Kooperativer Bibliotheksverbund

Berlin Brandenburg

and
and

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
Type of Medium
Language
Year
  • 1
    Language: English
    In: Functional & Integrative Genomics, 2008, Vol.8(1), pp.43-53
    Description: The increased availability of microarray data has been calling for statistical methods to integrate findings across studies. A common goal of microarray analysis is to determine differentially expressed genes between two conditions, such as treatment vs control. A recent Bayesian metaanalysis model used a prior distribution for the mean log-expression ratios that was a mixture of two normal distributions. This model centered the prior distribution of differential expression at zero, and separated genes into two groups only: expressed and nonexpressed. Here, we introduce a Bayesian three-component truncated normal mixture prior model that more flexibly assigns prior distributions to the differentially expressed genes and produces three groups of genes: up and downregulated, and nonexpressed. We found in simulations of two and five studies that the three-component model outperformed the two-component model using three comparison measures. When analyzing biological data of Bacillus subtilis, we found that the three-component model discovered more genes and omitted fewer genes for the same levels of posterior probability of differential expression than the two-component model, and discovered more genes for fixed thresholds of Bayesian false discovery. We assumed that the data sets were produced from the same microarray platform and were prescaled.
    Keywords: Bayesian statistics ; Metaanalysis ; Microarray data ; Mixture model
    ISSN: 1438-793X
    E-ISSN: 1438-7948
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 2
    Language: English
    In: PLoS ONE, 01 January 2014, Vol.9(9), p.e108425
    Description: Recent advances in big data and analytics research have provided a wealth of large data sets that are too big to be analyzed in their entirety, due to restrictions on computer memory or storage size. New Bayesian methods have been developed...
    Keywords: Sciences (General)
    E-ISSN: 1932-6203
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 3
    Language: English
    In: PLoS ONE, 01 January 2012, Vol.7(12), p.e52137
    Description: Current Bayesian microarray models that pool multiple studies assume gene expression is independent of other genes. However, in prokaryotic organisms, genes are arranged in units that are co-regulated (called operons). Here, we introduce a new Bayesian model for pooling gene expression studies...
    Keywords: Sciences (General)
    E-ISSN: 1932-6203
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 4
    Language: English
    In: PLoS ONE, 01 January 2012, Vol.7(11)
    Description: Please view the correct Figure S1 here: Download corrected item. https://doi.org/10.1371/annotation/5cba04eb-5172-43a7-ad92-10efcd3858c9.s001.cn [^] Citation: Blanchard JL, Wholey W-Y, Conlon EM, Pomposiello PJ (2012) Correction: Rapid Changes in Gene Expression Dynamics in Response to Superoxide Reveal...
    Keywords: Sciences (General)
    ISSN: PLoS ONE
    E-ISSN: 1932-6203
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 5
    In: Stat (2015), 4, 304-319
    Description: Recent developments in big data and analytics research have produced an abundance of large data sets that are too big to be analyzed in their entirety, due to limits on computer memory or storage capacity. To address these issues, communication-free parallel Markov chain Monte Carlo (MCMC) methods have been developed for Bayesian analysis of big data. These methods partition data into manageable subsets, perform independent Bayesian MCMC analysis on each subset, and combine the subset posterior samples to estimate the full data posterior. Current approaches to combining subset posterior samples include sample averaging, weighted averaging, and kernel smoothing techniques. Although these methods work well for Gaussian posteriors, they are not well-suited to non-Gaussian posterior distributions. Here, we develop a new direct density product method for combining subset marginal posterior samples to estimate full data marginal posterior densities. Using a commonly-implemented distance metric, we show in simulation studies of Bayesian models with non-Gaussian posteriors that our method outperforms the existing methods in approximating the full data marginal posteriors. Since our method estimates only marginal densities, there is no limitation on the number of model parameters analyzed. Our procedure is suitable for Bayesian models with unknown parameters with fixed dimension in continuous parameter spaces. Comment: 32 pages, 3 figures
    Keywords: Statistics - Methodology
    ISSN: 20491573
    E-ISSN: 20491573
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 6
    Description: Due to the escalating growth of big data sets in recent years, new Bayesian Markov chain Monte Carlo (MCMC) parallel computing methods have been developed. These methods partition large data sets by observations into subsets. However, for Bayesian nested hierarchical models, typically only a few parameters are common for the full data set, with most parameters being group-specific. Thus, parallel Bayesian MCMC methods that take into account the structure of the model and split the full data set by groups rather than by observations are a more natural approach for analysis. Here, we adapt and extend a recently introduced two-stage Bayesian hierarchical modeling approach, and we partition complete data sets by groups. In stage 1, the group-specific parameters are estimated independently in parallel. The stage 1 posteriors are used as proposal distributions in stage 2, where the target distribution is the full model. Using three-level and four-level models, we show in both simulation and real data studies that results of our method agree closely with the full data analysis, with greatly increased MCMC efficiency and greatly reduced computation times. The advantages of our method versus existing parallel MCMC computing methods are also described. Comment: 30 pages, 2 figures. New simulation example for logistic regression. MCMC efficiency measure added. Details of convergence diagnostics added. One additional table
