DOCSLIB.ORG

Museum of Spatial Transcriptomics

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Museum of Spatial Transcriptomics Museum of Spatial Transcriptomics Lambda Moses, [email protected] Lior Pachter, [email protected] 2021-05-11 1mailto:[email protected] 2mailto:[email protected] 2 Contents Preface 5 1 Introduction 7 I Prequel era 11 2 Prequel era 13 2.1 Enhancer and gene traps ....................... 14 2.2 In situ reporter ............................ 18 2.3 ISH and WMISH atlases ....................... 20 2.4 Databases of the prequel era ..................... 27 2.5 Geography of the prequel era .................... 29 3 Data analysis in the prequel era 33 3.1 Gene patterns ............................. 34 3.2 Spatial regions ............................ 38 3.3 Gene interactions ........................... 38 3.4 Decline ................................ 39 3.5 Geography of prequel data analysis ................. 40 II Current era 43 4 From the past to the present 45 3 4 CONTENTS 5 Current era technologies 57 5.1 Microdissection ............................ 57 5.2 Single molecular FISH ........................ 66 5.3 In situ sequencing .......................... 86 5.4 In situ array capture ......................... 94 5.5 No imaging .............................. 102 5.6 Spatial multi-omics .......................... 104 5.7 Databases of the current era ..................... 104 6 Text mining LCM transcriptomics abstracts 107 6.1 Topic modeling ............................ 109 6.2 Changes of word usage through time ................ 113 6.3 Changes of topic prevalence through time ............. 115 6.4 Association of topics with city .................... 118 6.5 GloVe word embedding ....................... 122 7 Data analysis in the current era 127 7.1 Preprocessing ............................. 133 7.2 Exploratory data analysis ...................... 142 7.3 Spatial reconstruction of scRNA-seq data ............. 145 7.4 Cell type deconvolution ....................... 154 7.5 Spatially variable genes ....................... 157 7.6 Gene patterns ............................. 163 7.7 Spatial regions ............................ 165 7.8 Cell-cell interaction .......................... 167 7.9 Gene-gene interaction ........................ 171 7.10 Subcellular transcript localization .................. 172 7.11 Gene expression imputation from H&E ............... 174 III Future perspectives 177 8 From the past to the present to the future 179 References 183 Preface This supplement to the paper Museum of Spatial Transcriptomics and the associated database of spatial transcriptomics literature1 is inspired by mu- seum catalogs that provide insight and detail to further understanding of the exhibits. The results presented are based on code that can be run interac- tively on RStudio Cloud2. We present key analyses of metadata curated for the database, and provide further analyses and results beyond what could be included here in the more_analyses directory of this repository. The mark- down that generates this text is on GitHub, and is version controlled so that its development can be tracked now and in the future. Please notify us of errors, omissions, or other suggestions via submission of issues on GitHub: https://github.com/pachterlab/LP_2021 This document is built with the bookdown package from a collection of R Markdown files. How some of figures look depends on parameters thatcan be changed, such as size of bins when binning number of publications in time to show a trend. The source code is on RStudio Cloud3. The dependencies are pre-installed in the RStudio Cloud project. By default, when the database is queried by code, the most up to date version is used, which can be newer than the rendered static version on github.io. To build the document in RStudio Cloud (i.e., both the web page gitbook version and a PDF), run this in the R console: bookdown::render_book("index.Rmd", output_format = c("bookdown::gitbook", "bookdown::pdf_book")) If you are cloning this repo into a fresh RStudio Cloud project or a fresh machine, install the packages required to build the book as follows: First install remotes with install.packages("remotes"). Then use remotes:install_deps(dependencies = TRUE) to install all required pack- ages from CRAN, Bioconductor, and GitHub. So in short, 