COMPUTATIONAL GENOME ANALYSIS PROJECT: Microarray data analysis

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COMPUTATIONAL GENOME ANALYSIS PROJECT Microarray
data analysis

• Müge Erdogmus
• Zeynep Isik

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DISEASE EMPHYSEMA

• Emphysema is a lung disease that is included in a
  group of diseases that are called chronic
  obstructive pulmonary disease.
• The ability of the lungs to expel air is
  diminished for the patients with emphysema.
• Lungs loose their elasticity thus, they become
  less contractile.
• In emphysema, the lung tissues which are
  responsible for supporting the physical shape and
  function of the lungs are damaged.

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EMPHYSEMA

• The lung tissue around the smaller airways
  bronchioles and the alveoli?targets for the
  destructions.
• Normally the lungs are very elastic and spongy
  but not in emphsema!

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Causes of Emphysema

• Alpha-1-antitrypsin deficiency
• Cigarette smoking
• 1)It damages the lung tissue in various ways? The
  cells in the airway which are responsible for the
  clearance of the mucus and other secretions are
  influenced by cigarette smoking.
• 2) Enhanced mucus secretion? rich source of food
  for bacteria but immune cells are negatively
  influenced by the cigarette in their fight for
  infection.
• Destructive enzymes from the immune cells?
  loss of proteins associated with elasticity.

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The Microarray Experiment

• The gene expression dataset is composed of 30
  samples that
• are retrieved from NCBIs Gene Expression
  Omnibus.
• The RNA transcripts that are utilized for
  measuring the
• expression signals are taken from Homo-sapien
  organism.
• 18 slides?Severely emphysematous tissue removed
  at LVRS
• 12 slides? normal or mildly emphysematous lung
  tissue taken
• from smokers with nodules suspicious for lung
  cancer.
• More than 33,000 best characterized human genes
  were
• represented in the dataset that include 1,000,000
  unique
• oligonucleotide features.

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Data Analysis
Read XLS Files
Normalization
Outlier Removal
Check Normal Dist.
Hypothesis Testing
PCA
Correlation Based
Classification
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Data Normalization

• Why do we normalize data?
• Scale data for reasonable comparisons
• Map the expression values of each probe between
  0,1 range
• Do not disturb the underlying distribution of the
  data

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Outlier Removal

• Why do we need to remove outliers?
• They can distort the mean of the data -gt wrong
  clusterings, wrongs significance values
• What is an outlier?
• Expression values that are three or more standard
  deviation away from the mean are outliers
• Replace outliers with the mean of remaining
  expression values for the probe
• Detected outliers in 5331 probes

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Clustering Before Feature Reduction

• Clustering on samples using all existing features
  to see how bad the situation is
• K-means clustering with euclidean distance
• For selection of initial k centers
• KCENTRES algorithm
• select k center objects from the distance matrix
  such that the distance between the most distant
  object and the center that is closest to that
  object is minimized.

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Clustering Before Feature Reduction
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Clustering Before Feature Reduction

• Really low clustering performance
• Data set is noisy
• Reduce features so as to have only the pobes that
  are really significant
• Differentially expressed among classes
• ...can be used to differentiate between the two
  classes of samples

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Searching for Normally Distributed Probes

• Why do we need to identify which probes are
  normally distributed and which ones are not?
• Apply different tests of significance
• Lilliefors test (95 significance)
• Tests the normality of the distribution via
  examining the signal intensity data for a probe
• Modified version of Kolmogorov-Smirnov test
• No need to specify the parameters of the
  underlying distribution of the data
• approximates the underlying distribution
• 14787 probes have normal distribution
• 7428 probes do not have normal distribution

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Tests of Significance

• For probes that have normal distribution
• T-test or Z-test
• 30 samples -gt t-test (95 significance)
• For probes that do not have normal distribution
• Non-parametric test
• Wilcoxon rank-sum test (95significance)
• ... sorts all intensity values for a probe
• ... gives each intensity value a rank
• ... sums up the ranks for the signal values for
  both classes
• ... compares the sums to decide whether the two
  samples come from the same distribution.

