1. Lecture 10. Microarray and RNA-seq
: Identification of Differentially Expressed Genes (DEG)
Hyun Seok Kim, Ph.D.
Assistant Professor,
Severance Biomedical Research Institute,
Yonsei University College of Medicine
MES Genome Informatics I (2015 Spring)

2. Gene expression

3. MES7594-01 Genome Informatics I (2015 Spring)
What is a microarray?
Platforms
Platforms
Glass slides (cDNA array)
Chips (Affymetrix)
Glass beads (Illumina)
10000s of oligonuceotide (or cDNA) probes are fixed on the surface of the platforms.
Microarrays can detect and quantify
mRNA
microRNA
SNP
LOH
CNV …
cDNA
Affymetrix
Illumina
MES Genome Informatics I (2015 Spring)

4. MES7594-01 Genome Informatics I (2015 Spring)
Questions of Interest
Determine steady-state gene expression levels of a sample in whole transcriptome scale.
Identify differentially expressed genes between samples.
Identify differentially regulated pathways or protein complexes.
MES Genome Informatics I (2015 Spring)

5. Affymetrix GeneChip for mRNA quantification
About Affy GeneChip platform
Probes (25 mers) are synthesized on a chip using a photolithographic manufacturing process.
At each x, y location of a GeneChip, a particular oligonucleotide is synthesized with millions of copies.
Each gene is represented by a unique set of probe pairs (PM and MM). MM helps increase specificity of the PM signal.
MES Genome Informatics I (2015 Spring)

6. Affymetrix GeneChip for mRNA quantification
About Affy workflow
Isolate total RNA (need biological replicates)
Sample amplification and labeling
Sample injected into microarray
Probe array hybridization, washing
Probe array scanning and intensity quantification
Intensity translated into nucleic acid abundance
MES Genome Informatics I (2015 Spring)

7. Illumina BeadArray for mRNA quatification
Beadchip platform
Each bead has one type of oligo and thousands of these oligos/bead
Bead is deposited on wells in glass slides. The beads are decoded by a step by proprietary technology
MES Genome Informatics I (2015 Spring)

8. MES7594-01 Genome Informatics I (2015 Spring)
Affy vs Illumina
Affymetrix GeneChip
Illumina BeadArray
25 mer
Longer oligo
Probe synthesized on chips
Bead technology
Multiple probes/probeset
Single probe
Multiple probes/transcript
.dat, .cel, .cdf, .chp file types
Image file processed by Bead Studio
Normalization by MAS5, RMA, GC-RMA etc.
Normalization by average, quantile, RSN etc.
TXT output for downstream analysis
Annotations can be updated
MES Genome Informatics I (2015 Spring)
Adapted from Dr.

9. RNA sequencing Isolate RNAs
Generate cDNA, fragment, size select, add linkers
Samples of interest
Condition 1
(normal colon)
Condition 2
(colon tumor)
Sequence ends
Map to genome, transcriptome, and predicted exon junctions
100s of millions of paired reads
10s of billions bases of sequence
Downstream analysis
Adapted from Canadian Bioinformatics Workshop

10. Pros and Cons of RNA-seq (versus microarray)
More powerful in detecting low expressing genes
Detect splicing variants and fusion transcripts
Measure allele specific expression
Discover mutation
Cons
Biased to highly expressed genes (e.g. ribosomal, mitochondrial genes)
More complicated analysis workflow (mapping to reference genome)
More expensive e.g. Hiseq2500, 100bpX2, 4Gb -> $700/sample (vs. < $500/array)

11. RNA-seq: Experimental Design
Single end read: one read sequenced from one end of each sample cDNA insert
Paired end read: two reads (one from each end) sequenced from each sample cDNA insert. Better to map reads over repetitive regions. Detect fusions and novel transcripts.

12. RNA-seq workflows Sequencing: obtain raw data (fastq format)
Quality control (optional): FASTX
Workflow 1: tophat2 (align) -> cufflinks (transcript assembly) -> cuffdiff (DEGs), cuffmerge (merge assemblies) Workflow 2: bowtie2 (align) -> HTSeq-count (count by gene) -> edgeR or DESeq (DEGs)
Fusion detection (optional): “chimerascan” or “defuse”

13. Normalization method (old)
RPKM: Reads per kilobase per million mapped reads (sigle end)
RPKM = (10^9*C)/(N*L) C = number of reads mapped to a gene N = total mapped reads in the experiment L = exon length in kb for a gene
RPKM measure is inconsistent among samples.
FPKM: Fragments per kilobase per million fragments reads (paired end).
RPKM and FPKM based DEG discovery is affected by gene length (no more recommended).