1https://docs.google.com/spreadsheets/d/1sJDb9B7AtYmfKv4-m8XR7uc3XXw_ k4kGSout8cqZ8bY/edit?usp=sharing 2https://rstudio.cloud/project/2492054 3https://rstudio.cloud/project/2492054 5 6 CONTENTS install.packages("remotes") remotes::install_deps(dependencies = TRUE) Because many packages are installed, the installation can be sped up with the argument Ncpus in install_deps() to specify the number of CPU cores to use to install packages in parallel, such as Ncpus = 2L for 2 cores. The free plan of RStudio Cloud only has 1 core, but this argument can be used when multiple cores are available. By default, the most up to date version of the database is downloaded for analy- ses in this book. However, as the museumst R package written for these analyses contains a cached version of the database, historical versions of the database can be viewed by installing older versions of museumst and setting update = FALSE when calling museumst::read_metadata() when running code from this book on RStudio Cloud or your computer. Older versions of museumst can be installed with remotes::install_github("pachterlab/museumst", ref = "v0.0.0.9016") where ref refers to a release. Release history of museumst can be seen here4. Documentation of museumst can be seen here5. 4https://github.com/pachterlab/museumst/releases 5https://pachterlab.github.io/museumst/ Chapter 1 Introduction The spatial organization of the components of biological systems is crucial for their proper function. For instance, morphogen gradients in embryos are tightly regulated to ensure that the right cell types differentiate at the right place. In adults, spatial organization of cells in tissues is important to proper functions of organs. For instance, the liver lobule is divided in labor according to distance from the portal triad as such distance affects suitability of different tasks. Both oxygen level and morphogen gradient regulate zonation of metabolism (Geb- hardt 2014); there is more oxidative phosphorylation and gluconeogenesis in the more oxygenated periportal region and more glycolysis in the more deoxy- genated pericentral region. How cell types and cellular functions vary in space is measured by quantifying gene expression in space. Conversely, the expression of an unknown gene in space can give clues to its function. Gene expression is usually quantified by quantifying proteins or transcripts encoded by the gene, and high throughput spatial methods exist for both protein and transcripts. In other words, cellular function exemplifies the maxim that “the whole is greater than the sum of its parts”, and in large part this follows from “location, location, location”. Here we focus on spatial transcriptomics (the field of spatial proteomics is cov- ered elsewhere (Lundberg and Borner 2019; Baharlou et al. 2019; Buchberger et al. 2018)). Even spatial transcriptomics is a vast field, and it is useful to begin by considering the scope of what it contains. Naïvely, one may say, spa- tial transcriptomics means quantifying the complete set of RNAs encoded by the genome in space. Usually the “in space” is at some microscopic resolution rather than geospatial as often assumed in the term “spatial statistics”; the resolution is usually cellular, though sometimes subcellular. The “spatial” is in contrast to other transcriptomics methods that by virtue of the nature of their assays, lose information of tissue structure in space. That is the case with microarray technology for bulk tissue analysis, for bulk RNA-seq, and single cell RNA-seq (scRNA-seq) that is based on dissociation of tissue – the “spatial” 7 8 CHAPTER 1. INTRODUCTION usually means tissue structure in space. More broadly, the “spatial” can mean knowing spatial context of samples although the spatial context is only a label and the coordinates are not collected or not used, such as in some laser capture microdissection (LCM) literature (Baccin et al. 2020; Nichterwitz et al. 2016), Niche-seq (Medaglia et al. 2017), and APEX-seq (Fazal et al. 2019). The “spatial” can also mean preserving spatial coordinates of samples within tissue, though the coordinates may or may not be explicitly used in data analysis, such as in the various single molecular fluorescent in situ hybridization (smFISH) based technologies such as seqFISH (Lubeck et al. 2014) and MERFISH (K. H. Chen et al. 2015) and array based technologies