Our goal....to understanding (wisdom)...to knowledge...to information data

Transcription

1 Knowledge Discovery

2 Our goal...to understanding (wisdom)...to knowledge...to information data

3 Why do we need Knowledge Discovery? Data Explosion: web usage, automated data collec?on tools, mature database technology Too much data and too liale knowledge Humans not able to sid through the data effec?vely Computa?onal approaches to data analysis are required for the con?nually increasing, accumulated data

4 Poten?al Applica?ons Market analysis, customer rela?onship management Risk analysis and management Fraud detec?on Text mining newsgroups, , documents Web mining of logs, data streams for customiza?on, adver?sing, marke?ng Biology and Medicine many types of highthroughput data for diagnos?cs, predic?ve and personalized medicine

5 Link to image reference

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8 Even BeAer Consult the Domain Expert(s)

9 The Process Guided Discovery PBL Knowledge Discovery Learn through examples and prac?ce Same general approach may be applied to many different problem domains Select appropriate methods to customize approach No one right answer!

10 Running Example of KD Gene Expression Data Why a good example? Biotechnology advances created huge influx of data Biologists not equipped to analyze the data Computa?onal scien?sts didn t understand the biology KDD process sorely needed Has significantly advanced over the last 10 years

11 Papers Data preprocessing and transforma?on Quackenbush Need for standards MAGE ML Mining large datasets for paaerns Molecular Classifica?on of Cancer Golub et al.

12 A Typical Scenario Biologist designs and runs an experiment and delivers samples (along with $$) to the Func?onal Genomics lab for high throughput gene expression analysis. A couple weeks later biologist picks up a CD with mul?ple files containing the raw data and some preprocessed data not knowing how to analyze the data biologist calls in your help Where do we start? Understand the domain and the problems

13 High Throughput Systems for Studying Global Gene Expression are Complex Need to learn about and consider: the biology behind the experiments & the interpreta?on of the experiments How the data is acquired (biotechnology) the data issues 13

14 Biology Basics: The Flow of Informa?on A gene is expressed in 2 steps: DNA is transcribed into RNA (mrna) RNA is translated into protein 14

15 Genotype to Phenotype Individual cells in an organism have the same genes (DNA) the genotype but.not all genes are ac?ve (expressed) in each cell It is the expression of thousands of genes and their products (RNA, proteins), func?oning in a complicated and orchestrated way, that make a specific cell what it is. the phenotype 15

16 Gene Expression Depends on Context The subsets of genes that are expressed (RNA/ protein) will differ among cells,?ssues, organs, condi?ons the subset expressed confers unique proper?es to the cell neuron liver muscle muscle 16

17 Differen?al Gene Expression The level of expression of genes also differs with the cellular context i.e. the amount of a given RNA will vary We can think of gene expression (in higher organisms) as having both an on/off switch and volume control 17

18 What Biologists Want to Know: Specific PaAerns of Gene Expression Tissue/Cell type specific e.g. skin cell vs. brain cell e.g. kera?nocyte vs. melanocyte Developmental stage e.g. embryonic skin cell vs. adult skin cell Disease state e.g. normal skin cell vs. skin tumor cell Environment specific (drugs, toxins) e.g. skin cell untreated vs. treated 18

19 But also, the more difficult problem: Gene Networks Genes and their products are related through their roles in: metabolic pathways cell signalling networks 19

20 Metabolic Pathway From KEGG Database 20

21 Cell Signalling Networks dortmund.mpg.de/departments/dep1/signaltransduk?on/image3.gif 21

22 What can we learn by studying global paaerns of gene expression? Individual gene expression pa1erns Classifica5ons: for diagnosis, predic?on Groups of Genes Molecular taxonomy of disease Gene Networks/Pathways: Reconstruc?on of metabolic & regulatory pathways 22

23 Now that we have some understanding of the domain and goals What about the data? How are the data generated? Data type? Data quality? Need for data cleaning and preprocessing?