    Keywords: Statistics - Methodology ; Computer Science - Distributed, Parallel, And Cluster Computing ; Statistics - Computation ; Statistics - Machine Learning
    Source: Cornell University
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 7
    Language: English
    In: Proceedings of the National Academy of Sciences of the United States of America, 18 March 2003, Vol.100(6), pp.3339-44
    Description: We propose motif regressor for discovering sequence motifs upstream of genes that undergo expression changes in a given condition. The method combines the advantages of matrix-based motif finding and oligomer motif-expression regression analysis, resulting in high sensitivity and specificity. motif regressor is particularly effective in discovering expression-mediating motifs of medium to long width with multiple degenerate positions. When applied to Saccharomyces cerevisiae, motif regressor identified the ROX1 and YAP1 motifs from Rox1p and Yap1p overexpression experiments, respectively; predicted that Gcn4p may have increased activity in YAP1 deletion mutants; reported a group of motifs (including GCN4, PHO4, MET4, STRE, USR1, RAP1, M3A, and M3B) that may mediate the transcriptional response to amino acid starvation; and found all of the known cell-cycle regulation motifs from 18 expression microarrays over two cell cycles.
    Keywords: Gene Expression Profiling -- Statistics & Numerical Data
    ISSN: 0027-8424
    E-ISSN: 10916490
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 8
    Description: Recent developments in data science and big data research have produced an abundance of large data sets that are too big to be analyzed in their entirety, due to limits on either computer memory or storage capacity. Here, we introduce our R package 'BayesSummaryStatLM' for Bayesian linear regression models with Markov chain Monte Carlo implementation that overcomes these limitations. Our Bayesian models use only summary statistics of data as input; these summary statistics can be calculated from subsets of big data and combined over subsets. Thus, complete data sets do not need to be read into memory in full, which removes any physical memory limitations of a user. Our package incorporates the R package 'ff' and its functions for reading in big data sets in chunks while simultaneously calculating summary statistics. We describe our Bayesian linear regression models, including several choices of prior distributions for unknown model parameters, and illustrate capabilities and features of our R package using both simulated and real data sets. Comment: Updated URL in reference [12]; added to description of zero.intercept on p. 11; added minor clarifications throughout; results unchanged
    Keywords: Statistics - Applications ; Statistics - Computation
    Source: Cornell University
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 9
    Language: English
    In: PLoS ONE, 2007, Vol.2(11), p.e1186
    Description: SoxR and SoxS constitute an intracellular signal response system that rapidly detects changes in superoxide levels and modulates gene expression in E. coli . A time series microarray design was used to identify co-regulated SoxRS-dependent and independent genes modulated by superoxide minutes after exposure to stress. ; mRNA levels surged to near maximal levels within the first few minutes of exposure to paraquat, a superoxide-producing compound, followed by a rise in mRNA levels of known SoxS-regulated genes. Based on a new method for determining the biological significance of clustering results, a total of 138 genic regions, including several transcription factors and putative sRNAs were identified as being regulated through the SoxRS signaling pathway within 10 minutes of paraquat treatment. A statistically significant two-block SoxS motif was identified through analysis of the SoxS-regulated genes. The SoxRS-independent response included members of the OxyR, CysB, IscR, BirA and Fur regulons. Finally, the relative sensitivity to superoxide was measured in 94 strains carrying deletions in individual, superoxide-regulated genes. ; By integrating our microarray time series results with other microarray data, databases and the primary literature, we propose a model of the primary transcriptional response containing 226 protein-coding and sRNA sequences. From the SoxS dependent network the first statistically significant SoxS-related motif was identified.
    Keywords: Research Article ; Genetics And Genomics -- Bioinformatics ; Genetics And Genomics -- Gene Expression ; Microbiology -- Microbial Physiology And Metabolism
    E-ISSN: 1932-6203
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 10
    Language: English
    In: Genome announcements, 25 January 2018, Vol.6(4)
    Description: sp. strain GAS474 was isolated from the mineral soil of a temperate deciduous forest in central Massachusetts. Here, we present the complete genome sequence of this phylogenetically novel organism, which consists of a total of 3,763,444 bp on a single scaffold, with a 65.8% GC content and 3,273 predicted open reading frames.
    Keywords: Prokaryotes;
    ISSN: 2169-8287
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. Further information can be found on the KOBV privacy pages