Biomedical subjects

Magnus Rattray

Publications and source records attributed to Magnus Rattray.

15 recordsLinked to original sources

PubMed2006

Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities.

MOTIVATION: Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular processes. Recent experimental high-throughput techniques, such as Chromatin Immunoprecipitation (ChIP) provide important information about the architecture of the regulatory networks in the cell. However, it is very difficult to measure the concentration levels of transcription factor proteins and determine their regulatory effect on gene transcription. It is therefore an important computational challenge to infer these quantities using gene expression data and network architecture data. RESULTS: We develop a probabilistic state space model that allows genome-wide inference of both transcription factor protein concentrations and their effect on the transcription rates of each target gene from microarray data. We use variational inference techniques to learn the model parameters and perform posterior inference of protein concentrations and regulatory strengths. The probabilistic nature of the model also means that we can associate credibility intervals to our estimates, as well as providing a tool to detect which binding events lead to significant regulation. We demonstrate our model on artificial data and on two yeast datasets in which the network structure has previously been obtained using ChIP data. Predictions from our model are consistent with the underlying biology and offer novel quantitative insights into the regulatory structure of the yeast cell. AVAILABILITY: MATLAB code is available from http://umber.sbs.man.ac.uk/resources/puma

Algorithms↗

PubMed2006

A tumor progression model for hepatocellular carcinoma: bioinformatic analysis of genomic data.

BACKGROUND & AIMS: It is widely recognized that genomic abnormalities underpin the development of human cancers. Aberrant patterns of chromosomal changes may represent useful information that can be used in classifying the complex traits of liver cancer cases for the genetic events involved in tumor carcinogenesis, tumor progression, and prognosis. METHODS: Genome-wide chromosomal aberrations of 158 hepatitis B virus-associated hepatocellular carcinoma (HCC) were studied by comparative genomic hybridization (CGH). By application of a self-organizing tree algorithm, statistically significant CGH events were used to construct an evolutionary tree that could infer patient subgroups with different degrees of tumor progression. The key CGH events in the subgroups were identified. The clinical significance of the groupings and the key CGH events were examined. RESULTS: Based on the patterns of significant chromosomal aberrations derived, 3 HCC subgroups organized in an evolutionary tree were identified. The groupings possessed information reflecting the degrees of tumor progression, including numbers of chromosomal aberrations, tumor stages, tumor sizes, and disease outcome. Gains of 1q21-23 and 8q22-24 were identified as genomic events associated with the early development of HCC. Gain of 3q22-24, however, was identified as 1 of the late genomic events found to be associated with tumor recurrence and poor overall patient survival. CONCLUSIONS: A tumor progression model for HCC was constructed and revealed chromosomal imbalances that were significantly associated with clinical pathologic characteristics of the disease. This model explains a significant part of the variations in clinical outcome among HCC patients.

Adult↗

PubMed2006

Probe-level measurement error improves accuracy in detecting differential gene expression.

MOTIVATION: Finding differentially expressed genes is a fundamental objective of a microarray experiment. Numerous methods have been proposed to perform this task. Existing methods are based on point estimates of gene expression level obtained from each microarray experiment. This approach discards potentially useful information about measurement error that can be obtained from an appropriate probe-level analysis. Probabilistic probe-level models can be used to measure gene expression and also provide a level of uncertainty in this measurement. This probe-level measurement error provides useful information which can help in the identification of differentially expressed genes. RESULTS: We propose a Bayesian method to include probe-level measurement error into the detection of differentially expressed genes from replicated experiments. A variational approximation is used for efficient parameter estimation. We compare this approximation with MAP and MCMC parameter estimation in terms of computational efficiency and accuracy. The method is used to calculate the probability of positive log-ratio (PPLR) of expression levels between conditions. Using the measurements from a recently developed Affymetrix probe-level model, multi-mgMOS, we test PPLR on a spike-in dataset and a mouse time-course dataset. Results show that the inclusion of probe-level measurement error improves accuracy in detecting differential gene expression. AVAILABILITY: The MAP approximation and variational inference described in this paper have been implemented in an R package pplr. The MCMC method is implemented in Matlab. Both software are available from http://umber.sbs.man.ac.uk/resources/puma.

Algorithms↗

PubMed2006

A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription.

