scholarly journals Rapid Transient Transcriptional Adaptation to Hypergravity in Jurkat T Cells Revealed by Comparative Analysis of Microarray and RNA-Seq Data

2021 ◽  
Vol 22 (16) ◽  
pp. 8451
Author(s):  
Christian Vahlensieck ◽  
Cora S. Thiel ◽  
Jan Adelmann ◽  
Beatrice A. Lauber ◽  
Jennifer Polzer ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Research Field ◽  
Data Sets ◽  
Rna Seq ◽  
Altered Gravity ◽  
Chromosomal Distribution ◽  
Data Set ◽  
Gene Expression Response ◽  
Temporal Adaptation ◽  
Transcriptional Adaptation

Cellular responses to micro- and hypergravity are rapid and complex and appear within the first few seconds of exposure. Transcriptomic analyses are a valuable tool to analyze these genome-wide cellular alterations. For a better understanding of the cellular dynamics upon altered gravity exposure, it is important to compare different time points. However, since most of the experiments are designed as endpoint measurements, the combination of cross-experiment meta-studies is inevitable. Microarray and RNA-Seq analyses are two of the main methods to study transcriptomics. In the field of altered gravity research, both methods are frequently used. However, the generation of these data sets is difficult and time-consuming and therefore the number of available data sets in this research field is limited. In this study, we investigated the comparability of microarray and RNA-Seq data and applied the results to a comparison of the transcriptomics dynamics between the hypergravity conditions during two real flight platforms and a centrifuge experiment to identify temporal adaptation processes. We performed a comparative study on an Affymetrix HTA2.0 microarray and a paired-end RNA-Seq data set originating from the same Jurkat T cell RNA samples from a short-term hypergravity experiment. The overall agreeability was high, with better sensitivity of the RNA-Seq analysis. The microarray data set showed weaknesses on the level of single upregulated genes, likely due to its normalization approach. On an aggregated level of biotypes, chromosomal distribution, and gene sets, both technologies performed equally well. The microarray showed better performance on the detection of altered gravity-related splicing events. We found that all initially altered transcripts fully adapted after 15 min to hypergravity and concluded that the altered gene expression response to hypergravity is transient and fully reversible. Based on the combined multiple-platform meta-analysis, we could demonstrate rapid transcriptional adaptation to hypergravity, the differential expression of the ATPase subunits ATP6V1A and ATP6V1D, and the cluster of differentiation (CD) molecules CD1E, CD2AP, CD46, CD47, CD53, CD69, CD96, CD164, and CD226 in hypergravity. We could experimentally demonstrate that it is possible to develop methodological evidence for the meta-analysis of individual data.

scholarly journals Solving for X: evidence for sex-specific autism biomarkers across multiple transcriptomic studies

2018 ◽  
Author(s):  
Samuel C. Lee ◽  
Thomas P. Quinn ◽  
Jerry Lai ◽  
Sek Won Kong ◽  
Irva Hertz-Picciotto ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Genetic Disorders ◽  
Autism Spectrum ◽  
Large Female ◽  
Data Sets ◽  
Post Mortem ◽  
Rna Seq ◽  
Data Set ◽  
Post Mortem Brain ◽  
High Concordance

AbstractAutism spectrum disorder (ASD) is a markedly heterogeneous condition with a varied phenotypic presentation. Its high concordance among siblings, as well as its clear association with specific genetic disorders, both point to a strong genetic etiology. However, the molecular basis of ASD is still poorly understood, although recent studies point to the existence of sex-specific ASD pathophysiologies and biomarkers. Despite this, little is known about how exactly sex influences the gene expression signatures of ASD probands. In an effort to identify sex-dependent biomarkers (and characterise their function), we present an analysis of a single paired-end post-mortem brain RNA-Seq data set and a meta-analysis of six blood-based microarray data sets. Here, we identify several genes with sex-dependent dysregulation, and many more with sex-independent dysregulation. Moreover, through pathway analysis, we find that these sex-independent biomarkers have substantially different biological roles than the sex-dependent biomarkers, and that some of these pathways are ubiquitously dysregulated in both post-mortem brain and blood. We conclude by synthesizing the discovered biomarker profiles with the extant literature, by highlighting the advantage of studying sex-specific dysregulation directly, and by making a call for new transcriptomic data that comprise large female cohorts.