such as Spatial Transciptomics (ST) (Ståhl et al. 2016). There is more complexity in defining “transcriptomics”. While some technologies usually called “spatial transcriptomics” are indeed transcriptome-wide, such as ST, Visium, and LCM followed by RNA-seq, many technologies that only profile a panel of usually a few hundred genes are nevertheless considered part of “spa- tial transcriptomics”. Here “transcriptomics” actually means high-throughput quantification of gene expression, preferably highly multiplexed, quantifying nu- merous genes within the same piece of tissue at the same time. However, what counts as “high-throughput”? Is there a minimum number of genes required? Should 50 genes be enough? Or a hundred genes? The threshold number of genes required to be considered “high-throughput” is difficult to define; here, by “high-throughput”, we mean the intent to quantify expression of more genes than normally done with fluorescent in situ hybridization (FISH)
Load more

Recommended publications
  • Hail the TR100! These 100 Brilliant Young Innovators—All Under 35 As of Jan
    TR100/2002 All hail the TR100! These 100 brilliant young innovators—all under 35 as of Jan. 1, 2002—are visitors from the future, living among us here and now. Their innova- tions will have a deep impact on how we live, work and think in the century to come. This is the second time Technology Review pages, come from those five areas. These inno- has picked such a group. The first was in vators are first grouped alphabetically 1999, our magazine’s centennial year. and then indexed by their areas of That was a wonderful experience, work (p. 95). but we’ve learned a lot in the last In addition to this offering in three years, and we think this our magazine, we’ve posted an installment is even more exciting augmented version of the TR100 than the first. special section on our Web site, For one thing, we’ve chosen a with more information about all special theme for this version of the honorees and a rich set of links the TR100: transforming existing to sites pertaining to their original industries and creating new ones. We research (www.technologyreview. looked for technology’s impact on the real com/tr100/feature). Choosing this group economy, as opposed to the now moribund has been a painstaking process that began “new economy.” The major hot spots where we more than a year ago. We could not have succeeded think a fundamental transformation is in progress include without our distinguished panel of judges (p. 97).But it’s information technology, biotechnology and medicine, been worth it.
    [Show full text]
  • Research Article Advanced Heat Map and Clustering Analysis Using Heatmap3
    Hindawi Publishing Corporation BioMed Research International Volume 2014, Article ID 986048, 6 pages http://dx.doi.org/10.1155/2014/986048 Research Article Advanced Heat Map and Clustering Analysis Using Heatmap3 Shilin Zhao, Yan Guo, Quanhu Sheng, and Yu Shyr Center for Quantitative Sciences, Vanderbilt University, Nashville, TN 37232, USA Correspondence should be addressed to Yu Shyr; [email protected] Received 6 June 2014; Accepted 2 July 2014; Published 16 July 2014 Academic Editor: Leng Han Copyright © 2014 Shilin Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Heat maps and clustering are used frequently in expression analysis studies for data visualization and quality control. Simple clustering and heat maps can be produced from the “heatmap” function in R. However, the “heatmap” function lacks certain functionalities and customizability, preventing it from generating advanced heat maps and dendrograms. To tackle the limitations of the “heatmap” function, we have developed an R package “heatmap3” which significantly improves the original “heatmap” function by adding several more powerful and convenient features. The “heatmap3” package allows users to produce highly customizable state of the art heat maps and dendrograms. The “heatmap3” package is developed based on the “heatmap” function in R, and itis completely compatible with it. The new features of “heatmap3” include highly customizable legends and side annotation, a wider range of color selections, new labeling features which allow users to define multiple layers of phenotype variables, and automatically conducted association tests based on the phenotypes provided.
