Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

Abstract Background An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. Results We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. Conclusion Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.

BayGO: Bayesian analysis of ontology term enrichment in microarray data

Abstract Background The search for enriched (aka over-represented or enhanced) ontology terms in a list of genes obtained from microarray experiments is becoming a standard procedure for a system-level analysis. This procedure tries to summarize the information focussing on classification designs such as Gene Ontology, KEGG pathways, and so on, instead of focussing on individual genes. Although it is well known in statistics that association and significance are distinct concepts, only the former approach has been used to deal with the ontology term enrichment problem. Results BayGO implements a Bayesian approach to search for enriched terms from microarray data. The R source-code is freely available at http://blasto.iq.usp.br/~tkoide/BayGO in three versions: Linux, which can be easily incorporated into pre-existent pipelines; Windows, to be controlled interactively; and as a web-tool. The software was validated using a bacterial heat shock response dataset, since this stress triggers known system-level responses. Conclusion The Bayesian model accounts for the fact that, eventually, not all the genes from a given category are observable in microarray data due to low intensity signal, quality filters, genes that were not spotted and so on. Moreover, BayGO allows one to measure the statistical association between generic ontology terms and differential expression, instead of working only with the common significance analysis.

Clinical judgment to estimate pretest probability in the diagnosis of Cushing's syndrome under a Bayesian perspective

OBJECTIVE: To estimate the pretest probability of Cushing's syndrome (CS) diagnosis by a Bayesian approach using intuitive clinical judgment. MATERIALS AND METHODS: Physicians were requested, in seven endocrinology meetings, to answer three questions: "Based on your personal expertise, after obtaining clinical history and physical examination, without using laboratorial tests, what is your probability of diagnosing Cushing's Syndrome?"; "For how long have you been practicing Endocrinology?"; and "Where do you work?". A Bayesian beta regression, using the WinBugs software was employed. RESULTS: We obtained 294 questionnaires. The mean pretest probability of CS diagnosis was 51.6% (95%CI: 48.7-54.3). The probability was directly related to experience in endocrinology, but not with the place of work. CONCLUSION: Pretest probability of CS diagnosis was estimated using a Bayesian methodology. Although pretest likelihood can be context-dependent, experience based on years of practice may help the practitioner to diagnosis CS. Arq Bras Endocrinol Metab. 2012;56(9):633-7

Spatio-temporal patterns of tuberculosis incidence in Ribeirão Preto, State of São Paulo, southeast Brazil, and their relationship with social vulnerability: a Bayesian analysis

INTRODUCTION: The purpose of this ecological study was to evaluate the urban spatial and temporal distribution of tuberculosis (TB) in Ribeirão Preto, State of São Paulo, southeast Brazil, between 2006 and 2009 and to evaluate its relationship with factors of social vulnerability such as income and education level. METHODS: We evaluated data from TBWeb, an electronic notification system for TB cases. Measures of social vulnerability were obtained from the SEADE Foundation, and information about the number of inhabitants, education and income of the households were obtained from Brazilian Institute of Geography and Statistics. Statistical analyses were conducted by a Bayesian regression model assuming a Poisson distribution for the observed new cases of TB in each area. A conditional autoregressive structure was used for the spatial covariance structure. RESULTS: The Bayesian model confirmed the spatial heterogeneity of TB distribution in Ribeirão Preto, identifying areas with elevated risk and the effects of social vulnerability on the disease. We demonstrated that the rate of TB was correlated with the measures of income, education and social vulnerability. However, we observed areas with low vulnerability and high education and income, but with high estimated TB rates. CONCLUSIONS: The study identified areas with different risks for TB, given that the public health system deals with the characteristics of each region individually and prioritizes those that present a higher propensity to risk of TB. Complex relationships may exist between TB incidence and a wide range of environmental and intrinsic factors, which need to be studied in future research.

Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series

In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method.

The Gemini planet-finding campaign: the frequency of giant planets aroud debris disk stars

We have completed a high-contrast direct imaging survey for giant planets around 57 debris disk stars as part of the Gemini NICI Planet-Finding Campaign. We achieved median H-band contrasts of 12.4 mag at 0.''5 and 14.1 mag at 1'' separation. Follow-up observations of the 66 candidates with projected separation <500 AU show that all of them are background objects. To establish statistical constraints on the underlying giant planet population based on our imaging data, we have developed a new Bayesian formalism that incorporates (1) non-detections, (2) single-epoch candidates, (3) astrometric and (4) photometric information, and (5) the possibility of multiple planets per star to constrain the planet population. Our formalism allows us to include in our analysis the previously known β Pictoris and the HR 8799 planets. Our results show at 95% confidence that <13% of debris disk stars have a ≥5 M Jup planet beyond 80 AU, and <21% of debris disk stars have a ≥3 M Jup planet outside of 40 AU, based on hot-start evolutionary models. We model the population of directly imaged planets as d 2 N/dMdavpropm α a β, where m is planet mass and a is orbital semi-major axis (with a maximum value of a max). We find that β < –0.8 and/or α > 1.7. Likewise, we find that β < –0.8 and/or a max < 200 AU. For the case where the planet frequency rises sharply with mass (α > 1.7), this occurs because all the planets detected to date have masses above 5 M Jup, but planets of lower mass could easily have been detected by our search. If we ignore the β Pic and HR 8799 planets (should they belong to a rare and distinct group), we find that <20% of debris disk stars have a ≥3 M Jup planet beyond 10 AU, and β < –0.8 and/or α < –1.5. Likewise, β < –0.8 and/or a max < 125 AU. Our Bayesian constraints are not strong enough to reveal any dependence of the planet frequency on stellar host mass. Studies of transition disks have suggested that about 20% of stars are undergoing planet formation; our non-detections at large separations show that planets with orbital separation >40 AU and planet masses >3 M Jup do not carve the central holes in these disks.

The Gemini NICI planet-finding campaign: the frequency of giant planets around young B and A stars

We have carried out high contrast imaging of 70 young, nearby B and A stars to search for brown dwarf and planetary companions as part of the Gemini NICI Planet-Finding Campaign. Our survey represents the largest, deepest survey for planets around high-mass stars (≈1.5-2.5 M ☉) conducted to date and includes the planet hosts β Pic and Fomalhaut. We obtained follow-up astrometry of all candidate companions within 400 AU projected separation for stars in uncrowded fields and identified new low-mass companions to HD 1160 and HIP 79797. We have found that the previously known young brown dwarf companion to HIP 79797 is itself a tight (3 AU) binary, composed of brown dwarfs with masses 58$^{+21}_{-20}$ M Jup and 55$^{+20}_{-19}$ M Jup, making this system one of the rare substellar binaries in orbit around a star. Considering the contrast limits of our NICI data and the fact that we did not detect any planets, we use high-fidelity Monte Carlo simulations to show that fewer than 20% of 2 M ☉ stars can have giant planets greater than 4 M Jup between 59 and 460 AU at 95% confidence, and fewer than 10% of these stars can have a planet more massive than 10 M Jup between 38 and 650 AU. Overall, we find that large-separation giant planets are not common around B and A stars: fewer than 10% of B and A stars can have an analog to the HR 8799 b (7 M Jup, 68 AU) planet at 95% confidence. We also describe a new Bayesian technique for determining the ages of field B and A stars from photometry and theoretical isochrones. Our method produces more plausible ages for high-mass stars than previous age-dating techniques, which tend to underestimate stellar ages and their uncertainties.