A systematical approach to classification problems with feature space heterogeneity

Kybernetes ◽  
2019 ◽  
Vol 48 (9) ◽  
pp. 2006-2029
Author(s):  
Hongshan Xiao ◽  
Yu Wang
Keyword(s):  
Factor Analysis ◽  
Meta Analysis ◽  
Feature Space ◽  
Classification Performance ◽  
Classification Algorithm ◽  
Significant Feature ◽  
Data Sets ◽  
Data Set ◽  
Classification Techniques ◽  
Content Type

Purpose Feature space heterogeneity exists widely in various application fields of classification techniques, such as customs inspection decision, credit scoring and medical diagnosis. This paper aims to study the relationship between feature space heterogeneity and classification performance. Design/methodology/approach A measurement is first developed for measuring and identifying any significant heterogeneity that exists in the feature space of a data set. The main idea of this measurement is derived from a meta-analysis. For the data set with significant feature space heterogeneity, a classification algorithm based on factor analysis and clustering is proposed to learn the data patterns, which, in turn, are used for data classification. Findings The proposed approach has two main advantages over the previous methods. The first advantage lies in feature transform using orthogonal factor analysis, which results in new features without redundancy and irrelevance. The second advantage rests on samples partitioning to capture the feature space heterogeneity reflected by differences of factor scores. The validity and effectiveness of the proposed approach is verified on a number of benchmarking data sets. Research limitations/implications Measurement should be used to guide the heterogeneity elimination process, which is an interesting topic in future research. In addition, to develop a classification algorithm that enables scalable and incremental learning for large data sets with significant feature space heterogeneity is also an important issue. Practical implications Measuring and eliminating the feature space heterogeneity possibly existing in the data are important for accurate classification. This study provides a systematical approach to feature space heterogeneity measurement and elimination for better classification performance, which is favorable for applications of classification techniques in real-word problems. Originality/value A measurement based on meta-analysis for measuring and identifying any significant feature space heterogeneity in a classification problem is developed, and an ensemble classification framework is proposed to deal with the feature space heterogeneity and improve the classification accuracy.

scholarly journals Has open data arrived at the British Medical Journal (BMJ)? An observational study

BMJ Open ◽  
2016 ◽  
Vol 6 (10) ◽  
pp. e011784 ◽  
Author(s):  
Anisa Rowhani-Farid ◽  
Adrian G Barnett
Keyword(s):  
Medical Journal ◽  
British Medical Journal ◽  
Observational Study ◽  
Data Sharing ◽  
Meta Analysis ◽  
Open Data ◽  
Research Articles ◽  
Data Sets ◽  
Data Set ◽  
Article 50

ObjectiveTo quantify data sharing trends and data sharing policy compliance at the British Medical Journal (BMJ) by analysing the rate of data sharing practices, and investigate attitudes and examine barriers towards data sharing.DesignObservational study.SettingThe BMJ research archive.Participants160 randomly sampled BMJ research articles from 2009 to 2015, excluding meta-analysis and systematic reviews.Main outcome measuresPercentages of research articles that indicated the availability of their raw data sets in their data sharing statements, and those that easily made their data sets available on request.Results3 articles contained the data in the article. 50 out of 157 (32%) remaining articles indicated the availability of their data sets. 12 used publicly available data and the remaining 38 were sent email requests to access their data sets. Only 1 publicly available data set could be accessed and only 6 out of 38 shared their data via email. So only 7/157 research articles shared their data sets, 4.5% (95% CI 1.8% to 9%). For 21 clinical trials bound by the BMJ data sharing policy, the per cent shared was 24% (8% to 47%).ConclusionsDespite the BMJ's strong data sharing policy, sharing rates are low. Possible explanations for low data sharing rates could be: the wording of the BMJ data sharing policy, which leaves room for individual interpretation and possible loopholes; that our email requests ended up in researchers spam folders; and that researchers are not rewarded for sharing their data. It might be time for a more effective data sharing policy and better incentives for health and medical researchers to share their data.

scholarly journals Bias, robustness and scalability in differential expression analysis of single-cell RNA-seq data

2017 ◽  
Author(s):  
Charlotte Soneson ◽  
Mark D. Robinson
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Statistical Methods ◽  
Expression Analysis ◽  
Method Development ◽  
Differential Expression Analysis ◽  
Data Sets ◽  
Rna Seq ◽  
Data Set ◽  
Extensive Evaluation

