1. Microarray Type Analyses using Second Generation Sequencing
Adam B. Olshen
Helen Diller Comprehensive Cancer Center
UCSF Division of Biostatistics
5/18/11
Spring 2011, BMI mini course - Statistical Methods for Array and Sequence Data

2. Outline
RNA
DNA
Methylation

3. RNA

4. RNA Sequencing Pipeline
Experiment
Millions of short reads
Map Reads
Summarize Counts
Normalize Counts
Test for Differential
Expression
Analyze Gene List
Stolen from D McCarthy via Terry Speed

5. Mapped Reads

6. Summarizing Counts
Counts are typically binned to annotated exons, genes, or transcripts.
Summarizing to unannotated regions is more difficult.

7. Summarized Counts

8. Normalization
Normalization is the process in which components of experiments are made comparable before statistical analysis.
It is important in sequencing as it was in microarrays!
A couple issues in normalization are different sequencing depth (library size) and distributions of reads (long right tails).

9. Simple RPKM Normalization
Proportion of reads: number of reads (n) mapping to an exon (gene) divided by the total number of reads (N), n/N.
RPKM: Reads Per Kilobase of exon (gene) per Million mapped sequence reads, 109n/(NL),
where L is the length of the transcriptional unit in bp (Mortazavi et al., Nat. Meth., 2008).

10. Summarized Counts

11. TMM Normalization

12. TMM Thought Experiment
Suppose samples A and B are sequenced to the same depth, say 9000 reads
90 genes are expressed in A and B truly at the same level
10 genes are expressed at high levels in B but not in A, and no other genes are expressed
Possible scenario
All 90 genes get about 100 reads for A
First 90 genes for B get about 50 reads, while the other 10 genes get about 450 reads each
It would appear that the first 90 are expressed twice as high in A as in B!
The reason for this result is that there is a fixed amount of sequencing real estate

13. TMM Example

14. TMM Solution
Trim off the genes with extreme M values
Compute scale factor from remaining genes
Others normalize by 75th percentile
(Bullard et al., BMC Bioinformatics, 2010)

15. Differential Expression
We may want to test for differential expression between/among conditions, disease types, etc.
Need a parametric test because few replicates (often 2 or 3 these days)
In a parametric test a statistical distribution is assumed for the test statistic (such as Gaussian) unlike nonparametric tests where ranks are used

16. Methods Based on Counts
For microarrays Gaussian-based methods are most common
Because sequencing data is counts, statistical distributions for discrete data are used
Relevant distributions are
Binomial distribution
Poisson distribution
Negative binomial distribution

17. The Poisson probability mass function is
Poisson Distribution
The Poisson probability mass function is
Pr(N)=exp(-λ)λN/N!, for rate parameter λ
The mean and variance of a Poisson random variable is the same: λ
The consensus is that this model is appropriate for technical replicates but that biological replicates have extra variability.

18. Negative Binomial Distribution
The negative binomial distribution is common when count data has variance significantly greater than its mean (overdispersed)
The NB distribution has mean λ and variance λ + φλ; as φ goes to 0 it goes to a Poisson
It is used to model biological replicates

19. Negative Binomial Methods
Different dispersion (φ) for every gene – not enough data to estimates this
Common dispersion (Robinson and Smyth, Biostatistics, 2008) – good, but does not include any gene level variability
Moderated dispersion (Robinson and Smyth, Bioinformatics, 2007) – best, but hard to weight gene level vs common dispersion

20. The Test
Say there are two classes, A and B, with counts for gene g of ZgA and ZgB
Model the counts as NB taking into account the number of libraries sequenced, the size of those libraries, and the NB parameters λ and φ
Test whether ZgA and ZgB are significantly different conditional on the total ZgA + ZgB

21. EdgeR-Robinson’s Methods
R Package
Normalization DE

22. RNA Seq vs Microarrays
Mortazavi et al., Nature Methods, 2008

23. DNA

24. Copy Number by Sequencing
Shen and Zhang, Stanford Statistics Technical Report

25. Complications of Copy Number by Sequencing
Over what region should copy number be sampled? Microarrays sample at a fixed number of probes/SNPs
Coverage is highly variable
Potentially, a huge amount of computation

26. Copy Number by Sequencing
Let μt represent a non-homogeneous Poisson process representing counts from a case.
Let λt represent a non-homogeneous Poisson process representing counts from a control.
Let p(t)= μt/(μt+λt).
Look for changes in p(t).

