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Introduction to Microarray Data Analysis
Sajid khan
Chapter : 6
GPGCM Abbottabad
Outline
1. Introduction
2. Microarrays Technology3. Types and Uses of Microarrays4. Microarrays for the Study of Gene Expression5. Fabrication
1. Spotted microarrays2. Oligonucletide microarrays
7. Experiments with Microarrays1. Flow chart of a experiment with microarrays
8. Software for Microarray Data Analysis
Introduction (1)Brief review of molecular biology...
Most life forms are made of cells. Each individual has a very large indefinite number of cells.
Each cell containschromosomes
(e.g. human cellscontain 23 pairs of
chromosomes).These organized
structures of DNAand inherited information.
proteins are the carriers of
A chromosome is a single piece of coiled DNA containing many genes, regulatory elements and other nucleotide sequences.
The genome of an organism is inscribed in DNA or RNA in some virus
A gene is the basic unit of heredity in a living organism and is the portion of the DNA that codes for a protein or an RNA
Each protein-coding gene is a gene transcribed into RNA in some molecules and in turn mRNA is translated into at least one protein in some cells
Introduction (2)What else?
Introduction (3)The Central Dogma of Molecular Biology
• Information flow from DNA to RNAtoprotein occurs in four stages
1. Replication
The DNA replicates its information in a process that involves many enzymes
2. Transcription
The DNA codes for the production of messenger RNA (mRNA)
3. Splicing
In eucaryotic cells, the mRNA is processedand migrates from the nucleus to the cytoplasm.
4. Translation
Messenger RNA carries coded information to ribosomes. The ribosomes read this information and use it for protein synthesis.
Introduction (4)
• Molecular biology has developed multiple techniques to measure levels of RNA, DNA, proteins or metabolites, such as
–
–
–
–
–
Southern Blot
Northern Blot
Differential display
SAGE
…
• Post-genomics era is perform and toanalyze
characterized by its capability todata sets from large-scale
experiments simultaneously
Techniques in Molecular Biology
Introduction (5)
• With the same resources we obtain a picture with lower resolution but with a view of the whole context
vs
The paradigm shift
Based on “The paradigm shift” slide from J. Dopazo (CNIO)
Introduction (6)
• Biology used to “spy” on genes everything in deep and individually (i.e. gene by gene)
To draw an analogy with pre-genomics era
Introduction (7)
Nowadays, a lot of genes can be “spied” at the same time... but...
To draw an analogy with post-genomics era
… How can we split the wheat from the chaff?
Microarrays Technology (1)
Microarrays are a variety of platformsin which high density assays performed in parallel on a support.
are solid
This technology has changed the waybiologists approach problems
and newchallenges
forhuge each
introduces statisticians quantity of experiment
becauseof the datageneratedin
They have been used for all kinds of biological problems
The literature contains almost 8000 papers using microarray word in the title
Broadly speaking...
1998
2000
2002
2004
2006
2008
2010
0
200
400
600
800
1000
year
num
ber
of
publ
ica
tion
s
Publications in PubMed with microarray word in the title
524
83
259
544
747
988
1091 1080
1000 986
920
171
Microarrays Technology (2)
• It is the same one that allows DNA double helices toprovide the basis for heredity
• Sequences of DNA or RNA molecules containing complementary base pairs have a natural tendency to bind together.
...AAAAAGCTAGTCGATGCTAG...
...TTTTTCGATCAGCTACGATC...
• If we know the mRNA sequence, we can build a probe for it using the complementary sequence.
The biological principle of microarrays involved in...
Microarrays Technology (3)
It consist of a large set (thousands to ten of thousands) of specific sequences (known as probes or features) attached in order (array) to microscopic spots on a solid support (nylon or silicon glass,...).
A probe (that can be a gene, a protein, a metabolite,...) is used to hybridize a molecule of a nucleic acid sample (called target) under high-stringency conditions.
