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Items: 1 to 20 of 107

1.

Should we abandon the t-test in the analysis of gene expression microarray data: a comparison of variance modeling strategies.

Jeanmougin M, de Reynies A, Marisa L, Paccard C, Nuel G, Guedj M.

PLoS One. 2010 Sep 3;5(9):e12336. doi: 10.1371/journal.pone.0012336.

2.

Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments.

Sartor MA, Tomlinson CR, Wesselkamper SC, Sivaganesan S, Leikauf GD, Medvedovic M.

BMC Bioinformatics. 2006 Dec 19;7:538.

3.

Evaluation of a statistical equivalence test applied to microarray data.

Qiu J, Cui X.

J Biopharm Stat. 2010 Mar;20(2):240-66. doi: 10.1080/10543400903572738.

PMID:
20309757
4.

A structural mixed model for variances in differential gene expression studies.

Jaffrézic F, Marot G, Degrelle S, Hue I, Foulley JL.

Genet Res. 2007 Feb;89(1):19-25.

PMID:
17517156
5.

Comparative evaluation of gene-set analysis methods.

Liu Q, Dinu I, Adewale AJ, Potter JD, Yasui Y.

BMC Bioinformatics. 2007 Nov 7;8:431.

6.
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8.

VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data.

Delmar P, Robin S, Daudin JJ.

Bioinformatics. 2005 Feb 15;21(4):502-8. Epub 2004 Sep 16.

PMID:
15374871
9.

Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data.

Shedden K, Chen W, Kuick R, Ghosh D, Macdonald J, Cho KR, Giordano TJ, Gruber SB, Fearon ER, Taylor JM, Hanash S.

BMC Bioinformatics. 2005 Feb 10;6:26.

10.

Sample size calculation through the incorporation of heteroscedasticity and dependence for a penalized t-statistic in microarray experiments.

Hirakawa A, Hamada C, Yoshimura I.

J Biopharm Stat. 2012;22(2):260-75. doi: 10.1080/10543406.2010.528820.

PMID:
22251173
11.

Differential analysis of DNA microarray gene expression data.

Hatfield GW, Hung SP, Baldi P.

Mol Microbiol. 2003 Feb;47(4):871-7. Review.

12.

Comparison of small n statistical tests of differential expression applied to microarrays.

Murie C, Woody O, Lee AY, Nadon R.

BMC Bioinformatics. 2009 Feb 3;10:45. doi: 10.1186/1471-2105-10-45.

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14.

A Bayesian approach for decision making on the identification of genes with different expression levels: an application to Escherichia coli bacterium data.

Saraiva EF, Louzada F, Milan LA, Meira S, Cobre J.

Comput Math Methods Med. 2012;2012:953086. doi: 10.1155/2012/953086. Epub 2012 Mar 5.

15.

A comparison of statistical tests for detecting differential expression using Affymetrix oligonucleotide microarrays.

Vardhanabhuti S, Blakemore SJ, Clark SM, Ghosh S, Stephens RJ, Rajagopalan D.

OMICS. 2006 Winter;10(4):555-66.

PMID:
17233564
16.

Shrinkage estimation of effect sizes as an alternative to hypothesis testing followed by estimation in high-dimensional biology: applications to differential gene expression.

Montazeri Z, Yanofsky CM, Bickel DR.

Stat Appl Genet Mol Biol. 2010;9:Article23. doi: 10.2202/1544-6115.1504. Epub 2010 Jun 8.

PMID:
20597849
17.

Adjustments and measures of differential expression for microarray data.

Tsodikov A, Szabo A, Jones D.

Bioinformatics. 2002 Feb;18(2):251-60.

PMID:
11847073
18.

Microarray data analysis: a hierarchical T-test to handle heteroscedasticity.

de Menezes RX, Boer JM, van Houwelingen HC.

Appl Bioinformatics. 2004;3(4):229-35.

PMID:
15702953
19.

An investigation on performance of Significance Analysis of Microarray (SAM) for the comparisons of several treatments with one control in the presence of small-variance genes.

Lin D, Shkedy Z, Burzykowski T, Ion R, Göhlmann HW, Bondt AD, Perer T, Geerts T, Van den Wyngaert I, Bijnens L.

Biom J. 2008 Oct;50(5):801-23. doi: 10.1002/bimj.200710467.

PMID:
18932139
20.

Assessing gene significance from cDNA microarray expression data via mixed models.

Wolfinger RD, Gibson G, Wolfinger ED, Bennett L, Hamadeh H, Bushel P, Afshari C, Paules RS.

J Comput Biol. 2001;8(6):625-37.

PMID:
11747616

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