Empirical Bayes Gene Screening Tool for Time-Course or Dose–Response Microarray Data

2004 ◽  
Vol 14 (3) ◽  
pp. 647-670 ◽  
Author(s):  
J. E. Eckel ◽  
C. Gennings ◽  
V. M. Chinchilli ◽  
L. D. Burgoon ◽  
T. R. Zacharewski
Keyword(s):  
Dose Response ◽  
Microarray Data ◽  
Empirical Bayes ◽  
Time Course ◽  
Screening Tool ◽  
Gene Screening

USING WEIGHTED PERMUTATION SCORES TO DETECT DIFFERENTIAL GENE EXPRESSION WITH MICROARRAY DATA

2005 ◽  
Vol 03 (04) ◽  
pp. 989-1006 ◽  
Author(s):  
XU GUO ◽  
WEI PAN
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Microarray Data ◽  
Empirical Bayes ◽  
Time Course ◽  
Null Distribution ◽  
Posterior Probabilities ◽  
Test Statistic ◽  
Microarray Experiments ◽  
Differential Gene

A class of nonparametric statistical methods, including a nonparametric empirical Bayes (EB) method, the Significance Analysis of Microarrays (SAM) and the mixture model method (MMM) have been proposed to detect differential gene expression for replicated microarray experiments. They all depend on constructing a test statistic, for example, a t-statistic, and then using permutation to draw inferences. However, due to special features of microarray data, using standard permutation scores may not estimate the null distribution of the test statistic well, leading to possibly too conservative inferences. We propose a new method of constructing weighted permutation scores to overcome the problem: posterior probabilities of having no differential expression from the EB method are used as weights for genes to better estimate the null distribution of the test statistic. We also propose a weighted method to estimate the false discovery rate (FDR) using the posterior probabilities. Using simulated data and real data for time-course microarray experiments, we show the improved performance of the proposed methods when implemented in MMM, EB and SAM.

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 Identifying significant temporal variation in time course microarray data without replicates

2009 ◽  
Vol 10 (1) ◽  
Author(s):  
Stephen C Billups ◽  
Margaret C Neville ◽  
Michael Rudolph ◽  
Weston Porter ◽  
Pepper Schedin
Keyword(s):  
Temporal Variation ◽  
Microarray Data ◽  
Time Course

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

scholarly journals Cytotoxic Effect of RecombinantMycobacterium tuberculosisCFP-10/ESAT-6 Protein on the Crucial Pathways of WI-38 Cells

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Kun-Nan Tsai ◽  
Err-Cheng Chan ◽  
Tsung-Yeh Tsai ◽  
Kuei-Tien Chen ◽  
Chun-Yu Chen ◽  
...  
Keyword(s):  
Focal Adhesion ◽  
Microarray Data ◽  
Cytotoxic Effect ◽  
Time Course ◽  
Essential Role ◽  
Topological Properties ◽  
Regulatory Effect ◽  
Qrt Pcr ◽  
Focal Adhesion Pathway ◽  
Pathway Databases

To unravel the cytotoxic effect of the recombinant CFP-10/ESAT-6 protein (rCFES) on WI-38 cells, an integrative analysis approach, combining time-course microarray data and annotated pathway databases, was proposed with the emphasis on identifying the potentially crucial pathways. The potentially crucial pathways were selected based on a composite criterion characterizing the average significance and topological properties of important genes. The analysis results suggested that the regulatory effect of rCFES was at least involved in cell proliferation, cell motility, cell survival, and metabolisms of WI-38 cells. The survivability of WI-38 cells, in particular, was significantly decreased to 62% with 12.5 μMrCFES. Furthermore, the focal adhesion pathway was identified as the potentially most-crucial pathway and 58 of 65 important genes in this pathway were downregulated by rCFES treatment. Using qRT-PCR, we have confirmed the changes in the expression levels of LAMA4, PIK3R3, BIRC3, and NFKBIA, suggesting that these proteins may play an essential role in the cytotoxic process in the rCFES-treated WI-38 cells.

scholarly journals A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters

2016 ◽  
Vol 14 (03) ◽  
pp. 1650007 ◽  
Author(s):  
Matthias Gerstgrasser ◽  
Sarah Nicholls ◽  
Michael Stout ◽  
Katherine Smart ◽  
Chris Powell ◽  
...  
Keyword(s):  
High Throughput ◽  
Bayesian Approach ◽  
Microarray Data ◽  
Time Course ◽  
Microbial Growth ◽  
Mcmc Methods ◽  
Lag Phase ◽  
Maximum Output ◽  
Phenotype Microarray ◽  
Phenotype Microarrays

Biolog phenotype microarrays (PMs) enable simultaneous, high throughput analysis of cell cultures in different environments. The output is high-density time-course data showing redox curves (approximating growth) for each experimental condition. The software provided with the Omnilog incubator/reader summarizes each time-course as a single datum, so most of the information is not used. However, the time courses can be extremely varied and often contain detailed qualitative (shape of curve) and quantitative (values of parameters) information. We present a novel, Bayesian approach to estimating parameters from Phenotype Microarray data, fitting growth models using Markov Chain Monte Carlo (MCMC) methods to enable high throughput estimation of important information, including length of lag phase, maximal “growth” rate and maximum output. We find that the Baranyi model for microbial growth is useful for fitting Biolog data. Moreover, we introduce a new growth model that allows for diauxic growth with a lag phase, which is particularly useful where Phenotype Microarrays have been applied to cells grown in complex mixtures of substrates, for example in industrial or biotechnological applications, such as worts in brewing. Our approach provides more useful information from Biolog data than existing, competing methods, and allows for valuable comparisons between data series and across different models.

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