Identification of significant periodic genes in microarray gene expression data

Background  

One frequent application of microarray experiments is in the study of monitoring gene activities in a cell during cell cycle or cell division. A new challenge for analyzing the microarray experiments is to identify genes that are statistically significantly periodically expressed during the cell cycle. Such a challenge occurs due to the large number of genes that are simultaneously measured, a moderate to small number of measurements per gene taken at different time points, and high levels of non-normal random noises inherited in the data.     

OpenDMAP: An open source, ontology-driven concept analysis engine, with applications to capturing knowledge regarding protein transport, protein interactions and cell-type-specific gene expression

Background

Microarray technology provides an efficient means for globally exploring physiological processes governed by the coordinated expression of multiple genes. However, identification of genes differentially expressed in microarray experiments is challenging because of their potentially high type I error rate. Methods for large-scale statistical analyses have been developed but most of them are applicable to two-sample or two-condition data.

Results

We developed a large-scale multiple-group F-test based method, named ranking analysis of F-statistics (RAF), which is an extension of ranking analysis of microarray data (RAM) for two-sample t-test. In this method, we proposed a novel random splitting approach to generate the null distribution instead of using permutation, which may not be appropriate for microarray data. We also implemented a two-simulation strategy to estimate the false discovery rate. Simulation results suggested that it has higher efficiency in finding differentially expressed genes among multiple classes at a lower false discovery rate than some commonly used methods. By applying our method to the experimental data, we found 107 genes having significantly differential expressions among 4 treatments at <0.7% FDR, of which 31 belong to the expressed sequence tags (ESTs), 76 are unique genes who have known functions in the brain or central nervous system and belong to six major functional groups.

Conclusion

Our method is suitable to identify differentially expressed genes among multiple groups, in particular, when sample size is small.     

Polynomial model approach for resynchronization analysis of cell-cycle gene expression data

MOTIVATION: Identification of genes expressed in a cell-cycle-specific periodical manner is of great interest to understand cyclic systems which play a critical role in many biological processes. However, identification of cell-cycle regulated genes by raw microarray gene expression data directly is complicated by the factor of synchronization loss, thus remains a challenging problem. Decomposing the expression measurements and extracting synchronized expression will allow to better represent the single-cell behavior and improve the accuracy in identifying periodically expressed genes. RESULTS: In this paper, we propose a resynchronization-based algorithm for identifying cell-cycle-related genes. We introduce a synchronization loss model by modeling the gene expression measurements as a superposition of different cell populations growing at different rates. The underlying expression profile is then reconstructed through resynchronization and is further fitted to the measurements in order to identify periodically expressed genes. Results from both simulations and real microarray data show that the proposed scheme is promising for identifying cyclic genes and revealing underlying gene expression profiles. AVAILABILITY: Contact the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at: http://dsplab.eng.umd.edu/~genomics/syn/     

Bayesian detection of periodic mRNA time profiles without use of training examples

Background  

Detection of periodically expressed genes from microarray data without use of known periodic and non-periodic training examples is an important problem, e.g. for identifying genes regulated by the cell-cycle in poorly characterised organisms. Commonly the investigator is only interested in genes expressed at a particular frequency that characterizes the process under study but this frequency is seldom exactly known. Previously proposed detector designs require access to labelled training examples and do not allow systematic incorporation of diffuse prior knowledge available about the period time.     

Towards the uniform distribution of null P values on Affymetrix microarrays

Methods to control false-positive rates require that P values of genes that are not differentially expressed follow a uniform distribution. Commonly used microarray statistics can generate P values that do not meet this assumption. We show that poorly characterized variance, imperfect normalization, and cross-hybridization are among the many causes of this non-uniform distribution. We demonstrate a simple technique that produces P values that are close to uniform for nondifferentially expressed genes in control datasets.     

Isolation of mRNA from specific tissues of Drosophila by mRNA tagging

To study the function of specific cells or tissues using genomic tools like microarray analyses, it is highly desirable to obtain mRNA from a homogeneous source. However, this is particularly challenging for small organisms, like Caenorhabditis elegans and Drosophila melanogaster. We have optimized and applied a new technique, mRNA tagging, to isolate mRNA from specific tissues of D.melanogaster. A FLAG-tagged poly(A)-binding protein (PABP) is expressed in a specific tissue and mRNA from that tissue is thus tagged by the recombinant PABP and separated from mRNA in other tissues by co-immunoprecipitation with a FLAG-tag specific antibody. The fractionated mRNA is then amplified and used as probe in microarray experiments. As a test system, we employed the procedures to identify genes expressed in Drosophila photoreceptor cells. We found that most known photoreceptor cell-specific mRNAs were identified by mRNA tagging. Furthermore, at least 11 novel genes have been identified as enriched in photoreceptor cells. mRNA tagging is a powerful general method for profiling gene expression in specific tissues and for identifying tissue-specific genes.     

