SISTEMA: A large and standardized collection of transcriptome data sets for human pluripotent stem cell research.
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ABSTRACT:
SUBMITTER: Jarrige M
PROVIDER: S-EPMC8271161 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The past decade has witnessed an extremely rapid increase in the number of newly established stem cell lines. However, due to the lack of a standardized format, data exchange among stem cell line resources has been challenging, and no system can search all stem cell lines across resources worldwide. To solve this problem, we have developed the Integrated Collection of Stem Cell Bank data (ICSCB) (http://icscb.stemcellinformatics.org/), the largest database search portal for stem cell line information, based on the standardized data items and terms of the MIACARM framework. Currently, ICSCB can retrieve >16,000 cell lines from four major data resources in Europe, Japan, and the United States. ICSCB is automatically updated to provide the latest cell line information, and its integrative search helps users collect cell line information for over 1,000 diseases, including many rare diseases worldwide, which has been a formidable task, thereby distinguishing itself from other database search portals.
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Project description:There exist a large number of cohort studies that have been used to identify genetic and biological risk factors for developing Alzheimer's disease (AD). However, there is a disagreement between studies as to how strongly these risk factors affect the rate of progression through diagnostic groups toward AD. We have calculated the probability of transitioning through diagnostic groups in six studies and considered how uncertainty around the strength of the effect of these risk factors affects estimates of the distribution of individuals in each diagnostic group in an AD clinical trial simulator. In this work, we identify the optimal choice of widely collected variables for comparing data sets and calculating probabilities of progression toward AD. We use the estimated transition probabilities to inform stochastic simulations of AD progression that are based on a Markov model and compare predicted incidence rates to those in a community-based study, the Cardiovascular Health Study.
| S-EPMC6804515 | biostudies-literature
Project description:Background: During the 2016 Assisi Think Tank Meeting (ATTM) on breast cancer, the panel of experts proposed developing a validated system, based on rapid learning health care (RLHC) principles, to standardize inter-center data collection and promote personalized treatments for breast cancer. Material and Methods: The seven-step Breast LArge DatabasE (BLADE) project included data collection, analysis, application, and evaluation on a data-sharing platform. The multidisciplinary team developed a consensus-based ontology of validated variables with over 80% agreement. This English-language ontology constituted a breast cancer library with seven knowledge domains: baseline, primary systemic therapy, surgery, adjuvant systemic therapies, radiation therapy, follow-up, and toxicity. The library was uploaded to the BLADE domain. The safety of data encryption and preservation was tested according to General Data Protection Regulation (GDPR) guidelines on data from 15 clinical charts. The system was validated on 64 patients who had undergone post-mastectomy radiation therapy. In October 2018, the BLADE system was approved by the Ethical Committee of Fondazione Policlinico Gemelli IRCCS, Rome, Italy (Protocol No. 0043996/18). Results: From June 2016 to July 2019, the multidisciplinary team completed the work plan. An ontology of 218 validated variables was uploaded to the BLADE domain. The GDPR safety test confirmed encryption and data preservation (on 5000 random cases). All validation benchmarks were met. Conclusion:BLADE is a support system for follow-up and assessment of breast cancer care. To successfully develop and validate it as the first standardized data collection system, multidisciplinary collaboration was crucial in selecting its ontology and knowledge domains. BLADE is suitable for multi-center uploading of retrospective and prospective clinical data, as it ensures anonymity and data privacy.
| S-EPMC7926376 | biostudies-literature
Project description:The collection of material flow data is the first step in the evaluation of the circular economy performance and material metabolism at the city level. However, Chinese statistical data are published by Chinese National Bureau of Statistics, and provincial and municipal Bureau of Statistics. This resulted in data being scattered in dispersed sources and varying between cities, even brings about mistakes. Therefore, we established a standardized data collection and accounting method with regular data sources for Chinese cities. In this data collection method, material flow accounting mainly consists of three parts: direct material input, material recycling, and waste disposal. It covers four types of materials, including fossil fuels, biomass, metal minerals, and non-metallic minerals with 155 items. We combined the data sources for the material flow accounting within a standardized Excel spreadsheet with detailed information on statistical data sources and equations to convert the information into material flow data. The statistical data were derived from the China City Statistical Yearbook, the provincial Statistic Yearbook, and the city's statistical yearbook. The estimated data in the material accounting were obtained by converting statistical data using relative coefficients. According to the main sources and features of materials use, the intersectoral material flows can also be estimated following this standardized method for urban metabolism analysis, circular economy performance evaluation, and ecological network analysis. The standardized method for material flow data collection was adopted in the article "H. Gao, X. Tian, Y. Zhang, L. Shi, F. Shi, 2021. Evaluating circular economy performance based on ecological network analysis: A framework and application at city level. 105257. Resources, Conservation & Recycling. https://doi.org/10.1016/j.resconrec.2020.105257".
