Summer Inst. Of Epidemiology and Biostatistics, 2010: Gene Expression Data Analysis 1:30pm – 5:00pm in Room W2015 Carlo Colantuoni [email protected]

Summer Inst. Of Epidemiology and Biostatistics, 2010:

Gene Expression Data Analysis

1:30pm – 5:00pm in Room W2015

Carlo [email protected]

http://www.illuminatobiotech.com/GEA2010/GEA2010.htm

Class Outline• Basic Biology & Gene Expression Analysis Technology

• Data Preprocessing, Normalization, & QC

• Measures of Differential Expression

• Multiple Comparison Problem

• Clustering and Classification

• The R Statistical Language and Bioconductor

• GRADES – independent project with Affymetrix data.

http://www.illuminatobiotech.com/GEA2010/GEA2010.htm

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• Basic Biology & Gene Expression Analysis Technology– The Biology of Our Genome & Transcriptome– Genome and Transcriptome Structure & Databases– Gene Expression & Microarray Technology

• Data Preprocessing, Normalization, & QC– Intensity Comparison & Ratio vs. Intensity Plots (log transformation)– Background correction (PM-MM, RMA, GCRMA)– Global Mean Normalization– Loess Normalization– Quantile Normalization (RMA & GCRMA)– Quality Control: Batches, plates, pins, hybs, washes, and other artifacts– Quality Control: PCA and MDS for dimension reduction– SVA: Surrogate Variable Analysis

• Measures of Differential Expression– Basic Statistical Concepts– T-tests and Associated Problems– Significance analysis in microarrays (SAM) [ & Empirical Bayes]– Complex ANOVA’s (limma package in R)

• Multiple Comparison Problem– Bonferroni– False Discovery Rate Analysis (FDR)

• Differential Expression of Functional Gene Groups– Functional Annotation of the Genome– Hypergeometric test?, Χ2, KS, pDens, Wilcoxon Rank Sum– Gene Set Enrichment Analysis (GSEA)– Parametric Analysis of Gene Set Enrichment (PAGE)– geneSetTest– Notes on Experimental Design

• Clustering and Classification– Hierarchical clustering– K-means– Classification

• LDA (PAM), kNN, Random Forests• Cross-Validation

• Additional Topics• eQTL (expression + SNPs)• Next-Gen Sequencing data: RNAseq, ChIPseq• Epigenetics?– The R Statistical Language: http://www.r-project.org/– Bioconductor : http://www.bioconductor.org/docs/install/– Affymetrix data processing example

Questions for you:

• Student’s training and experience:• Statistics or Biology• MS or MD or PhD

• Student’s goals

• Student’s data?

• R Statistic Language?• other programming experience?

• Extra topics: Student’s interests

DAY #1:DAY #1:

Genome BiologyGenome Biology

The TranscriptomeThe Transcriptome

Microarray TechnologyMicroarray Technology

The Human Genome

DAD MOM

YOU

• 2 copies of the entire genome in each cell:

• 3.3 billion ”bases” (Gb)• ~30K genes• millions of variants

• We each get 1 copy from MOM & 1 from DAD. Each parent passes on a ”mixed copy” (from their parents).

• Each copy of the genome is contained in 23 chromosomes: 22+XorY (2 copies = 46 / cell).

• All in DNA!

DNADNA• A deoxyribonucleic acid or

DNA molecule is a double-stranded polymer composed of four basic molecular units called nucleotides.

• Each nucleotide contains a phosphate group, a deoxyribose sugar, and one of four nitrogen bases: adenine (A), guanine (G), cytosine (C), and thymine (T).

• The two chains are held together by hydrogen bonds.

• Base-pairing occurs according to the following rule: G pairs with C, and A pairs with T.

• Directionality & Complementarity: Reverse Complements hybridize.

How do these molecular

interactions influence

directionality and complementarity?

G-C pairs are “stickier” than A-T pairs (3 vs. 2 H-

bonds).

A + G = purines (2 rings)T + C + U= pyrimidines (1 ring)

(T in DNA, U in RNA)

Another Another View of View of

DNADNA

Where does an individual gene lie in this schematic?


DNADNA


DNADNA

Central Dogma of Modern Cellular & Molecular Biology:

TranscriptionTranscription

From DNA to mRNA:Transcription occurs at Genes

(T in DNA => U in RNA)

Transcript Processing

TranslationTranslation

From RNA to Protein: In the exons of protein coding genes (and their mRNA intermediates), each codon (3 base pairs) encodes 1 amino acid in the protein.