    [Show full text]
  • Program Book
    Pacific Symposium on Biocomputing 2016 January 4-8, 2016 Big Island of Hawaii Program Book PACIFIC SYMPOSIUM ON BIOCOMPUTING 2016 Big Island of Hawaii, January 4-8, 2016 Welcome to PSB 2016! We have prepared this program book to give you quick access to information you need for PSB 2016. Enclosed you will find • Logistics information • Menus for PSB hosted meals • Full conference schedule • Call for Session and Workshop Proposals for PSB 2017 • Poster/abstract titles and authors • Participant List Conference materials are also available online at http://psb.stanford.edu/conference-materials/. PSB 2016 gratefully acknowledges the support the Institute for Computational Biology, a collaborative effort of Case Western Reserve University, the Cleveland Clinic Foundation, and University Hospitals; the National Institutes of Health (NIH), the National Science Foundation (NSF); and the International Society for Computational Biology (ISCB). If you or your institution are interested in sponsoring, PSB, please contact Tiffany Murray at [email protected] If you have any questions, the PSB registration staff (Tiffany Murray, Georgia Hansen, Brant Hansen, Kasey Miller, and BJ Morrison-McKay) are happy to help you. Aloha! Russ Altman Keith Dunker Larry Hunter Teri Klein Maryln Ritchie The PSB 2016 Organizers PACIFIC SYMPOSIUM ON BIOCOMPUTING 2016 Big Island of Hawaii, January 4-8, 2016 SPEAKER INFORMATION Oral presentations of accepted proceedings papers will take place in Salon 2 & 3. Speakers are allotted 10 minutes for presentation and 5 minutes for questions for a total of 15 minutes. Instructions for uploading talks were sent to authors with oral presentations. If you need assistance with this, please see Tiffany Murray or another PSB staff member.
    [Show full text]
  • Conference Proceedingssmall
    1 COMMITTEES Steering Committee Phil Bourne - University of California, San Diego Eric Davidson - California Institute of Technology Steven Salzberg - The Institute for Genomic Research John Wooley - University of California San Diego, San Diego Supercomputer Center Organizing Committee Pat Blauvelt – LSS Membership Director Karen Hauge – Palo Alto Medical Foundation, Local Arangements Kass Goldfein - Finance Consultant AlishaHolloway – The J. David Gladstone Institutes, Tutorial Chair Sami Khuri – San Jose State University, Poster Chair Ann Loraine – University of North Carolina at Charlotte, CSB Publication Chair Fenglou Mao – University of Georgia, On-Line Registration and Refereeing Website Peter Markstein – in silico Labs, Program Co-Chair Vicky Markstein - Life Sciences Society, Conference Chair, LSS President Jean Tsukamoto - Graphics Design Bill Wang - Sun Microsystems Inc, LSS Information Technology Director Ying Xu – University of Georgia, Program Co-Chair Program Committee Tatsuya Akutsu – Kyoto University Chris Bailey-Kellogg – Dartmouth College Pierre Baldi – University of California Irvine Liming Cai – University of Georgia Bill Cannon – Pacific Northwest National Laboratory Jake Chen – Indiana University Bhaskar DasGupta – University of Illinois Chicago Andrey Gorin – Oak Ridge National Laboratory Matt Hibbs – Princeton University Wen-Lian Hsu – Academia Sinica Tamer Kahveci – University of Florida Carl Kingsford – University of Maryland Christina Leslie – Memorial Sloan-Kettering Cancer Center Jing Li – Case Western Reserve
    [Show full text]
  • From the Editor
    From the Editor Arthur N. Popper I want to thank the 758 Acoustical Th e fourth article is by J. Lauren Ruoss, Catalina Bazacliu, Society of America (ASA) members Daphna Yasova Barbeau, and Philip Levy. Th ey discuss who responded to the recent Acous- the value of using ultrasound in clinical diagnosis, with tics Today (AT) survey. As promised a focus on dealing with high-risk newborns in neonatal in the survey, we awarded $50 gift intensive care units (NICUs). Although the use of ultra- cards (using an online random number generator) to sound is widespread in medicine, its use in the NICU has fi ve ASA members. Th ey are David Bonnett, Raymond special importance because of the fragility of the babies H. Dye, Gordon Ebbitt, Zhe-chen Guo, and Guillermo and their special needs. Rus. Th e results from the survey are discussed on page 84 of this issue. Lately, I have been seeking out editors of some of the Special Issues that have been published or will be pub- Th is issue contains a very important statement about the lished in Th e Journal of the Acoustical Society of America. ASA and future meetings by President Diane Kewley- Th e goal of these articles is to provide summaries of the Port. Although I realize (from the survey) that only about broad topic of the Special Issue to introduce the whole 60% of members read the From the President column ASA membership to the topic. Th us, these articles focus (and perhaps 70% read this column), I would like to less on the papers in the issue than on the overall topic.
