Cell subset prediction for blood genomic studies

BMC Bioinformatics

Volumn 12, Issue , 2011, Pages

  Bolen, Christopher R ^a   Uduman, Mohamed ^a   Kleinstein, Steven H ^a,b  

^a YALE UNIVERSITY   (United States)

^b YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFERENTIAL EXPRESSIONS; GENE EXPRESSION PROFILES; GENE SIGNATURES; GENOMIC STUDIES; MYELOID CELLS; PERIPHERAL BLOOD MONONUCLEAR CELLS; RNA-SEQ DATUM; TRANSCRIPTIONAL PROFILING;

BLOOD; CYTOLOGY; DISEASE CONTROL; FORECASTING; GENE EXPRESSION; GENE THERAPY; INTERFERONS; PATIENT TREATMENT; RNA; SET THEORY;

CELLS;

HEPATITIS C VIRUS;

ARTICLE; BIOLOGY; GENE EXPRESSION PROFILING; HEPATITIS C; HUMAN; METABOLISM; METHODOLOGY; MONONUCLEAR CELL; PATHOLOGY; SINGLE NUCLEOTIDE POLYMORPHISM;

COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; HEPATITIS C, CHRONIC; HUMANS; LEUKOCYTES, MONONUCLEAR; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 79959478958     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-12-258     Document Type: Article

References (31)

1. Chaussabel D, et al. A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. Immunity 2008, 29(1):150-64. 10.1016/j.immuni.2008.05.012, 2727981, 18631455.
2. Ramilo O, et al. Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood 2007, 109(5):2066-77. 10.1182/blood-2006-02-002477, 1801073, 17105821.
3. Berry MP, et al. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature 2010, 466(7309):973-7. 10.1038/nature09247, 20725040.
4. Querec TD, et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol 2009, 10(1):116-25. 10.1038/ni.1688, 19029902.
5. Sarasin-Filipowicz M, et al. Interferon signaling and treatment outcome in chronic hepatitis C. Proc Natl Acad Sci USA 2008, 105(19):7034-9. 10.1073/pnas.0707882105, 2383932, 18467494.
6. Hutcheson J, et al. Combined deficiency of proapoptotic regulators Bim and Fas results in the early onset of systemic autoimmunity. Immunity 2008, 28(2):206-17. 10.1016/j.immuni.2007.12.015, 18275831.
7. Grigoryev YA, 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 5(10):e13358.
8. Autissier P, et al. Evaluation of a 12-color flow cytometry panel to study lymphocyte, monocyte, and dendritic cell subsets in humans. Cytometry A 77(5):410-9.
9. Schlenke P, et al. Evaluation of a novel mononuclear cell isolation procedure for serological HLA typing. Clin Diagn Lab Immunol 1998, 5(6):808-13. 96206, 9801339.
10. Abbas AR, et al. 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.
11. Shen-Orr SS, et al. Cell type-specific gene expression differences in complex tissues. Nat Methods 2010, 7(4):287-9. 10.1038/nmeth.1439, 20208531.
12. Abbas AR, et al. Immune response in silico (IRIS): immune-specific genes identified from a compendium of microarray expression data. Genes Immun 2005, 6(4):319-31. 10.1038/sj.gene.6364173, 15789058.
13. Palmer C, et al. Cell-type specific gene expression profiles of leukocytes in human peripheral blood. BMC Genomics 2006, 7:115. 10.1186/1471-2164-7-115, 1479811, 16704732.
14. Fall N, et al. Gene expression profiling of peripheral blood from patients with untreated new-onset systemic juvenile idiopathic arthritis reveals molecular heterogeneity that may predict macrophage activation syndrome. Arthritis Rheum 2007, 56(11):3793-804. 10.1002/art.22981, 17968951.
15. Holland SM, et al. STAT3 mutations in the hyper-IgE syndrome. N Engl J Med 2007, 357(16):1608-19. 10.1056/NEJMoa073687, 17881745.
16. Knowlton N, et al. The meaning of clinical remission in polyarticular juvenile idiopathic arthritis: gene expression profiling in peripheral blood mononuclear cells identifies distinct disease states. Arthritis Rheum 2009, 60(3):892-900. 10.1002/art.24298, 2758237, 19248118.
17. Radom-Aizik S, et al. Brief bout of exercise alters gene expression in peripheral blood mononuclear cells of early- and late-pubertal males. Pediatr Res 2009, 65(4):447-52. 10.1203/PDR.0b013e3181993473, 19127215.
18. Radom-Aizik S, et al. A brief bout of exercise alters gene expression and distinct gene pathways in peripheral blood mononuclear cells of early- and late-pubertal females. J Appl Physiol 2009, 107(1):168-75. 10.1152/japplphysiol.00121.2009, 2711785, 19407257.
19. Taylor MW, et al. Changes in Gene Expression during Pegylated Interferon and Ribavirin Therapy of Chronic Hepatitis C Virus Distinguish Responders from Nonresponders to Antiviral Therapy. J Virol 2007, 81(7):3391-3401. %U, 10.1128/JVI.02640-06, 1866036, 17267482., http://jvi.asm.org/cgi/content/abstract/81/7/3391
20. Taylor M, et al. Cyclic changes in gene expression induced by Peg-interferon alfa-2b plus ribavirin in peripheral blood monocytes (PBMC) of hepatitis C patients during the first 10 weeks of treatment. Journal of Translational Medicine 2008, 6(1).
21. Subramanian A, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005, 102(43):15545-50. 10.1073/pnas.0506580102, 1239896, 16199517.
22. Poynard T, et al. Viral hepatitis C. Lancet 2003, 362(9401):2095-100. 10.1016/S0140-6736(03)15109-4, 14697814.
23. Zeuzem S. Interferon-based therapy for chronic hepatitis C: current and future perspectives. Nat Clin Pract Gastroenterol Hepatol 2008, 5(11):610-22.
24. Asselah T, et al. Gene Expression and Hepatitis C Virus Infection. Gut 2009,
25. He XS, et al. Global transcriptional response to interferon is a determinant of HCV treatment outcome and is modified by race. Hepatology 2006, 44(2):352-9. 10.1002/hep.21267, 16871572.
26. Watkins NA, et al. A HaemAtlas: characterizing gene expression in differentiated human blood cells. Blood 2009, 113(19):e1-9. 10.1182/blood-2008-06-162958, 2680378, 19228925.
27. Clarke J, Seo P, Clarke B. Statistical expression deconvolution from mixed tissue samples. Bioinformatics 26(8):1043-9.
28. Erkkila T, et al. Probabilistic analysis of gene expression measurements from heterogeneous tissues. Bioinformatics 26(20):2571-7.
29. Wu Z, Irizarry RA. Preprocessing of oligonucleotide array data. Nat Biotechnol 2004, 22(6):656-8. author reply 658..
30. Gentleman RC, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004, 5(10):R80. 10.1186/gb-2004-5-10-r80, 545600, 15461798.
31. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004, 3(1):Article3.