Basic Introduction to Microarrays

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Basic Introduction to Microarrays

• Chitta Baral
• Arizona State University
• Feb 3, 2003

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Basics of Microarrays

• From The magic of microarrays, S. Friend and R.
  Stoughton
• (Scientific American, Feb 2002)
• A microarray has thousands of spots (or array
  elements)
• Each spot has thousands to millions of copies of
  a particular single stranded DNA representing a
  particular gene.
• Thousands of genes are each assigned a unique
  spot.
• From 2 cell samples (say one treated with a drug
  and another untreated) collect mRNA, make more
  stable cDNA from them and add fluorescent labels
  (green to untreated and red to treated).
• Apply the labeled cDNAs to the chip.
• Binding occurs when cDNA from a sample finds its
  complementary sequence of bases on the chip.
• Such binding means that the gene represented by
  the chip DNA was active or expressed in the
  sample.
• Put the chip in a scanner.
• Calculate the ratio of red to green at each spot
  and generate a color coded readout.
• Red Gene that strongly increased activity in
  treated cells
• Green Gene that strongly decreased activity in
  treated cells
• Yellow Gene that was equally active in treated
  and untreated cells.
• Black Gene that was inactive in both groups.

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Determining the impact of new drug

• Suppose the last experiment was about determining
  quickly whether a potential new drug is likely to
  harm the liver.
• Are the red or green genes correspond to ways
  (gene functions) that reflect liver damage. I.e.,
  do those genes make proteins whose concentration
  (high for the green ones, and low for the red
  ones) reflects liver damage?
• Or compare the overall expression pattern with
  the patterns produced when those genes (? cells)
  react to known liver toxins.
• Close similarity would indicate that the new drug
  is probably toxic as well.

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Application of comparative cDNA hybridization

• Tissue-specific genes
• Comparative hybridization experiments (CHE) can
  reveal genes which are preferentially expressed
  in specific tissues. Some of the genes implement
  the behaviors that distinguish the cells tissue
  type while other controlling genes make sure
  that the cell only performs the function for its
  type.
• Regulatory gene defects in cancer
• CHE can pinpoint the transcription differences
  responsible for the change from normal to
  cancerous cells
• CHE can distinguish different patterns of
  abnormal transcription in heterogenous cancers.
• Cellular responses to the environment
• CHE can point to genes whose transcription
  changes in response to an environmental stimulus.
• Temporal studies can also identify the order of
  changes providing evidence about which genes
  control the response directly and which are only
  indirectly affected by it.
• Cell cycle variations
• CHE can be used to distinguish genes that are
  expressed at different times in the cell cycle.
  Thus pathways responsible for basic life
  processes can be uncovered.

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Microarray applications and uses

• Characterization of the temporal order of gene
  expression within a cell
• Determination of the cellular location of gene
  products
• Prediction of the function of resulting proteins
• Prediction of the effect of perturbations of the
  cellular environment on the program of gene
  expression by changing environmental conditions
  or administering drugs.

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Computational challenges interpreting the
scanned image

• End point of a CHE is a scanned array image.
• Intensities can be quantified by measuring the
  average or integrated intensities of the spots
• Subject to noise from irregular spots, dust on
  the slide, and nonspecific hybridization.
• Deciding the threshold (between spots and
  background) can be difficult, especially when the
  spots fade gradually around the edges.
• Detection efficiency may not be uniform across
  the slide, leading to excessive red intensity on
  one side and excessive green on the other.
• Ratio of fluoroscent intensities for a spot is
  interpreted as the ratio of concentrations for
  its corresponding mRNA in the two cell
  populations.
• Low levels of cDNA due to reverse transcription
  bias, sample loss, or an inherently rare mRNA can
  cause large uncertainties in these ratios.

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A typical Microarray data set

• Includes expression levels for thousands of genes
  across hundreds of conditions such as
• From cells of different cell lines
• From cells under different conditions
• Pathological tissue specimens from different
  patients
• Serial time points following a stimulus to a cell
  or organism.
• Imagine a 2D array of measurements
• Rows measurements associated with individual
  genes
• Columns measurements associate with conditions
• Profile list of measurements along each row or
  column
• Features individual expression measurements
  within each profile. (some features more valuable
  than others and sometimes focusing on a subset
  improves results)

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Analyzing microarray data

• Any dataset can be analyzed in 2 ways.
• Eg. 47 expression profiles of 4026 genes
  collected from lymphoma specimens (Nature 403,
  503-511, 2000)
• 47 cancer profiles with 4026 available features
• 4026 gene profiles with 47 available features
• Supervised and unsupervised methods
• Supervised the genes or conditions are
  associated with labels coming from outside the
  experiment --that provide information about a
  preexisting classification. The information may
  include knowledge of gene function or regulation,
  disease sub type or tissue origin of a cell type.
• Classification information is used to drive the
  analysis
• Used for predicting accurate labels for new
  genes.
• Unsupervised No additional information
• Geared towards the discovery of patterns in the
  data, unbiased by outside knowledge.
• Used for exploratory tasks.
• Clustering

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Unsupervised grouping clustering