    [Show full text]
  • Visualization and Exploration of Transcriptomics Data Nils Gehlenborg
    Visualization and Exploration of Transcriptomics Data 05 The identifier 800 year identifier Nils Gehlenborg Sidney Sussex College To celebrate our 800 year history an adaptation of the core identifier has been commissioned. This should be used on communications in the time period up to and including 2009. The 800 year identifier consists of three elements: the shield, the University of Cambridge logotype and the 800 years wording. It should not be redrawn, digitally manipulated or altered. The elements should not be A dissertation submitted to the University of Cambridge used independently and their relationship should for the degree of Doctor of Philosophy remain consistent. The 800 year identifier must always be reproduced from a digital master reference. This is available in eps, jpeg and gif format. Please ensure the appropriate artwork format is used. File formats European Molecular Biology Laboratory, eps: all professionally printed applications European Bioinformatics Institute, jpeg: Microsoft programmes Wellcome Trust Genome Campus, gif: online usage Hinxton, Cambridge, CB10 1SD, Colour United Kingdom. The 800 year identifier only appears in the five colour variants shown on this page. Email: [email protected] Black, Red Pantone 032, Yellow Pantone 109 and white October 12, 2010 shield with black (or white name). Single colour black or white. Please try to avoid any other colour combinations. Pantone 032 R237 G41 B57 Pantone 109 R254 G209 B0 To Maureen. This dissertation is my own work and contains nothing which is the outcome of work done in collaboration with others, except as specified in the text and acknowledgements. This dissertation is not substantially the same as any I have submit- ted for a degree, diploma or other qualification at any other university, and no part has already been, or is currently being submitted for any degree, diploma or other qualification.
    [Show full text]
  • From Research to Engagement to Translation: Words Are Cheap. Part 2 – a Case Study Timothy G
    TRANSACTIONS OF THE IMF 2020, VOL. 98, NO. 5, 217–220 https://doi.org/10.1080/00202967.2020.1805187 GUEST EDITORIAL From research to engagement to translation: words are cheap. Part 2 – a case study Timothy G. Leighton Institute of Sound and Vibration Research, University of Southampton, Southampton, UK Introduction larger competitors that wish to bury a However, early in the course of rival technology. Before selecting a developing sensors5–7,12 for these The first of these paired editorials1 intro- funder for SWT, I turned down dozens ocean studies, in the late 1980s I discov- duced ‘the virtuous circle’, where tax- of short term investors whose proposed ered the new acoustic signal13 that led payer funded research, including that model was to form a company (shared directly to the invention described by in the surface finishing field, can 50–50 between us) with a nominal Malakoutikhah et al.3 That signal was produce benefits to society, which in value of £1 M, then (after I had done a at a frequency v +v /2 and was scat- turn not only benefits the health of i p year of advertising) declare that the tered off a bubble when it was driven society as a whole and its individual company had grown in value, and we by two acoustic frequencies, a ‘pump’ members, but also generates tax were looking for an investor to buy frequency v close to the bubble reson- income that can be re-invested into p half of it for £10 M. That sale would ance for its pulsation mode of oscil- the research and development base to reduce my share to 25%, but now of a lation, and an ‘imaging’ signal v continue this onward progress.
    [Show full text]
  • Bringing 'Bee-Cological' Data to Life Through a Relational Database and an Interactive Visualization Tool By
    Bringing 'Bee-cological' Data to Life through a Relational Database and an Interactive Visualization Tool by Xiaojun Wang A Thesis Submitted to the Faculty of the WORCESTER POLYTECHNIC INSTITUTE in partial fulfillment of the requirements for the Degree of Master of Science in Bioinformatics and Computational Biology Aug 2018 APPROVED BY: Dr. Carolina Ruiz Dr. Robert J. Gegear Dr. Elizabeth F. Ryder 1 Abstract Over the past decade, bumblebees have rapidly declined in abundance and geographic distribution at an alarming rate, raising major social, economic and ecological concern worldwide. However, we presently lack effective bumblebee conservation strategies due to a lack of information on the specific ecological needs of each species. The ‘Beecology Project’ was created to fill this knowledge gap by utilizing citizen scientists to collect data on floral resource use patterns of foraging bees in naturally occurring mixed species communities across Massachusetts. In addition to its research goals, the Beecology Project also has the educational goal of providing a modular, integrated biology - computer science framework (a BIO-CS bridge) to assist teachers in developing curricula to meet the next generation biology and computer science standards at the high school level. The Beecology team has developed Android and Web mobile apps to assist citizen scientists to collect and submit field data on bumblebee and plant species interactions. Other Beecology team members also collected a substantial amount of bumblebee data through field research and online digital museum collections. However, there was no central location dedicated to the storage of such data. There was also no way for users such as researchers, educators, and the general public to access all of the collected data in an ecologically-meaningful way.