24 Knowledge Discovery Process Consult the Domain Expert(s)

25 GeneChip Oligonucleo?de Array High throughput gene expression analysis 25

26 Recall that DNA and RNA are composed of strings of nucleo?des A gene of interest will have a specific nucleo?de sequence DNA and RNA sequences can form bonds with complementary bases on another string called basepairing. When we do this experimentally we call it hybridiza?on and we can detect it by labeling one of the strings (aka strands) 26

27 GeneChip Expression Analysis Hybridiza?on and Staining Array Hybridized Array crna Target Streptavidin phycoerythrin conjugate Courtesy of M. Hessner, CAAGED Workshop

28 How do Affymetrix microarrays work? probes are picked to interrogate a gene, the idea is to get mul?ple measurements. Each probe is a 25mer oligonucleo?de that binds to a gene The collec?on of probes that are designed to hybridize to the same gene is called a probe set.may be tens of thousands of these probesets on a given chip Probe set names have iden?fica?on names called Affymetrix Ids, and look like 10329_g_at, etc. On any Genechip, some probesets are dedicated for Quality Control, these begin with AFFX_ Take home message: have to learn a lot of terminology

29 Affymetrix Chips ,000 Probes Perfect Match and Mismatch Average Difference Values Courtesy of J. Glasner CAAGED Workshop

30 Affymetrix Analysis High resolu?on image of the scanned microarray generates a DAT file Since the probes are laid out in a grid fashion, and each probe posi?on determined in terms of its X Y co ordinates, one can compute the PM and MM probe intensi?es from the pixelated image The CDF (chip defini?on file) library file contains the XY layout of every probe

31 Affymetrix Data Flow Hybridized GeneChip CDF file CHP file Scan Chip DAT file EXP file Process Image (GCOS) CEL file MAS5 (GCOS) TXT file RPT file GeneChip Opera?ng SoDware (GCOS) Affymetrix hap://

32 Affymetrix File Types DAT file: Raw (TIFF) op?cal image of the hybridized chip CDF File (Chip Descrip?on File): Provided by Affy, describes layout of chip CEL File: Processed DAT file (intensity/posi?on values) hap:// AffxFileFormats/cel.html CHP File: The CHP file contains summarized gene expression scores ader probe cells are analyzed; format is: Gene Avg. D Presence AFFX_CreX_at 48 A AFFX_BioB_at 149 P TXT File: Probeset expression values with annota?on (CHP file in text format) RPT File Generated by Affy sodware, report of QC info

33 Knowledge Discovery Process Consult the Domain Expert(s)

34 Data Quality Most data mining techniques can tolerate some level of imperfec?on in the data, but improving data quality can improve quality of analyses Main issues Noise Outliers Missing values Duplicate data Inconsistent data

35 There are Many Problems Facing Expression Analysis on the Biotech side Standardiza?on & quality control in the experiments (affects data quality at many levels) Cost 35

36 Problem in reproducibility of Lots of varia?on in arrays experimental data more than 100 experimental steps Sources of varia?on biological variability in each RNA extract each labeling reac?on is different each slide is a separate hybridiza?on spots on the slide are variable across slides (and within slides when double spoaed) each color is scanned separately Need Replicates and Sta?s?cs! 36

37 Outcome Noisy data Data preprocessing is necessary normaliza?on scaling Heavy reliance on sta?s?cs today 37

38 What do the spots (intensity measurements) represent? Fluorescence intensity is a measure of the rela?ve abundance of individual mrnas (expressed genes) in given samples e.g. experimental rela?ve to control But, gene expression experiments are run on mul?ple samples Why? We are trying to understand a dynamic process each sample only represents a snapshot Compare among samples (different arrays) Compare across a?me course of related samples

39 How can we use the data? We can only really depend on between sample fold change for Microarrays not absolute values or within sample comparisons (> fold change, in general) Take home message: Have to be careful when comparing between arrays; from experiment to experiment.

