Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors.
about
Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational SoftwareRNA-Seq technology and its application in fish transcriptomicsModerated estimation of fold change and dispersion for RNA-seq data with DESeq2Evaluating statistical analysis models for RNA sequencing experiments.A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments.NPEBseq: nonparametric empirical bayesian-based procedure for differential expression analysis of RNA-seq dataTransforming RNA-Seq data to improve the performance of prognostic gene signatures.Robustly detecting differential expression in RNA sequencing data using observation weights.ShrinkBayes: a versatile R-package for analysis of count-based sequencing data in complex study designs.A comparative study of techniques for differential expression analysis on RNA-Seq data.From Gigabyte to Kilobyte: A Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data.Detecting Differentially Expressed Genes with RNA-seq Data Using Backward Selection to Account for the Effects of Relevant Covariates.What if we ignore the random effects when analyzing RNA-seq data in a multifactor experiment.Animal models and integrated nested Laplace approximationsConditional estimation of local pooled dispersion parameter in small-sample RNA-Seq data improves differential expression test.Differential expression analysis for RNAseq using Poisson mixed models.A comparison of methods for differential expression analysis of RNA-seq dataAnalysis of small-sample clinical genomics studies using multi-parameter shrinkage: application to high-throughput RNA interference screening.tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalitiesdeGPS is a powerful tool for detecting differential expression in RNA-sequencing studies.Generalized empirical Bayesian methods for discovery of differential data in high-throughput biology.Getting the most out of RNA-seq data analysis.Root Type-Specific Reprogramming of Maize Pericycle Transcriptomes by Local High Nitrate Results in Disparate Lateral Root Branching Patterns.Robust differential expression analysis by learning discriminant boundary in multi-dimensional space of statistical attributes.Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.Recurrent deletions of IKZF1 in pediatric acute myeloid leukemiaHierarchical Modeling and Differential Expression Analysis for RNA-seq Experiments with Inbred and Hybrid Genotypes.MicroRNA-106b~25 cluster is upregulated in relapsed MLL-rearranged pediatric acute myeloid leukemia.Ambivalent role of pFAK-Y397 in serous ovarian cancer--a study of the OVCAD consortium.Complexity and specificity of the maize (Zea mays L.) root hair transcriptomeExtensive tissue-specific transcriptomic plasticity in maize primary roots upon water deficitTranscriptomic and anatomical complexity of primary, seminal, and crown roots highlight root type-specific functional diversity in maize (Zea mays L.).Biomarker detection and categorization in ribonucleic acid sequencing meta-analysis using Bayesian hierarchical models.Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq.Gene Network Reconstruction using Global-Local Shrinkage Priors.An empirical Bayes approach to network recovery using external knowledge.Non-syntenic genes drive RTCS-dependent regulation of the embryo transcriptome during formation of seminal root primordia in maize (Zea mays L.).Stability of Single-Parent Gene Expression Complementation in Maize Hybrids upon Water Deficit Stress.Genome sequence of the small brown planthopper, Laodelphax striatellus.Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size.
P2860
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P2860
Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors.
description
2012 nî lūn-bûn
@nan
2012 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
name
Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors.
@ast
Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors.
@en
type
label
Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors.
@ast
Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors.
@en
prefLabel
Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors.
@ast
Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors.
@en
P2093
P356
P1433
P1476
Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors
@en
P2093
Aad W Van Der Vaart
Håvard Rue
Luba Pardo
Mark A Van De Wiel
Wessel N Van Wieringen
P304
P356
10.1093/BIOSTATISTICS/KXS031
P577
2012-09-17T00:00:00Z