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"DE analysis"
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Stratégies d’investissement bas-carbone
Les investisseurs du monde entier sont aujourd’hui de plus en plus nombreux à prendre en considération le risque climatique dans leurs décisions d’investissement. Plusieurs approches ont été développées pour intégrer cette problématique dans l’analyse des investissements et la constitution d’un portefeuille. Dans cet article, nous analysons deux stratégies standards dédiées à l’optimisation de l’empreinte carbone d’un portefeuille : l’exclusion et l’optimisation. Nos simulations révèlent que l’optimisation permet une importante réduction de CO2 en portefeuille, sans accroître son risque et ce avec un faible biais sectoriel. L’optimisation améliore le profil de risque ESG global du portefeuille et répond aux enjeux climatiques soulevés par les investisseurs. Cette approche devrait selon nous devenir la norme dans un avenir proche.
More and more investors the world over are basing their investment choices on an appreciation of climate risk. Several approaches have been developed to include the issue in the investment analysis and portfolio construction process. In this article, we analyse two standard strategies for optimising a portfolio’s carbon footprint: exclusion and optimisation. Our simulations show that optimisation considerably reduces a portfolio’s CO2 content, without increasing portfolio risk and yet with a low sectorial bias. Optimisation both enhances the portfolio’s overall ESG risk profile while addressing investors’ climate-related qualms. The optimisation approach should, in our opinion, shortly become the norm.
Journal Article
Data driven identification of networks of dynamic systems
\"The identification of network connected dynamic systems is currently a hot research topic within the community of systems and control. Other engineering areas, social sciences and system biology are putting a lot of effort in the study of network connected systems. Modeling such networks and the identification of these models from acquired measurements is crucial in the analysis or understanding of the dynamics. Based on these models, synthesis to modify the behavior of the network can also be performed. This book gives a unique overview of state of the art research in the field of identifying networks of linear dynamical systems. This overview combines many of the pioneering contributions from the authors with those of other researchers that play a crucial role in the development of this new field\"-- Provided by publisher.
Book
iGEAK: an interactive gene expression analysis kit for seamless workflow using the R/shiny platform
Background
The use of microarrays and RNA-seq technologies is ubiquitous for transcriptome analyses in modern biology. With proper analysis tools, the differential gene expression analysis process can be significantly accelerated. Many open-source programs provide cutting-edge techniques, but these often require programming skills and lack intuitive and interactive or graphical user interfaces. To avoid bottlenecks impeding seamless analysis processing, we have developed an Interactive Gene Expression Analysis Kit, we term iGEAK, focusing on usability and interactivity. iGEAK is designed to be a simple, intuitive, light-weight that contrasts with heavy-duty programs.
Results
iGEAK is an R/Shiny-based client-side desktop application, providing an interactive gene expression data analysis pipeline for microarray and RNA-seq data. Gene expression data can be intuitively explored using a seamless analysis pipeline consisting of sample selection, differentially expressed gene prediction, protein-protein interaction, and gene set enrichment analyses. For each analysis step, users can easily alter parameters to mine more relevant biological information.
Conclusion
iGEAK is the outcome of close collaboration with wet-bench biologists who are eager to easily explore, mine, and analyze new or public microarray and RNA-seq data. We designed iGEAK as a gene expression analysis pipeline tool to provide essential analysis steps and a user-friendly interactive graphical user interface. iGEAK enables users without programing knowledge to comfortably perform differential gene expression predictions and downstream analyses. iGEAK packages, manuals, tutorials, sample datasets are available at the iGEAK project homepage (
https://sites.google.com/view/iGEAK
).
Journal Article
Cinnamomi ramulus inhibits cancer cells growth by inducing G2/M arrest
Introduction: Cinnamomi ramulus (CR) is one of the most widely used traditional Chinese medicine (TCM) with anti-cancer effects. Analyzing transcriptomic responses of different human cell lines to TCM treatment is a promising approach to understand the unbiased mechanism of TCM. Methods: This study treated ten cancer cell lines with different CR concentrations, followed by mRNA sequencing. Differential expression (DE) analysis and gene set enrichment analysis (GSEA) were utilized to analyze transcriptomic data. Finally, the in silico screening results were verified by in vitro experiments. Results: Both DE and GSEA analysis suggested the Cell cycle pathway was the most perturbated pathway by CR across these cell lines. By analyzing the clinical significance and prognosis of G2/M related genes (PLK1, CDK1, CCNB1, and CCNB2) in various cancer tissues, we found that they were up-regulated in most cancer types, and their down-regulation showed better overall survival rates in cancer patients. Finally, in vitro experiments validation on A549, Hep G2, and HeLa cells suggested that CR can inhibit cell growth by suppressing the PLK1/CDK1/ Cyclin B axis. Discussion: This is the first study to apply transcriptomic analysis to investigate the cancer cell growth inhibition of CR on various human cancer cell lines. The core effect of CR on ten cancer cell lines is to induce G2/M arrest by inhibiting the PLK1/CDK1/Cyclin B axis.
Journal Article
Stability of methods for differential expression analysis of RNA-seq data
Background
As RNA-seq becomes the assay of choice for measuring gene expression levels, differential expression analysis has received extensive attentions of researchers. To date, for the evaluation of DE methods, most attention has been paid on validity. Yet another important aspect of DE methods, stability, is overlooked and has not been studied to the best of our knowledge.