| S-EPMC8039539 | biostudies-literature
Project description:Hematopoietic stem cells (HSCs) are an essential source and reservoir for normal hematopoiesis, and their function is compromised in many blood disorders. HSC research has benefitted from the recent development of single-cell molecular profiling technologies, where single-cell RNA sequencing (scRNA-seq) in particular has rapidly become an established method to profile HSCs and related hematopoietic populations. The classic definition of HSCs relies on transplantation assays, which have been used to validate HSC function for cell populations defined by flow cytometry. Flow cytometry information for single cells, however, is not available for many new high-throughput scRNA-seq methods, thus highlighting an urgent need for the establishment of alternative ways to pinpoint the likely HSCs within large scRNA-seq data sets. To address this, we tested a range of machine learning approaches and developed a tool, hscScore, to score single-cell transcriptomes from murine bone marrow based on their similarity to gene expression profiles of validated HSCs. We evaluated hscScore across scRNA-seq data from different laboratories, which allowed us to establish a robust method that functions across different technologies. To facilitate broad adoption of hscScore by the wider hematopoiesis community, we have made the trained model and example code freely available online. In summary, our method hscScore provides fast identification of mouse bone marrow HSCs from scRNA-seq measurements and represents a broadly useful tool for analysis of single-cell gene expression data.
| S-EPMC6900257 | biostudies-literature
Project description:As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.
| S-EPMC7038760 | biostudies-literature
Project description:Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we present a measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination (R(2)) of the data relative to the regression function. MIC belongs to a larger class of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships. We apply MIC and MINE to data sets in global health, gene expression, major-league baseball, and the human gut microbiota and identify known and novel relationships.
| S-EPMC3325791 | biostudies-other
Project description:BackgroundMulti-dimensional scaling (MDS) is aimed to represent high dimensional data in a low dimensional space with preservation of the similarities between data points. This reduction in dimensionality is crucial for analyzing and revealing the genuine structure hidden in the data. For noisy data, dimension reduction can effectively reduce the effect of noise on the embedded structure. For large data set, dimension reduction can effectively reduce information retrieval complexity. Thus, MDS techniques are used in many applications of data mining and gene network research. However, although there have been a number of studies that applied MDS techniques to genomics research, the number of analyzed data points was restricted by the high computational complexity of MDS. In general, a non-metric MDS method is faster than a metric MDS, but it does not preserve the true relationships. The computational complexity of most metric MDS methods is over O(N2), so that it is difficult to process a data set of a large number of genes N, such as in the case of whole genome microarray data.ResultsWe developed a new rapid metric MDS method with a low computational complexity, making metric MDS applicable for large data sets. Computer simulation showed that the new method of split-and-combine MDS (SC-MDS) is fast, accurate and efficient. Our empirical studies using microarray data on the yeast cell cycle showed that the performance of K-means in the reduced dimensional space is similar to or slightly better than that of K-means in the original space, but about three times faster to obtain the clustering results. Our clustering results using SC-MDS are more stable than those in the original space. Hence, the proposed SC-MDS is useful for analyzing whole genome data.ConclusionOur new method reduces the computational complexity from O(N3) to O(N) when the dimension of the feature space is far less than the number of genes N, and it successfully reconstructs the low dimensional representation as does the classical MDS. Its performance depends on the grouping method and the minimal number of the intersection points between groups. Feasible methods for grouping methods are suggested; each group must contain both neighboring and far apart data points. Our method can represent high dimensional large data set in a low dimensional space not only efficiently but also effectively.
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Project description:Induced pluripotent stem cells (iPSCs) and human embryonic stem cells (hESCs) differentiated into hepatocyte-like cells (HLCs) provide a defined and renewable source of cells for drug screening, toxicology and regenerative medicine. We previously reprogrammed human fetal foreskin fibroblast cells (HFF1) into iPSCs employing an episomal plasmid-based integration-free approach, this iPSC-line and the hESC lines H1 and H9 were used to model hepatogenesis in vitro. Biochemical characterisation confirmed glycogen storage, ICG uptake and release, urea and bile acid production, as well as CYP3A4 activity. Microarray-based transcriptome analyses was carried out using RNA isolated from the undifferentiated pluripotent stem cells and subsequent differentiation stages- definitive endoderm (DE) hepatic endoderm (HE) and HLCs. K-means identified 100 distinct clusters, for example, POU5F1/OCT4 marking the undifferentiated stage, SOX17 the DE stage, HNF4α the HE stage, and ALB specific to HLCs, fetal liver and primary human hepatocytes (PHH). This data descriptor describes these datasets which should be useful for gaining new insights into the molecular basis of hepatogenesis and associated gene regulatory networks.
| S-EPMC5848791 | biostudies-literature
Project description:Various public transport (PT) agencies publish their route and timetable information with the General Transit Feed Specification (GTFS) as the standard open format. Timetable data are commonly used for PT passenger routing. They can also be used for studying the structure and organization of PT networks, as well as the accessibility and the level of service these networks provide. However, using raw GTFS data is challenging as researchers need to understand the details of the GTFS data format, make sure that the data contain all relevant modes of public transport, and have no errors. To lower the barrier for using GTFS data in research, we publish a curated collection of 25 cities' public transport networks in multiple easy-to-use formats including network edge lists, temporal network event lists, SQLite databases, GeoJSON files, and the GTFS data format. This collection promotes the study of how PT is organized across the globe, and also provides a testbed for developing tools for PT network analysis and PT routing algorithms.
| S-EPMC5952869 | biostudies-literature