesn’t aim to find every differentially expressed gene Conservative test Guaranteed to never return large numbers of hits

35 Assumptions Noise is related to observation level
Similar to DESeq Differences between conditions are either A direct response to stimulus Noise, either technical or biological Find points whose differences aren’t explained by general disruption

36 Method

37 Results

38 Exercises Look at raw QC Mapping with tophat
Small test data Quantitation and visualisation with SeqMonk Larger replicated data Differential expression with DESeq Review in SeqMonk

39 Useful links FastQC Tophat SeqMonk Cufflinks DESeq Bioconductor RSEM

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