Literature DB >> 14668232

Using credibility intervals instead of hypothesis tests in SAGE analysis.

Ricardo Z N Vêncio1, Helena Brentani, Carlos A B Pereira.   

Abstract

MOTIVATION: Statistical methods usually used to perform Serial Analysis of Gene Expression (SAGE) analysis are based on hypothesis testing. They answer the biologist's question: 'what are the genes with differential expression greater than r with P-value smaller than P?'. Another useful and not yet explored question is: 'what is the uncertainty in differential expression ratio of a gene?'.
RESULTS: We have used Bayesian model for SAGE differential gene expression ratios as a more informative alternative to hypothesis tests since it provides credibility intervals. AVAILABILITY: The model is implemented in R statistical language script and is available under GNU/GLP copyleft at supplemental web site. SUPPLEMENTARY INFORMATION: http://www.ime.usp.br/~rvencio/SAGEci/

Mesh:

Year:  2003        PMID: 14668232     DOI: 10.1093/bioinformatics/btg357

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  16 in total

1.  Gene discovery and expression profile analysis through sequencing of expressed sequence tags from different developmental stages of the chytridiomycete Blastocladiella emersonii.

Authors:  Karina F Ribichich; Silvia M Salem-Izacc; Raphaela C Georg; Ricardo Z N Vêncio; Luci D Navarro; Suely L Gomes
Journal:  Eukaryot Cell       Date:  2005-02

2.  ProbFAST: Probabilistic functional analysis system tool.

Authors:  Israel T Silva; Ricardo Z N Vêncio; Thiago Y K Oliveira; Greice A Molfetta; Wilson A Silva
Journal:  BMC Bioinformatics       Date:  2010-03-30       Impact factor: 3.169

3.  Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.

Authors:  Davis J McCarthy; Yunshun Chen; Gordon K Smyth
Journal:  Nucleic Acids Res       Date:  2012-01-28       Impact factor: 16.971

4.  BayGO: Bayesian analysis of ontology term enrichment in microarray data.

Authors:  Ricardo Z N Vêncio; Tie Koide; Suely L Gomes; Carlos A de B Pereira
Journal:  BMC Bioinformatics       Date:  2006-02-23       Impact factor: 3.169

5.  Identification and characterization of microRNAs and endogenous siRNAs in Schistosoma japonicum.

Authors:  Lili Hao; Pengfei Cai; Ning Jiang; Heng Wang; Qijun Chen
Journal:  BMC Genomics       Date:  2010-01-21       Impact factor: 3.969

6.  A comparative study of small RNAs in Toxoplasma gondii of distinct genotypes.

Authors:  Jielin Wang; Xiaolei Liu; Boyin Jia; Huijun Lu; Shuai Peng; Xianyu Piao; Nan Hou; Pengfei Cai; Jigang Yin; Ning Jiang; Qijun Chen
Journal:  Parasit Vectors       Date:  2012-09-03       Impact factor: 3.876

7.  Identification of genes related to agarwood formation: transcriptome analysis of healthy and wounded tissues of Aquilaria sinensis.

Authors:  Yanhong Xu; Zheng Zhang; Mengxi Wang; Jianhe Wei; Hongjiang Chen; Zhihui Gao; Chun Sui; Hongmei Luo; Xingli Zhang; Yun Yang; Hui Meng; Wenlan Li
Journal:  BMC Genomics       Date:  2013-04-08       Impact factor: 3.969

8.  Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework.

Authors:  Michael A Gilchrist; Hong Qin; Russell Zaretzki
Journal:  BMC Bioinformatics       Date:  2007-10-18       Impact factor: 3.169

9.  A feature selection approach for identification of signature genes from SAGE data.

Authors:  Junior Barrera; Roberto M Cesar; Carlos Humes; David C Martins; Diogo F C Patrão; Paulo J S Silva; Helena Brentani
Journal:  BMC Bioinformatics       Date:  2007-05-22       Impact factor: 3.169

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

Authors:  Ricardo Z N Vêncio; Helena Brentani; Diogo F C Patrão; Carlos A B Pereira
Journal:  BMC Bioinformatics       Date:  2004-08-31       Impact factor: 3.169

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.