Concept: Exploratory data analysis
The overall goal of the study was to identify functional and behavioral differences between individuals with higher tinnitus distress and individuals with lower tinnitus distress. Subsequent exploratory analyses were conducted to investigate the role physical activity may have on the observed results between high and low distress groups. The purpose of the experiment was to identify brain regions to be targeted in future intervention studies for tinnitus.
Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is important, it is not always clear how to share data in a manner that allows other labs to understand and reproduce published findings. Here we report a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a diffusion MRI study. Because AFQ-Browser is portable-it runs in any web-browser-it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of the dataset (anatomy, diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, aggregating data across labs, and publishing data alongside manuscripts.
Pyteomics-a Python Framework for Exploratory Data Analysis and Rapid Software Prototyping in Proteomics
- Journal of the American Society for Mass Spectrometry
- Published almost 7 years ago
Pyteomics is a cross-platform, open-source Python library providing a rich set of tools for MS-based proteomics. It provides modules for reading LC-MS/MS data, search engine output, protein sequence databases, theoretical prediction of retention times, electrochemical properties of polypeptides, mass and m/z calculations, and sequence parsing. Pyteomics is available under Apache license; release versions are available at the Python Package Index http://pypi.python.org/pyteomics , the source code repository at http://hg.theorchromo.ru/pyteomics , documentation at http://packages.python.org/pyteomics . Pyteomics.biolccc documentation is available at http://packages.python.org/pyteomics.biolccc/ . Questions on installation and usage can be addressed to pyteomics mailing list: firstname.lastname@example.org.
Exploratory analyses of a 126-item self-report assessment of difficulty of spatial behaviours (revision of the Everyday Spatial Behavioral Questionnaire, ESBQ) were used to examine principal components and the underlying root structure of perceived spatial competencies. We also examined laterality measures (handedness, footedness, and earedness), sex, and age as predictors of spatial behaviour component scores. 12 principal components were identified that represented facets of spatial behaviour and perception. Canonical analysis revealed 2 underlying dimensions of perceived difficulty in performing spatial behaviours: difficulty with spatial relations in the context of movement and difficulty with judging how things relate to each other or to a larger surround. Sex was more closely related to the former dimension; laterality measures and age were more closely related to the latter. With respect to specific components, women tended to report more difficulty with making judgements in relation to earth-fixed axes but less difficulty in judging relations to nearby objects and how objects fit together or within a surround. Right-handedness was associated with more perceived difficulty in judging spatial relations while driving, overlaying surfaces, and moving in relation to other objects in nearby space. Future confirmatory analyses will be needed to establish subscales of the ESBQ and their usefulness for practical applications.
Large studies profiling microbial communities and their association with healthy or disease phenotypes are now commonplace. Processed data from many of these studies are publicly available but significant effort is required for users to effectively organize, explore and integrate it, limiting the utility of these rich data resources. Effective integrative and interactive visual and statistical tools to analyze many metagenomic samples can greatly increase the value of these data for researchers. We present Metaviz, a tool for interactive exploratory data analysis of annotated microbiome taxonomic community profiles derived from marker gene or whole metagenome shotgun sequencing. Metaviz is uniquely designed to address the challenge of browsing the hierarchical structure of metagenomic data features while rendering visualizations of data values that are dynamically updated in response to user navigation. We use Metaviz to provide the UMD Metagenome Browser web service, allowing users to browse and explore data for more than 7000 microbiomes from published studies. Users can also deploy Metaviz as a web service, or use it to analyze data through the metavizr package to interoperate with state-of-the-art analysis tools available through Bioconductor. Metaviz is free and open source with the code, documentation and tutorials publicly accessible.
There has been a rapid increase in the use of technology-based activity trackers to promote behavior change. However, little is known about how individuals use these trackers on a day-to-day basis or how tracker use relates to increasing physical activity.
Instead of testing predefined hypotheses, the goal of exploratory data analysis (EDA) is to find what data can tell us. Following this strategy, we re-analyzed a large body of genomic data to study the complex gene regulation in mouse pre-implantation development (PD).
A workshop held at the 2015 annual meeting of the Canadian Society of Microbiologists highlighted compositional data analysis methods and the importance of exploratory data analysis for the analysis of microbiome data sets generated by high-throughput DNA sequencing. A summary of the content of that workshop, a review of new methods of analysis, and information on the importance of careful analyses are presented herein. The workshop focussed on explaining the rationale behind the use of compositional data analysis, and a demonstration of these methods for the examination of 2 microbiome data sets. A clear understanding of bioinformatics methodologies and the type of data being analyzed is essential, given the growing number of studies uncovering the critical role of the microbiome in health and disease and the need to understand alterations to its composition and function following intervention with fecal transplant, probiotics, diet, and pharmaceutical agents.
Despite high levels of equipment distribution through Needle and Syringe Programmes (NSPs) in Australia, the levels of reuse of equipment among people who inject drugs remain concerning. This paper used an exploratory analysis to examine the needs of NSP client that could be addressed by NSPs to enhance service impact and blood-borne virus risk practices.
With demand increasing for dissemination and implementation (D&I) training programs in the USA and other countries, more structured, competency-based, and tested curricula are needed to guide training programs. There are many benefits to the use of competencies in practice-based education such as the establishment of rigorous standards as well as providing an additional metrics for development and growth. As the first aim of a D&I training grant, an exploratory study was conducted to establish a new set of D&I competencies to guide training in D&I research.