Strategies for aggregating gene expression data: The collapseRows R function

BMC Bioinformatics

Volumn 12, Issue , 2011, Pages

  Miller, Jeremy A ^a,b   Cai, Chaochao ^a,b   Langfelder, Peter ^a,b   Geschwind, Daniel H ^b   Kurian, Sunil M ^c   Salomon, Daniel R ^c   Horvath, Steve ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c SCRIPPS RESEARCH INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURATE PREDICTION; BIOLOGICAL INTERPRETATION; CO-EXPRESSION NETWORKS; GENE EXPRESSION DATA; GENE EXPRESSION PROFILES; GENOMIC APPLICATIONS; PROBE MEASUREMENTS; STATISTICAL SUMMARY;

BLOOD; CELL CULTURE; CELLS; PROBES;

GENE EXPRESSION;

ANIMAL; ARTICLE; BLOOD; BRAIN; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; HUMAN; META ANALYSIS (TOPIC); METABOLISM; METHODOLOGY; MOUSE; STATISTICAL MODEL;

ANIMALS; BLOOD; BRAIN; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; HUMANS; META-ANALYSIS AS TOPIC; MICE; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS;

EID: 79961116748     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-12-322     Document Type: Article

References (29)

1. Langfelder P, Horvath S. Eigengene networks for studying the relationships between co-expression modules. BMC Syst Biol 2007, 1(1):54. 10.1186/1752-0509-1-54, 2267703, 18031580.
2. Ge H, Liu Z, Church GM, Vidal M. Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nature Genetics 2001, 29(4):482-486. 10.1038/ng776, 11694880.
3. Miller J, Horvath S, Geschwind D. Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proceedings of the National Academy of Sciences of the United States of America 2010, 107(28):12698-12703. 10.1073/pnas.0914257107, 2906579, 20616000.
4. Oldham M, Konopka G, Iwamoto K, Langfelder P, Kato T, Horvath S, Geschwind D. Functional organization of the transcriptome in human brain. Nature Neuroscience 2008, 11(11):1271-1282. 10.1038/nn.2207, 2756411, 18849986.
5. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9(1):559. 10.1186/1471-2105-9-559, 2631488, 19114008.
6. Miller J, Langfelder P, Chaochao C, Horvath S. The collapseRows function. , http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/collapseRows
7. Abbas A, Wolslegel K, Seshasayee D, Modrusan Z, Clark H. Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus. PloS one 2009, 4(7):e6098. 10.1371/journal.pone.0006098, 2699551, 19568420.
8. Dumeaux V, Olsen K, Nuel G, Paulssen R, Borresen-Dale AL, Lund E. Deciphering normal blood gene expression variation--The NOWAC postgenome study. PLoS Genet 2010, 6(3):e1000873. 10.1371/journal.pgen.1000873, 2837385, 20300640.
9. Goring H, Curran J, Johnson M, Dyer T, Charlesworth J, Cole S, Jowett J, Abraham L, Rainwater D, Comuzzie A, et al. Discovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes. Nature Genetics 2007, 39(10):1208-1216. 10.1038/ng2119, 17873875.
10. Grigoryev Y, Kurian S, Avnur Z, Borie D, Deng J, Campbell D, Sung J, Nikolcheva T, Quinn A, Schulman H, et al. Deconvoluting post-transplant immunity: cell subset-specific mapping reveals pathways for activation and expansion of memory T, monocytes and B cells. PloS one 2010, 5(10):e13358. 10.1371/journal.pone.0013358, 2954794, 20976225.
11. Pankla R, Buddhisa S, Berry M, Blankenship D, Bancroft G, Banchereau J, Lertmemongkolchai G, Chaussabel D. Genomic transcriptional profiling identifies a candidate blood biomarker signature for the diagnosis of septicemic melioidosis. Genome Biol 2009, 10(11):R127. 10.1186/gb-2009-10-11-r127, 3091321, 19903332.
12. Saris C, Horvath S, van Vught P, van Es M, Blauw H, Fuller T, Langfelder P, DeYoung J, Wokke J, Veldink J, et al. Weighted gene co-expression network analysis of the peripheral blood from Amyotrophic Lateral Sclerosis patients. BMC Genomics 2009, 10(1):405. 10.1186/1471-2164-10-405, 2743717, 19712483.