AbstractBackgroundAs single-cell RNA-seq (scRNA-seq) is becoming increasingly common, the amount of publicly available data grows rapidly, generating a useful resource for computational method development and extension of published results. Although processed data matrices are typically made available in public repositories, the procedure to obtain these varies widely between data sets, which may complicate reuse and cross-data set comparison. Moreover, while many statistical methods for performing differential expression analysis of scRNA-seq data are becoming available, their relative merits and the performance compared to methods developed for bulk RNA-seq data are not sufficiently well understood.ResultsWe present conquer, a collection of consistently processed, analysis-ready public single-cell RNA-seq data sets. Each data set has count and transcripts per million (TPM) estimates for genes and transcripts, as well as quality control and exploratory analysis reports. We use a subset of the data sets available in conquer to perform an extensive evaluation of the performance and characteristics of statistical methods for differential gene expression analysis, evaluating a total of 30 statistical approaches on both experimental and simulated scRNA-seq data.ConclusionsConsiderable differences are found between the methods in terms of the number and characteristics of the genes that are called differentially expressed. Pre-filtering of lowly expressed genes can have important effects on the results, particularly for some of the methods originally developed for analysis of bulk RNA-seq data. Generally, however, methods developed for bulk RNA-seq analysis do not perform notably worse than those developed specifically for scRNA-seq.

scholarly journals Construction of competing endogenous RNA networks from paired RNA-seq data sets by pointwise mutual information

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Chaowang Lan ◽  
Hui Peng ◽  
Gyorgy Hutvagner ◽  
Jinyan Li
Keyword(s):  
Breast Cancer ◽  
Mutual Information ◽  
Noncoding Rna ◽  
Regulation Mechanism ◽  
Data Sets ◽  
Rna Seq ◽  
Competing Endogenous Rna ◽  
Data Set ◽  
Cerna Network ◽  
Pointwise Mutual Information

Abstract Background A long noncoding RNA (lncRNA) can act as a competing endogenous RNA (ceRNA) to compete with an mRNA for binding to the same miRNA. Such an interplay between the lncRNA, miRNA, and mRNA is called a ceRNA crosstalk. As an miRNA may have multiple lncRNA targets and multiple mRNA targets, connecting all the ceRNA crosstalks mediated by the same miRNA forms a ceRNA network. Methods have been developed to construct ceRNA networks in the literature. However, these methods have limits because they have not explored the expression characteristics of total RNAs. Results We proposed a novel method for constructing ceRNA networks and applied it to a paired RNA-seq data set. The first step of the method takes a competition regulation mechanism to derive candidate ceRNA crosstalks. Second, the method combines a competition rule and pointwise mutual information to compute a competition score for each candidate ceRNA crosstalk. Then, ceRNA crosstalks which have significant competition scores are selected to construct the ceRNA network. The key idea, pointwise mutual information, is ideally suitable for measuring the complex point-to-point relationships embedded in the ceRNA networks. Conclusion Computational experiments and results demonstrate that the ceRNA networks can capture important regulatory mechanism of breast cancer, and have also revealed new insights into the treatment of breast cancer. The proposed method can be directly applied to other RNA-seq data sets for deeper disease understanding.

Investigation into the development and historical roots of collection development by referenced publication year spectroscopy

2020 ◽  
Vol 39 (4) ◽  
pp. 97-103
Author(s):  
Basharat Ahmad Malik ◽  
Ashiya Ahmadi
Keyword(s):  
Research Work ◽  
Research Field ◽  
Library Science ◽  
Data Sets ◽  
Collection Development ◽  
Data Set ◽  
Content Type ◽  
Abstract Author ◽  
Time Period ◽  
History Of