Probe-Targetdetermine therelative
hybridization is usedto
of
abundancenucleic acid sequences in the targets (e.g.to determine sequences,to
detectvariations in gene sequences, levels, gene mapping,...).
expression
But... What is a microarray?
gene 1 gene 2 gene 3 gene 4probe probe probeprobe1 32 4
probe probeprobeprobe 5
6
7
8
probe probeprobeprobe 9
10
11
12probe
k-3probe
probeprobe k-2
k-1
k
...
spots
Microarray
molecule sample 1
molecule sample 2
molecule sample r...
Types and Uses of Microarrays (1)
Microarrays spatially arranged on a solid surface aremost widely used.`
Bead arrays are created by
– either impregnating beads with differentconcentrations of fluorescent dye,
– or some type of barcoding technology.
The beads are addressable and usedto binding events that occur on their surface.
identify specific
Types of microarrays
Types and Uses of Microarrays (2)
Expression analysis
– The process of measuring gene expression via RNA (or cDNA after reverse transcription) is called expression analysis or expression profiling.
In this experiments the expression levels of thousands of genes are simultaneously monitored to study the effects of certain treatments, diseases, and developmental stages on gene expression.
–
Comparative Genomic Hybridization
– Comparative genomic hybridization (CGH) or Chromosomal Microarray Analysis (CMA) is used for the analysis of copy number changes (increases or decreases) of the important chromosomal fragments harboring genes involved in diseases.
Mutation analysis
– A single base difference between two sequences is known as Single Nucleotide Polymorphism (SNP) and detecting them is known as SNP detection.
With gDNA this kind of arrays try to detect genes that might differ from each other by as less as a single nucleotide base.
–
Uses of Microarrays (1)
Types and Uses of Microarrays (2)
Uses of Microarrays (2)
cDNA Nylon Membrane Array
SNP Array Affymetrix
CNV Array Illumina
CGH Arrays
Expression Arrays
Protein Array Tissue Array
cDNA Agilent Array GeneChip Affymetrix Array
Types and Uses of Microarrays (3)
Gene discovery
Identification of new genes, know about their functioning and expression levels under different conditions.
Molecular classification of complex diseases
To classify the types of cancer on the basis of the patterns of gene activity in the tumor cells, to develop more effective drugs.
Drug discovery
Comparative analysis of the genes from a diseased and a normal cell help the identification of the biochemical constitution of the proteins synthesized by the diseased genes. This information can be used to synthesize drugs that combat with these proteins and reduce their effect.
Toxicological research
Microarray technology provides a robust platform for the research of the impact of toxins on the cells and their passing on to the progeny.
Application of Microarrays
Microarrays for the Study of Gene Expression (1)
• The gene expression is the presence of the gene products of a gene, in the form of mRNA (or protein), in a cell
• To put it straight: Since cells contain the same genetic information, what makes different brain cells from heart cells is the gene expression.
What is the gene expression?
Microarrays for the Study of Gene Expression (2)
To find genes that display a large difference in gene expression between two conditions and are homogeneous within them
– Typically statistical tests (t-test, Wilcoxon test) are used
If there are more than two conditions, or if conditions are nested, the appropriate statistical method is ANOVA
pvalues from these tests have to be corrected for multiple testing
Finding Differentially Expressed Genes (DEG)
Cell Line 1 Cell Line 2Control Treatment Control Treatment
Microarrays for the Study of Gene Expression (3)
To find groups that are not defined yet (e.g. novel disease
subtypes) Methods
–
–
–
–
from this field were the first to be used for microarray data
should be used only if no prior knowledge exists that could be incorporated
find patterns in the data, but any patterns, whether they are meaningful or not
include
•
•Clustering (hierarchical and partitioning)
Projection (PCA, MDS)
Exploratory data analysis (1)
Alizadeh et al. Nature 403:503–511 (2000)
Microarrays for the Study of Gene Expression (4)
Usually used to find patterns of co-expressed genes
The meaning of time series is different for– Biologists: 2-10 time points
– statisticians: >200 time points
“Non-optimal” solution: to use clustering methods to find such patterns
Note that they are by no means exhaustive, and that no significance measure can be attached to them
In contrast to Estimation of Distribuiton Methods (EDA), partitioning cluster methods are more popular (e.g. K- means or Self-organizing maps)
To seek genes whose expression profile is similar to that of a paradigmatic gene, correlations can be calculated, and sort by them. There is no need for clustering.
