By Carolin Loos
Carolin bathrooms introduces novel methods for the research of single-cell facts. either methods can be utilized to review mobile heterogeneity and as a result strengthen a holistic knowing of organic strategies. the 1st approach, ODE limited mix modeling, allows the id of subpopulation constructions and assets of variability in single-cell image 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 knowledge in addition to lineage information.
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Extra info for Analysis of Single-Cell Data. ODE Constrained Mixture Modeling and Approximate Bayesian Computation
In the following, we propose ODE-MMs, which can simultaneously analyze multivariate measurements and therefore account for correlated behavior. Additionally, we demonstrate and tackle the numerically instability of the likelihood calculation arising due to the use of mixture probabilities (Problem 5). 5) e,k,j s=1 with x˙ es = f (xes , ψ es , ue ) , xes (0) = x0 (ψ es , ue ) , ϕes = h (xes , ψ es , ue ) . 10: Scatterplot and marginals of measurands A and B with (A) positive correlation and (B) negative correlation.
H2 considers diﬀerent weightings for the experimental condition, while H3 and H4 include diﬀerent responses to stimulation with NGF. The higher response is modeled by multiplying parameter k3 [TrkA]0 by a parameter κ, which describes the stimulus-dependent response. To obtain estimates of the parameters we perform multi-start local optimization with 100 multi-starts. If the optimizer ﬁnds the same (possibly local) optimum less than 5 times, we increase the number of multi-starts and repeat the optimization.
2. 1 a likelihood function for ODE-MMs with MEs to study univariate measurements y ∈ R. Additionally, we describe how the MEs can be linked to a normal and log-normal mixture distribution. 2, we validate the method for diﬀerent scenarios of a conversion process. Besides that, we compare the results of the method with those obtained using RREs for the description of the mechanisms of the system. 2) e,k,j s=1 with x˙ es = f (xes , ψ es , ue ) , xes (0) = x0 (ψ es , ue ) , ϕes = h(xes , ψ es , ue ) .