Molecular profiling reveals prognostically significant subtypes of canine lymphoma

We performed genomewide gene expression analysis of 35 samples representing 6 common histologic subtypes of canine lymphoma and bioinformatics analyses to define their molecular characteristics. Three major groups were defined on the basis of gene expression profiles: (1) low-grade T-cell lymphoma, composed entirely by T-zone lymphoma; (2) high-grade T-cell lymphoma, consisting of lymphoblastic T-cell lymphoma and peripheral T-cell lymphoma not otherwise specified; and (3) B-cell lymphoma, consisting of marginal B-cell lymphoma, diffuse large B-cell lymphoma, and Burkitt lymphoma. Interspecies comparative analyses of gene expression profiles also showed that marginal B-cell lymphoma and diffuse large B-cell lymphoma in dogs and humans might represent a continuum of disease with similar drivers. The classification of these diverse tumors into 3 subgroups was prognostically significant, as the groups were directly correlated with event-free survival. Finally, we developed a benchtop diagnostic test based on expression of 4 genes that can robustly classify canine lymphomas into one of these 3 subgroups, enabling a direct clinical application for our results.

Probabilities for separating sets of order statistics

Consider a set of order statistics that arise from sorting samples from two different populations, each with their own, possibly different distribution functions. The probability that these order statistics fall in disjoint, ordered intervals and that of the smallest statistics, a certain number come from the first populations is given in terms of the two distribution functions. The result is applied to computing the joint probability of the number of rejections and the number of false rejections for the Benjamini-Hochberg false discovery rate procedure.

Rapid dissection of a complex phenotype through genomic-scale mapping of fitness altering genes

The understanding and engineering of complex phenotypes is a critical issue in biotechnology. Conventional approaches for engineering such phenotypes are often resource intensive, marginally effective, and unable to generate the level of biological understanding desired. Here, we report a new approach for rapidly dissecting a complex phenotype that is based upon the combination of genome-scale growth phenotype data, precisely targeted growth selections, and informatic strategies for abstracting and summarizing data onto coherent biological processes. We measured at high resolution (125 NT) and for the entire genome the effect of increased gene copy number on overall biological fitness corresponding to the expression of a complex phenotype (tolerance to 3-hydroxypropionic acid (3-HP) in Escherichia coli). Genetic level fitness data were then mapped according to various definitions of gene-gene interaction in order to generate network-level fitness data. When metabolic pathways were used to define interactions, we observed that genes within the chorismate and threonine super-pathways were disproportionately enriched throughout selections for 3-HP tolerance. Biochemical and genetic studies demonstrated that alleviation of inhibition of either of these super-pathways was sufficient to mitigate 3-HP toxicity. These data enabled the design of combinatorial modifications that almost completely offset 3-HP toxicity in minimal medium resulting in a 20 g/L and 25-fold increase in tolerance and specific growth, respectively.

Fast Computation by Block Permanents of Cumulative Distribution Functions of Order Statistics from Several Populations

The joint cumulative distribution function for order statistics arising from several different populations is given in terms of the distribution functions of the populations. The computational cost of our formula in the case of two populations is still exponential in the worst case, but it is a dramatic improvement compared to the general formula by Bapat and Beg. In the case when only the joint distribution function of a subset of the order statistics of fixed size is needed, the complexity is polynomial, for the case of two populations.

Expected Power for the False Discovery Rate with Independence

The Benjamini-Hochberg procedure is widely used in multiple comparisons. Previous power results for this procedure have been based on simulations. This article produces theoretical expressions for expected power. To derive them, we make assumptions about the number of hypotheses being tested, which null hypotheses are true, which are false, and the distributions of the test statistics under each null and alternative. We use these assumptions to derive bounds for multiple dimensional rejection regions. With these bounds and a permanent based representation of the joint density function of the largest p-values, we use the law of total probability to derive the distribution of the total number of rejections. We derive the joint distribution of the total number of rejections and the number of rejections when the null hypothesis is true. We give an analytic expression for the expected power for a false discovery rate procedure that assumes the hypotheses are independent.