13. Horvath S, Dong J. Geometric interpretation of gene coexpression network analysis. PLoS computational biology 2008, 4(8):e1000117. 10.1371/journal.pcbi.1000117, 2446438, 18704157.
14. Oldham MC, Horvath S, Geschwind DH. Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc Natl Acad Sci U S A 2006, 103(47):17973-17978. 10.1073/pnas.0605938103, 1693857, 17101986.
15. Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 2005, 4:Article17.
16. Wang L, Tang H, Thayanithy V, Subramanian S, Oberg A, Cunningham J, Cerhan J, Steer C, Thibodeau SN. Gene Networks and microRNAs Implicated in Aggressive Prostate Cancer. Cancer Research 2009, 69(24):9490-9497. 10.1158/0008-5472.CAN-09-2183, 2795036, 19996289.
17. Ma S, Kosorok M, Huang J, Dai Y. Incorporating higher-order representative features improves prediction in network-based cancer prognosis analysis. BMC Med Genomics 2011, 4(1):5. 10.1186/1755-8794-4-5, 3037289, 21226928.
18. Ivliev A, 't Hoen P, Sergeeva M. Coexpression network analysis identifies transcriptional modules related to proastrocytic differentiation and sprouty signaling in glioma. Cancer Research 2010, 70(24):10060-10070. 10.1158/0008-5472.CAN-10-2465, 21159630.
19. Weston D, Gunter L, Rogers A, Wullschleger S. Connecting Genes, Coexpression Modules, and Molecular Signatures to Environmental Stress Phenotypes in Plants. BMC Syst Biol 2008, 2(1):16. 10.1186/1752-0509-2-16, 2277374, 18248680.
20. Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 2008, 24(5):719-720. 10.1093/bioinformatics/btm563, 18024473.
21. Clarke J, Seo P, Clarke B. Statistical expression deconvolution from mixed tissue samples. Bioinformatics 2010, 26(8):1043-1049. 10.1093/bioinformatics/btq097, 2853690, 20202973.
22. Lu P, Nakorchevskiy A, Marcotte E. Expression deconvolution: a reinterpretation of DNA microarray data reveals dynamic changes in cell populations. Proceedings of the National Academy of Sciences of the United States of America 2003, 100(18):10370-10375. 10.1073/pnas.1832361100, 193568, 12934019.
23. Stuart R, Wachsman W, Berry C, Wang-Rodriguez J, Wasserman L, Klacansky I, Masys D, Arden K, Goodison S, McClelland M, et al. In silico dissection of cell-type-associated patterns of gene expression in prostate cancer. Proceedings of the National Academy of Sciences of the United States of America 2004, 101(2):615-620. 10.1073/pnas.2536479100, 327196, 14722351.
24. Ballester B, Johnson N, Proctor G, Flicek P. Consistent annotation of gene expression arrays. BMC Genomics 2010, 11(1):294. 10.1186/1471-2164-11-294, 2894801, 20459806.
25. Chen R, Li L, Butte A. AILUN: reannotating gene expression data automatically. Nature methods 2007, 4(11):879-879. 10.1038/nmeth1107-879, 2716375, 17971777.
26. Dai M, Wang P, Boyd A, Kostov G, Athey B, Jones E, Bunney W, Myers R, Speed T, Akil H, et al. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucl Acids Res 2005, 33(20):e175-e175. 10.1093/nar/gni179, 1283542, 16284200.
27. Tsai J, Sultana R, Lee Y, Pertea G, Karamycheva S, Antonescu V, Cho J, Parvizi B, Cheung F, Quackenbush J. RESOURCERER: a database for annotating and linking microarray resources within and across species. Genome Biol 2001, 2(11):SOFTWARE0002. 138985, 16173164.
28. Liefeld T, Reich M, Gould J, Zhang P, Tamayo P, Mesirov J. GeneCruiser: a web service for the annotation of microarray data. Bioinformatics 2005, 21(18):3681-3682. 10.1093/bioinformatics/bti587, 16030072.
29. Pan F, Kamath K, Zhang K, Pulapura S, Achar A, Nunez-Iglesias J, Huang Y, Yan X, Han J, Hu H, et al. Integrative Array Analyzer: a software package for analysis of cross-platform and cross-species microarray data. Bioinformatics 2006, 22(13):1665-1667. 10.1093/bioinformatics/btl163, 16672260.