Generation of Synthetic Transcriptome Data with Defined Statistical Properties for the Development and Testing of New Analysis Methods

Genomics, Proteomics and Bioinformatics

Volumn 5, Issue 1, 2007, Pages 45-52

  Brysbaert, Guillaume ^a   Noth, Sebastian ^a   Benecke, Arndt ^a  

^a INSTITUT DES HAUTES ÉTUDES SCIENTIFIQUES   (France)

Author keywords

distribution modeling; microarray analysis; parameter approximation; signal variance distribution; synthetic data generation; transcriptome

Indexed keywords

TRANSCRIPTOME;

ACCURACY; ANALYTIC METHOD; ARTICLE; COMPUTER MODEL; DATA ANALYSIS; DATA SYNTHESIS; EVALUATION; INFORMATION PROCESSING; METHODOLOGY; MICROARRAY ANALYSIS; PARAMETER; PRODUCT DEVELOPMENT; SENSITIVITY AND SPECIFICITY; STANDARD; STATISTICAL DISTRIBUTION; TRANSCRIPTOMICS; VARIANCE;

INTERNET; MODELS, GENETIC; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; TRANSCRIPTION, GENETIC;

EID: 34250190944     PISSN: 16720229     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1672-0229(07)60013-8     Document Type: Article

References (9)

1. Noth S., et al. High-sensitivity transcriptome data structure and implications for analysis and biologic interpretation. Genomics Proteomics Bioinfor-matics 4 (2006) 212-229
2. Benecke A. Chromatin code, local non-equilibrium dynamics, and the emergence of transcription regulatory programs. Eur. Phys. J. E (Soft Matter) 19 (2006) 353-366
3. Kaern M., et al. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6 (2005) 451-464
4. Kurakin A. Self-organization vs Watchmaker: stochastic gene expression and cell differentiation. Dev. Genes Evol. 215 (2005) 46-52
5. Konishi T. Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatment. BMC Bioinformatics 5 (2004) 5
6. Dean N., and Raftery A.E. Normal uniform mixture differential gene expression detection for cDNA microarrays. BMC Bioinformatics 6 (2005) 173
7. Noth S., et al. Normalization using weighted negative second order exponential error functions (NeONORM) provides robustness against asymmetries in comparative transcriptome profiles and avoids false calls. Genomics Proteomics Bioinformatics 4 (2006) 90-109
8. Bilmes J.A., 1998. A gentle tutorial of the EM algorithm and its appliaction to parameter estimation for Gaussian mixture and hidden Markov models. Technical report, ICSI TR-97-021, University of California at Berkley, USA.
9. Shewchuk, J.R,. 1994. An introduction to the conjugate gradient method without the agonizing pain. Technical report, CS-94-125, Carnegie Mellon University, Pittsburgh, USA.