Microarray Data Preprocessing and Clustering Analysis Liangjiang (LJ) Wang [email protected] KSU...
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Transcript of Microarray Data Preprocessing and Clustering Analysis Liangjiang (LJ) Wang [email protected] KSU...
Microarray Data Preprocessing and
Clustering Analysis
Liangjiang (LJ) Wang
KSU Bioinformatics Center, Biology Division
June, 2005
Spotted Microarray Workshop
Outline
• Overview of microarray data analysis.
• Microarray data preprocessing.
• Statistical inference of significant genes.
• Clustering analysis and visualization.
• Microarray databases and standards.
Spotted Microarray
Extract mRNA
Reference Cells
Experimental Cells
Array image data
Make and label cDNA
Hybridize
Probes
Gen
es
Samples
Gene expression
matrix (ratios)
Overview of Microarray Data Analysis
Microarray experiment
Image analysis and data normalization
Statistical inference of significant genes
Sample classification
Clustering analysis of co-expressed genes
List of significant or co-expressed genes
Promoter analysis, gene function prediction, and pathway analysis
Microarray Image Analysis• Spot finding: place a grid to identify spot locations.
• Segmentation: separate each spot (foreground) from the background.
• Spot intensity extraction: often use mean or median intensity of all the pixels within a spot.
• Background subtraction: may subtract local background or globally estimated background.
Microarray Data Normalization• To remove the systemic bias in the data so that
meaningful biological comparisons can be made:
– Unequal quantities of starting RNA.
– Differences in labeling (e.g., Cy3 versus Cy5).
– Different detection efficiencies between the dyes.
– Differences in hybridization and washing.
– Other experimental variations.
• Normalization is based on some assumptions:
– A subset of genes (housekeeping genes) is assumed to be constant.
– The total intensity or overall intensity distributions between the two channels are comparable.
Global Normalization• Total intensity normalization:
– A normalization factor is calculated by summing the measured intensities in both channels and then taking the ratio:
– All the intensities in one channel are multiplied by the normalization factor:
• A subset of genes (housekeeping genes) may be also used for the global normalization.
N
i i
N
i i
G
R
1
1
iiii RRGG and
Scatter Plot of Cy3 vs Cy5 Intensities
Intensities from “self-self” hybridization
Before normalization
After normalization
(Quackenbush, 2001)
Lowess Normalization• Probably the most widely used approach for
spotted microarray normalization.
• A locally weighted linear repression is used to estimate the systematic bias in the data.
Ratio-Intensity (R-I) plot (also called MA plot)
After lowess
)*(log intensity, logMean 1021 GR
log
ratio
, lo
g 2(R
/ G
)0
(Quackenbush, 2001)
Raw data
)*(log intensity, logMean 1021 GR
log
ratio
, lo
g 2(R
/ G
)
0
(Quackenbush, 2001)
Why Log Transformation?• Log 2 (R / G) treats up-regulated and down-
regulated genes in a similar fashion:
– If R / G = 4, log 2 (R / G) = 2.
– If R / G = 1/4 = 0.25, log 2 (1/4) = -2.
• Log normalizes distribution.
Finding Significant Genes• Fold change: uses a single fold change threshold to
select genes; does not take into account the biological and experimental variability.
• Statistical tests: t test, SAM and ANOVA; require a number of replicates for each condition.
Volcano Plot
Larger fold changes does not necessarily mean higher significance levels.
Sta
tist
ical
sig
nif
ican
ce →
hig
h
(Wolfinger et al., 2001)
Student’s t Test• To test whether there is a significant difference in
gene expression measurements between two conditions (A and B):
– H0: no difference in gene expression,
– H1: the gene is differentially expressed,
• Test statistic:
• Calculate the probability (p value) of the t statistic with degree of freedom, df = nA + nB - 2.
• Assume a 95% confidence level (i.e., 5% false positive rate). If p ≤ 0.05, reject the null hypothesis.
BA XX
BA XX
B
B
A
A
BA
d
BA
nn
XXXXt
22
Problem of Multiple Testing
Suppose that you have 5,000 genes on your microarray, and you select the genes with p ≤ 0.05 (i.e., 5% false positive rate). Because you have applied 5,000 times of the t test, you may have 5,000 x 0.05 = 250 false positives!
Correction for Multiple Testing• Bonferroni correction:
– Set the significance cutoff, p' = α / N, where α is the false positive rate, and N is the number of genes.
– For example, if you have 5,000 genes in your microarray, and you expect 5% of false positives, the significance cutoff, p' = 0.05 / 5000 = 1.0 E -5.
• False Discovery Rate (FDR):– Rank all the genes by significance (p value) so that
the top gene has the most significant p value.– Start from the top of the list, and accept the genes if
qN
ip
i: the rank of the gene in the list.N: the number of genes in the array.q: the desired FDR.
SAM: Significance Analysis of Microarrays• SAM (http://www-stat.stanford.edu/~tibs/SAM/) is a modified t test.
• The observed d statistic is computed from the data, and the expected d statistic is assessed by permutation.
• With a user-defined FDR, SAM derives the significance cutoffs for selecting up- and down-regulated genes.
Down-regulated
Up-regulated
Expected d statistic
Ob
serv
ed d
sta
tist
ic
SAM Plot
Observed d = expected d
Significance cutoffs
ANOVA• ANalysis Of VAriance (ANOVA) is used to find
significant genes in more than two conditions:
• For each gene, compute the F statistic.
