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Items: 1 to 20 of 87

1.

OSAT: a tool for sample-to-batch allocations in genomics experiments.

Yan L, Ma C, Wang D, Hu Q, Qin M, Conroy JM, Sucheston LE, Ambrosone CB, Johnson CS, Wang J, Liu S.

BMC Genomics. 2012 Dec 10;13:689. doi: 10.1186/1471-2164-13-689.

2.

Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction.

Parker HS, Leek JT, Favorov AV, Considine M, Xia X, Chavan S, Chung CH, Fertig EJ.

Bioinformatics. 2014 Oct;30(19):2757-63. doi: 10.1093/bioinformatics/btu375. Epub 2014 Jun 6.

3.

The practical effect of batch on genomic prediction.

Parker HS, Leek JT.

Stat Appl Genet Mol Biol. 2012;11(3):Article 10.

4.

BatchQC: interactive software for evaluating sample and batch effects in genomic data.

Manimaran S, Selby HM, Okrah K, Ruberman C, Leek JT, Quackenbush J, Haibe-Kains B, Bravo HC, Johnson WE.

Bioinformatics. 2016 Dec 15;32(24):3836-3838. Epub 2016 Aug 18.

5.

A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis.

Reese SE, Archer KJ, Therneau TM, Atkinson EJ, Vachon CM, de Andrade M, Kocher JP, Eckel-Passow JE.

Bioinformatics. 2013 Nov 15;29(22):2877-83. doi: 10.1093/bioinformatics/btt480. Epub 2013 Aug 19.

6.

Removing batch effects from purified plasma cell gene expression microarrays with modified ComBat.

Stein CK, Qu P, Epstein J, Buros A, Rosenthal A, Crowley J, Morgan G, Barlogie B.

BMC Bioinformatics. 2015 Feb 25;16:63. doi: 10.1186/s12859-015-0478-3.

7.

ARTS: automated randomization of multiple traits for study design.

Maienschein-Cline M, Lei Z, Gardeux V, Abbasi T, Machado RF, Gordeuk V, Desai AA, Saraf S, Bahroos N, Lussier Y.

Bioinformatics. 2014 Jun 1;30(11):1637-9. doi: 10.1093/bioinformatics/btu075. Epub 2014 Feb 3.

8.

The sva package for removing batch effects and other unwanted variation in high-throughput experiments.

Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD.

Bioinformatics. 2012 Mar 15;28(6):882-3. doi: 10.1093/bioinformatics/bts034. Epub 2012 Jan 17.

9.

Risk-conscious correction of batch effects: maximising information extraction from high-throughput genomic datasets.

Oytam Y, Sobhanmanesh F, Duesing K, Bowden JC, Osmond-McLeod M, Ross J.

BMC Bioinformatics. 2016 Sep 1;17(1):332. doi: 10.1186/s12859-016-1212-5.

10.

svaseq: removing batch effects and other unwanted noise from sequencing data.

Leek JT.

Nucleic Acids Res. 2014 Dec 1;42(21). doi: 10.1093/nar/gku864. Epub 2014 Oct 7.

11.

PatternCNV: a versatile tool for detecting copy number changes from exome sequencing data.

Wang C, Evans JM, Bhagwate AV, Prodduturi N, Sarangi V, Middha M, Sicotte H, Vedell PT, Hart SN, Oliver GR, Kocher JP, Maurer MJ, Novak AJ, Slager SL, Cerhan JR, Asmann YW.

Bioinformatics. 2014 Sep 15;30(18):2678-80. doi: 10.1093/bioinformatics/btu363. Epub 2014 May 29.

12.

Assessing batch effects of genotype calling algorithm BRLMM for the Affymetrix GeneChip Human Mapping 500 K array set using 270 HapMap samples.

Hong H, Su Z, Ge W, Shi L, Perkins R, Fang H, Xu J, Chen JJ, Han T, Kaput J, Fuscoe JC, Tong W.

BMC Bioinformatics. 2008 Aug 12;9 Suppl 9:S17. doi: 10.1186/1471-2105-9-S9-S17.

13.

Practical impacts of genomic data "cleaning" on biological discovery using surrogate variable analysis.

Jaffe AE, Hyde T, Kleinman J, Weinbergern DR, Chenoweth JG, McKay RD, Leek JT, Colantuoni C.

BMC Bioinformatics. 2015 Nov 6;16:372. doi: 10.1186/s12859-015-0808-5. Erratum in: BMC Bioinformatics. 2016;17:302.

14.

Evaluation of genomic island predictors using a comparative genomics approach.

Langille MG, Hsiao WW, Brinkman FS.

BMC Bioinformatics. 2008 Aug 5;9:329. doi: 10.1186/1471-2105-9-329.

15.

Review of microarray experimental design strategies for genetical genomics studies.

Rosa GJ, de Leon N, Rosa AJ.

Physiol Genomics. 2006 Dec 13;28(1):15-23. Epub 2006 Sep 19. Review.

PMID:
16985008
16.

MultiMSOAR 2.0: an accurate tool to identify ortholog groups among multiple genomes.

Shi G, Peng MC, Jiang T.

PLoS One. 2011;6(6):e20892. doi: 10.1371/journal.pone.0020892. Epub 2011 Jun 21.

17.

Specimen allocation in longitudinal biomarker studies: controlling subject-specific effects by design.

Tworoger SS, Yasui Y, Chang L, Stanczyk FZ, McTiernan A.

Cancer Epidemiol Biomarkers Prev. 2004 Jul;13(7):1257-60.

18.

virtualArray: a R/bioconductor package to merge raw data from different microarray platforms.

Heider A, Alt R.

BMC Bioinformatics. 2013 Mar 2;14:75. doi: 10.1186/1471-2105-14-75.

19.

Modelling homogeneous and heterogeneous microbial contaminations in a powdered food product.

Jongenburger I, Reij MW, Boer EP, Zwietering MH, Gorris LG.

Int J Food Microbiol. 2012 Jun 15;157(1):35-44. doi: 10.1016/j.ijfoodmicro.2012.04.009. Epub 2012 Apr 20.

PMID:
22591548
20.

DNannotator: Annotation software tool kit for regional genomic sequences.

Liu C, Bonner TI, Nguyen T, Lyons JL, Christian SL, Gershon ES.

Nucleic Acids Res. 2003 Jul 1;31(13):3729-35.

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