arXiv subjects

Peter Mueller

Publications and source records attributed to Peter Mueller.

At least 55 records · Page 3Linked to original sources

arXiv2017

PairClone: A Bayesian Subclone Caller Based on Mutation Pairs

Tumor cell populations can be thought of as being composed of homogeneous cell subpopulations, with each subpopulation being characterized by overlapping sets of single nucleotide variants (SNVs). Such subpopulations are known as subclones and are an important target for precision medicine. Reconstructing such subclones from next-generation sequencing (NGS) data is one of the major challenges in precision medicine. We present PairClone as a new tool to implement this reconstruction. The main idea of PairClone is to model short reads mapped to pairs of proximal SNVs. In contrast, most existing methods use only marginal reads for unpaired SNVs. Using Bayesian nonparametric models, we estimate posterior probabilities of the number, genotypes and population frequencies of subclones in one or more tumor sample. We use the categorical Indian buffet process (cIBP) as a prior probability model for subclones that are represented as vectors of categorical matrices that record the corresponding sets of mutation pairs. Performance of PairClone is assessed using simulated and real datasets. An open source software package can be obtained at http://www.compgenome.org/pairclone.

stat.AP↗

arXiv2016

High accuracy position response calibration method for a micro-channel plate ion detector

We have developed a position response calibration method for a micro-channel plate (MCP) detector with a delay-line anode position readout scheme. Using an {\em in situ} calibration mask, an accuracy of 8~$μ$m and a resolution of 85~$μ$m (FWHM) have been achieved for MeV-scale $α$ particles and ions with energies of $\sim$10~keV. At this level of accuracy, the difference between the MCP position responses to high-energy $α$ particles and low-energy ions is significant. The improved performance of the MCP detector can find applications in many fields of AMO and nuclear physics. In our case, it helps reducing systematic uncertainties in a high-precision nuclear $β$-decay experiment.

physics.ins-det↗

arXiv2016

Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis

Constructing gene regulatory networks is a fundamental task in systems biology. We introduce a Gaussian reciprocal graphical model for inference about gene regulatory relationships by integrating mRNA gene expression and DNA level information including copy number and methylation. Data integration allows for inference on the directionality of certain regulatory relationships, which would be otherwise indistinguishable due to Markov equivalence. Efficient inference is developed based on simultaneous equation models. Bayesian model selection techniques are adopted to estimate the graph structure. We illustrate our approach by simulations and two applications in ZODIAC pairwise gene interaction analysis and colon adenocarcinoma pathway analysis.

stat.ME↗

arXiv2016

A Decision-Theoretic Comparison of Treatments to Resolve Air Leaks After Lung Surgery Based on Nonparametric Modeling

We propose a Bayesian nonparametric utility-based group sequential design for a randomized clinical trial to compare a gel sealant to standard care for resolving air leaks after pulmonary resection. Clinically, resolving air leaks in the days soon after surgery is highly important, since longer resolution time produces undesirable complications that require extended hospitalization. The problem of comparing treatments is complicated by the fact that the resolution time distributions are skewed and multi-modal, so using means is misleading. We address these challenges by assuming Bayesian nonparametric probability models for the resolution time distributions and basing the comparative test on weighted means. The weights are elicited as clinical utilities of the resolution times. The proposed design uses posterior expected utilities as group sequential test criteria. The procedure's frequentist properties are studied by extensive simulations.

stat.AP↗

arXiv2016

Fluid transport and mixing by an unsteady microswimmer

We study the fluid drift due to a time-dependent dumbbell model of a microswimmer. The model captures important aspects of real microswimmers such as a time-dependent flagellar motion and a no-slip body. The model consists of a rigid sphere for the body and a time-dependent moving Stokeslet representing the flagella. We analyze the paths of idealized fluid particles displaced by the swimmer. The simplicity of the model allows some asymptotic calculations very near and far away from the swimmer. The displacements of particles near the swimmer diverge in a manner similar to an isolated no-slip sphere, but with a smaller coefficient due to the action of the flagellum. Far from the swimmer, the time dependence becomes negligible due to both being very fast and decaying with distance. Finally, we compute the probability distribution of particle displacements, and find that our model has fatter tails than previous steady models, due to the presence of a no-slip surface that drags particles along.

