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Clustering and Classification In Gene Expression Data. Carlo Colantuoni ccolantu@jhsph.edu. Slide Acknowledgements:

Clustering and Classification In Gene Expression Data

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Clustering and Classification In Gene Expression Data

Carlo Colantuoni

ccolantu@jhsph.edu

Slide Acknowledgements:

Elizabeth Garrett-Mayer, Rafael Irizarry, Giovanni Parmigiani, David Madigan, Kevin Coombs, Richard Simon, Ingo Ruczinski.Classification based in part on Chapter 10 of Hand, Manilla, & Smyth and Chapter 7 of Han and Kamber

Data from Garber et al.

PNAS (98), 2001.

Clustering

- Clustering is an exploratory tool to see who's running with who: Genes and Samples.
- “Unsupervized”
- NOT for classification of samples.
- NOT for identification of differentially expressed genes.

- Clustering organizes things that are close into groups.
- What does it mean for two genes to be close?
- What does it mean for two samples to be close?
- Once we know this, how do we define groups?
- Hierarchical and K-Means Clustering

- We need a mathematical definition of distance between two points
- What are points?
- If each gene is a point, what is the mathematical definition of a point?

1 2 . . . . . . . N

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- Gene1= (E11, E12, …, E1N)’
- Gene2= (E21, E22, …, E2N)’
- Sample1= (E11, E21, …, EG1)’
- Sample2= (E12, E22, …, EG2)’
- Egi=expression gene g, sample i

DATA MATRIX

- Euclidean distance
- Example distance between gene 1 and 2:
- Sqrt of Sum of (E1i -E2i)2, i=1,…,N

- When N is 2, this is distance as we know it:

Baltimore

Distance

DC

When N is 20,000 you have to think abstractly

- Pearson Correlation
- Spearman Correlation
- Uncentered Correlation
- Absolute Value of Correlation

The difference is that, if you have two vectors X and Y with identical

shape, but which are offset relative to each other by a fixed value,

they will have a standard Pearson correlation (centered correlation)

of 1 but will not have an uncentered correlation of 1.

1 2 ………………………………...G

1 2 ……….N

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GENE SIMILARITY MATRIX

DATA MATRIX

1 2 ……….N

1 2 …………..N

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SAMPLE SIMILARITY MATRIX

DATA MATRIX

- Do you want all genes included?
- What to do about replicates from the same individual/tumor?
- Genes that contribute noise will affect your results.
- Including all genes: dendrogram can’t all be seen at the same time.
- Perhaps screen the genes?

- Hierarchical clustering
- Dendrogram (red-green picture)
- Allows us to cluster both genes and samples in one picture and see whole dataset “organized”

- K-means/K-medoids
- Partitioning method
- Requires user to define K = # of clusters a priori
- No picture to (over)interpret

- The most overused statistical method in gene expression analysis
- Gives us pretty red-green picture with patterns
- But, pretty picture tends to be pretty unstable.
- Many different ways to perform hierarchical clustering
- Tend to be sensitive to small changes in the data
- Provided with clusters of every size: where to “cut” the dendrogram is user-determined

- Agglomerative clustering (bottom-up)
- Starts with as each gene in its own cluster
- Joins the two most similar clusters
- Then, joins next two most similar clusters
- Continues until all genes are in one cluster

- Divisive clustering (top-down)
- Starts with all genes in one cluster
- Choose split so that genes in the two clusters are most similar (maximize “distance” between clusters)
- Find next split in same manner
- Continue until all genes are in single gene clusters

- Single Linkage: join clusters whose distance between closest genes is smallest (elliptical)
- Complete Linkage: join clusters whose distance between furthest genes is smallest (spherical)
- Average Linkage: join clusters whose average distance is the smallest.

Dendrogram Creation + Interpretation

Dendrogram Creation + Interpretation

Dendrogram Creation + Interpretation

Cluster Assignment

Simulated Data with 4 clusters: 1-10, 11-20, 21-30, 31-40

450 relevant genes + 450 “noise” genes.

450 relevant genes.

- Partitioning Method
- Don’t get pretty picture
- MUST choose number of clusters K a priori
- More of a “black box” because output is most commonly looked at purely as assignments
- Each object (gene or sample) gets assigned to a cluster
- Begin with initial partition
- Iterate so that objects within clusters are most similar

- Euclidean distance most often used
- Spherical clusters.
- Can be hard to choose or figure out K.
- Not unique solution: clustering can depend on initial partition
- No pretty figure to (over)interpret

1. Choose K centroids at random

2. Make initial partition of objects into k clusters by assigning objects to closest centroid

- Calculate the centroid (mean) of each of the k clusters.
- a. For object i, calculate its distance to each of
the centroids.

b. Allocate object i to cluster with closest

centroid.

c. If object was reallocated, recalculate centroids based

on new clusters.

4. Repeat 3 for object i = 1,….N.

- Repeat 3 and 4 until no reallocations occur.
- Assess cluster structure for fit and stability

We start with some data

Interpretation:

We are showing expression for two samples for 14 genes

We are showing expression for two genes for 14 samples

This is with 2 genes.

Iteration = 0

Choose K centroids

These are starting values that the user picks.

