Time-course microarray transcriptome data of in vitro cultured testes and age-matched in vivo testes.
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ABSTRACT:
SUBMITTER: Abe T
PROVIDER: S-EPMC7674299 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Publications
Time-course microarray transcriptome data of <i>in vitro</i> cultured testes and age-matched <i>in vivo</i> testes.
Abe Takeru T Nishimura Hajime H Sato Takuya T Suzuki Harukazu H Ogawa Takehiko T Suzuki Takahiro T
Data in brief 20201031
<i>In vitro</i> spermatogenesis, which produces fertile spermatozoa, has been successfully performed using an organ culture method from murine tissue. Here, we provide a dataset of time-course microarray transcriptome data of <i>in vitro</i> cultured neonate murine testes and age-matched <i>in vivo</i>-derived testes. The dataset presented here is related to the article titled "Transcriptome analysis reveals inadequate spermatogenesis and immediate radical immune reactions during organ culture i ...[more]
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Project description:Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes. This method is applied to two studies on humans. In one study, genes are identified that show differential expression over time in response to in vivo endotoxin administration. By using our method, 7,409 genes are called significant at a 1% false-discovery rate level, whereas several existing approaches fail to identify any genes. In another study, 417 genes are identified at a 10% false-discovery rate level that show expression changing with age in the kidney cortex. Here it is also shown that as many as 47% of the genes change with age in a manner more complex than simple exponential growth or decay. The methodology proposed here has been implemented in the freely distributed and open-source edge software package.
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Project description:This article presents a new method for analyzing microarray time courses by identifying genes that undergo abrupt transitions in expression level, and the time at which the transitions occur. The algorithm matches the sequence of expression levels for each gene against temporal patterns having one or two transitions between two expression levels. The algorithm reports a P-value for the matching pattern of each gene, and a global false discovery rate can also be computed. After matching, genes can be sorted by the direction and time of transitions. Genes can be partitioned into sets based on the direction and time of change for further analysis, such as comparison with Gene Ontology annotations or binding site motifs. The method is evaluated on simulated and actual time-course data. On microarray data for budding yeast, it is shown that the groups of genes that change in similar ways and at similar times have significant and relevant Gene Ontology annotations.
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Project description:BACKGROUND: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data. RESULTS: In this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data. CONCLUSION: Profiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements.
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Project description:BACKGROUND: Time-course microarray experiments are widely used to study the temporal profiles of gene expression. Storey et al. (2005) developed a method for analyzing time-course microarray studies that can be applied to discovering genes whose expression trajectories change over time within a single biological group, or those that follow different time trajectories among multiple groups. They estimated the expression trajectories of each gene using natural cubic splines under the null (no time-course) and alternative (time-course) hypotheses, and used a goodness of fit test statistic to quantify the discrepancy. The null distribution of the statistic was approximated through a bootstrap method. Gene expression levels in microarray data are often complicatedly correlated. An accurate type I error control adjusting for multiple testing requires the joint null distribution of test statistics for a large number of genes. For this purpose, permutation methods have been widely used because of computational ease and their intuitive interpretation. RESULTS: In this paper, we propose a permutation-based multiple testing procedure based on the test statistic used by Storey et al. (2005). We also propose an efficient computation algorithm. Extensive simulations are conducted to investigate the performance of the permutation-based multiple testing procedure. The application of the proposed method is illustrated using the Caenorhabditis elegans dauer developmental data. CONCLUSION: Our method is computationally efficient and applicable for identifying genes whose expression levels are time-dependent in a single biological group and for identifying the genes for which the time-profile depends on the group in a multi-group setting.
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Project description:Small RNA including microRNA (miRNA) and Piwi-interacting RNA (piRNA) play important roles in germline maintenance and maturation in wide variety of species. Relatively little however is known about role played by miRNA in male germline maturation in humans and closely related primate species. Here we focused on rhesus macaques as a model species closely related to humans to study small RNA expression in testis samples throughout postnatal development and maturation. We observe clear transition in miRNA and piRNA expression resulting in the overall increase of piRNA expression levels and corresponding decrease in miRNA expression. Unexpectedly, this transition takes place at approximately one year of age – far earlier then rhesus macaque sexual maturation occurring at 4-5 years of age. Notably, contradictory to the overall trend of expression decline, a group of 29 miRNA showed marked expression increase in macaque testis at approximately five years of age – the time interval associated with sexual maturation. Keywords: miRNA piRNA Age Series
2013-03-05 | GSE44820 | GEO
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