Example data – MALDI-TOF - PowerPoint Presentation

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Example data – MALDI-TOF

Peptide intensity vs m/z

Previous Lecture:

Proteomics Informatics

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Gene Expression Analysis (I)

This Lecture

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Learning Objectives

Microarray experimental detailsMicroarray data formatsQC analysis and data explorationNormalizationDifferential expressionFunctional enrichmentDatabases

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protein

RNA

DNA

transcription

translation

replication

The Central Dogma of Molecular Biology

DNA is

transcribed

into RNA which is then

translated

into protein

Measured by Microarray

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What is a Microarray

A simple concept: Dot Blot + Northern Reverse the hybridization - put the probes on the filter and label the bulk RNA Make probes for lots of genes - a massively parallel experimentMake it tiny so you don’t need so much RNA from your experimental cells.Make quantitative measurements

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Microarrays are Popular

At NYU Med Center we are now collecting about 3 GB of microarray data per week (60 chips, 6-10 different experiments)PubMed search "microarray"= 13,948 papers2005 = 44062004 = 35092003 = 24212002 = 15572001 = 8342000 = 294

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A Filter Array

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DNA Chip Microarrays

Put a large number (~100K) of cDNA sequences or synthetic DNA oligomers onto a glass slide (or other subtrate) in known locations on a grid.Label an RNA sample and hybridize Measure amounts of RNA bound to each square in the gridMake comparisonsCancerous vs. normal tissueTreated vs. untreatedTime courseMany applications in both basic and clinical research

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cDNA Microarray Technologies

Spot cloned cDNAs onto a glass microscope slideusually PCR amplified segments of plasmidsLabel 2 RNA samples with 2 different colors of flourescent dye - control vs. experimentalMix two labeled RNAs and hybridize to the chipMake two scans - one for each colorCombine the images to calculate ratios of amounts of each RNA that bind to each spot

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Spot your own Chip

(plans available for free from Pat Brown’s website)

Robot spotter

Ordinary glass

microscope slide

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Combine scans for Red & Green

False color image is made from digitized fluorescence data,

not by superimposing scanned images

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cDNA Spotted Microarrays

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Data Acquisition

Scan the arraysQuantitate each spotSubtract backgroundNormalizeExport a table of fluorescent intensities for each gene in the array

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Affymetrix “Gene chip” system

Uses 25 base oligos synthesized in place on a chip (20 pairs of oligos for each gene)RNA labeled and scanned in a single “color”one sample per chipCan have as many as 20,000 genes on a chipArrays get smaller every year (more genes)Chips are expensiveProprietary system: “black box” software, can only use their chips

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Affymetrix Gene Chip

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Affymetrix Technology

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Affymetrix Software

Affymetrix System is totally automatedComputes a single value for each gene from 40 probes - (using surprisingly kludgy math)Highly reproducible (re-scan of same chip or hyb. of duplicate chips with same labeled sample gives very similar results)Incorporates false results due to image artefactsdust, bubblespixel spillover from bright spot to neighboring dark spots

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Affymetrix Pivot Table

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Plot of raw data (PM probes)

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Plot of log2 data (PM probes)

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MA plot: log of fold change (M) vs log of Intensity (A)

Hypox1 vs

Hypox2

Hypox3

Norm1

Norm2

Norm3

M = log

2

(A/B)

A = ½ log

2

(A*B) = ½ (log

2

(A) + log

2

(B))

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Goals of a Microarray Experiment

Find the genes that change expression between experimental and control samplesClassify samples based on a gene expression profileFind patterns: Groups of biologically related genes that change expression together across samples/treatments

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Basic Data Analysis

Fold change (relative increase or decrease in intensity for each gene)Set cutoff filter for low values (background +noise)Cluster genes by similar changes - only really meaningful across multiple treatments or time pointsCluster samples by similar gene expression profiles

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Streamlined Affy Analysis

Normalize

Raw data

Filter

Classification

Significance

Clustering

Gene lists

Function

(Genome Ontology)

(RMA)

•Present/Absent

•Minimum value

•Fold change

•t-test

•SAM

•Rank Product

•PAM

•Machine learning

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Scatter plot of all genes in a simple comparison of two control (A) and two treatments (B: high vs. low glucose) showing changes in expression greater than 2.2 and 3 fold.