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Tests of Significance

• 2339 of 22215 probes are differentially expressed
  
• eliminated 19876 probes

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Clustering Before Feature Reduction

• Clustering on samples using only differentially
  expressed features to see whether feature
  reduction proved to be useful
• K-means clustering with same procedure

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Clustering Before Feature Reduction
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Further Feature Reduction

• 2339 features is a high number
• Try to reduce number of features while preserving
  clustering accuracy
• Two methods
• Correlation based feature reduction
• Principal component analysis

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Correlation Based Feature Reduction

• Extract uncorelated features
• Cluster uncorrelated features
• K-means
• SOM
• Prior to clustering find value of k from
  hierarchical clustering
• Different distance metrics
• Different hierarchical clustering methods
• From each cluster of each clustering select
  certain genes
• These genes will be used for classification

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Correlation Based Feature Reduction

• Extract uncorelated features
• Find correlation matrix
• Keep one of the highly correlated features and
  remove the others.
• Highly correlated gt85
• Keep the feature that is closest to all other
  features
• 1689 probes are left in our feature set

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Correlation Based Feature Reduction

• Prior to clustering find value of k from
  hierarchical clustering
• Different distance metrics
• Manhattan
• Euclidean
• Mahalanobis
• Chebyshev
• Correlation coefficients
• Different hierarchical clustering methods
• Complete linkage (max distance clustering)
• Average linkage (average distance clustering)

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Correlation Based Feature Reduction
Complete Linkage with Manhattan Distance
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Correlation Based Feature Reduction
Average Linkage with Manhattan Distance
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Correlation Based Feature Reduction
Complete Linkage with Euclidean Distance
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Correlation Based Feature Reduction
Average Linkage with Euclidean Distance
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Correlation Based Feature Reduction
Complete Linkage with Chebyshev Distance
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Correlation Based Feature Reduction
Average Linkage with Chebyshev Distance
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Correlation Based Feature Reduction
Complete Linkage with Correlation Coeff.
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Correlation Based Feature Reduction
Average Linkage with Correlation Coeff.
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Correlation Based Feature Reduction

• complete linkage method is more successful in
  separating between clusters
• the probes that are very similar to each other
  are put into the same clusters, the ones that are
  very different are put into different clusters
• focused on the trees that are formed using
  complete linkage with euclidean distance and
  correlation coefficients as distance metrics
• From which level we should cut the trees?
• Examine the trees

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Correlation Based Feature Reduction
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Correlation Based Feature Reduction
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Correlation Based Feature Reduction

• cut from a level above the lower bound lines
• have a small number of clusters
• insufficient to explain the closeness of probes.
• cut from a level below the upper bound lines
• have a high number of clusters
• forcing the clustering algorithm to divide
  clusters that consist of very similar samples
• lower bound 40 clusters
• upper bound 95 clusters

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Correlation Based Feature Reduction

• To find optimal k value
• run several k-means algorithms for each k value
  between 40 and 95
• produces the clustering of highest quality
• High quality small intra-cluster distance
• k is found to be 80
• store the clustering result that is produced when
  k 80
• run SOM clustering with 9x9 map and store the
  clustering result

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Correlation Based Feature Reduction

• How do we select the signature probes from the
  clusters?
• select the ones that are most significant
• Select how many probes form each cluster ?
• select the n most significant probes from each
  cluster, where n is depenedent on the quality
  of the cluster
• Quality of cluster intra-cluster distance
• Quality value is high -gt intra-cluster similarity
  is low -gt cluster is loose
• Quality value is low -gt intra-cluster similarity
  is high -gt cluster is tight
• Take more probes from clusters that are loose in
  order to represent those clusters better

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Correlation Based Feature Reduction

• From clusters formed by k-means
• 144 probes
• From clusters formed by SOM
• 141 probes
• we formed another set of 88 probes that are found
  both in probes selected from k-means clustering
  and SOM clustering (named as common probes)

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Principal Component Analysis

• set of statistically significant probes are
  directly given to prtools' pca function
• 99, 90 and 85 data preservation
• Number of resulting principal components

Percentage 99 90 85
of PCs 29 22 20
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Classification

• Feature sets from feature reduction based on
  correlation that can be used by classifiers
• K-means probe set
• Som probe set
• Common probe set
• Algorithms
• Linear classifier
• Support vector machine
• 1-narest neighbor classifier
• 3-nearest neighbor classifier

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Classification

• 30 samples in our data set -gt k-fold cross
  validation
• bias caused by random selection of samples for
  training and testing sets
• Repeatedly perform 100 classifications for each
  classifier
• Report the average classification error

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Classification (k-means probe set)
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Classification (k-means probe set)
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Classification (SOM probe set)
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Classification (common probe set)
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Classification

