Stan Pounds

Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values

MOTIVATION The occurrence of false positives and false negatives in a microarray analysis could be easily estimated if the distribution of p-values were approximated and then expressed as a mixture of null and alternative densities. Essentially any distribution of p-values can be expressed as such a mixture by extracting a uniform density from it. RESULTS The occurrence of false positives and false negatives in a microarray analysis could be easily estimated if the distribution of p-values were approximated and then expressed as a mixture of null and alternative densities. Essentially any distribution of p-values can be expressed as such a mixture by extracting a uniform density from it. AVAILABILITY An S-plus function library is available from http://www.stjuderesearch.org/statistics.

Reference alignment of SNP microarray signals for copy number analysis of tumors

UNLABELLED A new procedure to align single nucleotide polymorphism (SNP) microarray signals for copy number analysis is proposed. For each individual array, this reference alignment procedure (RAP) uses a set of selected markers as internal references to direct the signal alignment. RAP aligns the signals so that each array has a similar signal distribution among its reference markers. An accompanying reference selection algorithm (RSA) uses genotype calls and initial signal intensities to choose two-copy markers as the internal references for each array. After RSA and RAP are applied, each array has a similar distribution of signals of two-copy markers so that across-array signal comparisons are biologically meaningful. An upper bound for a statistical metric of signal misalignment is derived and provides a theoretical basis to choose RSA-RAP over other alignment procedures for copy number analysis of cancers. In our study of acute lymphoblastic leukemia, RSA-RAP gives copy number analysis results that show substantially better concordance with cytogenetics than do two other alignment procedures. AVAILABILITY Documented R code is freely available from www.stjuderesearch.org/depts/biostats/refnorm.

Sample size determination for the false discovery rate

MOTIVATION There is not a widely applicable method to determine the sample size for experiments basing statistical significance on the false discovery rate (FDR). RESULTS We propose and develop the anticipated FDR (aFDR) as a conceptual tool for determining sample size. We derive mathematical expressions for the aFDR and anticipated average statistical power. These expressions are used to develop a general algorithm to determine sample size. We provide specific details on how to implement the algorithm for a k-group (k > or = 2) comparisons. The algorithm performs well for k-group comparisons in a series of traditional simulations and in a real-data simulation conducted by resampling from a large, publicly available dataset. AVAILABILITY Documented S-plus and R code libraries are freely available from www.stjuderesearch.org/depts/biostats.

Severe cardiopulmonary complications consistent with systemic inflammatory response syndrome caused by leukemia cell lysis in childhood acute myelomonocytic or monocytic leukemia

Life‐threatening pulmonary complications that coincide with cell lysis during early chemotherapy and that mimic systemic inflammatory response syndrome (SIRS) have been reported in patients with acute myeloid leukemia (AML).

PROMISE: a tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables

MOTIVATION In some applications, prior biological knowledge can be used to define a specific pattern of association of multiple endpoint variables with a genomic variable that is biologically most interesting. However, to our knowledge, there is no statistical procedure designed to detect specific patterns of association with multiple endpoint variables. RESULTS Projection onto the most interesting statistical evidence (PROMISE) is proposed as a general procedure to identify genomic variables that exhibit a specific biologically interesting pattern of association with multiple endpoint variables. Biological knowledge of the endpoint variables is used to define a vector that represents the biologically most interesting values for statistics that characterize the associations of the endpoint variables with a genomic variable. A test statistic is defined as the dot-product of the vector of the observed association statistics and the vector of the most interesting values of the association statistics. By definition, this test statistic is proportional to the length of the projection of the observed vector of correlations onto the vector of most interesting associations. Statistical significance is determined via permutation. In simulation studies and an example application, PROMISE shows greater statistical power to identify genes with the interesting pattern of associations than classical multivariate procedures, individual endpoint analyses or listing genes that have the pattern of interest and are significant in more than one individual endpoint analysis. AVAILABILITY Documented R routines are freely available from www.stjuderesearch.org/depts/biostats and will soon be available as a Bioconductor package from www.bioconductor.org.

An R package that automatically collects and archives details for reproducible computing

BackgroundIt is scientifically and ethically imperative that the results of statistical analysis of biomedical research data be computationally reproducible in the sense that the reported results can be easily recapitulated from the study data. Some statistical analyses are computationally a function of many data files, program files, and other details that are updated or corrected over time. In many applications, it is infeasible to manually maintain an accurate and complete record of all these details about a particular analysis.ResultsTherefore, we developed the rctrack package that automatically collects and archives read only copies of program files, data files, and other details needed to computationally reproduce an analysis.ConclusionsThe rctrack package uses the trace function to temporarily embed detail collection procedures into functions that read files, write files, or generate random numbers so that no special modifications of the primary R program are necessary. At the conclusion of the analysis, rctrack uses these details to automatically generate a read only archive of data files, program files, result files, and other details needed to recapitulate the analysis results. Information about this archive may be included as an appendix of a report generated by Sweave or knitR. Here, we describe the usage, implementation, and other features of the rctrack package. The rctrack package is freely available from http://www.stjuderesearch.org/site/depts/biostats/rctrack under the GPL license.