Special methods exist for periodic changes (⇒ cell cycle), e.g. Fourier analysis
Time series, partitioning clustering and correlation
Microarrays for the Study of Gene Expression (5)
When information about grouping of the samples is available, it can (and should) be used to get improved results
Groupings may be:–
–
–
–
–
–
–
–
Treatment and Control
Disease and Normal
Disease stage 1, 2, 3
Mutant and Wild Type
Good and Poor Outcome,
Therapy success or failure
...
One learns characteristic patterns from a training set and evaluate by predicting classes of a test set
Classification
Microarrays for the Study of Gene Expression (6)
To find patterns that are associated with prolonged patients’survival time
Instead of treating outcome as a binary variable, can be used–
–The overall survival time or
The event free survival timeas continuous variables, and try to estimate it by regression
Since the risk to suffer from relapse is decreasing with time, linear regression models are almost always inappropriate specialized models would be better
–
–Cox regression
Regression
trees
Survival Analysis
Microarrays for the Study of Gene Expression (7)
To find molecular predictors that tell about probable success (or failure) of a certain therapy. e.g.
–
–estrogen receptor status for tamoxifen (antihormone)
therapy HER2/NEU status for herceptin therapy in breast
cancer One may regard treatment outcome as a discrete variable and
use classification methods
Sometimes, it’s convenient not to wait for the finalendpoint (which may be years away), but to use surrogate variables, e.g.–
–the drop of the blood level of a certain protein
reduction in tumor volume
Pharmacogenomics
Fabrication
There are many types of technologies, but principles are the same
The most used are spotted arrays and In situ arrays
Spotted arrays (aka cDNA arrays or Stanford arrays)– Previously synthesized cDNAs or oligonucleotides are deposited on the
chip
Based on “printing-like” technologies–
In situ arrays (aka oligo arrays or Affy arrays)–
–
–
Probes are synthesized directly on the chip
Based on photolithographic techniques
Affymetrix arrays are the best-known... but not the only one!
Two main technologies
1. Chip Design and Production2. Sample Preparation3. Hybridization4. Scanning and Capturing Images5. Image Analysis6. Quantification
From the chips to the images
Spotted Arrays (1)
Spotted Arrays (2)
Production begins with the selection of the "probes" to be printed on the array
In general: chosen from– GenBank (http://www.ncbi.nlm.nih.gov/)
– dbEST (http://www.ncbi.nlm.nih.gov/UniGene/index.html)
cDNA’s are printed on the array
– Each spot can contain unique sequences
– Printing” means adhering sequences to the spots
Chip design and construction
A movie of the printing process is available here
Spotted Arrays (3)
1. RNA is extracted from the samples
2. This RNA is converted to fluorescently labeled cDNA by reverse transcription in presence of fluorescently labeled nucleotide precursors
3. RNA from each samplesarelabelled fluorescent Cy-5)to
with dyes
different (e.g. Cy-3,allow
directcomparison
4. After labeling, they are mixedand hybridized
sequences onthe (probes)
with array
Sample preparation
Spotted Arrays (4)Hybridization with probes
Targets labeled and combined
A movie of the hybridization process is available here
Spotted Arrays (5)
After hybridization each DNA spot is illuminated and fluorescence measures taken for each dye separately
These measurements will be used, after the appropriate quality controls, to determine the relative abundance, of the sequence of each specific gene in the two mRNA or DNA samples
Scanning and capturing image
Spotted Arrays (6)
TIFF images are processed by image analysis programs–
–
–
SPOT,
GenePix
...
