Often NIEHS scientists are interested in studying the effect of a chemical on a tissue or a cell or a gene expression, etc. Accordingly, they conduct suitable dose-response or time-course experiments. Based on the available scientific knowledge, a researcher may hypothesize certain patterns of mean response with respect to dose and/or time. In some instances a researcher may also be interested in detecting the lowest dose or time point at which a significant effect is seen. For example, the National Toxicology Program (NTP) routinely conducts dose-response studies to investigate the carcinogenic and toxic effects of various chemicals. Using the responses obtained at each dose, the researchers are interested in determining if tumor incidence increases with dose. Similarly, researchers in the National Center for Toxicogenomics (NCT) and in the NTP are also interested in understanding the changes in gene expression in a tissue or cell line when an animal or a cell line is exposed to a compound at various doses and for various duration of times. Accordingly, a variety of dose-response and time-course microarray experiments are conducted to understand the gene expression profiles over duration of exposure and/or dose of exposure. Usually, the null hypothesis is a flat response and one can express the alternative hypotheses using mathematical inequalities, known as order restrictions, between the unknown parameters of interest. Order restrictions can often be expressed using a graph where each unknown parameter is denoted by a circle, and the inequality, between two unknown parameters, is denoted by an arrow that points towards the larger parameter. Order-restricted statistical inference refers to statistical procedures that take into consideration the order restrictions on the parameter space. In this research program we are developing statistical procedures that can be useful for analyzing data, routinely generated by NIEHS researchers, with the above feature. The new procedures are generally more sensitive than some of the commonly used statistical procedures.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Intramural Research (Z01)
Project #
1Z01ES101744-02
Application #
7174390
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
2
Fiscal Year
2005
Total Cost
Indirect Cost
Name
U.S. National Inst of Environ Hlth Scis
Department
Type
DUNS #
City
State
Country
United States
Zip Code
Larriba, Yolanda; Rueda, Cristina; Fernández, Miguel A et al. (2018) A Bootstrap Based Measure Robust to the Choice of Normalization Methods for Detecting Rhythmic Features in High Dimensional Data. Front Genet 9:24
Weiss, Sophie; Xu, Zhenjiang Zech; Peddada, Shyamal et al. (2017) Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5:27
Kaul, Abhishek; Mandal, Siddhartha; Davidov, Ori et al. (2017) Analysis of Microbiome Data in the Presence of Excess Zeros. Front Microbiol 8:2114
Peddada, Shyamal (2017) Seasonal change in the gut. Science 357:754-755
Mandal, Siddhartha; Godfrey, Keith M; McDonald, Daniel et al. (2016) Fat and vitamin intakes during pregnancy have stronger relations with a pro-inflammatory maternal microbiota than does carbohydrate intake. Microbiome 4:55
Larriba, Yolanda; Rueda, Cristina; Fernández, Miguel A et al. (2016) Order restricted inference for oscillatory systems for detecting rhythmic signals. Nucleic Acids Res 44:e163
Rueda, Cristina; Fernández, Miguel A; Barragán, Sandra et al. (2016) Circular piecewise regression with applications to cell-cycle data. Biometrics 72:1266-1274
Grandhi, Anjana; Guo, Wenge; Peddada, Shyamal D (2016) A multiple testing procedure for multi-dimensional pairwise comparisons with application to gene expression studies. BMC Bioinformatics 17:104
Reber, Stefan O; Siebler, Philip H; Donner, Nina C et al. (2016) Immunization with a heat-killed preparation of the environmental bacterium Mycobacterium vaccae promotes stress resilience in mice. Proc Natl Acad Sci U S A 113:E3130-9
Zhao, Haibing; Peddada, Shyamal D; Cui, Xinping (2015) Mixed directional false discovery rate control in multiple pairwise comparisons using weighted p-values. Biom J 57:144-58

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