GraviTIE: Exploratory Analysis of Large-Scale Heterogeneous Image Collections

2019 ◽  
Author(s):  
Sean T. Yang ◽  
Luke Rodriguez ◽  
Jevin D. West ◽  
Bill Howe
Keyword(s):  
Large Scale ◽  
Exploratory Analysis

scholarly journals ComplexBrowser: a tool for identification and quantification of protein complexes in large scale proteomics datasets

2019 ◽  
Author(s):  
Wojciech Michalak ◽  
Vasileios Tsiamis ◽  
Veit Schwämmle ◽  
Adelina Rogowska-Wrzesińska
Keyword(s):  
T Cell Activation ◽  
Protein Complex ◽  
Quantitative Proteomics ◽  
Large Scale ◽  
Protein Complexes ◽  
Quantitative Measure ◽  
Exploratory Analysis ◽  
List Type ◽  
Link Type ◽  
Complex Components

AbstractWe have developed ComplexBrowser, an open source, online platform for supervised analysis of quantitative proteomics data that focuses on protein complexes. The software uses information from CORUM and Complex Portal databases to identify protein complex components. Based on the expression changes of individual complex subunits across the proteomics experiment it calculates Complex Fold Change (CFC) factor that characterises the overall protein complex expression trend and the level of subunit co-regulation. Thus up- and down-regulated complexes can be identified. It provides interactive visualisation of protein complexes composition and expression for exploratory analysis. It also incorporates a quality control step that includes normalisation and statistical analysis based on Limma test. ComplexBrowser performance was tested on two previously published proteomics studies identifying changes in protein expression in human adenocarcinoma tissue and during activation of mouse T-cells. The analysis revealed 1519 and 332 protein complexes, of which 233 and 41 were found co-ordinately regulated in the respective studies. The adopted approach provided evidence for a shift to glucose-based metabolism and high proliferation in adenocarcinoma tissues and identification of chromatin remodelling complexes involved in mouse T-cell activation. The results correlate with the original interpretation of the experiments and also provide novel biological details about protein complexes affected. ComplexBrowser is, to our knowledge, the first tool to automate quantitative protein complex analysis for high-throughput studies, providing insights into protein complex regulation within minutes of analysis.A fully functional demo version of ComplexBrowser v1.0 is available online via http://computproteomics.bmb.sdu.dk/Apps/ComplexBrowser/The source code can be downloaded from: https://bitbucket.org/michalakw/complexbrowserHighlightsAutomated analysis of protein complexes in proteomics experimentsQuantitative measure of the coordinated changes in protein complex componentsInteractive visualisations for exploratory analysis of proteomics resultsIn briefComplexBrowser is capable of identifying protein complexes in datasets obtained from large scale quantitative proteomics experiments. It provides, in the form of the CFC factor, a quantitative measure of the coordinated changes in complex components. This facilitates assessing the overall trends in the processes governed by the identified protein complexes providing a new and complementary way of interpreting proteomics experiments.

scholarly journals Analyzing history-related posts in twitter

2020 ◽  
Author(s):  
Yasunobu Sumikawa ◽  
Adam Jatowt
Keyword(s):  
Large Scale ◽  
Exploratory Analysis ◽  
Content Dissemination ◽  
Content Sharing ◽  
The Past ◽  
Twitter Data ◽  
Convenient Means ◽  
Content Recommendation ◽  
Share Information ◽  
Time Aware

Abstract Microblogging platforms such as Twitter have been increasingly used nowadays to share information between users. They are also convenient means for propagating content related to history. Hence, from the research viewpoint they can offer opportunities to analyze the way in which users refer to the past, and how as well when such references appear and what purposes they serve. Such study could allow to quantify the interest degree and the mechanisms behind content dissemination. We report the results of a large scale exploratory analysis of history-oriented posts in microblogs based on a 28-month-long snapshot of Twitter data. The results can increase our understanding of the characteristics of history-focused content sharing in Twitter. They can also be used for guiding the design of content recommendation systems as well as time-aware search applications.

scholarly journals Linking the Endocannabinoidome with Specific Metabolic Parameters in an Overweight and Insulin-Resistant Population: From Multivariate Exploratory Analysis to Univariate Analysis and Construction of Predictive Models

Cells ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 71
Author(s):  
Clara Depommier ◽  
Nicolas Flamand ◽  
Rudy Pelicaen ◽  
Dominique Maiter ◽  
Jean-Paul Thissen ◽  
...  
Keyword(s):  
Large Scale ◽  
Cohort Analysis ◽  
Univariate Analysis ◽  
Principal Component ◽  
Exploratory Analysis ◽  
Metabolic Parameters ◽  
Good Prediction ◽  
Bioactive Lipids ◽  
Metabolic Complications ◽  
Insulin Resistant