MOTIVATION: Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular processes. This task, however, is difficult for a number of reasons: transcription factors' expression levels are often low and noisy, and many transcription factors are post-transcriptionally regulated. It is therefore useful to infer the activity of the transcription factors from the expression levels of their target genes. RESULTS: We introduce a novel probabilistic model to infer transcription factor activities from microarray data when the structure of the regulatory network is known. The model is based on regression, retaining the computational efficiency to allow genome-wide investigation, but is rendered more flexible by sampling regression coefficients independently for each gene. This allows us to determine the strength with which a transcription factor regulates each of its target genes, therefore providing a quantitative description of the transcriptional regulatory network. The probabilistic nature of the model also means that we can associate credibility intervals to our estimates of the activities. We demonstrate our model on two yeast datasets. In both cases the network structure was obtained using chromatin immunoprecipitation data. We show how predictions from our model are consistent with the underlying biology and offer novel quantitative insights into the regulatory structure of the yeast cell. AVAILABILITY: MATLAB code is available from http://umber.sbs.man.ac.uk/resources/puma.

Computer Simulation↗

PubMed2006

Analysis of tag-position bias in MPSS technology.

BACKGROUND: Massively Parallel Signature Sequencing (MPSS) technology was recently developed as a high-throughput technology for measuring the concentration of mRNA transcripts in a sample. It has previously been observed that the position of the signature tag in a transcript (distance from 3' end) can affect the measurement, but this effect has not been studied in detail. RESULTS: We quantify the effect of tag-position bias in Classic and Signature MPSS technology using published data from Arabidopsis, rice and human. We investigate the relationship between measured concentration and tag-position using nonlinear regression methods. The observed relationship is shown to be broadly consistent across different data sets. We find that there exist different and significant biases in both Classic and Signature MPSS data. For Classic MPSS data, genes with tag-position in the middle-range have highest measured abundance on average while genes with tag-position in the high-range, far from the 3' end, show a significant decrease. For Signature MPSS data, high-range tag-position genes tend to have a flatter relationship between tag-position and measured abundance. Thus, our results confirm that the Signature MPSS method fixes a substantial problem with the Classic MPSS method. For both Classic and Signature MPSS data there is a positive correlation between measured abundance and tag-position for low-range tag-position genes. Compared with the effects of mRNA length and number of exons, tag-position bias seems to be more significant in Arabadopsis. The tag-position bias is reflected both in the measured abundance of genes with a significant tag count and in the proportion of unexpressed genes identified. CONCLUSION: Tag-position bias should be taken into consideration when measuring mRNA transcript abundance using MPSS technology, both in Classic and Signature MPSS methods.

Arabidopsis↗

PubMed2006

Propagating uncertainty in microarray data analysis.

Microarray technology is associated with many sources of experimental uncertainty. In this review we discuss a number of approaches for dealing with this uncertainty in the processing of data from microarray experiments. We focus here on the analysis of high-density oligonucleotide arrays, such as the popular Affymetrix GeneChip array, which contain multiple probes for each target. This set of probes can be used to determine an estimate for the target concentration and can also be used to determine the experimental uncertainty associated with this measurement. This measurement uncertainty can then be propagated through the downstream analysis using probabilistic methods. We give examples showing how these credibility intervals can be used to help identify differential expression, to combine information from replicated experiments and to improve the performance of principal component analysis.

Computational Biology↗

PubMed2005

On the correlation between composition and site-specific evolutionary rate: implications for phylogenetic inference.

Model-based phylogenetic reconstruction methods traditionally assume homogeneity of nucleotide frequencies among sequence sites and lineages. Yet, heterogeneity in base composition is a characteristic shared by most biological sequences. Compositional variation in time, reflected in the compositional biases among contemporary sequences, has already been extensively studied, and its detrimental effects on phylogenetic estimates are known. However, fewer studies have focused on the effects of spatial compositional heterogeneity within genes. We show here that different sites in an alignment do not always share a unique compositional pattern, and we provide examples where nucleotide frequency trends are correlated with the site-specific rate of evolution in RNA genes. Spatial compositional heterogeneity is shown to affect the estimation of evolutionary parameters. With standard phylogenetic methods, estimates of equilibrium frequencies are found to be biased towards the composition observed at fast-evolving sites. Conversely, the ancestral composition estimates of some time-heterogeneous but spatially homogeneous methods are found to be biased towards frequencies observed at invariant and slow-evolving sites. The latter finding challenges the result of a previous study arguing against a hyperthermophilic last universal ancestor from the low apparent G + C content of its rRNA sequences. We propose a new model to account for compositional variation across sites. A Gaussian process prior is used to allow for a smooth change in composition with evolutionary rate. The model has been implemented in the phylogenetic inference software PHASE, and Bayesian methods can be used to obtain the model parameters. The results suggest that this model can accurately capture the observed trends in present-day RNA sequences.

Evolution, Molecular↗

PubMed2005

Accounting for probe-level noise in principal component analysis of microarray data.