cond-mat.soft↗

arXiv2016

Improved limit on the $^{225}$Ra electric dipole moment

Background: Octupole-deformed nuclei, such as that of $^{225}$Ra, are expected to amplify observable atomic electric dipole moments (EDMs) that arise from time-reversal and parity-violating interactions in the nuclear medium. In 2015, we reported the first "proof-of-principle" measurement of the $^{225}$Ra atomic EDM. Purpose: This work reports on the first of several experimental upgrades to improve the statistical sensitivity of our $^{225}$Ra EDM measurements by orders of magnitude and evaluates systematic effects that contribute to current and future levels of experimental sensitivity. Method: Laser-cooled and trapped $^{225}$Ra atoms are held between two high voltage electrodes in an ultra high vacuum chamber at the center of a magnetically shielded environment. We observe Larmor precession in a uniform magnetic field using nuclear-spin-dependent laser light scattering and look for a phase shift proportional to the applied electric field, which indicates the existence of an EDM. The main improvement to our measurement technique is an order of magnitude increase in spin precession time, which is enabled by an improved vacuum system and a reduction in trap-induced heating. Results: We have measured the $^{225}$Ra atomic EDM to be less than $1.4\times10^{-23}$ $e$ cm (95% confidence upper limit), which is a factor of 36 improvement over our previous result. Conclusions: Our evaluation of systematic effects shows that this measurement is completely limited by statistical uncertainty. Combining this measurement technique with planned experimental upgrades we project a statistical sensitivity at the $1\times10^{-28}$ $e$ cm level and a total systematic uncertainty at the $4\times10^{-29}$ $e$ cm level.

nucl-ex↗

arXiv2015

Bayesian Inference for Latent Biologic Structure with Determinantal Point Processes (DPP)

We discuss the use of the determinantal point process (DPP) as a prior for latent structure in biomedical applications, where inference often centers on the interpretation of latent features as biologically or clinically meaningful structure. Typical examples include mixture models, when the terms of the mixture are meant to represent clinically meaningful subpopulations (of patients, genes, etc.). Another class of examples are feature allocation models. We propose the DPP prior as a repulsive prior on latent mixture components in the first example, and as prior on feature-specific parameters in the second case. We argue that the DPP is in general an attractive prior model for latent structure when biologically relevant interpretation of such structure is desired. We illustrate the advantages of DPP prior in three case studies, including inference in mixture models for magnetic resonance images (MRI) and for protein expression, and a feature allocation model for gene expression using data from The Cancer Genome Atlas. An important part of our argument are efficient and straightforward posterior simulation methods. We implement a variation of reversible jump Markov chain Monte Carlo simulation for inference under the DPP prior, using a density with respect to the unit rate Poisson process.

stat.ME↗

arXiv2014

MAD Bayes for Tumor Heterogeneity Feature Allocation with Non-Normal Sampling

We propose small-variance asymptotic approximations for the inference of tumor heterogeneity (TH) using next-generation sequencing data. Understanding TH is an important and open research problem in biology. The lack of appropriate statistical inference is a critical gap in existing methods that the proposed approach aims to fill. We build on a hierarchical model with an exponential family likelihood and a feature allocation prior. The proposed approach generalizes similar small-variance approximations proposed by Kulis and Jordan (2012) and Broderick et.al (2012) for inference with Dirichlet process mixture and Indian buffet prior models under normal sampling. We show that the new algorithm can successfully recover latent structures of different subclones and is also magnitude faster than available Markov chain Monte Carlo samplers, the latter often practically infeasible for high-dimensional genomics data. The proposed approach is scalable, simple to implement and benefits from the flexibility of Bayesian nonparametric models. More importantly, it provides a useful tool for the biological community for estimating cell subtypes in tumor samples.

stat.ME↗

arXiv2014

Bayesian Inference for Tumor Subclones Accounting for Sequencing and Structural Variants

