Analyzing Microarray Data with Methods from Statistics and Machine.ppt

Analyzing Microarray Data with Methods from Statistics and Machine Learning B-IT IPEC Winter School 2008 Prof. Dr. A. B. Cremers J?rg Zimmermann DNA Microarray Data Genome Chips containing a collection of microscopic DNA spots Simultaneous determination of 105 Gene Expression Levels Dramatic acceleration of data aquisition New possibilities for disease diagnosis, treatment studies, network analysis, … DNA Microarray Data The resulting data have the form: x11 x12 … x1n ( L1 ) . . . . . . xp1 xp2 … xpn ( Lp ) n = number of measured cell states (e.g. gene expression levels) p = number of samples xij = real number e.g. representing expression level of gene j in sample i Li = Label of sample i Challenges for Data Analysis Normalization (removing systematic measurement effects) Variable Selection (Identification of relevant Variables) Large sample Effects: Cluster Analysis Prediction Problem Classify data into known disease classes: Prediction Problem Data Analysis Methods Organisation * Analyzing Microarray Data with Methods from Statistics and Machine Learning Analyzing Microarray Data with Methods from Statistics and Machine Learning Analyzing Microarray Data with Methods from Statistics and Machine Learning Type I and Type II errors (False positives / False negatives) Dimensionality Reduction Identification of new disease classes Classification of data into known disease classes Finding Structure in data without labels (unsupervised learning) Analyzing Microarray Data with Methods from Statistics and Machine Learning Does a cluster characterize a (new) disease type? Supervised Learning Split data in Training and Test set Learn a model on the training set Evaluate model on the test set Analyzing Microarray Data with Methods from Statistics and Machine Learning Analyzing Microarray Data with Methods from Statistics and Machine Learning Under- and Overlearning: Analyzing