MOTIVATION: Principal Component Analysis (PCA) is one of the most popular dimensionality reduction techniques for the analysis of high-dimensional datasets. However, in its standard form, it does not take into account any error measures associated with the data points beyond a standard spherical noise. This indiscriminate nature provides one of its main weaknesses when applied to biological data with inherently large variability, such as expression levels measured with microarrays. Methods now exist for extracting credibility intervals from the probe-level analysis of cDNA and oligonucleotide microarray experiments. These credibility intervals are gene and experiment specific, and can be propagated through an appropriate probabilistic downstream analysis. RESULTS: We propose a new model-based approach to PCA that takes into account the variances associated with each gene in each experiment. We develop an efficient EM-algorithm to estimate the parameters of our new model. The model provides significantly better results than standard PCA, while remaining computationally reasonable. We show how the model can be used to 'denoise' a microarray dataset leading to improved expression profiles and tighter clustering across profiles. The probabilistic nature of the model means that the correct number of principal components is automatically obtained.

Algorithms↗

PubMed2005

A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips.

MOTIVATION: Affymetrix GeneChip arrays are currently the most widely used microarray technology. Many summarization methods have been developed to provide gene expression levels from Affymetrix probe-level data. Most of the currently popular methods do not provide a measure of uncertainty for the expression level of each gene. The use of probabilistic models can overcome this limitation. A full hierarchical Bayesian approach requires the use of computationally intensive MCMC methods that are impractical for large datasets. An alternative computationally efficient probabilistic model, mgMOS, uses Gamma distributions to model specific and non-specific binding with a latent variable to capture variations in probe affinity. Although promising, the main limitations of this model are that it does not use information from multiple chips and does not account for specific binding to the mismatch (MM) probes. RESULTS: We extend mgMOS to model the binding affinity of probe-pairs across multiple chips and to capture the effect of specific binding to MM probes. The new model, multi-mgMOS, provides improved accuracy, as demonstrated on some bench-mark datasets and a real time-course dataset, and is much more computationally efficient than a competing hierarchical Bayesian approach that requires MCMC sampling. We demonstrate how the probabilistic model can be used to estimate credibility intervals for expression levels and their log-ratios between conditions. AVAILABILITY: Both mgMOS and the new model multi-mgMOS have been implemented in an R package, which is available at http://www.bioinf.man.ac.uk/resources/puma.

Algorithms↗

PubMed2004

A comprehensive analysis of mammalian mitochondrial genome base composition and improved phylogenetic methods.

Phylogenetic analysis of mammalian species using mitochondrial protein genes has proved to be problematic in many previous studies. The high mutation rate of mitochondrial DNA and unusual base composition of several species has prompted us to conduct a detailed study of the composition of 69 mammalian mitochondrial genomes. Most major changes in base composition between lineages can be attributed to shifts between the proportions of C and T on the L-strand. These changes are significant at all codon positions and are shown to affect amino acid composition. Correlated changes in the base composition of the RNA loops and stems are also observed. Following up from previous studies, we investigate changes in the base composition of all 12 H-strand proteins and find that variability in proportions of C and T is correlated with location on the genome. Variation in base composition across genes and species is known to adversely affect the performance of phylogenetic inference methods. We have, therefore, developed a customized three-state general time-reversible DNA substitution model, implemented in the PHASE phylogenetic inference package, which lumps C and T into a composite pyrimidine state. We compare the phylogenetic tree obtained using the new three-state model with that obtained using a standard four-state model. Results using the three-state model are more congruent with recent studies using large sets of nuclear genes and help resolve some of the apparent conflicts between studies using nuclear and mitochondrial proteins.

Animals↗

PubMed2003

A model-based analysis of microarray experimental error and normalisation.

A statistical model is proposed for the analysis of errors in microarray experiments and is employed in the analysis and development of a combined normalisation regime. Through analysis of the model and two-dye microarray data sets, this study found the following. The systematic error introduced by microarray experiments mainly involves spot intensity-dependent, feature-specific and spot position-dependent contributions. It is difficult to remove all these errors effectively without a suitable combined normalisation operation. Adaptive normalisation using a suitable regression technique is more effective in removing spot intensity-related dye bias than self-normalisation, while regional normalisation (block normalisation) is an effective way to correct spot position-dependent errors. However, dye-flip replicates are necessary to remove feature-specific errors, and also allow the analyst to identify the experimentally introduced dye bias contained in non-self-self data sets. In this case, the bias present in the data sets may include both experimentally introduced dye bias and the biological difference between two samples. Self-normalisation is capable of removing dye bias without identifying the nature of that bias. The performance of adaptive normalisation, on the other hand, depends on its ability to correctly identify the dye bias. If adaptive normalisation is combined with an effective dye bias identification method then there is no systematic difference between the outcomes of the two methods.

Algorithms↗

PubMed2003

The evolution of tRNA-Leu genes in animal mitochondrial genomes.