• Calculate the p value for the F statistic.
• Adjust the significance cutoff for multiple testing.
Gene
Disease A Disease B Disease C
A1 A2 A3 B1 B2 B3 C1 C2 C3
g1 0.9 1.1 1.4 1.9 2.1 2.5 3.1 2.9 2.6
g2 4.2 3.9 3.5 5.1 4.6 4.3 1.8 2.4 1.5
g3 0.7 1.2 0.9 1.1 0.9 0.6 1.2 0.8 1.4
g4 2.0 1.2 1.7 4.0 3.2 2.8 6.3 5.7 5.1
∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙ ∙
Clustering Analysis• Clustering analysis is to partition a dataset into a
few groups (clusters) such that:– Homogeneity: objects in the same
cluster are similar to each other.
– Separation: dissimilar objects are placed in different clusters.
• In microarray data analysis, thismeans to find groups of genes (or samples) with similar gene expression patterns.
• Two key questions:
– How to measure similarity of gene expression?– How to find these gene clusters?
Distance Metrics• Expression vector: each gene can be represented as
a vector in the N-dimensionalhyperspace, where N is the number of samples.
• Euclidean distance:
• Vector angle:
• Pearson correlation coefficient:
Sample 1
Sam
ple
2 A a2
a1
B b2
b1
d
α
N
i ii bad1
2)(
N
i i
N
i i
N
i ii
ba
ba
1
2
1
2
1cos
1]. 1,[ ,)()(
))((
1
2
1
2
1
N
i i
N
i i
N
i ii
bbaa
bbaa
Z Transformation• If Euclidean distance is used for clustering
analysis, z transformation of the gene expression matrix may be necessary.
• For each gene, calculate the z scores of the expression values:
x
ix
xxz
i
Lo
g (r
atio
)
Samples
— Gene A— Gene B
dAB = 3.58
Z s
core
Samples
— Gene A— Gene B
dAB = 0.36
Hierarchical Clustering
a b
d e
c d e
a b c d eb
d
c
e
a
Step 0 Step 1 Step 2 Step 3 Step 4
Agglomerative approach
Initialization: each object is a cluster
Iteration
Merge two clusters which are most similar to each other
Until all objects are merged into a single cluster
Hierarchical Clustering (Cont’d)
• Calculating distances between clusters:
– Single linkage: takes the shortest distance between two clusters.
– Complete linkage: uses the largest distance between two clusters.
– Average linkage: uses the average distance between two clusters.
• The clustering results are visualized using a tree (called dendrogram) with color-coded gene expression levels.
• Hierarchical clustering can be applied to genes, samples, or both.
SL
AL
CL
Sample Clustering
Alizadeh, et al., 2000. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, 403:503-511.
k-Means ClusteringInitialization
User-defined k (# clusters)
Randomly place k vectors (called centroids) in the data space
Iteration
Each object is assigned to its closest centroid
Re-compute each centroid by taking the mean of data vectors currently assigned to the cluster
Until the cluster centroids no longer change
Iteration
0:
1:
2:
3:
k = 2
Self-Organizing Map (SOM)• The user defines an initial geometry of nodes (reference
vectors) for the partitions such as a 3 x 2 rectangular grid.
• During the iterative “training” process, the nodes migrate to fit the gene expression data.
• The genes are mapped to the most similar reference vector.
Clustering analysis of a yeast cell cycle time-series dataset
k-means SOM
237 genes 194 genes
Tools for Microarray Data Analysis• GenePix (http://www.axon.com/GN_GenePixSoftware.html):
commercial software for microarray image analysis.
• GeneSpring (http://www.silicongenetics.com/cgi/SiG.cgi/Products/GeneSpring/index.smf): commercial software for microarray data analysis.
• TIGR MeV (http://www.tm4.org/mev.html): free software for clustering, visualization, classification and statistical analysis of microarray data.
• Bioconductor (http://www.bioconductor.org/): open source, free software for the analysis of genomic data. For microarray data analysis, most of the statistical methods are implemented in R.
Microarray Databases
• Gene Expression Omnibus (GEO) at NCBI (http://www.ncbi.nlm.nih.gov/geo/): a public repository for high throughput gene expression data.
• ArrayExpress at EBI (http://www.ebi.ac.uk/arrayexpress/): a public repository for microarray gene expression data; MIAME compliant.
• Stanford Microarray Database (SMD at http://genome-www5.stanford.edu/): stores raw and normalized microarray data; provides data retrieval and online data processing.
The MIAME Standard• MIAME (Minimum Information About a Microarray
Experiment) is a microarray data standard proposed by the Microarray Gene Expression Database group (MGED, http://www.mged.org/).
• MIAME (http://www.mged.org/Workgroups/MIAME/) is needed to interpret the results from a microarray experiment and potentially to reproduce the microarray experiment.
• MIAME checklist helps authors, reviewers and editors of scientific journals to meet the MIAME requirements and to make microarray data available to the community in a useful way.
Summary• Image analysis and data normalization are
important preprocessing steps for microarray data analysis.
• Statistical methods are available for selecting significantly up- or down-regulated genes.
• Clustering analysis is widely used to explore and visualize microarray data.
• The resulting significant or co-expressed genes can be further investigated using Gene Ontology annotation and promoter analysis.