    [Show full text]
  • Pacific Symposium on Biocomputing 2020 January 3-7, 2020 Big Island of Hawaii
    Pacific Symposium on Biocomputing 2020 January 3-7, 2020 Big Island of Hawaii Program Book PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020 Big Island of Hawaii, January 3-7, 2020 Welcome to PSB 2020! We have prepared this program book to give you quick access to information you need for PSB 2020. Enclosed you will find: • Logistics information • Medical services information • Full conference schedule (see website for the latest version) • Agendas for workshops and JBrowse demo • Call for Session and Workshop Proposals for PSB 2021 • Poster/abstract titles and author index • Participant list The latest conference materials are available online at http://psb.stanford.edu/conference-materials/. PSB 2020 gratefully acknowledges the support of the Cleveland Institute for Computational Biology; UPenn Institute for Biomedical Informatics; Variant Bio; Penn Center for Precision Medicine, Penn Medicine; Cipherome; Translational Bioinformatics Conference (TBC); the National Institutes of Health (NIH); and the International Society for Computational Biology (ISCB). If you or your institution are interested in sponsoring, PSB, please contact Tiffany Murray at [email protected] If you have any questions, the PSB registration staff (Tiffany Murray, Kasey Miller, BJ Morrison-McKay, and Cindy Paulazzo) are happy to help you. Aloha! Russ Altman Keith Dunker Larry Hunter Teri Klein Maryln Ritchie The PSB 2020 Organizers PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020 Big Island of Hawaii, January 3-7, 2020 SPEAKER INFORMATION Oral presentations of accepted proceedings papers will take place in Salon 2 & 3. Speakers are allotted 10 minutes for presentation and 5 minutes for questions for a total of 15 minutes. Instructions for uploading talks were sent to authors with oral presentations.
    [Show full text]
  • Daniel Aalberts Scott Aa
    PLOS Computational Biology would like to thank all those who reviewed on behalf of the journal in 2015: Daniel Aalberts Jeff Alstott Benjamin Audit Scott Aaronson Christian Althaus Charles Auffray Henry Abarbanel Benjamin Althouse Jean-Christophe Augustin James Abbas Russ Altman Robert Austin Craig Abbey Eduardo Altmann Bruno Averbeck Hermann Aberle Philipp Altrock Ferhat Ay Robert Abramovitch Vikram Alva Nihat Ay Josep Abril Francisco Alvarez-Leefmans Francisco Azuaje Luigi Acerbi Rommie Amaro Marc Baaden Orlando Acevedo Ettore Ambrosini M. Madan Babu Christoph Adami Bagrat Amirikian Mohan Babu Frederick Adler Uri Amit Marco Bacci Boris Adryan Alexander Anderson Stephen Baccus Tinri Aegerter-Wilmsen Noemi Andor Omar Bagasra Vera Afreixo Isabelle Andre Marc Baguelin Ashutosh Agarwal R. David Andrew Timothy Bailey Ira Agrawal Steven Andrews Wyeth Bair Jacobo Aguirre Ioan Andricioaei Chris Bakal Alaa Ahmed Ioannis Androulakis Joseph Bak-Coleman Hasan Ahmed Iris Antes Adam Baker Natalie Ahn Maciek Antoniewicz Douglas Bakkum Thomas Akam Haroon Anwar Gabor Balazsi Ilya Akberdin Stefano Anzellotti Nilesh Banavali Eyal Akiva Miguel Aon Rahul Banerjee Sahar Akram Lucy Aplin Edward Banigan Tomas Alarcon Kevin Aquino Martin Banks Larissa Albantakis Leonardo Arbiza Mukul Bansal Reka Albert Murat Arcak Shweta Bansal Martí Aldea Gil Ariel Wolfgang Banzhaf Bree Aldridge Nimalan Arinaminpathy Lei Bao Helen Alexander Jeffrey Arle Gyorgy Barabas Alexander Alexeev Alain Arneodo Omri Barak Leonidas Alexopoulos Markus Arnoldini Matteo Barberis Emil Alexov
    [Show full text]
  • TWIPS -- Sonar Inspired by Dolphins