to acquire intensity values for each spot
These measures will be used, after the appropriate quality controls, to determine the relative abundance, of the sequence of each specific gene in the two mRNA or DNA samples
Image analysis (1)
Steps in Image Processing1. Addressing: Estimate location of spot centers
2. Segmentation: Classify each spot a
● foreground (signal) background (noise)●
3. Information extraction (quantification)
For each spot on the array, and each dye obtain– Signal measurements (R , G )
gg
–
–
Background measurements (bgR , bgG )g g
Quality indicators
Image analysis (2)
Spotted Arrays (7)
Spotted Arrays (8)
● Gene measured measures
expression isfrom
intensity as
therelative
(corrected)intensity of one dye vs the (corrected)relative intensity of the other
● Background correctionmay be according quality
needed, or
not, array
to the
Quantification
M = Rg , M
G g Corrected
R g−bgRg
= Gg −bgGg
Spotted Arrays (8)Overview of the process
A movie of the whole process is available here
1. Main Concepts2. Synthesis of Oligos on the Chip3. Sample Preparation4. Hybridization Process5. Scanning Images6. Output Images7. Quantification and Expression Measures
From the chips to the images
In situ Chips (1)
In situ Chips (1)
More advanced design than spotted cDNA arrays–
–They are NOT based on competitive hybridization. That is, one chip, one sample
They are NOT added on the chip after being synthesized in vitro
Main idea: Probes are synthesized in situ (on the chip)
Sequences are built up on the chip surface by sequentially elongating a growing chain with a single nucleotide using photolithography
Chemical yield of the stepwise elongation is limited
– Sequences can NOT grow to more than 25mers length (oligo)
– Need 16-20 different 25mer sequences to uniquely characterize a gene
•
•
Probe = Individual 25mer sequence
Probe set = Set of 25mers corresponding to a particular gene/EST
Main concepts (1)
In situ Chips (2)
Affymetrix (http://www.affymetrix.com) is the leader company of these kinds of chips. They call them GeneChips
Each gene is represented by a set of short sequences
Some of these chips contain whole genomes, that is > 50.000 probe sets
A probe set (usually denoted probeset) is used to measure the mRNA levels of a unique gene
Each probeset is made up of multiple probe cells–
–with millons of copies of one oligo de copias de un oligo (25bp)
Organized in probe pairs with•
•
a Perfect Match (PM): match perfectly with a piece of a gene
a Mismatch (MM): it is the same to PM but with the central nucleotide change by the
complementary
Main concepts (2)
In situ Chips (3)
More advanced design than spotted cDNA arrays–
–They are NOT based on competitive hybridization. That is, one chip, one sample
They are NOT added on the chip after being synthesized in vitro
Main idea: Probes are synthesized in situ (on the chip)
Sequences are built up on the chip surface by sequentially elongating a growing chain with a single nucleotide using photolithography
Chemical yield of the stepwise elongation is limited
– Sequences can NOT grow to more than 25mers length (oligo)
– Need 16-20 different 25mer sequences to uniquely characterize a gene
•
•
Probe = Individual 25mer sequence
Probe set = Set of 25mers corresponding to a particular gene/EST
Main concepts (1)
In situ Chips (4)GeneChip® expression array design
In situ Chips (5)One gene, one probe set
Probes are selected to be specific of the represented gene
The must have good properties of hybridization
gene sequence
In situ Chips (6)Synthesis of oligos on the chip (1)
GeneChip® probe arrays are manufactured through a unique and robust process, a combination of photolithography and combinational chemistry
Image courtesy of Affymetrix
In situ Chips (7)Synthesis of oligos on the chip (2)
Image from a course of Dan Nettleton
GeneChip
maskmaskmaskmaskmaskmaskmask
mask
G A A
T C
C A G
TTT
T AA C C
In situ Chips (8)Synthesis of oligos on the chip (3)
Several copies of a single feature are deposited in each cell
Image courtesy of Affymetrix
In situ Chips (9)Sample preparation