14. Negative binomial RNA-seq
Microarray data follows a Poisson distribution. However RNA seq does not. In RNA Seq genes with high mean counts (either because they’re long or highly expressed) tend to show more variance (between samples) than genes with low mean counts. Thus this data fits a Negative Binomial Distribution. edgeR and DESeq identfy DEGs based on negative binomial distribution.
Poisson
microarray
RNA-seq
Adapted from EMBL

15. A case study for practice session
GEO accession number: GSE41588
MES Genome Informatics I (2015 Spring)

16. GEO entry of GSE41558 Two platforms: Affy and HiSeq
Matrix file: processed data
Raw files: CEL, count files

17. MES7594-01 Genome Informatics I (2015 Spring)
Data Pre-processing
Affy produces CEL format file as raw data.
CEL file contains the feature quantifications
CEL file still has probes spread over the chip
Values still need to be summarized to probe set level; for example 90525_at = 250 units
Probe set: a collection of probes designed to interrogate a given sequence
250
MES Genome Informatics I (2015 Spring)

18. MES7594-01 Genome Informatics I (2015 Spring)
CEL file to TXT file
In going from .CEL to .TXT file to generate signal values, the multiple probes within a probe set are “averaged” to produce a single value for that gene/transcript.
the CEL files must first be normalized to account for technical variation between the arrays
MES Genome Informatics I (2015 Spring)

19. Robust Multi-array Average (RMA)
Background adjust PM values from .CEL files.
Take the base-2 log of each background-adjusted PM intensity.
Quantile normalize values from step 2 across all GeneChips.
Perform median polish separately for each probe set with rows indexed by GeneChip and columns indexed by probe.
For each row, find the average of the fitted values from step 4 to use as probe-set-specific expression measures for each GeneChip. -> .TXT files

20. Log Transformation Reason for working with log transformed intensities
Spread features more evenly across intensity range
Makes variability more constant across intensity range
Makes results close to normal distribution of intensities and errors

21. MES7594-01 Genome Informatics I (2015 Spring)
How to normalize?
Raw data
Many methods
Median scaling – median intensity for all chips should be the same
Known genes, house keeping, invariant genes
Quantile normalization: RMA (Robust Multiarray Averaging), GC- RMA
Normalization method may differ depending on array platform
(Reading materials) GC-RMA: Wu et al. (2004), JASA, 99, RMA: Irizarry et al. (2003), Nuc Acids Res, 31, e15.
After normalization
MES Genome Informatics I (2015 Spring)

22. RMA: Quantile Normalization
After background adjustment, find the smallest log2(PM) on each chip.
Average the values from step 1.
Replace each value in step 1 with the average computed in step 2.
Repeat steps 1 through 3 for the second smallest values, third smallest values,..., largest values.

23. RMA: Median Polish
For a given probe set with J probe pairs, let yij denote the background-adjusted, base-2-logged, and quantile- normalized value for GeneChip i and probe j.
Assume yij = μi + αj + eij where α1 + α αn = 0.
Perform Tukey’s Median Polish on the matrix of yij values with yij in the ith row and jth column.
μi from median polish is the probe-set-specific measure of expression for GeneChip i after correcting for array effect and probe effect.
residual for the
jth probe on the
ith GeneChip
gene expression
of the probe set
on GeneChip i
probe affinity
affect for the
jth probe in the
probe set

24. Differentially Expressed Genes (DEG)
Criteria for DEG discovery - Amount of difference: Fold change, Signal to noise ratio - Statistical significance: p-value, false discovery rate (FDR), odds ratio
Statistical Methods - Parametric: t-test - Non-parametric: Wilcoxon rank-sum tests - Significance Analysis of Microarrays (SAM; permutation based) - Empirical Bayesian (Linear Models of Microarrays, LIMMA, Affy data) - ANOVA (multiple factors; e.g. two different strains +/- drug)
Multiplicity of testing: p-value adjustments - Methods: FDR, bonferroni, etc.
MES Genome Informatics I (2015 Spring)

25. Limma & Empirical Bayesian
Limma is an R package to find DEGs
It uses linear models - Fitted to normalized intensities for each gene given a series of arrays - Design matrix: indicates which RNA samples have been applied to each array - Contrast matrix: specifies which comparisons you would like to make between the RNA samples - Can be used to compare two or more groups
Assumption: normal distribution
Uses empirical Bayesian analysis to improve power in small sample sizes - Borrowing information across genes
Output: p-values (adjusted for multiple testing)

26. Moderated/Bayesian t-test
Ordinary t-test is testing for differences in means between two groups given the variability within each group
Moderated/Bayesian t-test: rather than estimating within-group variability over and over again for each gene, pool the information from many similar genes.
Advantage: eliminate occurrence of accidentally large t-statistics due to accidentally small within-group variance.

27. Further reading
RNA-seq normalization: Dillies M-A et al. Briefings in Bioinformatics, 2012, 14,
Limma & eBayes: Smyth GK. Statistical Applications in Genetics and Molecular Biology, 2004, 3 (1), article 3.

28. MES7594-01 Genome Informatics I (2015 Spring)
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