92,541 research outputs found
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.
BackgroundSingle-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve.ResultsWe introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods.ConclusionsSlingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression
Halo detection via large-scale Bayesian inference
We present a proof-of-concept of a novel and fully Bayesian methodology
designed to detect halos of different masses in cosmological observations
subject to noise and systematic uncertainties. Our methodology combines the
previously published Bayesian large-scale structure inference algorithm, HADES,
and a Bayesian chain rule (the Blackwell-Rao Estimator), which we use to
connect the inferred density field to the properties of dark matter halos. To
demonstrate the capability of our approach we construct a realistic galaxy mock
catalogue emulating the wide-area 6-degree Field Galaxy Survey, which has a
median redshift of approximately 0.05. Application of HADES to the catalogue
provides us with accurately inferred three-dimensional density fields and
corresponding quantification of uncertainties inherent to any cosmological
observation. We then use a cosmological simulation to relate the amplitude of
the density field to the probability of detecting a halo with mass above a
specified threshold. With this information we can sum over the HADES density
field realisations to construct maps of detection probabilities and demonstrate
the validity of this approach within our mock scenario. We find that the
probability of successful of detection of halos in the mock catalogue increases
as a function of the signal-to-noise of the local galaxy observations. Our
proposed methodology can easily be extended to account for more complex
scientific questions and is a promising novel tool to analyse the cosmic
large-scale structure in observations.Comment: 17 pages, 13 figures. Accepted for publication in MNRAS following
moderate correction
Bayesian non-linear large scale structure inference of the Sloan Digital Sky Survey data release 7
In this work we present the first non-linear, non-Gaussian full Bayesian
large scale structure analysis of the cosmic density field conducted so far.
The density inference is based on the Sloan Digital Sky Survey data release 7,
which covers the northern galactic cap. We employ a novel Bayesian sampling
algorithm, which enables us to explore the extremely high dimensional
non-Gaussian, non-linear log-normal Poissonian posterior of the three
dimensional density field conditional on the data. These techniques are
efficiently implemented in the HADES computer algorithm and permit the precise
recovery of poorly sampled objects and non-linear density fields. The
non-linear density inference is performed on a 750 Mpc cube with roughly 3 Mpc
grid-resolution, while accounting for systematic effects, introduced by survey
geometry and selection function of the SDSS, and the correct treatment of a
Poissonian shot noise contribution. Our high resolution results represent
remarkably well the cosmic web structure of the cosmic density field.
Filaments, voids and clusters are clearly visible. Further, we also conduct a
dynamical web classification, and estimated the web type posterior distribution
conditional on the SDSS data.Comment: 18 pages, 11 figure
Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors
Meaningful quantification of data and structural uncertainties in conceptual rainfall-runoff modeling is a major scientific and engineering challenge. This paper focuses on the total predictive uncertainty and its decomposition into input and structural components under different inference scenarios. Several Bayesian inference schemes are investigated, differing in the treatment of rainfall and structural uncertainties, and in the precision of the priors describing rainfall uncertainty. Compared with traditional lumped additive error approaches, the quantification of the total predictive uncertainty in the runoff is improved when rainfall and/or structural errors are characterized explicitly. However, the decomposition of the total uncertainty into individual sources is more challenging. In particular, poor identifiability may arise when the inference scheme represents rainfall and structural errors using separate probabilistic models. The inference becomes illâposed unless sufficiently precise prior knowledge of data uncertainty is supplied; this illâposedness can often be detected from the behavior of the Monte Carlo sampling algorithm. Moreover, the priors on the data quality must also be sufficiently accurate if the inference is to be reliable and support meaningful uncertainty decomposition. Our findings highlight the inherent limitations of inferring inaccurate hydrologic models using rainfallârunoff data with large unknown errors. Bayesian total error analysis can overcome these problems using independent prior information. The need for deriving independent descriptions of the uncertainties in the input and output data is clearly demonstrated.Benjamin Renard, Dmitri Kavetski, George Kuczera, Mark Thyer, and Stewart W. Frank
A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes
Basketball games evolve continuously in space and time as players constantly
interact with their teammates, the opposing team, and the ball. However,
current analyses of basketball outcomes rely on discretized summaries of the
game that reduce such interactions to tallies of points, assists, and similar
events. In this paper, we propose a framework for using optical player tracking
data to estimate, in real time, the expected number of points obtained by the
end of a possession. This quantity, called \textit{expected possession value}
(EPV), derives from a stochastic process model for the evolution of a
basketball possession; we model this process at multiple levels of resolution,
differentiating between continuous, infinitesimal movements of players, and
discrete events such as shot attempts and turnovers. Transition kernels are
estimated using hierarchical spatiotemporal models that share information
across players while remaining computationally tractable on very large data
sets. In addition to estimating EPV, these models reveal novel insights on
players' decision-making tendencies as a function of their spatial strategy.Comment: 31 pages, 9 figure
A Statistical Inference Method for Interpreting the CLASP Observations
On 3rd September 2015, the Chromospheric Lyman-Alpha SpectroPolarimeter
(CLASP) successfully measured the linear polarization produced by scattering
processes in the hydrogen Lyman- line of the solar disk radiation,
revealing conspicuous spatial variations in the and signals. Via
the Hanle effect the line-center and amplitudes encode information
on the magnetic field of the chromosphere-corona transition region (TR), but
they are also sensitive to the three-dimensional structure of this corrugated
interface region. With the help of a simple line formation model, here we
propose a statistical inference method for interpreting the Lyman-
line-center polarization observed by CLASP.Comment: Accepted for publication in The Astrophysical Journa
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