Spectral estimation in unevenly sampled space of periodically expressed microarray time series data

Background  

Periodogram analysis of time-series is widespread in biology. A new challenge for analyzing the microarray time series data is to identify genes that are periodically expressed. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, and unevenly sampled time points. Most methods used in the literature operate on evenly sampled time series and are not suitable for unevenly sampled time series.     

A mRNA landscape of bovine embryos after standard and MAPK-inhibited culture conditions: a comparative analysis

Background

Genes and signalling pathways involved in pluripotency have been studied extensively in mouse and human pre-implantation embryos and embryonic stem (ES) cells. The unsuccessful attempts to generate ES cell lines from other species including cattle suggests that other genes and pathways are involved in maintaining pluripotency in these species. To investigate which genes are involved in bovine pluripotency, expression profiles were generated from morula, blastocyst, trophectoderm and inner cell mass (ICM) samples using microarray analysis. As MAPK inhibition can increase the NANOG/GATA6 ratio in the inner cell mass, additionally blastocysts were cultured in the presence of a MAPK inhibitor and changes in gene expression in the inner cell mass were analysed.

Results

Between morula and blastocyst 3,774 genes were differentially expressed and the largest differences were found in blastocyst up-regulated genes. Gene ontology (GO) analysis shows lipid metabolic process as the term most enriched with genes expressed at higher levels in blastocysts. Genes with higher expression levels in morulae were enriched in the RNA processing GO term. Of the 497 differentially expressed genes comparing ICM and TE, the expression of NANOG, SOX2 and POU5F1 was increased in the ICM confirming their evolutionary preserved role in pluripotency. Several genes implicated to be involved in differentiation or fate determination were also expressed at higher levels in the ICM. Genes expressed at higher levels in the ICM were enriched in the RNA splicing and regulation of gene expression GO term. Although NANOG expression was elevated upon MAPK inhibition, SOX2 and POU5F1 expression showed little increase. Expression of other genes in the MAPK pathway including DUSP4 and SPRY4, or influenced by MAPK inhibition such as IFNT, was down-regulated.

Conclusion

The data obtained from the microarray studies provide further insight in gene expression during bovine embryonic development. They show an expression profile in pluripotent cells that indicates a pluripotent, epiblast-like state. The inability to culture ICM cells as stem cells in the presence of an inhibitor of MAPK activity together with the reported data indicates that MAPK inhibition alone is not sufficient to maintain a pluripotent character in bovine cells.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1448-x) contains supplementary material, which is available to authorized users.     

Microarray gene expression analysis of Monochamus alternatus (Coleoptera: Cerambycidae) after treatment with a sublethal dose of chloramine phosphorus

Monochamus alternatus Hope (Coleoptera: cerambycidae), the Japanese pine sawyer beetle, is a serious pest management concern in pine stands. A new organophosphorus insecticide, chloramine phosphorus (CP), has been applied as an insecticide in China to control M. alternatus. In this study, we investigated gene expression changes in M. alternatus after 4 h of exposure to CP using a 60-mer oligonucleotide microarray. The results showed that 356 genes were differentially expressed, of which 76 were upregulated and 280 were downregulated. GO enrichment analysis of the differentially expressed genes indicated that those involved in structural molecule activity and transporter activity were among the most highly represented. Within the cellular component category of GO, cell and cell part were the most represented GO terms in upregulated genes. “Cellular process” was the most represented GO term in the biological process category among the downregulated genes. Molecular pathway analysis showed that a large portion of the differentially expressed genes were associated with metabolic pathways. This study sought to identify new biomarkers as nonacetylcholinesterase targets to assess the secondary effects of CP on M. alternatus and will provide a valuable resource for the scientific community to study the molecular toxicology of CP.     

Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity

Background  

To identify differentially expressed genes (DEGs) from microarray data, users of the Affymetrix GeneChip system need to select both a preprocessing algorithm to obtain expression-level measurements and a way of ranking genes to obtain the most plausible candidates. We recently recommended suitable combinations of a preprocessing algorithm and gene ranking method that can be used to identify DEGs with a higher level of sensitivity and specificity. However, in addition to these recommendations, researchers also want to know which combinations enhance reproducibility.     

Hypervariable genes—experimental error or hidden dynamics

In a homogeneous group of samples, not all genes of high variability stem from experimental errors in microarray experiments. These expression variations can be attributed to many factors including natural biological oscillations or metabolic processes. The behavior of these genes can tease out important clues about naturally occurring dynamic processes in the organism or experimental system under study. We developed a statistical procedure for the selection of genes with high variability denoted hypervariable (HV) genes. After the exclusion of low expressed genes and a stabilizing log-transformation, the majority of genes have comparable residual variability. Based on an F-test, HV genes are selected as having a statistically significant difference from the majority of variability stabilized genes measured by the ‘reference group’. A novel F-test clustering technique, further noted as ‘F-means clustering’, groups HV genes with similar variability patterns, presumably from their participation in a common dynamic biological process. F-means clustering establishes, for the first time, groups of co-expressed HV genes and is illustrated with microarray data from patients with juvenile rheumatoid arthritis and healthy controls.