Perspective: Biological Setup

Every cell in the human body contains the entire human genome: 3.3 Gb in which ~30K genes exist.

The investigation of gene expression is meaningful because different cells, in different environments,

doing different jobs express different genes.

Cellular “Plans”: DNA - RNA - PROTEIN

Cellular Biology, Gene Expression, and Microarray Analysis

DNA

RNA

Protein

A protein-coding gene is a segment of chromosomal DNA that directs the synthesis

of a protein via an mRNA intermediate.

How do we design and implement probes that will effectively assay expression of ALL

(most? many?) genes simultaneously.

Easy to sequence some genomic DNA.

Laboratory Methods:The Genome and The Transcriptome

Easy to sequence some expressed mRNA’s.

NOT EASY to catalogue all genomic DNA, all expressed mRNA’s, and to map out the exact

relations between all these sequences.

AAAAASTART STOPprotein coding

5’ UTR 3’ UTR

mRNA

GenomicDNA 3.3 Gb

Protein

Molecular Cell Biology:Components of the Central Dogma

Transcription

Translation

AAAAA

Gene: Protein coding unit of genomic DNA with an mRNA intermediate.

START STOP

protein coding5’ UTR 3’ UTR

mRNA

GenomicDNA 3.3 Gb

DNAProbe

~30K genes

Sequence is a Necessity.

Transcription

From Genomic DNA to mRNA Transcripts

EXONS INTRONS

RNA editing & SNPs

Alternative splicingAlternative start & stop sites in same RNA molecule

~30K

>30K

Transcript coverage Homology to other transcripts

Hybridization dynamics 3’ bias

Protein-coding genes are not easy to find - gene density is low, and exons are interrupted by introns.

Designing DNA Probes From Genomic DNA Sequence

Sequence & assemble the entire human genome.

Search for genes predicted to produce mRNA transcripts. Protein-coding genes are not easy to find - gene density is low, and exons are interrupted by introns.

Completeness?

Design DNA probes.

[ Genomic DNA databases & assembly ]

Designing DNA Probes From mRNA Sequences

Sequence ALL expressed mRNA molecules.

Completeness?

Design DNA probes.

Sequence Quality!

Redundancy!

Completeness?

Unsurpassed as source of expressed sequence

Chaos?!?


~30K

>30K

>>30K

Transcript-BasedGene-Centered Information



DAY #1:DAY #1:

Genome BiologyGenome Biology

The TranscriptomeThe Transcriptome

Microarray TechnologyMicroarray Technology

RNA Expression Measurement: Northern Blot

SAMPLE 1 SAMPLE 2

RNA 1 RNA 2

RNAExtraction

electrophoreric transfer to membrane

hybridization of labeled probe

electrophoreric separation

Design + construction of

labeled “probe”Seq DB

“target”

SEQUENCE knowledge is REQUIRED for BOTH!

MicroarrayNorthern

Target: unknown (sample)Probe: known (synthetic)

Target

Probe

Northern blots seek to interrogate the expression of

ONE gene in a SINGLE hybridization reaction.

Target

Probes

RNA Expression Measurement:Northern Blot & Microarrays

Microarrays seek to interrogate the expression of MANY genes

simultaneously in a MULTIPLEX hybridization reaction.

Hybridization on a Northen BlotHybridization on a Northen Blot

Labeled Probe

Unlabeled Targets

1

MANY

Hybrid

MEMBRANE MEMBRANE

1

Target: unknownProbe: known

Edwin Southern et al, Nature Genetics Suppl 1999

Labeled Target

Unlabeled Probes

MANY

Solid Support Solid Support

Hybridization on a MicroarrayHybridization on a Microarray

MANY MANY

Hybrids

Target: unknownProbe: known

Edwin Southern et al, Nature Genetics Suppl 1999

Essentials of Microarray Experimental Design:

• Probe sequence selection & design

• Probe deposition on solid support

• Target Labeling

• Target Hybridization

• Signal detectionMicroarray

Target

Probes

cDNA Microarray Fabrication

cDNA Microarray

Printing onto standard glass microscope slides or nylon

Bacterial clones in 96 well plates

cDNA Microarray Experimentation

Sample Standard

RNA

cDNA

HybridizedMicroarray

Scan

Cy5 Cy3

cDNA Microarray Scanning

Cy5 Cy3

Merged Image

Cy3 Channel DataCy5 Channel Data

Quantification

cDNA Microarray Quantification



Log Intensity

Lo

g I

nte

nsi

ty


Log Intensity [ ]+

Lo

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atio

/


[ ]


• Probe sequence selection / design


• Target Labeling


• Signal detectionMicroarray

Target

Probes

Agilent (HP) Microarrays

2-channel fluorescence on glass slides.

