scholarly journals Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference

2003 ◽  
Vol 19 (7) ◽  
pp. 834-841 ◽  
Author(s):  
S. D. Peddada ◽  
E. K. Lobenhofer ◽  
L. Li ◽  
C. A. Afshari ◽  
C. R. Weinberg ◽  
...  
Keyword(s):  
Dose Response ◽  
Time Course ◽  
Gene Selection ◽  
Order Restricted Inference ◽  
Microarray Experiments ◽  
Order Restricted

An Inference Methodology for Selecting and Clustering Genes Based on Likelihood Ratio Test

2016 ◽  
Vol 30 (08) ◽  
pp. 1650019
Author(s):  
Ruiyin Liu ◽  
Jian Tao ◽  
Dehui Wang
Keyword(s):  
Time Course ◽  
Type I Error ◽  
Likelihood Estimation ◽  
Ratio Test ◽  
Type I ◽  
Order Restricted Inference ◽  
Order Restrictions ◽  
Microarray Experiments ◽  
Constant Variance ◽  
Order Restricted

Peddada et al. (Gene selected and clustering for time-course and close-response microarray experiments using order-restricted inference, Bioinformatics 19 (2003): 834–841) proposed a new method for selecting and clustering genes according to their time-course or dose-response profiles. Their method necessitates the assumption of a constant variance through time or among dosages. This homoscedasticity assumption is, however, seldom satisfied in practice. In this paper, via the application of Shi’s algorithms and a modified bootstrap procedure (N. Z. Shi, Maximum likelihood estimation of means and variances from normal populations under simulations order restrictions (J. Multivariate Anal. 50 (1994) 282–293), we proposed a generalized order-restricted inference method which releases the homoscedasticity restriction. Simulation results show that procedures considered in this paper as well as those by Peddada et al. (Gene selected and clustering for time-course and close-response microarray experiments using order-restricted inference, Bioinformatics 19 (2003) 834–841) are generally comparable in terms of Type I error rate while our proposed algorithms are usually more powerful.

scholarly journals Order restricted inference for oscillatory systems for detecting rhythmic signals

2016 ◽  
Vol 44 (22) ◽  
pp. e163-e163 ◽  
Author(s):  
Yolanda Larriba ◽  
Cristina Rueda ◽  
Miguel A Fernández ◽  
Shyamal D Peddada
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Real Data ◽  
Order Restricted Inference ◽  
Mathematical Functions ◽  
R Language ◽  
Oscillatory Systems ◽  
Constrained Inference ◽  
Order Restricted ◽  
User Friendly

Abstract Motivation Many biological processes, such as cell cycle, circadian clock, menstrual cycles, are governed by oscillatory systems consisting of numerous components that exhibit rhythmic patterns over time. It is not always easy to identify such rhythmic components. For example, it is a challenging problem to identify circadian genes in a given tissue using time-course gene expression data. There is a great potential for misclassifying non-rhythmic as rhythmic genes and vice versa. This has been a problem of considerable interest in recent years. In this article we develop a constrained inference based methodology called Order Restricted Inference for Oscillatory Systems (ORIOS) to detect rhythmic signals. Instead of using mathematical functions (e.g. sinusoidal) to describe shape of rhythmic signals, ORIOS uses mathematical inequalities. Consequently, it is robust and not limited by the biologist's choice of the mathematical model. We studied the performance of ORIOS using simulated as well as real data obtained from mouse liver, pituitary gland and data from NIH3T3, U2OS cell lines. Our results suggest that, for a broad collection of patterns of gene expression, ORIOS has substantially higher power to detect true rhythmic genes in comparison to some popular methods, while also declaring substantially fewer non-rhythmic genes as rhythmic. Availability and Implementation A user friendly code implemented in R language can be downloaded from http://www.niehs.nih.gov/research/atniehs/labs/bb/staff/peddada/index.cfm. Contact [email protected]

Time course of insulin resistance associated with feeding dogs a high-fat diet

1997 ◽  
Vol 272 (1) ◽  
pp. E147-E154 ◽  
Author(s):  
A. P. Rocchini ◽  
P. Marker ◽  
T. Cervenka
Keyword(s):  
Insulin Resistance ◽  
Blood Pressure ◽  
Glucose Uptake ◽  
Dose Response ◽  
High Fat Diet ◽  
Time Course ◽  
Rapid Development ◽  
Diet Group ◽  
Whole Body ◽  
High Fat

The current study evaluated both the time course of insulin resistance associated with feeding dogs a high-fat diet and the relationship between the development of insulin resistance and the increase in blood pressure that also occurs. Twelve adult mongrel dogs were chronically instrumented and randomly assigned to either a control diet group (n = 4) or a high-fat diet group (n = 8). Insulin resistance was assessed by a weekly, single-dose (2 mU.kg-1.min-1) euglycemic-hyperinsulinemic clamp on all dogs. Feeding dogs a high-fat diet was associated with a 3.7 +/- 0.5 kg increase in body weight, a 20 +/- 4 mmHg increase in mean blood pressure, a reduction in insulin-mediated glucose uptake [(in mumol-kg-1.min-1) decreasing from 72 +/- 6 before to 49 +/- 7 at 1 wk, 29 +/- 3 at 3 wk, and 30 +/- 2 at 6 wk of the high-fat diet, P < 0.01]. and a reduced insulin-mediated increase in cardiac output. In eight dogs (4 high fat and 4 control), the dose-response relationship of insulin-induced glucose uptake also was studied. The whole body glucose uptake dose-response curve was shifted to the right, and the rate of maximal whole body glucose uptake was significantly decreased (P < 0.001). Finally, we observed a direct relationship between the high-fat diet-induced weekly increase in mean arterial pressure and the degree to which insulin resistance developed. In summary, the current study documents that feeding dogs a high-fat diet causes the rapid development of insulin resistance that is the result of both a reduced sensitivity and a reduced responsiveness to insulin.

