MOST: detecting cancer differential gene expression.
We propose a new statistics for the detection of differentially expressed genes when the genes are activated only in a subset of the samples. Statistics designed for this unconventional circumstance has proved to be valuable for most cancer studies, where oncogenes are activated for a small number of disease samples. Previous efforts made in this direction include cancer outlier profile analysis (Tomlins and others, 2005), outlier sum (Tibshirani and Hastie, 2007), and outlier robust t-statistics (Wu, 2007). We propose a new statistics called maximum ordered subset t-statistics (MOST) which seems to be natural when the number of activated samples is unknown. We compare MOST to other statistics and find that the proposed method often has more power then its competitors.
Lian
Heng
H
Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. henglian@ntu.edu.sg
D001699:Biometry / Q000379:methods; D001943:Breast Neoplasms / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods / Q000706:statistics & numerical data; D009857:Oncogenes; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000235:genetics; D012372:ROC Curve; D018401:Sample Size