40 Pre processing Gene filtering control genes uninforma?ve genes Normaliza?on and scaling allows comparisons across arrays scaling to control dynamic range Transforma?on logarithmic transforma?on for improved sta?s?cal proper?es 40

41 Normaliza?on Cy5 signal (log 2 ) Cy3 signal (log 2 )

42 Take home Message Important to remember that once preprocessing, normaliza?on, transforma?on of the data have occurred, all downstream mining will be affected.

43 Data Representa?on Flat file Vector data Sparse matrix (text) data Sequence data (e.g. web or genomic) Time series Image data Spa?o temporal

44 Three levels of microarray gene expression data processing Brazma et al., Nature Genetics, 29: , 2001

45 Outcomes of Microarray Analysis Large, complex data sets of high dimensionality example of a rou?ne study: 50,000 genes from 20 samples approx. 1 2 X 10 6 pieces of data challenges for Bioinforma?cs annota?on, storage, retrieval, sharing of data informa?on from the data

46 Knowledge Discovery Process Consult the Domain Expert(s)

47 State of Microarray Data Wide availability of technology has given rise to a large number of distributed databases data scaaered among many independent sites (accessible via Internet) or not publicly available at all Need for standardiza?on!

48 MGED Group and Standardiza?on Issues Microarray Gene Expression Database (MGED) Group MGED is taking on the challenge of standardiza?on Four major projects

49 MGED Projects MIAME The formula?on of the minimum informa?on about a microarray experiment required to interpret and verify the results. MAGE The establishment of a data exchange format (MAGE ML) and object model (MAGE OM) for microarray experiments.

50 MGED Projects Ontologies The development of ontologies for microarray experiment descrip?on and biological material (biomaterial) annota?on in par?cular. Normaliza?on The development of recommenda?ons regarding experimental controls and data normaliza?on methods.

51 MAGE ML the XML representa?on of the MAGE OM the DTD (document type defini?on) is what is specified in MAGE_ML rules or declara?ons what tags can be used what tags contain

52 MAGE OM hap:// mage om.html mapping of microarray experimental workflow to the OM

53 DTD hap:// dtd

54 MAGE STK sodware toolkit defines an API to MAGE OM in Java, Perl, C++ Used to export data to MAGE_ML to store data in rela?onal database input data to analysis tools Reader: MAGE ML docs into objects Writer: objects into MAGE ML

55 Knowledge Discovery Process Consult the Domain Expert(s)

56 Data Mining Techniques Exploratory data analysis Descrip?ve modeling Predic?ve modeling PaAern discovery others

57 Exploratory Data Analysis Interac?ve and visual Insight and feel for the data in a broad sense Provide summaries e.g. max/min, mean/median, variance etc Visualiza?on Histograms, scaaerplots Useful for data valida?on or verifica?on Simple exploratory data analysis is invaluable Always get a cursory view of the data before applying data mining algorithms

58 PaAern Discovery Discover interes?ng local paaerns in data rather than to characterize data globally Market basket data Discover that if customers buy wine and bread, they buy cheese with a 0.9 probability Known as associa?on rules

59 Descrip?ve Modeling Build model for underlying process Simulate the data if needed Cluster analysis to find natural groups in the data Bayesian network to find dependency models among variables

60 Predic?ve Modeling Predict a variable Y, given a p dimensional vector X Classifica?on: Y is categorical Regression: Y is real valued Much like func?on approxima?on Learning the rela?onship between Y and X Sta?s?cs and machine learning have many algorithms for predic?ve modeling Emphasis is oden on predic?ve accuracy rather than understanding the model itself.