Tumor samples are heterogeneous. They consist of different subclones that are characterized by differences in DNA nucleotide sequences and copy numbers on multiple loci. Heterogeneity can be measured through the identification of the subclonal copy number and sequence at a selected set of loci. Understanding that the accurate identification of variant allele fractions greatly depends on a precise determination of copy numbers, we develop a Bayesian feature allocation model for jointly calling subclonal copy numbers and the corresponding allele sequences for the same loci. The proposed method utilizes three random matrices, L, Z and w to represent subclonal copy numbers (L), numbers of subclonal variant alleles (Z) and cellular fractions of subclones in samples (w), respectively. The unknown number of subclones implies a random number of columns for these matrices. We use next-generation sequencing data to estimate the subclonal structures through inference on these three matrices. Using simulation studies and a real data analysis, we demonstrate how posterior inference on the subclonal structure is enhanced with the joint modeling of both structure and sequencing variants on subclonal genomes. Software is available at http://compgenome.org/BayClone2.

stat.ME↗

arXiv2014

Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times

Dynamic treatment regimes in oncology and other disease areas often can be characterized by an alternating sequence of treatments or other actions and transition times between disease states. The sequence of transition states may vary substantially from patient to patient, depending on how the regime plays out, and in practice there often are many possible counterfactual outcome sequences. For evaluating the regimes, the mean final overall time may be expressed as a weighted average of the means of all possible sums of successive transitions times. A common example arises in cancer therapies where the transition times between various sequences of treatments, disease remission, disease progression, and death characterize overall survival time. For the general setting, we propose estimating mean overall outcome time by assuming a Bayesian nonparametric regression model for the logarithm of each transition time. A dependent Dirichlet process prior with Gaussian process base measure (DDP-GP) is assumed, and a joint posterior is obtained by Markov chain Monte Carlo (MCMC) sampling. We provide general guidelines for constructing a prior using empirical Bayes methods. We compare the proposed approach with inverse probability of treatment weighting. These comparisons are done by simulation studies of both single-stage and multi-stage regimes, with treatment assignment depending on baseline covariates. The method is applied to analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for acute leukemia. An R program for implementing the DDP-GP-based Bayesian nonparametric analysis is freely available at https://www.ma.utexas.edu/users/yxu/.

stat.AP↗

arXiv2014

Radiometric 81Kr dating identifies 120,000 year old ice at Taylor Glacier, Antarctica

We present the first successful 81Kr-Kr radiometric dating of ancient polar ice. Krypton was extracted from the air bubbles in four ~350 kg polar ice samples from Taylor Glacier in the McMurdo Dry Valleys, Antarctica, and dated using Atom Trap Trace Analysis (ATTA). The 81Kr radiometric ages agree with independent age estimates obtained from stratigraphic dating techniques with a mean absolute age offset of 6 +/- 2.5 ka. Our experimental methods and sampling strategy are validated by 1) 85Kr and 39Ar analyses that show the samples to be free of modern air contamination, and 2) air content measurements that show the ice did not experience gas loss. We estimate the error in the 81Kr ages due to past geomagnetic variability to be below 3 ka. We show that ice from the previous interglacial period (MIS 5e, 130-115 ka before present) can be found in abundance near the surface of Taylor Glacier. Our study paves the way for reliable radiometric dating of ancient ice in blue ice areas and margin sites where large samples are available, greatly enhancing their scientific value as archives of old ice and meteorites. At present, ATTA 81Kr analysis requires a 40-80 kg ice sample; as sample requirements continue to decrease 81Kr dating of ice cores is a future possibility.

physics.geo-ph↗

arXiv2013

Ion Current as a Precise Measure of the Loading Rate of a Magneto-Optical Trap

We have demonstrated that the ion current resulting from collisions between metastable krypton atoms in a magneto-optical trap can be used to precisely measure the trap loading rate. We measured both the ion current of the abundant isotope Kr-83 (isotopic abundance = 11%) and the single-atom counting rate of the rare isotope Kr-85 (isotopic abundance ~ 1x10^-11), and found the two quantities to be proportional at a precision level of 0.9%. This work results in a significant improvement in using the magneto-optical trap as an analytical tool for noble-gas isotope ratio measurements, and will benefit both atomic physics studies and applications in the earth sciences.