    ADVERTISMENT Members Login RSS FEED Keyword > Advanced Search Win a Book Home > News > Breaking news 10 Nov 2011 Latest News Articles > Breaking news No items here. > Agriculture TWIPS -- sonar inspired by dolphins > Archaeology - 17 Nov 2010 By National Oceanography Centre, Southampton (UK) Page 1 of 2 Hydrographic > Atmospheric Science Survey Tool > Biology Scientists at the University of Southampton have developed a new kind of underwater Small AUV, > Cancer sonar device that can detect objects through bubble clouds that would effectively blind Operate from standard sonar. shore Low cost, > Chemistry and Physics easy to use, Just as ultrasound is used in medical imaging, conventional sonar 'sees' with sound. It uses > Earth Science differences between emitted sound pulses and their echoes to detect and identify targets. accurate www.iver-auv.com > Education These include submerged structures such as reefs and wrecks, and objects, including submarines and fish shoals. > Infectious Diseases However, standard sonar > Mathematics does not cope well with > Medicine & Health bubble clouds resulting from breaking waves or other > Nanotechnology causes, which scatter sound and clutter the sonar > Oceanography image. > Science Business Professor Timothy Leighton > Science Policy of the University of Southampton's Institute of > Social & Behavioural Sound and Vibration Research (ISVR), who led > Space the research, explained: > Technology "Cold War sonar was Articles developed mainly for use in deep water where bubbles Blogs and Opinions are not much of a problem, but many of today's applications involve shallow waters. Better Facts detection and classification of targets in bubbly waters are key goals of shallow-water sonar." Poems & Quotes Leighton and his colleagues have developed a new sonar concept called twin inverted Games & Quizzes pulse sonar (TWIPS).
    [Show full text]
  • How Do They Make and Interpret Those Dendrograms and Heat Maps; Differences Between Unsupervised Clustering and Classification
    BINF 636: Lecture 9: Clustering: How Do They Make and Interpret Those Dendrograms and Heat Maps; Differences Between Unsupervised Clustering and Classification. Description: Clustering, for the purpose of this lecture, is the exploratory partitioning of a set of data points into subgroups (clusters) such that members of each subgroup are relatively similar to each other and members of distinct clusters are relatively dissimilar. For example, one might have gene expression profiles from a set of samples of a particular type of tumor and wish to see if the samples separate out into distinct subgroups. In this case one could be looking to uncover evidence of previously unknown subtypes, or one might wish to see if the results of clustering the gene expression profiles are consistent with classification by histopathology. In this class we will describe how dendrograms, such as the example to the right, are constructed using hierarchical agglomerative clustering, where one starts with each of the data points as an individual cluster, and in successive steps combines the pair of clusters that are “closest” to each other into one new cluster. This requires specifying a distance measure between data points and between clusters. Each clustering step reduces by one the number of existing clusters until at the end of the final step there is one cluster containing all the data points. If one has ordered the data points along a line so that at each step the clusters that are joined together are adjacent to each other, one can draw a corresponding diagram (dendrogram) where the heights of the vertical lines reflect the distance between the pair of clusters joined at each stage of the procedure.
    [Show full text]