61 Mining of Expression Data Recall that: A gene expression paaern derived from a single microarray is simply a snapshot (one experimental sample vs reference) Usually want to understand a process or changes in expression over a collec?on of samples gene expression profile

62 Working with Gene Expression Data Hypothesis driven approaches Typically model oriented Descrip?ve sta?s?cs relying on prior knowledge and good design Discovery based Few, if any, a priori hypotheses Data driven and algorithm oriented Sta?s?cal algorithms Machine learning using heuris?c techniques 62

63 Tes?ng Hypotheses Based on prior biological knowledge Simplest look for individual differen?ally expressed genes fold changes ScaAerplot Sta?s?cal measures 63

64 64 ScaAerplot

65 Some simple sta?s?cs If we are looking at samples that seem to belong to two groups or condi?ons t test compares the means of two groups while accoun?ng for the standard error of the difference of the means ANOVA if want to extend the analysis to more than two groups 65

66 But, gene chips allow us to measure thousands of genes... Across mul?ple samples 66

67 Goal of Analysis of Expression Matrix Some sta?s?cal methods applied to: 1. Group similar genes together => groups of func?onally similar genes. 2. Group similar cell samples together. 3. Extract representa?ve genes in each group.

68 Typical approach Look for paaerns compare rows to find evidence for co regula?on of genes compare columns to find evidence for relatedness among samples 1) Choose a measure of similarity (distance) among the objects being compared each row or column is considered a vector in space 2) Then, group together objects (genes or samples) with similar proper?es is a mul?dimensional analysis

69 An experiment 12 Genes Expression values at 0, 2, 4, 6, 8 and 10 hours 69

70 Table 4.2 of Campbell/Heyer Name 0 hrs 2 hrs 4 hrs 6 hrs 8 hrs 10 hrs C D E F G H I J K L M N

71 Take logs C D E F G H I J K L M N Compare 71

72 How Similar are two Rows? How similar are the expressions of two genes? First we ll normalize each row Calculate the mean and standard devia?on for each gene Normalize each value by subtrac?ng the mean and dividing by the standard devia?on. 72

73 How Similar are two Rows? Calculate the Pearson Correla?on between pairs of rows Correla?on quan?fies the extent to which the expression paaerns of two genes go up or down together, regardless of their magnitudes. Calculated by taking the dot product of the two vectors > (pc '( ) ; row G '( )) ; row L 1.0 > (pc '( ) ; row G '( )) ; row D

74 Some other pairs Name 0 hrs 2 hrs 4 hrs 6 hrs 8 hrs 10 hrs C D E F G H I J K L M N > (pc '( ) ; row D '( )) ; row M > (pc '( ) ; row G '( )) ; row H

75 Pearson Correla?on pc(g,l) = 1 iden?cally expressed genes pc(g,d) =.897 similarly expressed genes pc(d,m) =.926 reciprocally expressed pc(g,h) =.909 also reciprocally expressed 75

76 Descrip?ve and Predic?ve Modeling Clustering Feature extrac?on/selec?on Classifica?on discrimina?on analysis

77 Analy?c Approaches Clustering: Identification of associations between data points; organization of data into groups Unsupervised Clustering: genes clustered by similarity/ correla?on, or other criteria based on X values no useful external informa?on about the Y variables ( the response), is used doesn t reveal groups of genes with special interest for?ssue discrimina?on Supervised Methods: grouping of variables (genes), controlled by informa?on about the X and Y variables supervised algorithms try to find gene clusters, whose average expression profile has great poten?al for explaining the response Y, i.e. for?ssue discrimina?on

78 Unsupervised Clustering Algorithms Hierarchical K means Self organizing maps Others

79 g e n e s samples Gene Expression Matrix & Hierarchical Clustering Eisen et al. content/full/95/25/14863

80 Theory Hierarchical Clustering works by sequen?ally joining the two nearest clusters and then hierarchically joining the next two closest clusters and so on in this fashion, joining the nearest clusters first and farthest clusters last. Ini?ally each individual data pt is set equal to one cluster

81 Hierarchical Clustering Algorithm Given a set of N items to be clustered, and an N*N distance (or similarity) matrix. 1. Start by assigning each item to a cluster, so that if you have N items, you will now have N clusters, each containing just one item. Let the distances (similari?es) between the clusters be defined as the same as the distances (similari?es) between the items they contain. 2. Find the closest (most similar) pair of clusters and merge them into a single cluster. You now have one cluster less. 3. Compute distances (similari?es) between the new cluster and each of the old clusters. 4. Repeat steps 2 and 3 un?l all items are clustered into a single cluster of size N.

