Microarray Data Analysis. Prof. Anastasios Bezerianos Andrei Dragomir Ioannis Maraziotis

Transcription

1 Microarray Data Analysis Prof. Anastasios Bezerianos Andrei Dragomir Ioannis Maraziotis

2 Process overview 1- sample 2- reference sample excitation scanning laser 1 laser 2 Cy3: ~550 nm Cy5: ~650 nm cdna probes cdna cy5 cdna cy3 emission printing Hybridization combination normalization of images Microarray Image analysis

3 Image analysis -Software needs to extract red and green intensities from each spot. -Also estimates background intensity in each wavelength band. - Easy for some spots, harder for others.

4 Image Analysis - Usually the company producing the microarrays offer image analysis software suits. - Some preprocessing and normalization are performed in order to make data acquisition reliable. - Normalization = transformations applied to data sets from different experiments to make them comparable, in terms of equal intensity for spots.

5 From array scans to data matrix

6 Gene expression data matrices Experiments (several to 100s) T1 T2 T3 T4 T5 T6 Genes (1000s of rows) At At At At At At At At At Cells hold expression values in arbitrary units

7 Microarray Data Analysis Level 1 Which genes are expressed/suppressed? Biology Level 2 Which genes are co-expressed/suppressed? Discovery of gene function Level 3 How are genes regulated? Discovery of Gene Regulation Network

8 Example of an expression profile for each gene : [mrna] ~ Cy5/Cy3 = r 5 _ 1 _ up-regulation induction down-regulation repression 0 Start of experiment time / h

9 Expression profiles Expression level Five experiments e.g. Red and black genes show similar expression profiles Blue gene is different T1 T2 T3 T4 T5 Gene Gene Gene Experiment

10 First level analysis Question : - is gene i significantly different between states A and B? - significant means statistical significance. - define sets of statistical parameters from experiment data: mean, variances, standard deviation data distributions Fig. Shows changes in expression greater than 2 and 10 fold

11 First level analysis - Answers the question : when, where and to what extent are genes expressed. - Techniques employed : 2-fold rule, statistical tests

12 Second level analysis More complicated questions : - which genes show related expression over a set of experiments? Co-regulation inference of function (genes belonging to same pathway exhibit same regulatory patterns) - which genes are differentially expressed? (e.g. disease / healthy tissue)

13 Similarity: blue e 2 - Distance between profiles of red and blue genes = d(a,b) red e 2 - most common: Euclidian distance but other measures can be used also (correlation, Manhattan distance etc) d( red, blue) = i red ( e e i blue i 2 )

14 Similarity? Large correlation Small correlation Large Correlation

15 Clustering Know-how to measure similarity between profiles Clustering is the process of gathering together similar profiles into clusters Each cluster is a set of genes with related expression patterns in the experiments concerned - Genes may be imagined as points in an n dim space E x p e r i m e n t 3 Experiment 2 Experiment 1

16 Clustering - supervised methods methods make use of a priori knowledge on data while unsupervised methods don t, they are exploratory techniques. Unsupervised clustering Supervised classification

17 Unsupervised Clustering - K means clustering 1. Assign genes randomly to K clusters and compute cluster centroids 2. Evaluate sum of squared distances to cluster centres 3. While (not finished) 4. Make a random change to the cluster assignments 5. Evaluate sum of squared distances to new cluster centres 6. If clustering improved then accept, else reject 7. End while

18 K-means clustering

19 Hierarchical clustering

20 Hierarchical clustering 1. Assign each gene to its own cluster (N genes gives N clusters). 2. Find the two clusters with the most similar expression profiles and cluster them together (reduces the number of clusters by 1). 3. Calculate distances between the new cluster and all existing clusters. 4. Repeat 2 and 3 until there is only one cluster containing all the genes.

21 Hierarchical clustering Variants: Single linkage distance between two clusters is the shortest possible pair-wise distance Complete linkage distance between two clusters is the longest possible pair-wise distance Average linkage (not shown) distance is the average pair-wise distance

22 Self Organizing Map - Well known unsupervised neural network algorithm. - used for analysis and organization of large data sets. - it offers improved visualizations by performing a dimensional reduction projecting data from the n dim input data space into a 2D node grid. - nodes represent clusters, node weights initialized with values from data set. - for each gene pattern the closest node is computed; gene assigned to that node. - update the node weights, perform the whole process for several weight until algorithm performance is satisfactory.

23 Self Organizing Map

24 Supervised data analysis - makes use of available information on genes, such as labels describing gene functions etc. - used in classification, diagnosis or prognosis. - Goal obtain a set of gene patterns on basis of which : - Make diagnosis - Predict future outcome - Predict future response to pharmacological intervention - Categorize tissue as part of a class (e.g malignant or healthy tissue)

25 Supervised Techniques - typically classifiers trained on a subset of data with a priori given classification and tested on other subsets with known classification and then applied to data of unknown class. (see J.Khan paper Nature 7, 2001 p ) - Support vector machines (SVM) (Brown paper PNAS 97, 2000 p ) - SVMs find a linear classifier for data that are impossible to separate by a hyper plane by mapping data into a higher dimension data space. - over-fitting is avoided by defining a maximum margin hyper plane, typically represented by a linear combination of training points.

26 Support Vector Machines - employ distance functions that operate in high dimensional space. - make advantage of prior knowledge in making distinction between one type of gene and another.

27 - Gene regulatory networks : Third level analysis εδοµένα Γονιδιακής Έκφρασης Οµαδοποίηση Κοινής Έκφρασης Εξαγωγή συνρύθµισης από συνέκφραση Βιολογική Γνώση Ανακάλυψη Λειτουργίας γονιδίων -priori ιολογική νώση εδοµένα αλληλούχησης DNA Εκτίµηση των DNA σηµείων δέσµευσης µε επιβλεπόµενη µάθηση Βιολογική Γνώση Ανακάλυψη Σηµάτων Ελέγχου Αρχική Σχεδίαση του ΓΡ Εκπαίδευση του ΓΡ από δεδοµένα έκφρασης Βιολογική Γνώση Ανακάλυψη ΓΡ

28 Genetic Networks - genetic network = a sophisticated regulatory web of cause and effect which describes the way genes interact with one another. - at different stages of development (or response to extra-cellular perturbation), different genes of a cell are switched on at different intensity levels. - parameters that characterize the genetic networks : -intracellular (mrna concentration of another gene) -intercellular (hormones secreted by an organ gives a signal to other organ) -extracellular (environmental conditions e.g. heat shock)