44,000 oligonucleotides (60 NT’s) synthesized in situ using inkjet printing and solid phase phosphoramidite chemistry.

NIA Microarray

10K Full Length cDNA’s

P33

One-Channel

Spotted on Nylon

Affymetrix GeneChip

One-channel data generated using biotin labeling.

1,300,000 oligonucleotides (25 NT’s) in 54,000 “probe sets” (11 PM’s and 11 MM’s).

Oligo’s synthesized in situ on a silicon wafer using photolithography.

Affymetrix GeneChip

Affymetrix Probe Set DesignAffymetrix Probe Set Design

5’ 3’

Reference sequence

…TGTGATGGTGCATGATGGGTCAGAAGGCCTCCGATGCGCCGATTGAGAAT…GTACTACCCAGTCTTCCGGAGGCTAGTACTACCCAGTGTTCCGGAGGCTA

Perfectmatch (PM)Mismatch (MM)

NSB & SB

NSB

NimbleGen Microarrays

Oligonucleotides synthesized in situ on a glass slide using maskless, digital micromirror device.

195,000 oligonucleotides (60 NT’s): 5 probes / gene.

One-channel data.

Amersham’s CodeLink Arrays

One-channel data.

54,841 oligonucleotides (30NT’s).

Spotted into a 3-D aqueous polyacrylamide gel surface

on a glass slide.

ABI’s Human Genome Survey Array

One-channel data using digoxigenin/AP.

Oligonucleotides spotted into a 3-D nylon matirx.

31,077 oligonucleotides (60 NT’s).

Illumina’s BeadChip

One-channel data using biotin.

Oligonucleotides anchored on beads distributed in random arrays of plasma etched pits in the silicon wafer.

1,700,000 oligonucleotides (50 NT’s) immobilized on beads and represented ~30 times (6 full arrays per glass slide).


• Probe sequence


• Target Labeling


• Signal detection

Microarray

Target

Probes

Oligo vs. cDNA (Design: follow-up)

1 vs. 2 channel most important for experimental and analysis design

Specifics of each technology will determine idiosyncrasies of data preprocessing.

Probe length:Specificity & Sensitivity

Signal? Amplification?

An Example to Remind us of Gene Structure and Gene Cross-Referencing Issues

2 independent probes (!) on your microarray

interrogate the same gene (!) and both show an

extreme expression change in your cell line following

treatment: YES!!!

However, the directionality of this change is opposite:

one probe shows induction while the other shows

repression: NO !?!

Log Intensity

Lo

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ty


Log Intensity

Lo

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Probes designed to interrogate expression

of the same gene!


SF1 in Entrez Gene (RefSeq):A Complex Transcriptional Profile

Lacks regulatory SPSP phosphorylation motif

Probe Decreased

Probe Increased

SF1 in AceView:A Complex Transcriptional Profile!

AAAAA

Gene: Protein coding unit of genomic DNA with an mRNA intermediate.

START STOP

protein coding5’ UTR 3’ UTR

mRNA

GenomicDNA 3.3 Gb

DNAProbe

~30K genes

Sequence is a Necessity.

Transcription


EXONS INTRONS

RNA editing & SNPs

Alternative splicingAlternative start & stop sites in same RNA molecule

~30K

>30K

Transcript coverage Homology to other transcripts

Hybridization dynamics 3’ bias

Protein-coding genes are not easy to find - gene density is low, and exons are interrupted by introns.

USCS Genome Browser:

Genes

Transcripts

Probes

(Live Web Demo)(Live Web Demo)

USCS example with genes, transcripts, and probe USCS example with genes, transcripts, and probe mapping – custom tracks.mapping – custom tracks.

Documents

Summer Inst. Of Epidemiology and Biostatistics, 2010: Gene Expression Data Analysis 1:30pm – 5:00pm in Room W2015 Carlo Colantuoni [email protected]