Artículo
Abstract:
The traditional approach with microarray data has been to apply transformations that approximately normalize them, with the drawback of losing the original scale. The alternative standpoint taken here is to search for models that fit the data, characterized by the presence of negative values, preserving their scale; one advantage of this strategy is that it facilitates a direct interpretation of the results. A new family of distributions named gpower-normal indexed by p ∈ R is introduced and it is proven that these variables become normal or truncated normal when a suitable gpower transformation is applied. Expressions are given for moments and quantiles, in terms of the truncated normal density. This new family can be used to model asymmetric data that include non-positive values, as required for microarray analysis. Moreover, it has been proven that the gpower-normal family is a special case of pseudo-dispersion models, inheriting all the good properties of these models, such as asymptotic normality for small variances. A combined maximum likelihood method is proposed to estimate the model parameters, and it is applied to microarray and contamination data. R codes are available from the authors upon request. © 2017 by the authors; licensee MDPI, Basel, Switzerland.
Registro:
| Documento: | Artículo |
| Título: | A new distribution family for microarray data |
| Autor: | Kelmansky, D.M.; Ricci, L. |
| Filiación: | Instituto de Cálculo, UBA-CONICET, Buenos Aires, Argentina Centro Marplatense de Investigaciones Matemáticas, UNMdP, Mar del Plata, Argentina |
| Palabras clave: | Combined maximum likelihood estimators; Data analysis; Gpower-normal; Microarrays; Pseudo-dispersion models; Truncated normal |
| Año: | 2017 |
| Volumen: | 6 |
| Número: | 1 |
| DOI: | http://dx.doi.org/10.3390/microarrays6010005 |
| Título revista: | Microarrays |
| Título revista abreviado: | Microarrays |
| ISSN: | 20763905 |
| Registro: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_20763905_v6_n1_p_Kelmansky |
Referencias:
- Speed, T., (2003) Statistical Analysis of Gene Expression Data, , Chapman and Hall: London, UK
- Smyth, G., Yang, Y., Speed, T., Statistical Issues in cDNA Microarray Data Analysis (2003) Methods Mol. Biol., 224, pp. 111-136
- Durbin, B., Hardin, J., Hawkins, D., Rocke, D., A variance-stabilizing transformation for gene-expression microarray data (2002) Bioinformatics, 18, pp. 247-252
- Huber, W.H., Sueltmann, H.A., Poustka, A., Vingron, M., Parameter estimation for the calibration and variance stabilization of microarray data (2003) Stat. Appl. Genet. Mol. Biol, 2
- Kelmansky, D.M., Martínez, E.J., Leiva, V.A., New variance stabilizing transformation for gene expression data (2013) Stat. Appl. Genet. Mol. Biol, 12, pp. 653-666
- Box, G.E.P., Cox, D.R., An Analysis of Transformations (1964) J. R. Stat. Soc. Ser. B (Meth.), 26, pp. 211-252
- Yang, Y., Dudoit, S., Luu, P., Lin, D., Peng, V., Ngai, J., Speed, T., Normalization for cDNA microarray data: A robust composite method addressing single and multiple slide systematic variation (2002) Nucleic Acids Res., 30, p. e15
- Allison, D.B., Cui, X., Page, G.P., Sabripour, M., Microarray data analysis: From disarray to consolidation and consensus (2006) Nat. Rev. Genet., 7, pp. 55-65
- Dabney, A.R., Storey, J.D., Normalization of two-channel microarrays accounting for experimental design and intensity-dependent relationships (2007) Genome Biol, 8, pp. 1-11
- Bengtsson, H., Hössjer, O., Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method (2006) BMC Bioinform, 7, pp. 1-18
- Leiva, V., Sanhueza, A., Kelmansky, D., Martinez, E., On the glog-normal distribution and its association with the gene expression problem (2009) Comput. Stat. Data Anal., 53, pp. 1613-1621
- Freeman, J., Modarres, S., Inverse Box-Cox: The power-normal distribution (2006) Stat. Probab. Lett, 76, pp. S105-S110
- Dhrymes, P.J., Moments of Truncated (Normal) Distributions 2005, , http://www.columbia.edu/lpjd1/l, Available online, (accessed on 15 May 2012)
- Jørgensen, B., (1997) The Theory of Dispersion Models, , Chapman and Hall: London, UK
- (2013) R: A Language and Environment for Statistical Computing, , R Foundation for Statistical Computing: Vienna, Austria
- (2007), ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE5nnn/GSE5350/suppl/GSE5350_MAQC_H25K_2_30GPRs.zip, Available online,(accessed on 2 February); (2013), http://www.pollutantdeposition.ceh.ac.uk/data, Available online, accessed on 5 June; Chaparro, M.A., Miranda, A.C., Chaparro, D.M., Gargiulo, J.L., Bohnel, H., Biomonitoreo Magnético de Polvos Antropogénicos en Árboles de Mar del Plata (Argentina) (2016) Proceedings of the Reunión Anual 2016 Unión Geofísica Mexicana, Puerto Vallarta, Jalisco, México, , 30 October–4 November
- Morris, C.N., Natural exponential families with quadratic variance functions (1982) Ann. Stat., 10, pp. 65-80
Citas:
---------- APA ----------
Kelmansky, D.M. & Ricci, L. (2017) . A new distribution family for microarray data. Microarrays, 6(1).http://dx.doi.org/10.3390/microarrays6010005
---------- CHICAGO ----------
Kelmansky, D.M., Ricci, L. "A new distribution family for microarray data" . Microarrays 6, no. 1 (2017).http://dx.doi.org/10.3390/microarrays6010005
---------- MLA ----------
Kelmansky, D.M., Ricci, L. "A new distribution family for microarray data" . Microarrays, vol. 6, no. 1, 2017.http://dx.doi.org/10.3390/microarrays6010005
---------- VANCOUVER ----------
Kelmansky, D.M., Ricci, L. A new distribution family for microarray data. Microarrays. 2017;6(1).http://dx.doi.org/10.3390/microarrays6010005
