PoolMC: Smart pooling of mRNA samples in microarray experiments

BMC Bioinformatics

Volumn 11, Issue , 2010, Pages

  Kainkaryam, Raghunandan M ^a   Bruex, Angela ^a   Gilbert, Anna C ^a   Schiefelbein, John ^a   Woolf, Peter J ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL SAMPLES; DECODING ALGORITHM; EXPERIMENTAL DEMONSTRATIONS; MICROARRAY EXPERIMENTS; ROBUSTNESS TO NOISE; SOFTWARE IMPLEMENTATION; THEORETICAL DEVELOPMENT; THEORETICAL FRAMEWORK;

MATLAB;

EXPERIMENTS;

MESSENGER RNA;

ARTICLE; CHEMISTRY; COMPUTER PROGRAM; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENETIC DATABASE; METHODOLOGY;

DATABASES, GENETIC; GENE EXPRESSION PROFILING; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; RNA, MESSENGER; SOFTWARE;

EID: 77952864810     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-11-299     Document Type: Article

References (28)

1. Peng X, Wood C, Blalock E, Chen K, Landfield P, Stromberg A. Statistical implications of pooling RNA samples for microarray experiments. BMC Bioinformatics 2003, 4:26. 10.1186/1471-2105-4-26, 166151, 12823867.
2. Shih JH, Michalowska AM, Dobbin K, Ye Y, Qiu TH, Green JE. Effects of pooling mRNA in microarray class comparisons. Bioinformatics 2004, bth391., http://bioinformatics.oxfordjournals.org/cgi/content/short/20/18/3318
3. Kendziorski C, Irizarry RAA, Chen KSS, Haag JDD, Gould MNN. On the utility of pooling biological samples in microarray experiments. Proc Natl Acad Sci USA 2005, 552978, 15755808.
4. Zhang SD, Gant TW. Effect of pooling samples on the efficiency of comparative studies using microarrays. Bioinformatics 2005, 21(24):4378-4383. 10.1093/bioinformatics/bti717, 16234321.
5. Mary-Huard T, Daudin JJJ, Baccini M, Biggeri A, Bar-Hen A. Biases induced by pooling samples in microarray experiments. Bioinformatics 2007, 23(13). 10.1093/bioinformatics/btm182, 17646312.
6. Zhang W, Carriquiry A, Nettleton D, Dekkers JC. Pooling mRNA in microarray experiments and its effect on power. Bioinformatics 2007, 23(10):1217-1224. 10.1093/bioinformatics/btm081, 17344238.
7. Westreich DJ, Hudgens MG, Fiscus SA, Pilcher CD. Optimizing Screening for Acute Human Immunodeficiency Virus Infection with Pooled Nucleic Acid Amplification Tests. J Clin Microbiol 2008, 46(5):1785-1792. 10.1128/JCM.00787-07, 2395100, 18353930.
8. Kainkaryam RM, Woolf PJ. poolHiTS: A Shifted Transversal Design based pooling strategy for high-throughput drug screening. BMC Bioinformatics 2008, 9. 10.1186/1471-2105-9-256, 2438324, 18513431.
9. Kainkaryam RM, Woolf PJ. Pooling in high-throughput drug screening. Current Opinion in Drug Discovery & Development 2009, 12(3):339-350., http://www.biomedcentral.com/content/pdf/cd-1002721.pdf
10. Jin F, Avramova L, Huang J, Hazbun T. A yeast two-hybrid smart-pool-array system for protein-interaction mapping. Nat Meth 2007, 4(5):405-407.
11. Xin X, Rual JF, Hirozane-Kishikawa T, Hill DE, Vidal M, Boone C, Thierry-Mieg N. Shifted Transversal Design smart-pooling for high coverage interactome mapping. Genome Research 2009, 19(7):1262-1269. 10.1101/gr.090019.108, 2704426, 19447967.
12. Erlich Y, Chang K, Gordon A, Ronen R, Navon O, Rooks M, Hannon GJ. DNA Sudoku--harnessing high-throughput sequencing for multiplexed specimen analysis. Genome Research 2009, 19(7):1243-1253. 10.1101/gr.092957.109, 2704425, 19447965.
13. Prabhu S, Pe'er I. Overlapping pools for high-throughput targeted resequencing. Genome Research 2009, 19(7):1254-1261. 10.1101/gr.088559.108, 2704440, 19447964.
14. Du D, Hwang F. Pooling Designs And Nonadaptive Group Testing: Important Tools for DNA Sequencing (Series on Applied Mathematics). 2006, World Scientific Publishing Company., http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-2%0\&path =ASIN/9812568220
15. Dai W, Sheikh MA, Milenkovic O, Baraniuk RG. Compressive sensing DNA microarrays. EURASIP journal on bioinformatics & systems biology 2009, 2625392, 19158952.
16. Candès EJ, Romberg JK, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics 2006, 59(8):1207-1223.
17. Candes EJ, Wakin MB. An Introduction To Compressive Sampling. Signal Processing Magazine, IEEE 2008, 25(2):21-30.
18. Du DZ, Hwang FK. Combinatorial Group Testing and Its Applications (Applied Mathematics) 2000, World Scientific Publishing Company.
19. Dinneny JR, Long TA, Wang JY, Jung JW, Mace D, Pointer S, Barron C, Brady SM, Schiefelbein J, Benfey PN. Cell Identity Mediates the Response of Arabidopsis Roots to Abiotic Stress. Science 2008, 320(5878):1153795-945.
20. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostat 2003, 4(2):249-264.
21. Dai M, Wang P, Boyd AD, Kostov G, Athey B, Jones EG, Bunney WE, Myers RM, Speed TP, Akil H, Watson SJ, Meng F. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucl Acids Res 2005, 33(20):e175. 10.1093/nar/gni179, 1283542, 16284200.
22. DeVore RA. Deterministic constructions of compressed sensing matrices. J Complex 2007, 23(4-6):918-925.
23. Berinde R, Indyk P. Sparse recovery using sparse random matrices. MIT-CSAIL Technical Report 2008,
24. Berinde R, Gilbert A, Indyk P, Karloff H, Strauss M. Combining geometry and combinatorics: A unified approach to sparse signal recovery. 46th Annual Allerton Conference on Communication, Control, and Computing 2008, 798-805.
25. Candes EJ, Romberg J. ℓ1-MAGIC: Recovery of Sparse Signals via Convex Programming. 2005, , http://www.acm.caltech.edu/l1magic
26. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences of the United States of America 2001, 98(9):5116-5121. 10.1073/pnas.091062498, 33173, 11309499.
27. Hackstadt A, Hess A. Filtering for increased power for microarray data analysis. BMC Bioinformatics 2009, 10:11. 10.1186/1471-2105-10-11, 2661050, 19133141.
28. Tibshirani R. The lasso method for variable selection in the Cox model. Statistics in Medicine 1997, 16(4):385-395. 10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3, 9044528.