3rd Workshop on Statistical and Algorithmic Challenges in Microbiome Data Analysis

Date & Time

Center for Computational Biology (Simons Foundation, NYU), and the Center for Microbiome Informatics and Therapeutics (at MIT) will hold their Third Workshop on Statistical and Algorithmic Challenges in Microbiome Data Analysis at The Simons Foundation, 160 Fifth Avenue, 2nd Floor, G.D.F. Auditorium, New York, New York 10010 on April 1 – 2, 2019.

As with previous years, the SACMDA workshop is a forum to advance research within the growing community of developers of computational and statistical methods from the microbiome field and related disciplines. Dissemination, exchange, and synthesis of ideas is encouraged by inviting by junior faculty members or postdoctoral associates to present the tools and algorithms that they conceived and developed. Plenty of time will be allocated for discussion so that peers and senior faculty can provide constructive feedback and initiate collaboration.

Susan Holmes has been selected as our Keynote to inspire the group to rise to new challenges.

  • Workshop Videos plus--large

    Workshop Videos Day 1

    Workshop Videos Day 2

  • Agendaplus--large

    See detailed schedule here.

  • Participantsplus--large
    Abigail ArmstrongUniversity of Denver
    Francesco AsnicarUniversity of Trento
    Bahar BehsazUniversity of California San Diego
    Antoine BodeinUniversité Laval
    Evan BolyenNorthern Arizona University
    Hector Corrada BravoUniversity of Maryland, College Park
    M. Luz Calle RosinganaUniversitat de Vic (Barcelona, Spain)
    Liu CaoCarnegie Mellon University
    David ClausenUniversity of Washington
    Rachel ColquhounThe European Molecular Biology Laboratory
    Christian DienerInstitute for Systems Biology
    Juan José Egozcue RubiTechnical University of Catalonia
    Georg GerberMassachusetts Host Microbiome Center
    Sean GibbonsInstitute for Systems Biology
    Travis E. GibsonMassachusetts Host Microbiome Center
    Greg GloorUniversity of Western Ontario
    Susan HolmesStanford University
    Jiyuan HuNew York University Langone Medical Center
    Lu HuangUniversity of Pennsylvania
    Pratheepa JeganathanStanford University
    Xiaofang JiangMassachusetts Institute of Technology / The Broad
    Devin JonesMontana State University
    Christopher KeefeNorthern Arizona University
    Kim-Anh Le CaoUniversity of Melbourne
    Hongzhe LeeUniversity of Pennsylvania
    Jeff LeekJohn Hopkins Bloomberg School of Public Health
    Huilin LiNew York University Langone Medical Center
    Catherine LozuponeUniversity of Denver
    Cameron MartinoUniversity of California, San Diego
    Rajita MenonBoston University
    Hosein MohimaniCarnegie Mellon University
    Senthil Kumar Muthiah.University of Maryland, College Park
    Vera Palowsky-GlahnUniversity of Girona
    Mihai PopUniversity of Maryland
    Christopher QuinceUniversity of Warwick
    Javier Rivera PintoUniversity of Girona
    Kimberly RocheDuke University
    Nicola SegataUniversity of Trento
    Nidhi ShahUniversity of Maryland
    Justin SilvermanDuke University
    Siruo WangJohn Hopkins Bloomberg School of Public Health
    Alex WashburneMontana State University
    Amy WillisUniversity of Washington
    Jakob WirbelThe European Molecular Biology Laboratory
  • Organizing Committeeplus--large

    Center for Microbiome Informatics & Therapeutics
    Eric Alm, Ph.D.
    Vicki Mountain
    Claire Duvallet

    Flatiron Institute / New York University
    Richard Bonneau, Ph.D.
    Michelle Badri

    University of California, San Diego
    Rob Knight, Ph.D.
    James Morton, Ph.D.

  • Posterplus--large


    Following the guidelines below, abstracts should be submitted to as a pdf files [LAST NAME-FIRST NAME.PDF]. DEADLINE: March 4. In order to be considered to present a poster, in addition to submitting your abstract, you must register for the conference. While registering, please indicate that you will be submitting an abstract and provide the abstract title.

    Guidelines must be followed for your abstract to be considered! Abstract Guidelines:

    1. Abstract Title: Arial, Bold, 12 pt., centered. Maximum of 150 characters, including spaces.
    2. Abstract Authors: Arial, 12 pt., centered. Include, all authors full name and affiliation. Use superscript to indicate multiple or varying affiliations.
    3. Authors Address(es): Arial, 11 pt., justified.
    4. TEXT ONLY abstract: Arial, 12 pt., justified. Maximum of 500 words.
    5. Maximum size 48″w x 42″h

    Submit here


    Microbes are STICKY – Inference and Topological analysis of microbial ecological networks

    Christian L. Müller1, Zachary D. Kurtz4, Emily R. Miraldi1, Richard Bonneau1, 2, 3

    1 Simons Center for Data Analysis, Simons Foundation, New York, NY 10010
    2 Department of Biology, Center for Genomics and Systems Biology, NYU, New York, NY 10003
    3 Courant Institute of Mathematical Sciences, New York University, New York, NY 10012
    4 Departments of Microbiology and Medicine, New York University School of Medicine, New York, NY 10016

    16S-ribosomal sequencing and metagenomic measurements of microbial communities reveal phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans) and associations between microbes, identification of underlying mechanisms and microbial ecological networks requires new statistical tools. A key challenge is that metagenomic and 16S data are typically compositional (counts are normalized to the total number of counts in the sample due to limits in sequencing capacity). Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. We will first present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method the inference of microbial ecological associations from metagenomic datasets that addresses both of these issues. SPIEC- EASI combines compositional data transformations with algorithms for sparse neighborhood and inverse covariance selection. Because no large-scale microbial ecological networks have been experimentally validated, SPIEC-EASI is accompanied by a set of computational tools to generate realistic OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods in terms of edge recovery and network properties on realistic synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project. Given this method we will then present a topological analysis of inferred networks (from > 50 different 16S data sets) that span several ecological environments, ranging from mammalian gut to fresh water habitats. Characterizing these networks based on induced sub-graph (graphlet) spectra reveals that the topology of microbial networks across most ecological habitats is STICKY, a network class that has so far only been reported to accurately describe selected subsets of protein-protein interaction networks. Lastly, implications on the required number of samples as a function of network type/topology will be discussed. For a preprint describing our inference method see: http://arxiv.org/pdf/1408.4158v2.pdf

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