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Mosteller, Frederick; Fisher, R. A.

doi:10.2307/2681650

Cited by 123 publications

(86 citation statements)

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“…Stouffer’s Method is a meta-analytic method for combining a set of Z-scores when the consistency of the sign of the effect is important. (Compare to Fisher’s method, Mosteller and Fisher (1948), where significance can be obtained even if the signs are inconsistent). If there are K independent Z -scores to combine, the method simply consists of computing the average Z -score and scaling by

\sqrt{k}

; the scaling is necessary so that the Stouffer’s measure is once again a Z-score under the null hypothesis.…”

Section: Methodsmentioning

confidence: 99%

Multi-modal characterization of rapid anterior hippocampal volume increase associated with aerobic exercise

et al. 2016

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The hippocampus has been shown to demonstrate a remarkable degree of plasticity in response to a variety of tasks and experiences. For example, the size of the human hippocampus has been shown to increase in response to aerobic exercise. However, it is currently unknown what underlies these changes. Here we scanned sedentary, young to middle-aged human adults before and after a six-week exercise intervention using nine different neuroimaging measures of brain structure, vasculature, and diffusion. We then tested two different hypotheses regarding the nature of the underlying changes in the tissue. Surprisingly, we found no evidence of a vascular change as has been previously reported. Rather, the pattern of changes is better explained by an increase in myelination. Finally, we show hippocampal volume increase is temporary, returning to baseline after an additional six weeks without aerobic exercise. This is the first demonstration of a change in hippocampal volume in early to middle adulthood suggesting that hippocampal volume is modulated by aerobic exercise throughout the lifespan rather than only in the presence of age related atrophy. It is also the first demonstration of hippocampal volume change over a period of only six weeks, suggesting gross morphometric hippocampal plasticity occurs faster than previously thought.

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\sqrt{k}

; the scaling is necessary so that the Stouffer’s measure is once again a Z-score under the null hypothesis.…”

Section: Methodsmentioning

confidence: 99%

Multi-modal characterization of rapid anterior hippocampal volume increase associated with aerobic exercise

et al. 2016

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“…The p values estimated based on the Cauchy−Lorentz distribution were combined using Fisher's combined probability test. 21 Label-Free Profiling of the Individual Nondepleted Serum Samples Used in the iTRAQ Labeling Experiment (Confirmation Step) Individual nondepleted serum samples were desalted by mRP chromatography reduced, alkylated, and digested for 16 h with 1 μg of trypsin. The digested samples were separated on a chip G4240−62030 with a 360 nL trap column and a 150 mm × 75 μm analytical column filled with Polaris C18-A, 3 μm sorbent (Agilent Technologies) (Appendix S1 in the Supporting Information).…”

Section: Database Search Protein Quantification and Statistical Anamentioning

confidence: 99%

A Panel of Regulated Proteins in Serum from Patients with Cervical Intraepithelial Neoplasia and Cervical Cancer

et al. 2014

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We developed a discovery-validation mass-spectrometry-based pipeline to identify a set of proteins that are regulated in serum of patients with cervical intraepithelial neoplasia (CIN) and squamous cell cervical cancer using iTRAQ, label-free shotgun, and targeted mass-spectrometric quantification. In the discovery stage we used a "pooling" strategy for the comparative analysis of immunodepleted serum and revealed 15 up- and 26 down-regulated proteins in patients with early- (CES) and late-stage (CLS) cervical cancer. The analysis of nondepleted serum samples from patients with CIN, CES, an CLS and healthy controls showed significant changes in abundance of alpha-1-acid glycoprotein 1, alpha-1-antitrypsin, serotransferrin, haptoglobin, alpha-2-HS-glycoprotein, and vitamin D-binding protein. We validated our findings using a fast UHPLC/MRM method in an independent set of serum samples from patients with cervical cancer or CIN and healthy controls as well as serum samples from patients with ovarian cancer (more than 400 samples in total). The panel of six proteins showed 67% sensitivity and 88% specificity for discrimination of patients with CIN from healthy controls, a stage of the disease where current protein-based biomarkers, for example, squamous cell carcinoma antigen (SCCA), fail to show any discrimination. Additionally, combining the six-protein panel with SCCA improves the discrimination of patients with CES and CLS from healthy controls.

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“…A small area implies a high number of low ranks. When more than one source of differential expression measurements are available, we use Fishers method (Mosteller and Fisher, 1948) to combine the P -values as

X = - 2 \sum_{i = 1}^{k} log p_{i},

where p i is the P -value computed from the i th experiment and k the total number of independent experiments. The random variable X follows a chi-square distribution with 2 k degrees of freedom.…”

Section: Methodsmentioning

confidence: 99%

Network-based integration of multi-omics data for prioritizing cancer genes

et al. 2018

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MotivationSeveral molecular events are known to be cancer-related, including genomic aberrations, hypermethylation of gene promoter regions and differential expression of microRNAs. These aberration events are very heterogeneous across tumors and it is poorly understood how they affect the molecular makeup of the cell, including the transcriptome and proteome. Protein interaction networks can help decode the functional relationship between aberration events and changes in gene and protein expression.ResultsWe developed NetICS (Network-based Integration of Multi-omics Data), a new graph diffusion-based method for prioritizing cancer genes by integrating diverse molecular data types on a directed functional interaction network. NetICS prioritizes genes by their mediator effect, defined as the proximity of the gene to upstream aberration events and to downstream differentially expressed genes and proteins in an interaction network. Genes are prioritized for individual samples separately and integrated using a robust rank aggregation technique. NetICS provides a comprehensive computational framework that can aid in explaining the heterogeneity of aberration events by their functional convergence to common differentially expressed genes and proteins. We demonstrate NetICS’ competitive performance in predicting known cancer genes and in generating robust gene lists using TCGA data from five cancer types.Availability and implementationNetICS is available at https://github.com/cbg-ethz/netics.Supplementary information Supplementary data are available at Bioinformatics online.

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Questions and Answers

Cited by 123 publications

References 0 publications

Multi-modal characterization of rapid anterior hippocampal volume increase associated with aerobic exercise

Multi-modal characterization of rapid anterior hippocampal volume increase associated with aerobic exercise

A Panel of Regulated Proteins in Serum from Patients with Cervical Intraepithelial Neoplasia and Cervical Cancer

Network-based integration of multi-omics data for prioritizing cancer genes

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