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Thomas Hudson, Montreal Genome Center

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Normalization

Can control for many of the experimental sources of variability (systematic, not random or gene specific)Bring each image to the same average brightnessCan use simple math or fancy - divide by the mean (whole chip or by sectors)LOESS (locally weighted regression)No sure biological standards

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RMA

Robust Multichip AverageBolstad, B.M., Irizarry R. A., Astrand, M., and Speed, T.P. (2003), A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance. Bioinformatics 19(2):185-193 log(medpol(PMij − BG)) = µ i + α

j + e ijfor (array i, probe j)

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Are the Treatments Different?

Analysis of microarray data has tended to focus on making lists of genes that are up or down regulated between treatmentsBefore making these lists, ask the question: "Are the treatments different?"PCA/MDS or cluster the samplesIf the treatment is responsible for differences, then use statistical methods to find the genes most responsibleIf there are not significant overall differences, then lists of genes with large fold changes may only reflect random variability.

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Statistics

When you have variability in measurements, you need replication and statistics to find real differencesIt’s not just the genes with 2 fold increase, but those with a significant p-value across replicatesNon-parametric (i.e. rank or permutation) or paired value statistics may be more appropriate (low number of samples, high standard deviation)

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Multiple Comparisons

In a microarray experiment, each gene (each probe or probe set) is really a separate experimentYet if you treat each gene as an independent comparison, you will always find some with significant differences(the tails of a normal distribution)Different genes are NOT independent

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False Discovery

Statisticians call false positives a "type 1 error" or a "False Discovery"The FDR must be smaller than the number of real differences that you find - which in turn depends on the size of the differences and variability of the measured expression valuesYou can’t know the true false discovery rate for your data, but it can be estimated in a number of different ways. In biology we tend to be comfortable with an estimated FDR of 5-10%

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SAM

Significance Analysis of MicroarraysTusher, Tibshirani and Chu (2001): Significance analysis of microarrays applied to the ionizing radiation response. PNAS 2001 98: 5116-5121, (Apr 24).

R package, Excel pluginFreePermutation basedMost published method of microarray data analysis

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SAM- procedure overview

Sample genesexpression scale

Define and calculate

a statistic, d(i)

Generate permutated

samples

Estimate attributes

of d(i)’s distribution

Identify potentially

Significant genes

Estimate FDR

Choose

Δ

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Calculate “relative difference” – a value that incorporates the change in expression between conditions and the variation of measurements in each condition

Calculate “expected relative difference” – derived from controls generated by permutations of data

Plot against each other, set cutoff to identify deviating genesCalculate FDR for chosen cutoff from the control permutations

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Relative Difference

Mean expression of gene

i

in conditions

I

and

U

Gene-specific scatter

Constant to reduce variation of low expressed genes

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Permutation tests

For each gene, compute the d-value (similar to a t-statistic). This is the

observed d-value (d

i) for that gene.

ii) Randomly shuffle the expression values between groups A and B. Compute the d-value for each randomized set.

iii)

Take the average of the randomized d-values for each gene. This is the ‘

expected relative difference’

(

d

E

) of that gene. Difference between

(di)

and (d

eE)

is used to measure significance.

iv) Plot d(i

) vs. dE(

i)

v) Calculate FDR = average number of genes that exceed in the permuted data.

Exp 1

Exp 2

Exp 3

Exp 4

Exp 5

Exp 6

Gene 1

Group A

Group B

Exp 1

Exp 4

Exp 5

Exp 2

Exp 3

Exp 6

Gene 1

Group A

Group B

Original grouping

Randomized grouping

SAM Two-Class Unpaired

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SAM Two-Class Unpaired

Significant positive genes

(i.e., mean expression of group B >mean expression of group A)

Significant negative genes

(i.e., mean expression of group A > mean expression of group B)

“Observed d = expected d” line

The more a gene deviates from the “observed = expected” line, the more likely it is to be significant. Any gene beyond the first gene in the +

ve

or –

ve

direction on the x-axis (including the first gene), whose observed exceeds the expected by at least delta, is considered significant.