• Three sets of principal components from feature
  reduction with principal component analysis can
  be used by classifiers
• Algorithms
• Linear classifier
• Support vector machine
• 1-narest neighbor classifier
• 3-nearest neighbor classifier

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Classification (PCA 99)
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Classification (PCA 90)
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Classification (PCA 85)
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Classification

• in all cases support vector machines provides us
  with the best clustering results

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Classification

• performance of support vector machines that
  utilize the principal component sets is worse
  than the ones that utilize the probe sets that
  are formed as the result of correlation based
  feature reduction method
• performance of support vector classifiers that
  utilize the k-means, SOM and common probe sets
  are more or less similar
• classify samples with 99 accuracy on the average
  
• use set of common probes as signature genes
• aim is to reduce the number of features without
  sacrificing classification performance

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Final feature reduction

• 88 features is still a high number
• use Fishers linear discriminant to further
  reduce number of features
• resulting signature gene set consisted of 26
  probes
• performance of classification even improved when
  the number of probes is reduced
• able to classify the 30 samples with 99.7-100
  accuracy on the average

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Final classification
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Signature Genes

• PTGDS?prostaglandin D2 synthase 21kDa (brain)
• Key enzyme for the generation of prostanoids in
  the immune system.
• Prostaglandins?various influences throughout the
  body such as taking role in inflammation , smooth
  muscle contraction.
• The expression of PTGDS is enhanced in patients
  with emphysema compared to the control patients.

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Signature Genes

• There are also cases in the literature that
  support the rise of prostaglandins as being
  potent proinflammatory mediators in response to
  various stimuli including the allergic airway
  inflammations.
• Thus considering that immune response is
  stimulated in response to the emphysema due to
  the bacterial infection, it is expected to
  observe a rise in the expression of
  prostaglandins due to its role in inflammation.

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Signature Genes

• BIRC4?Baculoviral IAP repeat-containing 4
• The gene that encodes that protein belongs to a
  family of proteins that blocks apoptosis.
• According to the literature information,15-
  deoxy- delta(12,14)- prostaglandin J2 leads to
  the reduction of BIRC4.
• The expression results indicate that, a decrease
  in BIRC4 is observed in patient with emphysema
  compared to the patients without emphysema.
• Speculation? In control cells there is a
  possibility that the cancer tissues influence the
  expression of BIRC4 therefore its expression may
  be analyzed as high.

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Signature Genes

• Spastin gene has a key role in cytoskeletol
  rearrangement and dynamic.
• The expression spastin is decreased in patients
  with emphysema compared to the patients that do
  not have.
• Considering that macrophages release substances
  that damages proteins that are responsible for
  contracting and expanding of lung,it is highly
  expected to observe a decrease in the level of
  spastin .

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Signature Genes

• FGF18 ? fibroblast growth factor 18
• According to literature data, (Decreased Lung
  Fibroblast
• Growth Factor 18 and Elastin in Human Congenital
  Diaphragmatic Hernia
• and Animal Models) FGF18 reduction has been
  associated with
• the decrease in elastin expression and impaired
  elastin
• deposition.
• Therefore, the decrease of FGF18 seen in
  patients with
• emphysema is an expected finding which is
  correlated with
• the loss of elasticity in the lungs, specifically
  with the alveoli.

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Signature Genes

• TCF12?transcription factor 12 (HTF4, helix-loop-
• helix transcription factors 4)
• Important regulators of gene regulation during
• lymphocyte development
• This encoded protein is expressed in many
  tissues,
• among them skeletal muscle, thymus, B- and
  T-cells
• The expression of that gene is enhanced in
  patients
• with emphysema compared to the patients without
• emphysema.
• That is correlated with emphysema pathogenesis
  due
• to the existence of the large scale infection
  associated with
• cigarette smoking.

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Signature Genes

• CSNK1A1?casein kinase 1, alpha 1
• Catalytic unit? involved in the transduction of
  the
• Wnt signal.
• According to the previous studies in literature,
  an inhibitor of
• Wnt signalling(secreted frizzled-related
  protein) is found to be
• expressed in lung tissue with emphysema but not
  in the healthy
• lung tissues.
• The reduction in the expression of that gene in
  patients with
• emphysema is expected and correlated with the
  literature data.
• Diminishing of CSNK1A1 leads to the ? prevention
  in
• Wnt signalling? rise in matrix proteases that
  are able to degrade
• all types of extracellular matrix proteins

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Signature Genes

• PPIB?peptidylprolyl isomerase B (cyclophilin B)
• These proteins are found in biological fluids in
  response
• to inflammatory stimuli, or oxidative stress.
• Cyclophilin B has a role in the T-lymphocyte
  function and
• recruitment.
• Therefore, the increase in that gene transcript
  in patients
• with emphysema compared to the patients who do
  not have
• the disease, is expected due to the existence of
  an immune
• response associated with the bacterial
  infection.

