Get Analysis of Single-Cell Data : ODE Constrained Mixture PDF

By Carolin Loos

ISBN-10: 3658132337

ISBN-13: 9783658132330

ISBN-10: 3658132345

ISBN-13: 9783658132347

Carolin bathrooms introduces novel methods for the research of single-cell information. either techniques can be utilized to review mobile heterogeneity and for that reason enhance a holistic figuring out of organic strategies. the 1st technique, ODE restricted combination modeling, allows the id of subpopulation buildings and assets of variability in single-cell picture information. the second one technique estimates parameters of single-cell time-lapse information utilizing approximate Bayesian computation and is ready to make the most the temporal cross-correlation of the information in addition to lineage info.

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Extra info for Analysis of Single-Cell Data : ODE Constrained Mixture Modeling and Approximate Bayesian Computation

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13A-C. The model can explain the data quite well. 13D shows that information is gained by using both measurements at the same time. We presented a method, which considers multivariate measurements simultaneously to infer parameters. This allows to capture correlated behavior between the measurements, which would not be possible if the measurements are considered separately. More information can be extracted from the data yielding higher confidence in the estimates.

The red circles mark the value of the loglikelihood function corresponding to the ML estimate. (A, B) show the optimizer output for the robust calculation. (A) All (local) optima found by the optimizer. (B) The best log-likelihood value is found in 71 out of 100 runs. The difference of the values is below 10−10 . (C) Only 1 initial value of 100 has a probabilty greater than 0 using the classical calculation. 2, comprising the conversion between two species A and B. Artificial Data As we want to analyze multivariate data, we measure both species A and B.

This is achieved by modeling subpopulation dynamics with RREs and treating different subpopulations as individual components of a mixture distribution. Combining these two approaches, the method benefits from both, the possibility to include distributional information and from getting mechanistic insights using ODEs. Using ODE-MMs, population snapshot data can be analyzed across different experimental conditions. Moreover, it has been shown that even the causal differences between subpopulations can be revealed.

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Analysis of Single-Cell Data : ODE Constrained Mixture Modeling and Approximate Bayesian Computation by Carolin Loos

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