Plot d(i) vs. d

E

(i)

For most of the genes:

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Higher Level

Microarray data analysisClustering and pattern detectionData mining and visualizationControls and normalization of resultsStatistical validatationLinkage between gene expression data and gene sequence/function/metabolic pathways databasesDiscovery of common sequences in co-regulated genesMeta-studies using data from multiple experiments

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Types of Clustering

HerarchicalLink similar genes, build up to a tree of allSelf Organizing Maps (SOM)Split all genes into similar sub-groupsFinds its own groups (machine learning)Principle Componentevery gene is a dimension (vector), find a single dimension that best represents the differences in the data

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Cluster by fold change

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GeneSpring

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SOM Clusters

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Classification

How to sort samples into two classes based on gene expression dataCancer vs. normalCancer sub-types (benign vs. malignant)Responds well to drug vs. poor response (i.e. tamoxifen for breast cancer)

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PAM: Prediction Analysis for Microarrays

Class Prediction and Survival Analysis for Genomic Expression Data MiningPerforms sample classification from gene expression data,via "nearest shrunken centroid method'' of Tibshirani, Hastie, Narasimhan and Chu (2002): "Diagnosis of multiple cancer types by shrunken centroids of gene expression" PNAS 2002 99:6567-6572 (May 14).

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BioConductor

All of these normalization, statistical, and clustering methods are available in a free software package called BioConductor, which is part of the R statistical environmentwww.bioconductor.orgcommand line interface> data(SpikeIn)> pms <- pm(SpikeIn)> mms <- mm(SpikeIn)> par(mfrow = c(1, 2))> concentrations <- matrix(as.numeric(sampleNames(SpikeIn)), 20,

+ 12, byrow = TRUE)> matplot(concentrations, pms, log = "xy", main = "PM", ylim = c(30,+ 20000))> lines(concentrations[1, ], apply(pms, 2, mean), lwd = 3)> matplot(concentrations, mms, log = "xy", main = "MM", ylim = c(30,+ 20000))> lines(concentrations[1, ], apply(mms, 2, mean), lwd = 3)

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Functional Genomics

Take a list of "interesting" genes and find their biological relationshipsGene lists may come from significance/classfication analysis of microarrays, proteomics, or other high-throughput methodsRequires a reference set of "biological knowledge"

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Genome Ontology

How to organize biological knowledge?Biologists work on a variety of different research organisms: yeast, fruit fly, mouse, … humanthe same gene can have very different functions (antennapedia)and very different names (sonic hedgehog…)

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GO

Biologists got together and developed a sensible system called Genome Ontology (GO)3 hierarchical sets of terminologyBiological ProcessCellular Component (location within cell)Molecular Functionabout 1000 categories of functions

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List (and convert) gene identifiers from many

genomic resources including NCBI, PIR and Uniprot/SwissProt as well as Illumina and Affymetrix gene IDsGene IDs matched to GO function annotations (for human)Test for enrichment of GO categories (or KEGG pathways, disease associations, etc.) in list.Groups significant categories into clusters

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DAVID uses a modified Fishers Exact text to get p-values for enrichment.

Basic idea: is enrichment of this category in this list greater than frequency of the category in the genome.DAVID enrichment score: EASE

A Hypothetical Example: In human genome background (20,000 gene total), 40 genes are involved in p53 signaling pathway. A given gene list has found that 3 out of 300 belong to p53 signaling pathway. Then  we ask the question if 3/300 is more than random chance comparing to the human background of 40/20000.Fisher Exact P-Value =  0.008.

However, EASE Score is more conservative. EASE Score =  0.06 (using 3-1 instead of 3). Since P-Value > 0.01, this user gene list is specifically associated (enriched) in p53 signaling pathway no more than random chance

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Microarray Databases

Large experiments may have hundreds of individual array hybridizationsCore lab at an institution or multiple investigators using one machine - data archive and validate across experimentsData-mining - look for similar patterns of gene expression across different experiments

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Public Databases

Gene Expression data is an essential aspect of annotating the genomePublication and data exchange for microarray experimentsData mining/Meta-studiesCommon data format - XMLMIAME (Minimal Information About a Microarray Experiment)

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Array Express

at EMBL

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GEO

at the NCBI

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Sumary

Microarray experimental detailsMicroarray data formatsQC analysis and data explorationNormalizationDifferential expressionFunctional enrichmentDatabases

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Next Lecture:

Next Generation Sequencing Informatics