There are some data driven ways to do it

Iteration = 0

Make first partition by finding the closest centroid for each point

This is where distance is used

Iteration = 1

Now re-compute the centroids by taking the middle of each cluster

Iteration = 2

Repeat until the centroids stop moving or until you get tired of waiting

Iteration = 3

- Final results depend on starting values
- How do we chose K? There are methods but not much theory saying what is best.
- Where are the pretty pictures?

- Most often ignored.
- Cluster structure is treated as reliable and precise
- Can be VERY sensitive to noise and to outliers
- Homogeneity and Separation
- Cluster Silhouettes: how similar genes within a cluster are to genes in other clusters (Rousseeuw Journal of Computation and Applied Mathematics, 1987)

- Silhouette of gene i is defined as:
- ai = average distance of gene i to other gene in same cluster
- bi = average distance of gene i to genes in its nearest neighbor cluster

Add perturbations to original data

Calculate the number of paired samples that cluster together in the original cluster that didn’t in the perturbed

Repeat for every cutoff (i.e. for each k)

Do iteratively

Estimate for each k the proportion of discrepant pairs.

Classification

- Diagnostic tests are good examples of classifiers
- A patient has a given disease or not
- The classifier is a machine that accepts some clinical parameters as input, and spits out an prediction for the patient
- D
- Not-D

- Classes must be mutually exclusive and exhaustive

- Select features (genes)
- Which genes will be included in the model

- Select type of classifier
- E.g. (D)LDA, SVM, k-Nearest-Neighbor, …

- Fit parameters for model (train the classifier)
- Quantify predictive accuracy: Cross-Validation

- Goal is to identify a small subset of genes which together give accurate predictions.
- Methods will vary depending on nature of classification problem
- Choose genes with significant t-statistics to distinguish between two simple classes e.g.

- In microarray classification, the number of features is (almost) always much greater than the number of samples.
- Overfitting is a distinct risk, and increases with more complicated methods.

- Complex classification algorithms such as neural networks that perform better elsewhere don’t do as well as simpler methods for expression data.
- Comparative studies have shown that simpler methods work as well or better for microarray problems because the number of candidate predictors exceeds the number of samples by orders of magnitude.
(Dudoit, Fridlyand and Speed JASA 2001)

- Demonstrating statistical significance of prognostic factors is not the same as demonstrating predictive accuracy.
- Demonstrating goodness of fit of a model to the data used to develop it is not a demonstration of predictive accuracy.
- Most statistical methods were not developed for p>>n prediction problems

- If there are K classes, simply draw lines (planes) to divide the space of expression profiles into K regions, one for each class.
- If profile X falls in region K, predict class K.

- To classify a new observation X, measure the distance d(X,Xi) between X and every sample Xi in training set
- Assign to X the class label of its “nearest neighbor” in the training set.

Build several “random” decision trees and have them vote to determine final classification

- Want to estimate the error rate when classifier is used to predict class of a new observation
- The ideal approach is to get a set of new observations, with known class label and see how frequently the classifier makes the correct prediction.
- Performance on the training set is a poor approach, and will deflate the error estimate.
- Cross validation methods are used to get less biased estimates of error using only the training data.

- Training-set
- Used to select features, select model type, determine parameters and cut-off thresholds

- Test-set
- Withheld until a single model is fully specified using the training-set.
- Fully specified model is applied to the expression profiles in the test-set to predict class labels.
- Number of errors is counted

- Divide data into V groups.
- Hold one group back, train the classifier on other V-1 groups, and use it to predict the last one.
- Rotate through all V points, holding each back.
- Error estimate is total error rate on all V test groups.

- Hold one data point back, train the classifier on other n-1 data points, and use it to predict the last one.
- Rotate through all n points, holding each back.
- Error estimate is total error rate on all n test values.

log-expression ratios

full data set

specimens

log-expression ratios

training set

specimens

test set

Non-cross-validated Prediction

1. Prediction rule is built using full data set.

2. Rule is applied to each specimen for class prediction.

Cross-validated Prediction (Leave-one-out method)

1. Full data set is divided into training and test sets (test set contains 1 specimen).

2. Prediction rule is built from scratch using the training set.

3. Rule is applied to the specimen in the test set for class prediction.

4. Process is repeated until each specimen has appeared once in the test set.

- If the sample is large enough, split into test and train groups.
- If sample is barely adequate for either testing or training, use leave one out
- In between consider V-fold. This method can give more accurate estimates than leave one out, but reduces the size of training set.

- Cross-validation of a model cannot occur after selecting the genes to be used in the model

- Publications are using all the data to select genes and then cross-validating only the parameter estimation component of model development
- Highly biased
- Many published complex methods which make strong claims based on incorrect cross-validation.
- Frequently seen in complex feature set selection algorithms
- Some software encourages inappropriate cross-validation

cDNA Microarrays

Parallel Gene Expression Analysis

Gene-Expression Profiles in Hereditary Breast Cancer

- Breast tumors studied:
- 7 BRCA1+ tumors
- 8 BRCA2+ tumors
- 7 sporadic tumors

- Log-ratios measurements of 3226 genes for each tumor after initial data filtering

RESEARCH QUESTION

Can we distinguish BRCA1+ from BRCA1– cancers and BRCA2+ from BRCA2– cancers based solely on their gene expression profiles?