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Signature Genes

• C1R?complement component 1, r subcomponent
• In order to begin and propagate the inflammatory
• response, an early acting mechanism the
  complement
• system fight against the microbial infections.
• Therefore, as being a component of this system,
  the
• increase of that transcript(C1R) in tissues of
  patients with
• emphysema compared to the patients without
• emphysema is an expected situation considering
  the
• elevated levels of infection in empysema patients.

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Signature Genes

• FLNB ?filamin B, beta (actin binding protein 278)
• The protein is responsible for connecting the
  cell
• membrane components to the actin cytoskeleton.It
  also
• fixes various transmembrane proteins to the actin
  
• cytoskeleton
• The transcript level of that gene is seemed to be
  
• enhanced in tissues of patients with emphysema
• compared to the tissues of patients without
  emphysema.
• This increase may be due to the compensation of
  these
• connective tissues that are damaged to the
  proteases
• released from immune cells.

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Signature Genes

• CST1?cystatin SN
• These proteins are found to be participated in
• mechanisms that guard lungs against injury or
  inflammation .
• As expected , the transcript level for that gene
  is
• enhanced in tissues of patients with emphysema
  compared
• to the tissues of patients without emphysema.

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THANK YOU ?
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REFERENCES

• http//www.emedicinehealth.com/emphysema/article_e
  m.htmEmphysema20Overview
• Effect of thymoquinone on cyclooxygenase
  expression and
• prostaglandin production in a mouse model of
  allergic airway inflammation
• Immunology Letters Volume 106, Issue 1, 15 July
  2006, Pages 72-81
• Decreased Lung Fibroblast Growth Factor 18 and
  Elastin in Human
• Congenital Diaphragmatic Hernia and Animal
  ModelsOlivier Boucherat1,2,
• Alexandra Benachi1,3, Anne-Marie Barlier-Mur1,2,
  Montoya1,2, Jelena
• Martinovic4, Bernard Thébaud5, Bernadette
  Chailley-American Journal of
• Respiratory and Critical Care Medicine Vol 175.
  pp. 1066-1077, (2007)
• http//www.abcam.com/index.html?datasheet33581
• en.wikipedia.org/wiki/Alveoli

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REFERENCES

• The basic helix-loop-helix transcription factor
  HEBAlt is expressed in
• pro-T cells and enhances the generation of T cell
  precursors Wang D,
• Claus CL, Vaccarelli G, Braunstein M, Schmitt TM,
  Zúñiga-Pflücker JC,
• Rothenberg EV, Anderson MK. J Immunol. 2006 Jul
  1177(1)109-19.
• Activation of an Embryonic Gene Product in
  Pulmonary
• Emphysema Identification of the Secreted
  Frizzled-Related Protein K.
• Imai, DMD, PhD, and J. DArmiento, MD, PhD Chest.
  2000117229S
• Regulated expression of the interstitial
  collagenase matrix
• metalloproteinase-1 (MMP-1) in the lung by MAP
  kinase and Wnt signaling
• Becky A Mercer, COLUMBIA UNIVERSITY
• http//en.wikipedia.org/wiki/Matrix_metalloprotein
  ase
• Interaction with glycosaminoglycans is required
  for cyclophilin B to trigger
• integrin-mediated adhesion of peripheral blood T
  lymphocytes to
• extracellular matrix PNAS , March 5, 2002 ,vol.
  99 , no. 5 ,2714-2719

65
REFERENCES

• Methods for treating conditions associated with
  masp-2 dependent complement activation U.S.
  Provisional Application No. 60/578,847, filed
  Jun. 10, 2004
• http//biomed.ngic.re.kr/cgibin/cards/carddisp.pl?
  geneFLNBsearchneurodegenerative20or20senilep
  ubmed114
• http//biomed.ngic.re.kr/cgibin/cards/carddisp.pl?
  geneFLNBsearchneurodegenerative20or20senilep
  ubmed114