Rattus norvegicus
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ABSTRACT:
PROVIDER: PRJNA113577 | ENA |
REPOSITORIES: ENA
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Project description:An important component of time course microarray studies is the identification of genes that demonstrate significant time-dependent variation in their expression levels. Until recently available methods for performing such significance tests required replicates of individual time points. This paper describes a replicate-free method that was developed as part of a study of the estrous cycle in the rat mammary gland in which no replicate data was collected. Keywords: developmental time course (time series) throughout the estrus cycle in rat mammary gland singlet data
2008-08-04 | GSE12289 | GEO
Project description:BACKGROUND: Dendritic cells (DC) play a central role in primary immune responses and become potent stimulators of the adaptive immune response after undergoing the critical process of maturation. Understanding the dynamics of DC maturation would provide key insights into this important process. Time course microarray experiments can provide unique insights into DC maturation dynamics. Replicate experiments are necessary to address the issues of experimental and biological variability. Statistical methods and averaging are often used to identify significant signals. Here a novel strategy for filtering of replicate time course microarray data, which identifies consistent signals between the replicates, is presented and applied to a DC time course microarray experiment. RESULTS: The temporal dynamics of DC maturation were studied by stimulating DC with poly(I:C) and following gene expression at 5 time points from 1 to 24 hours. The novel filtering strategy uses standard statistical and fold change techniques, along with the consistency of replicate temporal profiles, to identify those differentially expressed genes that were consistent in two biological replicate experiments. To address the issue of cluster reproducibility a consensus clustering method, which identifies clusters of genes whose expression varies consistently between replicates, was also developed and applied. Analysis of the resulting clusters revealed many known and novel characteristics of DC maturation, such as the up-regulation of specific immune response pathways. Intriguingly, more genes were down-regulated than up-regulated. Results identify a more comprehensive program of down-regulation, including many genes involved in protein synthesis, metabolism, and housekeeping needed for maintenance of cellular integrity and metabolism. CONCLUSIONS: The new filtering strategy emphasizes the importance of consistent and reproducible results when analyzing microarray data and utilizes consistency between replicate experiments as a criterion in both feature selection and clustering, without averaging or otherwise combining replicate data. Observation of a significant down-regulation program during DC maturation indicates that DC are preparing for cell death and provides a path to better understand the process. This new filtering strategy can be adapted for use in analyzing other large-scale time course data sets with replicates.
2010-08-26 | GSE21033 | GEO
Project description:Living organisms are intricate systems with dynamic internal processes. Their RNA, protein, and metabolite levels fluctuate in response to variations in health and environmental conditions. Among these, RNA expression is particularly accessible for comprehensive analysis, thanks to the evolution of high throughput sequencing technologies in recent years. This progress has enabled researchers to identify unique RNA patterns associated with various diseases, as well as to develop predictive and prognostic biomarkers for therapy response. Such cross-sectional studies allow for the identification of differentially expressed genes (DEGs) between groups, but they have limitations. Specifically, they often fail to capture the temporal changes in gene expression following individual perturbations and may lead to significant false discoveries due to inherent noise in RNA sequencing sample preparation and data collection. To address these challenges, our study hypothesized that frequent, longitudinal RNA sequencing (RNAseq) analysis of blood samples could offer a more profound understanding of the temporal dynamics of gene expression in response to drug interventions, while also enhancing the accuracy of identifying genes influenced by these drugs. In this research, we conducted RNAseq on 829 blood samples collected from 84 Sprague-Dawley lab rats. Excluding the control group, each rat was administered one of four different compounds known for liver toxicity: tetracycline, isoniazid, valproate, and carbon tetrachloride. We developed specialized bioinformatics tools to pinpoint genes that exhibit temporal variation in response to these treatments.
2023-12-13 | GSE250058 | GEO
Project description:Cumulus-oocyte complexes were isolated a seperate time-points to generate temporal complexes. Targets from two biological replicates at each time point (0h, 8h, 16h post-hCG treatment) were generated and the expression profiles were determined using Affymetrix GeneChip Mouse Genome 430 2.0 Arrays. Comparisons between the sample groups allow the identification of genes with temporal expression patterns. Keywords: time course
2006-02-17 | GSE4260 | GEO
Project description:Alpha-naphtylisothiocyanate administration time-course
| PRJNA87911 | ENA
Project description:We measured temporal changes of gene expression in hippocampus of rat after BCCAo using time-course microarray analysis
2013-02-14 | GSE44289 | GEO