Results
In this study, we empirically show the need of assessing stability of DE methods and propose a stability metric, called Area Under the Correlation curve (AUCOR), that generates the perturbed datasets by a mixture distribution and combines the information of similarities between sets of selected features from these perturbed datasets and the original dataset.
Conclusion
Empirical results support that AUCOR can effectively rank the DE methods in terms of stability for given RNA-seq datasets. In addition, we explore how biological or technical factors from experiments and data analysis affect the stability of DE methods. AUCOR is implemented in the open-source R package AUCOR, with source code freely available at
https://github.com/linbingqing/stableDE
.
Journal Article
BIAM: a new bio-inspired analysis methodology for digital ecosystems based on a scale-free architecture
Today we live in a world of digital objects and digital technology; industry and humanities as well as technologies are truly in the midst of a digital environment driven by ICT and cyber informatics. A digital ecosystem can be defined as a digital environment populated by interacting and competing digital species. Digital species have autonomous, proactive and adaptive behaviors, regulated by peer-to-peer interactions without central control point. An interconnecting architecture with few highly connected nodes (hubs) and many low connected nodes has a scale- free architecture. A new bio-inspired analysis methodology (BIAM) environment, an investigation strategy for information flow, fault and error tolerance detection in digital ecosystems based on a scale-free architecture is presented in this paper. In order to extract the information about modules and digital species role, the analysis methodology, inspired by metabolic network working, implements a set of three interacting techniques, i.e., topological analysis, flux balance analysis and extreme pathway analysis. Highly connected nodes, intermodule connectors and ultra-peripheral nodes can be identified by evaluating their impact on digital ecosystems behavior and addressing their strengthen, fault tolerance and protection countermeasures. Two real case studies of ecosystems have been analyzed in order to test the functionalities of the proposed (BIAM) environment and the goodness of this approach.
Journal Article
DEApp: an interactive web interface for differential expression analysis of next generation sequence data
Background
A growing trend in the biomedical community is the use of Next Generation Sequencing (NGS) technologies in genomics research. The complexity of downstream differential expression (DE) analysis is however still challenging, as it requires sufficient computer programing and command-line knowledge. Furthermore, researchers often need to evaluate and visualize interactively the effect of using differential statistical and error models, assess the impact of selecting different parameters and cutoffs, and finally explore the overlapping consensus of cross-validated results obtained with different methods. This represents a bottleneck that slows down or impedes the adoption of NGS technologies in many labs.
Results
We developed DEApp, an interactive and dynamic web application for differential expression analysis of count based NGS data. This application enables models selection, parameter tuning, cross validation and visualization of results in a user-friendly interface.
Conclusions
DEApp enables labs with no access to full time bioinformaticians to exploit the advantages of NGS applications in biomedical research. This application is freely available at
https://yanli.shinyapps.io/DEApp
and
https://gallery.shinyapps.io/DEApp
.
Journal Article
Targeted proteomics to identify cadmium-induced protein modifications in Glomus mosseae-inoculated pea roots
• Arbuscular mycorrhiza (AM) can increase plant tolerance to heavy metals. A targeted proteomic approach was used to determine the putative identity of some of the proteins induced/modulated by cadmium (Cd) and to analyse the impact of the mycorrhizal process. • The effect of Cd (100 mg Cd kg-1 substrate) applied either at planting or 15 d later on two pea (Pisum sativum) genotypes, differing in sensitivity to Cd inoculated or not with the AM fungus Glomus mosseae, was studied at three levels: plant biomass production, development of G. mosseae and root differential protein display with one- and two-dimensional gel electrophoresis (1-DE and 2-DE) analyses. • Cd-induced growth inhibition was significantly alleviated by mycorrhiza in the Cd-sensitive genotype. The AM symbiosis modulated the expression of several proteins, identified by liquid chromatography-tandem mass spectrometry, newly induced and upregulated or downregulated by Cd. • The protective effect of AM symbiosis towards Cd stress was observed in the Cd-sensitive genotype. Our results demonstrate the usefulness of proteomics to better understand the possible role of AM symbiosis in detoxification/response mechanisms towards Cd in pea plants.
Journal Article
STUDY ON DE-NOISING METHODS FOR SOIL COMPRESSIVE STRESS SIGNAL DURING VIBRATION COMPACTION
The compressive stress signal of soil during vibration compaction is an unstable and transient saltation signal accompanied by broadband noise, and the spectra of the signal and noise always overlap. To extract the ideal original signal from noisy data, this paper studies several signal de-noising methods such as low-pass filtering, multi-resolution wavelet transform, spectrum subtraction and independent component analysis. Experiments show that the traditional low-pass filter is only applicable when the spectra of the signal and noise can be separated in the frequency domain. The multi-resolution wavelet transform can decompose the signal into different frequency bands and remove the noise efficiently by extracting useful the frequency band of the signal, but this method is not reliable when the signal to noise ratio (SNR) is low. Spectrum subtraction can remove strong background noise with stationary statistical characteristics even if the noise level is high and the spectrum of the signal overlaps with that of the noise. Independent component analysis can extract weak signals which are combined with heavy noise and can separate the noise from signal effectively when the independent channel hypothesis holds. These de-noising methods are of great importance for further analysing vibration signals in engineering.
Journal Article