MAY 10, 2018 06:00 AM PDT

Differential Abundance Analysis for Microbial Marker-Gene Surveys

C.E. CREDITS: P.A.C.E. CE | Florida CE
Speakers
  • Research Fellow, Dana-Farber Cancer Institute
    Biography
      I am a Research Fellow in the Department of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute and Department of Biostatistics at the Harvard TH Chan School of Public Health under the guidance of Professor John Quackenbush. Prior to joining Harvard I was a National Science Foundation Graduate Research Fellow at the University of Maryland, College Park where I received my Ph.D. in Applied Mathematics, Statistics and Scientific Computation.
      As a computer scientist and computational biologist, my interests are to develop computational methods for the analysis of high-throughput sequencing data. I also desire to develop software and support these methods as open-source software for the broader scientific community through Bioconductor and popular domain tools such as QIIME and Phyloseq. MetagenomeSeq, is my most popular tool developed and is in the top 5% of all Bioconductor packages downloaded in the last year with over 5,000 unique users. I am excited to leverage statistical and network methodologies in accounting for technological when identifying disease markers.

    Abstract:

    We introduce a differential abundance analysis method for the analysis of sparse high-throughput data from large-scale surveys of marker genes for microbial communities. Our approach relies on cumulative sum scaling (CSS) normalization - a count data normalization technique - and the zero-inflated Gaussian (ZIG) model as a statistical method for detecting differential abundance of taxonomic features. ZIG differential abundance detection method accounts for bias introduced by the under-sampling of microbial communities commonly found in large-scale marker gene studies.  We have implemented these methods in the publicly available metagenomeSeq bioconductor package. In addition we highlight the utility of the method in a large scale study characterizing the diarrheal microbiome in young children from developing children. Diarrhea, a major cause of mortality and morbidity in young children from developing countries, leading to as many as 15% of all deaths in children under 5 years of age. While many causes of this disease are already known, conventional diagnostic approaches fail to detect a pathogen in up to 60% of diarrheal cases. Using our novel methodology Streptococci were found in our study to be statistically associated with diarrheal disease in general and more severe forms (such as dysentery) in particular.


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