82 Hierarchical in ac?on

83 Varia?ons of Hierarchical Algorithm Step 3 (compu?ng distances between the new cluster and each of the old clusters) can be done in several different ways. Single Linkage, average linkage and complete linkage. In single linkage the distance between clusters is equal to the shortest distance from any one member of one cluster to any one member of the other cluster. In Average linkage the distance between two clusters is defined as the average distance between any member of one cluster to any member of the other cluster. Complete linkage is defined as the the maximum distance from any one member of the first cluster to any one member of the second cluster.

84 Varia?ons of Hierarchical Algorithm Self Organizing Tree Algorithm Unsupervised neural network with a binary tree topology Combina?on of SOM and hierarchical clustering Run?me is approximately linear Faster than normal hierarchical method Uses divisive method In comparison to boaom up method of hierarchical

85 Advantages Hierarchical clustering results in a visual representa?on that is convenient for humans to analyze Unlike k means and SOM, does not have an a priori cluster number

86 Why cluster analysis may not be the answer Clustering methods typically require user inputs: Example: distance measure Clustering methods differ in the way that the number of clusters are specified. Clustering methods are oden sensi?ve to the ini?aliza?on condi?on (star?ng guess) Local vs. global sampling of clustering space

87 Cluster Analysis Challenges Noise in the data itself Large data sets most of the techniques currently used were not developed for mul?dimensional data What about networks? limita?on of cluster analysis: similarity in expression paaern suggests co regula?on but doesn t reveal cause effect rela?onships

88 Feature Selec?on & Classifica?on First, iden?fy features (genes) that discriminate between classes Then use features for classifica?on machine learning approach supervised analysis assignment of a new sample to a previously specified class, based on sample features and a trained classifier

89 Classic Example: Classifica?on of AML vs. ALL Comparing 2 acute leukemias acute myeloid leukemia (AML) acute lymphoid leukemia (ALL) Biological/Clinical Problems: previously, no single reliable test to dis?nguish them differ greatly in clinical course & response to treatments Golub et al., Science Oct :

90 Study Design Golub et al., Science Oct :

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92 The prediction of a new sample is based on 'weighted votes' of a set of informative genes

93 Results of the study 1) Clustering of microarray data using tumors of known type found 1100 of 6817 genes correlated with class dis?nc?on 2) Forma?on of a class predictor = 50 most informa?ve genes used as a training set classifica?on of unknown tumors Golub et al., Science Oct :

94 Results How to test the validity of class predictors? Cross valida?on tests: The 50 gene predictor assigned 36 of the 38 samples as either AML or ALL and the remaining two as uncertain (PS < 0.3). All 36 predic?ons agreed with the pa?ents' clinical diagnosis; Independent test: The 50 gene predictor was applied to an independent collec?on of 34 leukemia samples. The predictor assigned 29 of the 34 samples, and the accuracy was 100%; Predic?on strength: median PS = 0.77 in cross valida?on and 0.73 in independent test (Fig. 3A).

95 Results Class discovery If the AML ALL dis?nc?on were not already known, could it have been discovered simply on the basis of gene expression?

96 Results Two cluster analysis (1). Cluster tumors by gene expression: A two cluster SOM was applied to automa?cally group the 38 ini?al leukemia samples into two classes on the basis of the expression paaern of all 6817 genes.

97 Results Determine whether puta?ve classes produced are meaningful. The clusters were first evaluated by comparing them to the known AML ALL classes (Fig. 4A). Class A1 contained mostly ALL (24 of 25 samples) and class A2 contained mostly AML (10 of 13 samples). The SOM was thus quite effec?ve at automa?cally discovering the two types of leukemia.

98 Results How could one evaluate such puta?ve clusters if the "right" answer were not already known? Class discovery could be tested by class predic?on; If puta?ve classes reflect true structure, then a class predictor based on these classes should perform well.

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