9,749 research outputs found
A statistical framework for the design of microarray experiments and effective detection of differential gene expression
Four reasons why you might wish to read this paper: 1. We have devised a new
statistical T test to determine differentially expressed genes (DEG) in the
context of microarray experiments. This statistical test adds a new member to
the traditional T-test family. 2. An exact formula for calculating the
detection power of this T test is presented, which can also be fairly easily
modified to cover the traditional T tests. 3. We have presented an accurate yet
computationally very simple method to estimate the fraction of non-DEGs in a
set of genes being tested. This method is superior to an existing one which is
computationally much involved. 4. We approach the multiple testing problem from
a fresh angle, and discuss its relation to the classical Bonferroni procedure
and to the FDR (false discovery rate) approach. This is most useful in the
analysis of microarray data, where typically several thousands of genes are
being tested simultaneously.Comment: 9 pages, 1 table; to appear in Bioinformatic
A Sharp upper bound for the spectral radius of a nonnegative matrix and applications
In this paper, we obtain a sharp upper bound for the spectral radius of a
nonnegative matrix. This result is used to present upper bounds for the
adjacency spectral radius, the Laplacian spectral radius, the signless
Laplacian spectral radius, the distance spectral radius, the distance Laplacian
spectral radius, the distance signless Laplacian spectral radius of a graph or
a digraph. These results are new or generalize some known results.Comment: 16 pages in Czechoslovak Math. J., 2016. arXiv admin note: text
overlap with arXiv:1507.0705
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