Integrative analyses for omics data: a Bayesian mixture model to assess the concordance of ChIP-chip and ChIP-seq measurements.
The analysis of different variations in genomics, transcriptomics, epigenomics, and proteomics has increased considerably in recent years. This is especially due to the success of microarray and, more recently, sequencing technology. Apart from understanding mechanisms of disease pathogenesis on a molecular basis, for example in cancer research, the challenge of analyzing such different data types in an integrated way has become increasingly important also for the validation of new sequencing technologies with maximum resolution. For this purpose, a methodological framework for their comparison with microarray techniques in the context of smallest sample sizes, which result from the high costs of experiments, is proposed in this contribution. Based on an adaptation of the externally centered correlation coefficient ( Schäfer et al. 2009 ), it is demonstrated how a Bayesian mixture model can be applied to compare and classify measurements of histone acetylation that stem from chromatin immunoprecipitation combined with either microarray (ChIP-chip) or sequencing techniques (ChIP-seq) for the identification of DNA fragments. Here, the murine hematopoietic cell line 32D, which was transduced with the oncogene BCR-ABL, the hallmark of chronic myeloid leukemia, was characterized. Cells were compared to mock-transduced cells as control. Activation or inhibition of other genes by histone modifications induced by the oncogene is considered critical in such a context for the understanding of the disease.
Department of Statistics, TU Dortmund University, Dortmund, Germany. martin.schaefer@tu-dortmund.de
Martin
M
Schäfer
Otgonzul
O
Lkhagvasuren
Hans-Ulrich
HU
Klein
Christian
C
Elling
Torsten
T
Wüstefeld
Carsten
C
Müller-Tidow
Lars
L
Zender
Steffen
S
Koschmieder
Martin
M
Dugas
Katja
K
Ickstadt
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D053801:Capillary Electrochromatography; D047369:Chromatin Immunoprecipitation; D004247:DNA / Q000737:chemistry / Q000235:genetics; D003627:Data Interpretation, Statistical; D057890:Epigenomics / Q000379:methods* / Q000706:statistics & numerical data; D016044:Fusion Proteins, bcr-abl / Q000235:genetics; D023281:Genomics / Q000379:methods* / Q000706:statistics & numerical data; D006412:Hematopoietic Stem Cells / Q000378:metabolism; D006657:Histones / Q000235:genetics / Q000378:metabolism; D015464:Leukemia, Myelogenous, Chronic, BCR-ABL Positive / Q000235:genetics; D008390:Markov Chains; D051379:Mice; D046228:Microarray Analysis; D015233:Models, Statistical; D009010:Monte Carlo Method; D009857:Oncogenes / Q000235:genetics; D040901:Proteomics / Q000379:methods* / Q000706:statistics & numerical data; D018401:Sample Size; D017422:Sequence Analysis, DNA / Q000379:methods / Q000706:statistics & numerical data*; D014161:Transduction, Genetic
D006657:Histones; D004247:DNA; D016044:Fusion Proteins, bcr-abl