rhIGF-I Enhances Functional Recovery from Sciatic Crush Time-Course and Dose-Response Study

1993 ◽  
Vol 692 (1 The Role of I) ◽  
pp. 314-316 ◽  
Author(s):  
PATRICIA C. CONTRERAS ◽  
CATHY STEFFLER ◽  
JEFFRY L. VAUGHT
Keyword(s):  
Functional Recovery ◽  
Dose Response ◽  
Time Course ◽  
Dose Response Study

scholarly journals Rat uterine microbiochemistry: metabolic enzyme activities stimulated by 17-beta-estradiol are localized in epithelial cells.

1987 ◽  
Vol 35 (6) ◽  
pp. 657-662 ◽  
Author(s):  
J P Holt ◽  
E Rhe
Keyword(s):  
Enzyme Activity ◽  
Smooth Muscle ◽  
Epithelial Cells ◽  
Dose Response ◽  
Enzyme Activities ◽  
Time Course ◽  
Citrate Synthase ◽  
Ovariectomized Rats ◽  
Similar Response ◽  
Estradiol Treatment

Lactate dehydrogenase (LDH; EC 1.1.1.27), citrate synthase (CS; EC 4.1.3.7), and beta-hydroxyacyl-CoA-dehydrogenase (beta-OH-acyl-CoA-DH; EC 1.1.1.35) activities were determined in each of the three major cell types of rat uterus, i.e., epithelial, stromal, and smooth muscle, using quantitative microanalytical techniques. Adult ovariectomized rats were treated with 17-beta-estradiol to determine the time course and dose response (0.025-50 micrograms/300-g rat) effect of estrogen on enzyme activity of each type of uterine cell. The use of "oil well" and enzyme-cycling microtechniques to determine the time course and the dose responses of enzyme activity changes required microassays involving 1595 microdissected single cell specimens. Estradiol treatment increased epithelial LDH, CS and beta-OH-acyl-CoA-DH activity but had no effect on these enzymes in the stroma or in smooth muscle cells. The estradiol-stimulated peak enzyme activities on Day 4 in the intervention group are compared with those in the ovariectomized rat controls as follows: LDH, 44.5 +/- 3.5 vs 22.3 +/- 3.9; CS, 3.5 +/- 0.2 vs 1.5 +/- 0.6; beta-OH-acyl-CoA-H, 3.5 +/- 0.32 vs 2.2 +/- 0.2 (mean +/- standard deviation; mol/kg/hr). Stromal cell activities (LDH, 7.4 +/- 1.0; CS, 1.2 +/- 0.2; beta-OH-acyl-CoA-DH, 0.9 +/- 0.1) were significantly lower than epithelial cell levels and were similar to smooth muscle levels. Therefore, even in the ovariectomized animal epithelial cells have markedly higher metabolic activity compared with adjacent cells. The enzyme activities are expressed as moles of substrate reacting per kilogram of dry weight per hour. All three enzymes exhibited a 17-beta-estradiol-induced dose response between 0.025-0.15 micrograms/300-g rat. The three enzymes studied all had similar response patterns to estrogen. The effect of estradiol was restricted to epithelial cells, with enzyme activities increasing to maximal levels after approximately 96 hr of hormone treatment. This study therefore not only confirms the specific and differential metabolic responses of uterine cells to estradiol treatment, but clearly demonstrates that marked metabolic differences exist between epithelial cells and stromal or smooth muscle uterine cells.

scholarly journals A Bayesian Approach to Estimation and Testing in Time-course Microarray Experiments

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Claudia Angelini ◽  
Daniela De Canditiis ◽  
Margherita Mutarelli ◽  
Marianna Pensky
Keyword(s):  
Time Course ◽  
Expression Profiles ◽  
Cancer Cell Line ◽  
Real Data ◽  
Microarray Experiment ◽  
Computational Effort ◽  
Microarray Experiments ◽  
Analytic Expressions ◽  
Sampling Intervals ◽  
Nonparametric Techniques

The objective of the present paper is to develop a truly functional Bayesian method specifically designed for time series microarray data. The method allows one to identify differentially expressed genes in a time-course microarray experiment, to rank them and to estimate their expression profiles. Each gene expression profile is modeled as an expansion over some orthonormal basis, where the coefficients and the number of basis functions are estimated from the data. The proposed procedure deals successfully with various technical difficulties that arise in typical microarray experiments such as a small number of observations, non-uniform sampling intervals and missing or replicated data. The procedure allows one to account for various types of errors and offers a good compromise between nonparametric techniques and techniques based on normality assumptions. In addition, all evaluations are performed using analytic expressions, so the entire procedure requires very small computational effort. The procedure is studied using both simulated and real data, and is compared with competitive recent approaches. Finally, the procedure is applied to a case study of a human breast cancer cell line stimulated with estrogen. We succeeded in finding new significant genes that were not marked in an earlier work on the same dataset.

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