* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Array CGH Analysis
Koinophilia wikipedia , lookup
Genome evolution wikipedia , lookup
Polycomb Group Proteins and Cancer wikipedia , lookup
Gene therapy of the human retina wikipedia , lookup
Biology and consumer behaviour wikipedia , lookup
Public health genomics wikipedia , lookup
Human genetic variation wikipedia , lookup
Oncogenomics wikipedia , lookup
Genomic imprinting wikipedia , lookup
Vectors in gene therapy wikipedia , lookup
Therapeutic gene modulation wikipedia , lookup
Site-specific recombinase technology wikipedia , lookup
Genetic engineering wikipedia , lookup
Population genetics wikipedia , lookup
Gene expression profiling wikipedia , lookup
Genome (book) wikipedia , lookup
Artificial gene synthesis wikipedia , lookup
History of genetic engineering wikipedia , lookup
Nutriepigenomics wikipedia , lookup
Comparative genomic hybridization wikipedia , lookup
Designer baby wikipedia , lookup
Algorithms for Smoothing Array CGH data Kees Jong (VU, CS and Mathematics) Elena Marchiori (VU, Computer Science) Aad van der Vaart (VU, Mathematics) Gerrit Meijer (VUMC) Bauke Ylstra (VUMC) Marjan Weiss (VUMC) 1 Tumor Cell Chromosomes of tumor cell: 2 CGH Data C o p y # Clones/Chromosomes 3 Naïve Smoothing 4 “Discrete” Smoothing Copy numbers are integers 5 Why Smoothing ? • Noise reduction • Detection of Loss, Normal, Gain, Amplification • Breakpoint analysis Recurrent (over tumors) aberrations may indicate: –an oncogene or –a tumor suppressor gene 6 Is Smoothing Easy? Measurements are relative to a reference sample Printing, labeling and hybridization may be uneven Tumor sample is inhomogeneous •vertical scale is relative •do expect only few levels 7 Smoothing: example 8 Problem Formalization A smoothing can be described by • a number of breakpoints • corresponding levels A fitness function scores each smoothing according to fitness to the data An algorithm finds the smoothing with the highest fitness score. 9 Smoothing breakpoints variance levels 10 Fitness Function We assume that data are a realization of a Gaussian noise process and use the maximum likelihood criterion adjusted with a penalization term for taking into account model complexity We could use better models given insight in tumor pathogenesis 11 Fitness Function (2) CGH values: x1 , ... , xn breakpoints: 0 < y1< … < yN < xN levels: m1, . . ., mN error variances: s12, . . ., sN2 likelihood: 12 Fitness Function (3) Maximum likelihood estimators of μ and s 2 can be found explicitly Need to add a penalty to log likelihood to control number N of breakpoints penalty 13 Algorithms Maximizing Fitness is computationally hard Use genetic algorithm + local search to find approximation to the optimum 14 Algorithms: Local Search choose N breakpoints at random while (improvement) - randomly select a breakpoint - move the breakpoint one position to left or to the right 15 Genetic Algorithm Given a “population” of candidate smoothings create a new smoothing by - select two “parents” at random from population - generate “offspring” by combining parents (e.g. “uniform crossover” or “union”) - apply mutation to each offspring - apply local search to each offspring - replace the two worst individuals with the offspring 16 Experiments • Comparison of – – – – GLS GLSo Multi Start Local Search (mLS) Multi Start Simulated Annealing (mSA) • GLS is significantly better than the other algorithms. 17 Comparison to Expert algorithm expert 18 Relating to Gene Expression 19 Relating to Gene Expression 20 Algorithms for Smoothing Array CGH data Kees Jong (VU, CS and Mathematics) Elena Marchiori (VU, CS) Aad van der Vaart (VU, Mathematics) Gerrit Meijer (VUMC) Bauke Ylstra (VUMC) Marjan Weiss (VUMC) 21 22 Conclusion • Breakpoint identification as model fitting to search for most-likely-fit model given the data • Genetic algorithms + local search perform well • Results comparable to those produced by hand by the local expert • Future work: – Analyse the relationship between Chromosomal aberrations and Gene Expression 23 Example of a-CGH Tumor V a l u e Clones/Chromosomes 24 a-CGH vs. Expression a-CGH • DNA – In Nucleus – Same for every cell • DNA on slide • Measure Copy Number Variation Expression • RNA – In Cytoplasm – Different per cell • cDNA on slide • Measure Gene Expression 25 Breakpoint Detection • Identify possibly damaged genes: – These genes will not be expressed anymore • Identify recurrent breakpoint locations: – Indicates fragile pieces of the chromosome • Accuracy is important: – Important genes may be located in a region with (recurrent) breakpoints 26 Experiments • Both GAs are Robust: – Over different randomly initialized runs breakpoints are (mostly) placed on the same location • Both GAs Converge: – The “individuals” in the pool are very similar • Final result looks very much like (mean error = 0.0513) smoothing conducted by the local expert 27 Genetic Algorithm 1 (GLS) initialize population of candidate solutions randomly while (termination criterion not satisfied) - select two parents using roulette wheel - generate offspring using uniform crossover - apply mutation to each offspring - apply local search to each offspring - replace the two worst individuals with the offspring 28 Genetic Algorithm 2 (GLSo) initialize population of candidate solutions randomly while (termination criterion not satisfied) - select 2 parents using roulette wheel - generate offspring using OR crossover - apply local search to offspring - apply “join” to offspring - replace worst individual with offspring 29 Fitness function (2) CGH values: x1 , ... , xn breakpoints: 0 < y1< … < yN < xN levels: m1, . . ., mN error variances: s12, . . ., sN2 likelihood: 30