physics.atom-ph↗

arXiv2013

Low-degree planar monomials in characteristic two

Planar functions over finite fields give rise to finite projective planes and other combinatorial objects. They exist only in odd characteristic, but recently Zhou introduced an even characteristic analogue which has similar applications. In this paper we determine all planar functions on F_q of the form c-->uc^t, where q is a power of 2, t is an integer with 0<t<=q^{1/4}, and u is a nonzero element of F_q. This settles and sharpens a conjecture of Schmidt and Zhou.

math.NT↗

arXiv2012

A one--parameter family of polynomials with Galois group M24 over Q(t)

Granboulan computed an explicit polynomial whose Galois group over the rational function field Q(t) is the Mathieu group M24. By a result of Malle and Matzat, it was known before that such a polynomial exists. Even more, their proof showed that there is a 1-parameter family of such polynomials. In this note we compute this family.

math.NT↗

arXiv2008

On Ritt's polynomial decomposition theorems

Ritt studied the functional decomposition of a univariate complex polynomial f into prime (indecomposable) polynomials, f = u_1 o u_2 o ... o u_r. His main achievement was a procedure for obtaining any decomposition of f from any other by repeatedly applying certain transformations. However, Ritt's results provide no control on the number of times one must apply the basic transformations, which makes his procedure unsuitable for many theoretical and algorithmic applications. We solve this problem by giving a new description of the collection of all decompositions of a polynomial. Our results have been used by Ghioca, Tucker and Zieve (arXiv:0807.3576) to describe the polynomials f,g having orbits with infinite intersection; they have also been used by Medvedev and Scanlon to describe the affine curves invariant under a coordinatewise polynomial action.

math.AG↗

arXiv2008

Densities and filling factors of the DIG in the Solar neighbourhood

For the first time we have combined dispersion measures and emission measures towards 38 pulsars at KNOWN distances from which we derived the mean electron density in clouds, N_c, and their volume filling factor, F_v, averaged along the line of sight. The emission measures were corrected for absorption by dust and contributions from beyond the pulsar distance. Results: The scale height of the electron layer for our sample is 0.93+/-0.13 kpc and the midplane electron density is 0.023+/-0.004 cm^-3, in agreement with earlier results. The average density along the line of sight is = 0.018+/-0.002 cm^-3 and nearly constant. Since = F_v N_c, an inverse relationship between F_v and N_c is expected. We find F_v(N_c) = (0.011+/-0.003) N_c^{-1.20+/-0.13}, which holds for the ranges N_c = 0.05-1 cm^-3 and F_v = 0.4-0.01. Near the Galactic plane the dependence of F_v on N_c is significantly stronger than away from the plane. F_v does not systematically change along or perpendicular to the Galactic plane, but the spread about the mean value of 0.08+/-0.02 is considerable. Conclusions: The inverse F_v-N_c relation is consistent with a hierarchical, fractal density distribution in the diffuse ionized gas (DIG) caused by turbulence. The observed near constancy of then is a signature of fractal structure in the ionized medium, which is most pronounced outside the thin disk.

astro-ph↗

arXiv2006

Uniform existence of the integrated density of states for models on $\ZZ^d$

We provide an ergodic theorem for certain Banach-space valued functions on structures over $\ZZ^d$, which allow for existence of frequencies of finite patterns. As an application we obtain existence of the integrated density of states for associated finite-range operators in the sense of convergence of the distributions with respect to the supremum norm. These results apply to various examples including periodic operators, percolation models and nearest-neighbour hopping on the set of visible points. Our method gives explicit bounds on the speed of convergence in terms of the speed of convergence of the underlying frequencies. It uses neither von Neumann algebras nor a framework of random operators on a probability space.

math-ph↗

arXiv2003

Volume Filling Factors of the DIG

Combining dispersion measures, distances and emission measures for 157 pulsars lying above \mid b \mid > 5 degree and between 60 degree < l < 360 degree we find the mean volume filling factor (\bar{f_v}) of the diffused ionized gas in the Milky Way. This filling factor is inversely related to the mean electron density (\bar{n_c}) in the clouds, \bar{f_v} = (0.0184 +/- 0.0014) \bar{n_c}^{-1.07 +/- 0.03}, implying a nearly constant average electron density distribution within 3 kpc of the sun.

astro-ph↗