27. Copy Number by Sequencing

28. SeqCBS Software
The method of Shen and Zhang (Stanford Statistics Technical Report) for segmenting sequencing data is called SeqCBS
An R package for doing the analysis can be found at CRAN (

29. Methylation

30. What is Methylation?
~ 70% of CpGs are methylated in mammals; CpGs are relatively rare
A small fraction of the genome, CpG islands, shows near the expected CpG frequency

31. CpG islands often overlap promoters (sites of transcriptional initiation)
Definition of a CpG island (Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J. Mol. Biol Jul 20;196(2):261-82):
1. GC content of 50% or greater
2. length greater than 200 bp
3. ratio greater than 0.6 of observed number of CG dinucleotides to the expected number
Obs/Exp CpG = Number of CpG * N / (Number of C * Number of G),
where N = length of sequence.
The promoters of genes often overlap with a type of genomic sequence called a CpG island. Most of the genome has been depleted of CpGs because when the C is methylated, it can easily mutate. However, you can find clusters of CpGs, and they really stand out as landmarks in the genome. There are several definitions. Here is the one we are using. …. At the bottom is the gene gene GAPDH, which is a classis housekeeping gene. The arrows show it is being transcribed from left to right. Each exon is shown as a box. The intervening lines are introns. At the top is the genomic position on chromosome 12. Each nucleotide is numbered. The scale bar is 2 kilobases. You can see the GC percent and individual CpG dinucleotides. The beginning, or 5’ end, of GAPDH overlaps with a region with high GC and many CpGs. It’s about 1 kb in length, and meets the requirements for a CGI. However, CGIs can occur in other locations too – in gene bodies, or in regions where no genes are known.

32. The DNA Methylome: 28,848,753 CpG sites
(Rollins et al, 2006) 32

33. Current methods for genome-wide DNA methylation analysis
Bisulfite sequencing
Antibody- or affinity-based enrichment
Methyl-sensitive restriction enzymes
Limitations:
1. only a small number of the ~28 M CpGs can be interrogated (no longer true!)
2. difficult to analyze repetitive sequences
There are 3 common methods that are currently used to study DNA methylation on a genome-wide level, but these have limitations that have prevented analyses from being truly genome-wide. In bisulfite sequencing, chemical treatment converts unmethylated Cs to Us but methylated Cs are protected. Methyl-sensitive restriction enzymes cut DNA based on methylation status of a CpG site within the recognition sequence. Antibody against methylcytosine OR affinity purification methods using protein domains that are known to bind to methylated DNA. In their existing forms, these are all limited in their coverage of the 28 M CpG sites present in the haploid human genome. Also, it is difficult to analyze repetitive sequences, where ~half of DNA methylation occurs, and this is especially a problem with array-based approaches. We wanted to overcome some of these limitations by leveraging advancements in DNA sequencing technology.

34. Bisulfite Sequencing
Xi and Li, BMC Bioinformatics, 2009

35. Enrichment and Restriction Enzymes
Methyl DNA immunoprecipitation - sequencing (MeDIP-seq)
higher read density at methylated regions
Methyl-sensitive restriction enzyme – sequencing (MRE-seq)
each read is a single unmethylated CpG site
5MeC
MeDIP-seq
MRE-seq
MRE digestion

36. Methylome Methods Comparison
Shotgun bisulfite
Enrichment
Restriction Enzymes
Base resolution
Absolute quantitation
Higher cost/sample
150bp resolution
Relative quantitation
Much lower cost/sample
Low resolution
Can be combined with enrichment methods

37. MethylC, RRBS, MeDIP, MeDIP, MBD
Comparison of
MethylC, RRBS, MeDIP, MeDIP, MBD
Harris et al, NIH Roadmap Epigenome Consortium,
Nature Biotechnology, Oct 2010

38. Things Learned from Whole Genome Methylation Studies
Maunakea et al., Nature, 2010
5’ promoter regions of CpG island almost never methylated, while intragenic region can be
Methylation of intragenic regions appears to involve alternative promoters

39. Things Learned from Methylation Studies of Cancer
Aberrant methylation of promoter CpG islands can lead to gene silencing (before microarrays)
More soon!

40. Methylation in GBM

41. Whole Genome Methylation Data is Very Difficult to Analyze!
What is the proper scale:
CpG level
Bin level (how many bins?)
Adjacent CpGs or bins are correlated, but not as correlated as copy number where regional segmentation is possible
P-values from testing differences between conditions are correlated
Huge multiple comparisons problem (28m CpGs)
Come back next year for methods discussion

42. MethylC, RRBS, MeDIP, MeDIP, MBD
Comparison of
MethylC, RRBS, MeDIP, MeDIP, MBD
Harris et al, NIH Roadmap Epigenome Consortium,
Nature Biotechnology, Oct 2010

43. Methylation and Copy Number

44. References
Mortazavi et al., Nat. Meth.,
Robinson and Oshlack, Genome Biology,
Bullard et al., BMC Bioinformatics,
Robinson and Smyth, Biostatistics,
Robinson and Smyth, Bioinformatics,
Robinson et al., Bioinformatics,
Shen and Zhang, Stanford Statistics Technical Report,
Gardiner-Garden and Frommer, J. Mol. Biol.,
Rollins, Genome Res.,
Xi and Li, BMC Bioinformatics,
Harris et al., Nature Biotechnology,
Maunakea et al., Nature,