In situ Chips (8)Hybridization process
Once the oligos have been synthesized hybridization is performed by adding mRNA from the tissue to analyze on the chip
Image courtesy of Affymetrix
In situ Chips (9)Scanning Images
Scanning of tagged and un-tagged probes on an Affymetrix GeneChip®
microarray
Image courtesy of Affymetrix
In situ Chips (10)Output Image
Data from an experiment showing the expression of thousands of genes on a single GeneChip® probe array
Image courtesy of Affymetrix
Intensities from each element are extracted
Quantitative analysis of the hybridization results is performed by analyzing the hybridization pattern of the set of PM and MM probes of every gene
In contrast with spotted chips expression measures used here are absolute ones. That is, each chip is hybridized with only one tissue at a time
Quantification
In situ Chips (11)
Measures to determine the quantitative RNA abundance, i.e. the expression level based on the average of the differences PM minus MM for each probe family
Many alternatives have been introduced
Absolute expression measures
In situ Chips (12)
Avg.Diff = 1 ¿ j ∈ A PM −MM ∣A
∣
PRO's
••
CheaperFlexibility with the experimental designHigh signal intensity (large sequences)
•
CON's
• Low reproducibility• Cross-hybridization (low specificity)• High manupulation (ssibility of
contamination)
PRO's and CON's
Spotted vs In situ Arrays
PRO's
••••
Quick manufacture (automated) High reproducibilityHigh specificityA lot of probes/genes
CON's
• Requires more specialized equipmentExpensives Low flexibility
••
cDNA microarrays Oligo microarrays
In situ Chips (13)Overview of the process
A movie of the whole process is available here
Image courtesy of Affymetrix
Experiments with MicroarraysFlowchart of a experiment with microrrays
Software (1)
Microarray experiments generate huge quantitiesof data which have to be– Stored, managed, visualized, processed...
Many options available.
However... No tool satisfies all
user’s needs Trade-off. A tool
must be
– Powerful but user friendly
– Complete but without too many option
– Flexible but easy to start with and go further
– Available, to date, well documented but affordable
Which software for the analysis?
Software (2)
Partek–
–
–
Commercial market
leader Many useful
facilities Some infelicities Bioconductor
– Open Source R software package
– Vigorous development, new things show up here first
GEPAS
– Freely available
– Based on Bioconductor and R
Some options are...
Software (3)
R is an open-source system for statistical computationand graphics.
It consists of–A language
– A run-time environment with
– Graphics
– A debugger
–Access to certain system functions
It can be used
– either, interactively, through a command language
– or running programs stored in script files
So, what you need is R?
Software (4)
R is a popular tool between statisticians
Once they started to work with microarrays they
continued using it– To perform the analysis
– To implement new tools
This gave rise very fast to lots of free R-based software to analyze microarrays
The Bioconductor project groups many of these (but not all) developments
R and Microarrays
Software (5)
http://bioconductor.org
Open source and open development software project for the analysis and comprehension of genomic data
Most early developments as R packages
Extensive documentation and training material fromshort courses
Has reached very good stability...
But, what is now a standard may not be so in a future
The Bioconductor project
Software (6)
http://estbioinfo.stat.ub.es/resources/index.html
on-line resources
ReferencesEnjoy them!!!
Smyth, G.K.; Yang, Y.H.; Speed, T. (2003) Statistical Issues in cDNA Microarray Data Analysis. In: MJ Brownstein, AB Khodurski (eds.), Methods in Molecular Biology, Humana Press 2002.
http://www.stat.berkeley.edu/ t̃�erry/zarray/TechReport/mareview.pdf
Huber, W.; von Heydebreck, A.; Vingron, M. (2003) Analysis of microarray gene expression data. In: Handbook of Statistical Genetics, 2nd ed., Wiley 2003.
http://www.ebi.ac.uk/huber/docs/hvhv.pdf