Integrated analysis of pharmacologic, clinical and SNP microarray data using Projection Onto the Most Interesting Statistical Evidence with Adaptive Permutation Testing

We recently developed the Projection Onto the Most Interesting Statistical Evidence (PROMISE) procedure that uses prior biological knowledge to guide an integrated analysis of gene expression data with multiple biological and clinical endpoints. Here, PROMISE is adapted to the integrated analysis of pharmacologic, clinical and genome-wide genotype data. An efficient permutation-testing algorithm is introduced so that PROMISE is computationally feasible in this higher-dimension setting. In the analysis of a paediatric leukaemia data set, PROMISE effectively identifies genomic features that exhibit a biologically meaningful pattern of association with multiple endpoint variables.

A permutation-based method to identify loss-of-heterozygosity using paired genotype microarray data.

Background SNP genotyping microarrays may be used to detect regions of loss-of-heterozygosity (LOH). Genotype array data are collected for tumor tissue and germline tissue samples from each subject. For each subject, an initial call of LOH or non-LOH is generated for each marker via straightforward comparison of the genotype call across each tissue sample pair [1]. The genotype calls are generated with some error. Therefore, statistical models are used to analyze the pattern of LOH calls to infer regions of LOH for each subject [1].

A genomic random interval model for statistical analysis of genomic lesion data

MOTIVATION Tumors exhibit numerous genomic lesions such as copy number variations, structural variations and sequence variations. It is difficult to determine whether a specific constellation of lesions observed across a cohort of multiple tumors provides statistically significant evidence that the lesions target a set of genes that may be located across different chromosomes but yet are all involved in a single specific biological process or function. RESULTS We introduce the genomic random interval (GRIN) statistical model and analysis method that evaluates the statistical significance of the abundance of genomic lesions that overlap a specific locus or a pre-defined set of biologically related loci. The GRIN model retains certain biologically important properties of genomic lesions that are ignored by other methods. In a simulation study and two example analyses of leukemia genomic lesion data, GRIN more effectively identified important loci as significant than did three methods based on a permutation-of-markers model. GRIN also identified biologically relevant pathways with a significant abundance of lesions in both examples. AVAILABILITY An R package will be freely available at CRAN and www.stjuderesearch.org/site/depts/biostats/software.

Statistical analysis of data from retroviral clonal experiments in the developing retina.

Retroviral lineage studies have been widely used over the past decade to study retinal development in vivo and in explant culture [Donovan S.L., Dyer, M.A., 2006. Preparation and Square Wave Electroporation of Retinal Explant Cultures, Nature Protocols 1, 2710-2718; Donovan, S.L., Schweers, B., Martins, R., Johnson D., Dyer, M.A., 2001. Compensation by tumor suppressor genes during retinal development in mice and humans, BMC Biol 4 , 14; Dyer M.A., Cepko, C.L., 2001. p27Kip1 and p57Kip2 regulate proliferation in distinct retinal progenitor cell populations, J. of Neurosci 21, 4259-4271; Dyer M.A., Cepko, C.L., 2000. p57(Kip2) regulates progenitor cell proliferation and amacrine interneuron development in the mouse retina, Development 127, 3593-3605; Dyer, M.A., Livesey, F.J., Cepko C.L., Oliver, G., 2003. Prox1 function controls progenitor cell proliferation and horizontal cell genesis in the mammalian retina, Nat Genet 34, 53-58]. These approaches can provide important data on the proliferation, cell fate specification, differentiation and survival of individual neurons and glia derived from single infected retinal progenitor cells. In some experiments, these parameters are compared in retinae from animals with different targeted deletions or transgenes. Alternatively, the effect of ectopic expression of virally encoded transgenes may be studied at the level of individual retinal progenitor cells in vivo and in explant culture. One of the challenges with interpreting retroviral lineage studies is determining the statistical significance of differences in the proliferation, cell fate specification, differentiation of survival of retinal progenitor cells between experimental and control samples. In this study, we provide a clear step-by-step guide to the application of statistical methods to retroviral lineage analyses actual data sets. We anticipate that this will serve as a guide for future statistical analyses of retroviral lineage studies and will help to provide a uniform standard in the field.