10 research outputs found
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
An explosion of high-throughput DNA sequencing in the past decade has led to
a surge of interest in population-scale inference with whole-genome data.
Recent work in population genetics has centered on designing inference methods
for relatively simple model classes, and few scalable general-purpose inference
techniques exist for more realistic, complex models. To achieve this, two
inferential challenges need to be addressed: (1) population data are
exchangeable, calling for methods that efficiently exploit the symmetries of
the data, and (2) computing likelihoods is intractable as it requires
integrating over a set of correlated, extremely high-dimensional latent
variables. These challenges are traditionally tackled by likelihood-free
methods that use scientific simulators to generate datasets and reduce them to
hand-designed, permutation-invariant summary statistics, often leading to
inaccurate inference. In this work, we develop an exchangeable neural network
that performs summary statistic-free, likelihood-free inference. Our framework
can be applied in a black-box fashion across a variety of simulation-based
tasks, both within and outside biology. We demonstrate the power of our
approach on the recombination hotspot testing problem, outperforming the
state-of-the-art.Comment: 9 pages, 8 figure
Simultaneous identification of models and parameters of scientific simulators
Many scientific models are composed of multiple discrete components, and
scien tists often make heuristic decisions about which components to include.
Bayesian inference provides a mathematical framework for systematically
selecting model components, but defining prior distributions over model
components and developing associated inference schemes has been challenging. We
approach this problem in an amortized simulation-based inference framework: We
define implicit model priors over a fixed set of candidate components and train
neural networks to infer joint probability distributions over both, model
components and associated parameters from simulations. To represent
distributions over model components, we introduce a conditional mixture of
multivariate binary distributions in the Grassmann formalism. Our approach can
be applied to any compositional stochastic simulator without requiring access
to likelihood evaluations. We first illustrate our method on a simple time
series model with redundant components and show that it can retrieve joint
posterior distribution over a set of symbolic expressions and their parameters
while accurately capturing redundancy with strongly correlated posteriors. We
then apply our approach to drift-diffusion models, a commonly used model class
in cognitive neuroscience. After validating the method on synthetic data, we
show that our approach explains experimental data as well as previous methods,
but that our fully probabilistic approach can help to discover multiple
data-consistent model configurations, as well as reveal non-identifiable model
components and parameters. Our method provides a powerful tool for data-driven
scientific inquiry which will allow scientists to systematically identify
essential model components and make uncertainty-informed modelling decisions
Using deep learning to identify recent positive selection in malaria parasite sequence data.
BACKGROUND: Malaria, caused by Plasmodium parasites, is a major global public health problem. To assist an understanding of malaria pathogenesis, including drug resistance, there is a need for the timely detection of underlying genetic mutations and their spread. With the increasing use of whole-genome sequencing (WGS) of Plasmodium DNA, the potential of deep learning models to detect loci under recent positive selection, historically signals of drug resistance, was evaluated. METHODS: A deep learning-based approach (called "DeepSweep") was developed, which can be trained on haplotypic images from genetic regions with known sweeps, to identify loci under positive selection. DeepSweep software is available from https://github.com/WDee/Deepsweep . RESULTS: Using simulated genomic data, DeepSweep could detect recent sweeps with high predictive accuracy (areas under ROC curve > 0.95). DeepSweep was applied to Plasmodium falciparum (n = 1125; genome size 23 Mbp) and Plasmodium vivax (n = 368; genome size 29 Mbp) WGS data, and the genes identified overlapped with two established extended haplotype homozygosity methods (within-population iHS, across-population Rsb) (~ 60-75% overlap of hits at P < 0.0001). DeepSweep hits included regions proximal to known drug resistance loci for both P. falciparum (e.g. pfcrt, pfdhps and pfmdr1) and P. vivax (e.g. pvmrp1). CONCLUSION: The deep learning approach can detect positive selection signatures in malaria parasite WGS data. Further, as the approach is generalizable, it may be trained to detect other types of selection. With the ability to rapidly generate WGS data at low cost, machine learning approaches (e.g. DeepSweep) have the potential to assist parasite genome-based surveillance and inform malaria control decision-making
Neural Point Estimation for Fast Optimal Likelihood-Free Inference
Neural point estimators are neural networks that map data to parameter point
estimates. They are fast, likelihood free and, due to their amortised nature,
amenable to fast bootstrap-based uncertainty quantification. In this paper, we
aim to increase the awareness of statisticians to this relatively new
inferential tool, and to facilitate its adoption by providing user-friendly
open-source software. We also give attention to the ubiquitous problem of
making inference from replicated data, which we address in the neural setting
using permutation-invariant neural networks. Through extensive simulation
studies we show that these neural point estimators can quickly and optimally
(in a Bayes sense) estimate parameters in weakly-identified and
highly-parameterised models with relative ease. We demonstrate their
applicability through an analysis of extreme sea-surface temperature in the Red
Sea where, after training, we obtain parameter estimates and bootstrap-based
confidence intervals from hundreds of spatial fields in a fraction of a second
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
A likelihood-free inference framework for population genetic data using exchangeable neural networks
Inference for population genetics models is hindered by computationally intractable likelihoods. While this issue is tackled by likelihood-free methods, these approaches typically rely on hand-crafted summary statistics of the data. In complex settings, designing and selecting suitable summary statistics is problematic and results are very sensitive to such choices. In this paper, we learn the first exchangeable feature representation for population genetic data to work directly with genotype data. This is achieved by means of a novel Bayesian likelihood-free inference framework, where a permutation-invariant convolutional neural network learns the inverse functional relationship from the data to the posterior. We leverage access to scientific simulators to learn such likelihood-free function mappings, and establish a general framework for inference in a variety of simulation-based tasks. We demonstrate the power of our method on the recombination hotspot testing problem, outperforming the state-of-the-art
Training deep neural density estimators to identify mechanistic models of neural dynamics
Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators—trained using model simulations—to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics
Probabilistic symmetries and invariant neural networks
Treating neural network inputs and outputs as random variables, we
characterize the structure of neural networks that can be used to model data
that are invariant or equivariant under the action of a compact group. Much
recent research has been devoted to encoding invariance under symmetry
transformations into neural network architectures, in an effort to improve the
performance of deep neural networks in data-scarce, non-i.i.d., or unsupervised
settings. By considering group invariance from the perspective of probabilistic
symmetry, we establish a link between functional and probabilistic symmetry,
and obtain generative functional representations of probability distributions
that are invariant or equivariant under the action of a compact group. Our
representations completely characterize the structure of neural networks that
can be used to model such distributions and yield a general program for
constructing invariant stochastic or deterministic neural networks. We
demonstrate that examples from the recent literature are special cases, and
develop the details of the general program for exchangeable sequences and
arrays.Comment: Revised structure for clarity; fixed minor mistakes; incorporated
reviewer feedback for publicatio