Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a coarse graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16-hi,CD66b-lo neutrophil and IFNγ+,GranzymeB+ Th17 cell responses enriched in patients who die. Furthermore, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome.

STUDI PEMETAAN DATA BASE SPASIAL PENGGUNAAN LAHAN BERBASIS SISTEM INFORMASI GEOGRAFIS DI KELURAHAN BAURUNG

ABSTRACT: Geographic Information Systems not only handle maps or images, but most importantly is the ability to handle large volumes of databases. The database concept is the center of a Geographic Information System and is a simple system that can only produce output in the form of geographical and spatial data from a region. The Geographical Information System Database (GIS) is formed having a spatial data topology structure, and can be used as basic data. The database is formed automatically from the results of surveys and measurements and the results of digitization of high-resolution image maps so that spatial data information can be obtained, to produce a new Geographic Information System database as a result of the merging of survey data and the results of digitization of image maps.

The phylogenetic significance of the morphology of the syrinx, hyoid and larynx, of the Southern Cassowary, Casuarius casuarius (Aves, Palaeognathae)

The Palaeognathae are a basal clade within Aves and include the large and flightless ratites and the smaller, volant tinamous. Although much research has been conducted on various aspects of palaeognath morphology, ecology, and evolutionary history, there are still areas which require investigation. This study aimed to fill gaps in our knowledge of the Southern cassowary, Casuarius casuarius , for which information on the skeletal systems of the syrinx, hyoid and larynx is lacking despite these structures having been recognised as performing key functional roles associated with vocalisation, respiration and feeding. Previous research into the syrinx and hyoid have also indicated these structures to be valuable for determining evolutionary relationships among neognath taxa, and thus be informative for palaeognath phylogenetic analyses, which still exhibits strong conflict between morphological and molecular trees. We documented variation across palaeognaths in the skeletal character states of the syrinx, hyoid, and larynx, using both the literature and novel observations (e.g. of cassowary). Notably the molecular moa-tinamou clade was found to share morphological character traits including the ossification of the cricoid and arytenoid cartilages, and an additional cranial character, the articulation between the maxillary process of the nasal and the maxilla. These findings contributed to optimisation of syrinx, hyoid and larynx characters showing increased phylogenetic support for palaeognath relationships derived from a molecular and morphological combined-data topology.

A topological data analysis based classification method for multiple measurements

BackgroundMachine learning models for repeated measurements are limited. Using topological data analysis (TDA), we present a classifier for repeated measurements which samples from the data space and builds a network graph based on the data topology. When applying this to two case studies, accuracy exceeds alternative models with additional benefits such as reporting data subsets with high purity along with feature values.ResultsFor 300 examples of 3 tree species, the accuracy reached 80% after 30 datapoints, which was improved to 90% after increased sampling to 400 datapoints. Using data from 100 examples of each of 6 point processes, the classifier achieved 96.8% accuracy. In both datasets, the TDA classifier outperformed an alternative model.ConclusionsThis algorithm and software can be beneficial for repeated measurement data common in biological sciences, as both an accurate classifier and a feature selection tool.

Linking Interactome to Disease

The introduction of high-throughput gene expression profiling technologies (DNA microarrays) in molecular biology and their expected applications to the clinic have allowed the design of predictive signatures linked to a particular clinical condition or patient outcome in a given clinical setting. However, it has been shown that such signatures are prone to several problems: (i) they are heavily unstable and linked to the set of patients chosen for training; (ii) data topology is problematic with regard to the data dimensionality (too many variables for too few samples); (iii) diseases such as cancer are provoked by subtle misregulations which cannot be readily detected by current analysis methods. To find a predictive signature generalizable for multiple datasets, a strategy of superimposition of a large scale of protein-protein interaction data (human interactome) was devised over several gene expression datasets (a total of 2,464 breast cancer tumors were integrated), to find discriminative regions in the interactome (subnetworks) predicting metastatic relapse in breast cancer. This method, Interactome-Transcriptome Integration (ITI), was applied to several breast cancer DNA microarray datasets and allowed the extraction of a signature constituted by 119 subnetworks. All subnetworks have been stored in a relational database and linked to Gene Ontology and NCBI EntrezGene annotation databases for analysis. Exploration of annotations has shown that this set of subnetworks reflects several biological processes linked to cancer and is a good candidate for establishing a network-based signature for prediction of metastatic relapse in breast cancer.

Spatial Data Topology in GIS Database

Topology and its various benefits and functionality are fairly well understood within the context of 2D Geographical Information Systems. We summarize the principle of common 2D topology and the implementation of GIS databases. Existing topological frameworks and data models as a staring point to guide the review process, three key areas were studied for the purposes of requirements identification, namely existing 2D topological systems. However requirements in 3D have yet to be defined, with factors such as lack of familiarity with the potential of such functionality of 3D systems impeding this process. In this paper, we identify and review the requirements for topology in three-dimensional (3D) applications. Requirements for topological functionality in 3D were then grouped and categorized.