Abstract
We consider inference procedures for cell number assessment of a multinomial distribution using the least square principle and quadratic programming. We establish the consistency of the estimators under regularity conditions. Simulation results show that our approach yields a more accurate estimate than the often-used naive estimator. We also demonstrate the proposed methodology via two genomic data sets.
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References
Bortoluzzi S, d’Alessi F, Romualdi C, Danieli G (2001) Differential expression of genes coding for ribosomal proteins in different human tissues. Bioinformatics 17: 1152–1157
Hillier FS, Lieberman GJ (1990) Introduction to operations research, 5th edn. McGraw-Hill, New York, p 520
Martin KJ, Pardee AB (2000) Identifying expressed genes. Proc Natl Acad Sci USA 97: 3789–3791
Okubo K, Hori N, Matoba R, Niiyama T, Fukushima AM, Kojima Y, Matsubara K (1992) Large scale cDNA sequencing for analysis of quantitative and qualitative aspects of gene expression. Nat Genet 2: 173–179
Romualdi C, Bortoluzzi S, Danieli GA (2001) Detecting differentially expressed genes in multiple tag sampling experiments: comparative evaluation of statistical tests. Hum Mol Genet 10: 2133–2141
Shao J (1999) Mathematical statistics, 2nd edn. Springer, New York
Wang HY, Chien HC, Osada N, Hashimoto K, Sugano S, Gojobori T, Chou CK, Tsai SF, Wu C, Shen CK (2006) Rate of evolution in brain-expressed genes in humans and other primates. PLoS Biol 5: 335–342
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Li, Q., Li, G. & Xiong, S. Assessment of cell number for a multinomial distribution with application to genomic data. Metrika 71, 151–164 (2010). https://doi.org/10.1007/s00184-008-0223-2
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DOI: https://doi.org/10.1007/s00184-008-0223-2