Purpose The purpose of this study is the application of a recently developed quantitative method named Referenced Publication Year Spectroscopy (RPYS) in the spectrum of Collection Development. RPYS portrays peak years to be recognized in citations in a research field that guarantees to assist in the identification of significant contributions and groundbreaking revelations in a research field. Design/methodology/approach Preliminary data of the study has been extracted from Web of Science (WoS) by using two phrases “collection development” and “collection building” to search in terms of the topic (comprising four parts: title, abstract, author keywords and KeyWords Plus). The search was restricted to the time period 1974-2017, which formulated a data set of 1,682 documents covering 29,017 cited references. The program CRExplorer (www.crexplorer.net) was used for the extraction of cited references from the data sets downloaded from WoS. Further analysis was performed manually using MS-Excel 2016. Findings The present study identified seminal works, which contributed to a high extent to the evolution and development of collection development. The analysis of all cited references using the RPYS method showed nine peaks, which present historical roots of collection development and revealed that the basic idea of this very subfield of library science dates centuries back. Moreover, the results of the investigation on most effective documents (in the form of peaks) revealed that the field of collection development significantly influenced by the works of authors such as Gabriel Naudé, Gabriel Peignot, Giulio Petzholdt, P L Gross, E M Gross, Richard Trueswell, Allen Kent, Ross Atkinson, etc. Practical implications The analysis of works cited in publications helps to ascertain important intellectual contributions related to a particular domain of knowledge. It not only helps in extracting the most important works but also it helps to reconstruct the history of a specific research field by examining the specific role of the cited references. Therefore, the results of the study could be useful for researchers, practitioners, scholars and more specifically bibliophiles, bibliographers and librarians to gain a better understanding of seminal works in the spectrum of collection development. Originality/value To the best of authors’ knowledge, the present research work is unique and novel in the spectrum of collection development, which explored and examined the pivotal works in the field by using the RPYS method.

scholarly journals Combining linkage data sets for meta-analysis and mega-analysis: the GAW15 rheumatoid arthritis data set

2007 ◽  
Vol 1 (Suppl 1) ◽  
pp. S104 ◽  
Author(s):  
Ricardo Segurado ◽  
Marian L Hamshere ◽  
Beate Glaser ◽  
Ivan Nikolov ◽  
Valentina Moskvina ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Meta Analysis ◽  
Linkage Data ◽  
Data Sets ◽  
Data Set ◽  
Rheumatoid Arthritis Data

scholarly journals The Impact of Normalization Methods on RNA-Seq Data Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
J. Zyprych-Walczak ◽  
A. Szabelska ◽  
L. Handschuh ◽  
K. Górczak ◽  
K. Klamecka ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Data Sets ◽  
Complex Data ◽  
Rna Seq ◽  
Medical Problems ◽  
Data Set ◽  
Normalization Methods ◽  
Wide Range ◽  
The Impact ◽  
Selection Of

High-throughput sequencing technologies, such as the Illumina Hi-seq, are powerful new tools for investigating a wide range of biological and medical problems. Massive and complex data sets produced by the sequencers create a need for development of statistical and computational methods that can tackle the analysis and management of data. The data normalization is one of the most crucial steps of data processing and this process must be carefully considered as it has a profound effect on the results of the analysis. In this work, we focus on a comprehensive comparison of five normalization methods related to sequencing depth, widely used for transcriptome sequencing (RNA-seq) data, and their impact on the results of gene expression analysis. Based on this study, we suggest a universal workflow that can be applied for the selection of the optimal normalization procedure for any particular data set. The described workflow includes calculation of the bias and variance values for the control genes, sensitivity and specificity of the methods, and classification errors as well as generation of the diagnostic plots. Combining the above information facilitates the selection of the most appropriate normalization method for the studied data sets and determines which methods can be used interchangeably.

Missing data in open-data era – a barrier to multiomics integration

2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Monika Piwowar ◽  
Wiktor Jurkowski
Keyword(s):  
Quantitative Data ◽  
Open Data ◽  
Multidimensional Data ◽  
Data Sets ◽  
Biological Processes ◽  
Rna Seq ◽  
Data Set ◽  
Complex Interactions ◽  
Integrate Data ◽  
Generation Sequencing

AbstractThe exploration of complex interactions in biological systems is one of the main aims in nature science nowadays. Progress in this area is possible because of high-throughput omics technologies and the computational surge. The development of analytical methods “is trying to keep pace” with the development of molecular biology methods that provide increasingly large amounts of data – omics data. Specialized databases consist of ever-larger collections of experiments that are usually conducted by one next-generation sequencing technique (e.g. RNA-seq). Other databases integrate data by defining qualitative relationships between individual objects in the form of ontologies, interactions, and pathways (e.g. GO, KEGG, and String). However, there are no open-source complementary quantitative data sets for the biological processes studied, including information from many levels of the organism organization, which would allow the development of multidimensional data analysis methods (multiscale and insightful overviews of biological processes). In the paper, the lack of omics complementary quantitative data set, which would help integrate the defined qualitative biological relationships of individual biomolecules with statistical, computational methods, is discussed.

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