The global obesity epidemic continues to rise worldwide. In this context, unraveling new interconnections between biological systems involved in obesity etiology is highly relevant. Dysregulation of the endocannabinoidome (eCBome) is associated with metabolic complications in obesity. This study aims at deciphering new associations between circulating endogenous bioactive lipids belonging to the eCBome and metabolic parameters in a population of overweight or obese individuals with metabolic syndrome. To this aim, we combined different multivariate exploratory analysis methods: canonical correlation analysis and principal component analysis, revealed associations between eCBome subsets, and metabolic parameters such as leptin, lipopolysaccharide-binding protein, and non-esterified fatty acids (NEFA). Subsequent construction of predictive regression models according to the linear combination of selected endocannabinoids demonstrates good prediction performance for NEFA. Descriptive approaches reveal the importance of specific circulating endocannabinoids and key related congeners to explain variance in the metabolic parameters in our cohort. Analysis of quartiles confirmed that these bioactive lipids were significantly higher in individuals characterized by important levels for aforementioned metabolic variables. In conclusion, by proposing a methodology for the exploration of large-scale data, our study offers additional evidence of the existence of an interplay between eCBome related-entities and metabolic parameters known to be altered in obesity.

scholarly journals Non-motor correlates of wrist-worn wearable sensor use in Parkinson’s disease: an exploratory analysis

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Daniel J. van Wamelen ◽  
Shweta Hota ◽  
Aleksandra Podlewska ◽  
Valentina Leta ◽  
Dhaval Trivedi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gastrointestinal Tract ◽  
Large Scale ◽  
Disease Duration ◽  
Wearable Sensors ◽  
International Study ◽  
Exploratory Analysis ◽  
College Hospital ◽  
Gastrointestinal Problems

Abstract Wearable sensors are becoming increasingly more available in Parkinson’s disease and are used to measure motor function. Whether non-motor symptoms (NMS) can also be measured with these wearable sensors remains unclear. We therefore performed a retrospective, exploratory, analysis of 108 patients with a diagnosis of idiopathic Parkinson’s disease enroled in the Non-motor Longitudinal International Study (UKCRN No. 10084) at King’s College Hospital, London, to determine the association between the range and nature of NMS and an accelerometer-based outcome measure of bradykinesia (BKS) and dyskinesia (DKS). NMS were assessed by the validated NMS Scale, and included, e.g., cognition, mood and sleep, and gastrointestinal, urinary and sexual problems. Multiple linear regression modelling was used to identify NMS associated with BKS and DKS. We found that BKS was associated with domains 6 (gastrointestinal tract; p = 0.006) and 8 (sexual function; p = 0.003) of the NMS scale. DKS was associated with domains 3 (mood/cognition; p = 0.016), 4 (perceptual problems; p = 0.025), 6 (gastrointestinal tract; p = 0.029) and 9 (miscellaneous, p = 0.003). In the separate domains, constipation was significantly associated with BKS. Delusions, dysphagia, hyposmia, weight change and hyperhidrosis were identified as significantly associated with DKS. None of the NMSS domains were associated with disease duration (p ≥ 0.08). In conclusion, measures of BKS and DKS were mainly associated with gastrointestinal problems, independent of disease duration, showing the potential for wearable devices to pick up on these symptoms. These exploratory results deserve further exploration, and more research on this topic in the form of comprehensive large-scale studies is needed.

scholarly journals Revealing multi-scale population structure in large cohorts

2018 ◽  
Author(s):  
Alex Diaz-Papkovich ◽  
Luke Anderson-Trocmé ◽  
Simon Gravel
Keyword(s):  
Population Structure ◽  
Large Scale ◽  
Large Population ◽  
Computational Cost ◽  
General Purpose ◽  
Exploratory Analysis ◽  
Fine Scale ◽  
Using Data ◽  
The Uk ◽  
Unbalanced Sampling

AbstractGenetic structure in large cohorts results from technical, sampling and demographic variation. Visualisation is therefore a first step in most genomic analyses. However, existing data exploration methods struggle with unbalanced sampling and the many scales of population structure. We investigate an approach to dimension reduction of genomic data that combines principal components analysis (PCA) with uniform manifold approximation and projection (UMAP) to succinctly illustrate population structure in large cohorts and capture their relationships on local and global scales. Using data from large-scale genomic datasets, we demonstrate that PCA-UMAP effectively clusters closely related individuals while placing them in a global continuum of genetic variation. This approach reveals previously overlooked subpopulations within the American Hispanic population and fine-scale relationships between geography, genotypes, and phenotypes in the UK population. This opens new lines of investigation for demographic research and statistical genetics. Given its small computational cost, PCA-UMAP also provides a general-purpose approach to exploratory analysis in population-scale datasets.Author summaryBecause of geographic isolation, individuals tend to be more genetically related to people living nearby than to people living far. This is an example of population structure, a situation where a large population contains subgroups that share more than the average amount of DNA. This structure can tell us about human history, and it can also have a large effect on medical studies. We use a newly developed method (UMAP) to visualize population structure from three genomic datasets. Using genotype data alone, we reveal numerous subgroups related to ancestry and correlated with traits such as white blood cell count, height, and FEV1, a measure used to detect airway obstruction. We demonstrate that UMAP reveals previously unobserved patterns and fine-scale structure. We show that visualizations work especially well in large datasets containing populations with diverse backgrounds, which are rapidly becoming more common, and that unlike other visualization methods, we can preserve intuitive connections between populations that reflect their shared ancestries. The combination of these results and the effectiveness of the strategy on large and diverse datasets make this an important approach for exploratory analysis for geneticists studying ancestral events and phenotype distributions.

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