Paul DW Kirk | bioRxiv Science

bioRxiv2015

Inferring extrinsic noise from single-cell gene expression data using Approximate Bayesian Computation.

BackgroundGene expression is known to be an intrinsically stochastic process which can involve single-digit numbers of mRNA molecules in a cell at any given time. The modelling of such processes calls for the use of exact stochastic simulation methods, most notably the Gillespie algorithm. However, this stochasticity, also termed \"intrinsic noise\", does not account for all the variability between genetically identical cells growing in a homogeneous environment. Despite substantial experimental efforts, determining appropriate model parameters continues to be a challenge. Methods based on approximate Bayesian computation can be used to obtain posterior parameter distributions given the observed data. However, such inference procedures require large numbers of simulations of the model and exact stochastic simulation is computationally costly. In this work we focus on the specific case of trying to infer model parameters describing reaction rates and extrinsic noise on the basis of measurements of molecule numbers in individual cells at a given time point.\n\nResultsTo make the problem computationally tractable we develop an exact, model-specific, stochastic simulation algorithm for the commonly used two-state model of gene expression. This algorithm relies on certain assumptions and favourable properties of the model to forgo the simulation of the whole temporal trajectory of protein numbers in the system, instead returning only the number of protein and mRNA molecules present in the system at a specified time point. The computational gain is proportional to the number of protein molecules created in the system and becomes significant for systems involving hundreds or thousands of protein molecules. We employ this algorithm, approximate Bayesian computation, and published gene expression data for Escherichia coli to simultaneously infer the models rate parameters and parameters describing extrinsic noise for 86 genes.

Systems Biology↗

bioRxiv2015

A Graph Theoretical Approach to Data Fusion

The rapid development of high throughput experimental techniques has resulted in a growing diversity of genomic datasets being produced and requiring analysis. A variety of computational techniques allow us to analyse such data and to model the biological processes behind them. However, it is increasingly being recognised that we can gain deeper understanding by combining the insights obtained from multiple, diverse datasets. We therefore require scalable computational approaches for data fusion.\n\nWe propose a novel methodology for scalable unsupervised data fusion. Our technique exploits network representations of the data in order to identify (and quantify) similarities among the datasets. We may work within the Bayesian formalism, using Bayesian nonparametric approaches to model each dataset; or (for fast, approximate, and massive scale data fusion) can naturally switch to more heuristic modelling techniques. An advantage of the proposed approach is that each dataset can initially be modelled independently (and therefore in parallel), before applying a fast post-processing step in order to perform data fusion. This allows us to incorporate new experimental data in an online fashion, without having to rerun all of the analysis. The methodology can be applied to genomic scale datasets and we demonstrate its applicability on examples from the literature, using a broad range of genomic datasets, and also on a recent gene expression dataset from Sporadic inclusion body myositis Availability. Example R code and instructions are available from https://sites.google.com/site/gtadatafusion/.

Bioinformatics↗