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

Workshop Videos Day 1

Workshop Videos Day 2

See detailed schedule here.

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.

<font size="4.5"><u>Guidelines</u></font>

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

Example

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

Christian L. Müller¹, Zachary D. Kurtz⁴, Emily R. Miraldi¹, Richard Bonneau^{1, 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