• Goal Simplify large gene expression data sets
• Approach Group similar profiles together based
  on a distance metric (a formula for calculating
  the similarity of two profiles)
• Distance metrics distance between 2 list of
  numbers
• Euclidean distance (sqr root of the sum of
  squared differences)
• Statistical correlation coefficient (-1 to 1)
• Clustering strategy
• Hierarchical clustering calculate the distance
  between individual data points and then group
  together that are close.
• Distance between groups are computed and used to
  create groups of groups
• Easy to implement but suffer because the decision
  about where to create branches and in what order
  to present them is often arbitrary.
• K-means clustering requires a parameter k (the
  expected clusters)
• Initially cluster centers are selected randomly
• In each iteration of the algorithm, all of the
  profiles are assigned to clusters whose center
  they are nearest to, and then the cluster center
  is recalculated based on the profiles within the
  cluster.
• Self-organizing maps
• Instead of partitioning, they organize the
  clusters into a map where similar clusters are
  close to each other.
• No and topological configuration of the clusters
  are pre-specified
• Cluster centers are recalculated in each
  iteration using both the profiles within the
  cluster as well as the profiles in adjacent
  clusters.
• Clustering is sensitive to the features used to
  compute the distance metric.
• Applications identify co-regulated genes, genes
  with related functions, signatures of individual
  signalling pathways within the data set, etc.

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Supervised grouping classification

• Approach Take known groupings and create rules
  for reliably assigning genes or conditions to
  these groups.
• Eg. Problem of classifying unknown genes as
  ribosomal or non-ribosomal
• Success depends on whether high quality labeled
  sets are provided or not.
• Examples of methods (Machine learning)
• Logistic regression
• uses the feature value for different groups to
  estimate the parameters of a predictor function
  (a linear log-likelihood model)
• Neural networks
• Use a set of known examples to create a
  multi-layered computational network
• Linear discriminant analysis
• Use the labeled example from each set of
  classified cases to estimate a probability
  distribution for the values of the features in
  that set.
• Given a new example, it determines the closest
  distribution and assigns the example to this set.
• Inductive logic programming
• Decision trees

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Dimension reduction

• Involves removing features from the data set
• Removed because do not provide significant
  incremental information and can confuse and make
  analysis unnecessarily complex
• Feature selection often attempts to identify a
  minimum set of non-redundant features that are
  useful for classification.
• Eg. Dont select co-regulated genes.
• Unsupervised dimensional reduction pruning
  uninformative features several methods such as
• Principal component analysis (PCA)
• Automatically detects redundancies in the data
  and determines a new set of guarentedly
  non-redundant hybrid (multiple features
  condensed) features.
• Advantage Makes apparent the outliers and
  clusters in a data set and reduces the noise in a
  data set
• Disadvantage Throwing away weak signals that
  could be but important
• Independent component analysis
• Supervised dimension reduction feature selection
• Goals selecting relevant underlying features and
  reducing the number of features necessary to
  classify correctly
• A straight forward method
• Iteratively apply a supervised classification
  algorithm that reports weights on all features.
• After running the classification algorithm the
  first time, the feature with the lowest weight is
  removed from the data set.
• The algorithm is run again to determine the
  second least important feature.
• This process is repeated while monitoring the
  classification performance on known examples.

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Further Details

• Types of microarrays spotted cDNA microarrays,
  high-density Oligonucleotide microarrays
• Fluorescent dyes (for glass arrays) vs
  radioactive isotopes (for membrane arrays)
• Interpretations
• Identifying individual genes (regulated
  expression of which can explain particular
  biological phenomena) or assign potential
  function to new genes.
• Co-regulated genes (often identified using
  cluster analysis) allow functional classification
  (may participate in similar cellular processes or
  pathways),
• potential identification of common regulatory
  elements (DNA motifs) in promoter sequences.
• Assumption Genes with closely related expression
  patterns may be controlled by the same regulatory
  mechanism
• When one sees differential expression they may
  have knowledge about the probable function (from
  NCBI databases) of that gene and can make a
  hypothesis about the role that gene is playing in
  their system.
• One cluster of genes related to one pathway,
  another to another pathway hint about
  interconnection between such pathways
• Gene regulatory networks
• Comparing large number of samples for a global
  view
• Common control can be mixture of all samples used
• Combining many data sets and analyzing the whole
  set is very useful
• Comparing the expression profiles of tumour
  samples using many genes, it is possible to
  identify those genes whose expression
  characterizes a particular tumour type
• Compare the expression signature of a particular
  tumour type to data generated by measuring the
  responses of closely related cell lines in
  culture to many different stimuli, such as
  hormones, growth factors, etc. Using this
  strategy one can draw conclusions about which
  signalling pathways are activated in a particular
  tumour type, leading to the identification of
  pathways that might provide therauptic targets.

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References main sources used in this presentation

• Basic microarray analysis grouping and feature
  reduction. Raychaudhuri et al.Trends in
  Biotechnology vol 19, No 5, May 2001 189 193.
• Gene expression microarrays and the integration
  of biological knowledge. Noordewier and Warren.
  Trends in Biotechnology vol 19, No 10, Oct 2001
  412-415.
• http//www.cs.wustl.edu/jbuhler/research/array
• The magic of microarrays. Friend and Stoughton.
  Scientific American. Feb 2002.
• Other sources that I read.
• Navigating gene expression using microarrays a
  tech review. Schulze Downward. Nature cell
  biology. Vol3, Aug 01. E190
• http//www.fargo.ars.usda.gov/ps/micr_the.htm
• Analysis of gene expression by microarrays cell
  biologists gold mine or minefield? Schulze
  Downward. J. of Cell Science 113, 2000,
  4151-4156.
• Microarrays handling the deluge of data and
  extracting reliable information. Hess et al.
  Trends in Biotechnology. Vol 19, No 11, Nov 2001,
  463-468