Animal mitochondrial genomes usually have two transfer RNAs for leucine: one, with anticodon UAG, translates the four-codon family CUN, while the other, with anticodon UAA, translates the two-codon family UUR. These two genes must differ at the third anticodon position, but in some species the genes differ at many additional sites, indicating that these genes have been independent for a long time. Duplication and deletion of genes in mitochondrial genomes occur frequently during the evolution of the Metazoa. If a tRNA-Leu gene were duplicated and a substitution occurred in the anticodon, this would effectively turn one type of tRNA into the other. The original copy of the second tRNA type might then be lost by a deletion elsewhere in the genome. There are several groups of species in which the two tRNA-Leu genes occur next to one another (or very close) on the genome, which suggests that tandem duplication has occurred. Here we use RNA-specific phylogenetic methods to determine evolutionary trees for both genes. We present evidence that the process of duplication, anticodon mutation, and deletion of tRNA-Leu genes has occurred at least five times during the evolution of the metazoa-once in the common ancestor of all protostomes, once in the common ancestor of echinoderms and hemichordates, once in the hermit crab, and twice independently in mollusks.

Animals↗

PubMed2003

RNA-based phylogenetic methods: application to mammalian mitochondrial RNA sequences.

The PHASE software package allows phylogenetic tree construction with a number of evolutionary models designed specifically for use with RNA sequences that have conserved secondary structure. Evolution in the paired regions of RNAs occurs via compensatory substitutions, hence changes on either side of a pair are correlated. Accounting for this correlation is important for phylogenetic inference because it affects the likelihood calculation. In the present study we use the complete set of tRNA and rRNA sequences from 69 complete mammalian mitochondrial genomes. The likelihood calculation uses two evolutionary models simultaneously for different parts of the sequence: a paired-site model for the paired sites and a single-site model for the unpaired sites. We use Bayesian phylogenetic methods and a Markov chain Monte Carlo algorithm is used to obtain the most probable trees and posterior probabilities of clades. The results are well resolved for almost all the important branches on the mammalian tree. They support the arrangement of mammalian orders within the four supra-ordinal clades that have been identified by studies of much larger data sets mainly comprising nuclear genes. Groups such as the hedgehogs and the murid rodents, which have been problematic in previous studies with mitochondrial proteins, appear in their expected position with the other members of their order. Our choice of genes and evolutionary model appears to be more reliable and less subject to biases caused by variation in base composition than previous studies with mitochondrial genomes.

Animals↗

PubMed2002

Making sense of microarray data distributions.

MOTIVATION: Typical analysis of microarray data has focused on spot by spot comparisons within a single organism. Less analysis has been done on the comparison of the entire distribution of spot intensities between experiments and between organisms. RESULTS: Here we show that mRNA transcription data from a wide range of organisms and measured with a range of experimental platforms show close agreement with Benford's law (Benford, PROC: Am. Phil. Soc., 78, 551-572, 1938) and Zipf's law (Zipf, The Psycho-biology of Language: an Introduction to Dynamic Philology, 1936 and Human Behaviour and the Principle of Least Effort, 1949). The distribution of the bulk of microarray spot intensities is well approximated by a log-normal with the tail of the distribution being closer to power law. The variance, sigma(2), of log spot intensity shows a positive correlation with genome size (in terms of number of genes) and is therefore relatively fixed within some range for a given organism. The measured value of sigma(2) can be significantly smaller than the expected value if the mRNA is extracted from a sample of mixed cell types. Our research demonstrates that useful biological findings may result from analyzing microarray data at the level of entire intensity distributions.

Algorithms↗

PubMed2002

Stochastic trapping in a solvable model of on-line independent component analysis.

Previous analytical studies of on-line independent component analysis (ICA) learning rules have focused on asymptotic stability and efficiency. In practice, the transient stages of learning are often more significant in determining the success of an algorithm. This is demonstrated here with an analysis of a Hebbian ICA algorithm, which can find a small number of nongaussian components given data composed of a linear mixture of independent source signals. An idealized data model is considered in which the sources comprise a number of nongaussian and gaussian sources, and a solution to the dynamics is obtained in the limit where the number of gaussian sources is infinite. Previous stability results are confirmed by expanding around optimal fixed points, where a closed-form solution to the learning dynamics is obtained. However, stochastic effects are shown to stabilize otherwise unstable suboptimal fixed points. Conditions required to destabilize one such fixed point are obtained for the case of a single nongaussian component, indicating that the initial learning rate eta required to escape successfully is very low (eta = O(N(-2)) where N is the data dimension), resulting in very slow learning typically requiring O(N(3)) iterations. Simulations confirm that this picture holds for a finite system.

Journal Article↗