Functional Biogeography of Ocean Microbes Revealed through Non-Negative Matrix Factorization
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{"title"=>"Functional Biogeography of Ocean Microbes Revealed through Non-Negative Matrix Factorization", "type"=>"journal", "authors"=>[{"first_name"=>"Xingpeng", "last_name"=>"Jiang", "scopus_author_id"=>"55842694900"}, {"first_name"=>"Morgan G.I.", "last_name"=>"Langille", "scopus_author_id"=>"8693354000"}, {"first_name"=>"Russell Y.", "last_name"=>"Neches", "scopus_author_id"=>"36721861300"}, {"first_name"=>"Marie", "last_name"=>"Elliot", "scopus_author_id"=>"7004493695"}, {"first_name"=>"Simon A.", "last_name"=>"Levin", "scopus_author_id"=>"26643470900"}, {"first_name"=>"Jonathan A.", "last_name"=>"Eisen", "scopus_author_id"=>"35247902700"}, {"first_name"=>"Joshua S.", "last_name"=>"Weitz", "scopus_author_id"=>"7102347776"}, {"first_name"=>"Jonathan", "last_name"=>"Dushoff", "scopus_author_id"=>"55748313900"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"365684202", "sgr"=>"84866495952", "issn"=>"19326203", "pmid"=>"23049741", "scopus"=>"2-s2.0-84866495952", "doi"=>"10.1371/journal.pone.0043866", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"e5876125-92c7-3884-9a99-1988b877bb8a", "abstract"=>"The direct \"metagenomic\" sequencing of genomic material from complex assemblages of bacteria, archaea, viruses and microeukaryotes has yielded new insights into the structure of microbial communities. For example, analysis of metagenomic data has revealed the existence of previously unknown microbial taxa whose spatial distributions are limited by environmental conditions, ecological competition, and dispersal mechanisms. However, differences in genotypes that might lead biologists to designate two microbes as taxonomically distinct need not necessarily imply differences in ecological function. Hence, there is a growing need for large-scale analysis of the distribution of microbial function across habitats. Here, we present a framework for investigating the biogeography of microbial function by analyzing the distribution of protein families inferred from environmental sequence data across a global collection of sites. We map over 6,000,000 protein sequences from unassembled reads from the Global Ocean Survey dataset to [Formula: see text] protein families, generating a protein family relative abundance matrix that describes the distribution of each protein family across sites. We then use non-negative matrix factorization (NMF) to approximate these protein family profiles as linear combinations of a small number of ecological components. Each component has a characteristic functional profile and site profile. Our approach identifies common functional signatures within several of the components. We use our method as a filter to estimate functional distance between sites, and find that an NMF-filtered measure of functional distance is more strongly correlated with environmental distance than a comparable PCA-filtered measure. We also find that functional distance is more strongly correlated with environmental distance than with geographic distance, in agreement with prior studies. We identify similar protein functions in several components and suggest that functional co-occurrence across metagenomic samples could lead to future methods for de-novo functional prediction. We conclude by discussing how NMF, and other dimension reduction methods, can help enable a macroscopic functional description of marine ecosystems.", "link"=>"http://www.mendeley.com/research/functional-biogeography-ocean-microbes-revealed-through-nonnegative-matrix-factorization", "reader_count"=>110, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>8, "Librarian"=>1, "Student > Doctoral Student"=>6, "Researcher"=>31, "Student > Ph. D. Student"=>32, "Student > Postgraduate"=>4, "Student > Master"=>13, "Other"=>1, "Student > Bachelor"=>5, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>8, "Librarian"=>1, "Student > Doctoral Student"=>6, "Researcher"=>31, "Student > Ph. D. 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  • {"files"=>["https://ndownloader.figshare.com/files/304306", "https://ndownloader.figshare.com/files/304365", "https://ndownloader.figshare.com/files/304389", "https://ndownloader.figshare.com/files/304414", "https://ndownloader.figshare.com/files/304440", "https://ndownloader.figshare.com/files/304460", "https://ndownloader.figshare.com/files/304476", "https://ndownloader.figshare.com/files/304497", "https://ndownloader.figshare.com/files/304509", "https://ndownloader.figshare.com/files/304531"], "description"=>"<div><p>The direct “metagenomic” sequencing of genomic material from complex assemblages of bacteria, archaea, viruses and microeukaryotes has yielded new insights into the structure of microbial communities. For example, analysis of metagenomic data has revealed the existence of previously unknown microbial taxa whose spatial distributions are limited by environmental conditions, ecological competition, and dispersal mechanisms. However, differences in genotypes that might lead biologists to designate two microbes as taxonomically distinct need not necessarily imply differences in ecological function. Hence, there is a growing need for large-scale analysis of the distribution of microbial function across habitats. Here, we present a framework for investigating the biogeography of microbial function by analyzing the distribution of protein families inferred from environmental sequence data across a global collection of sites. We map over 6,000,000 protein sequences from unassembled reads from the Global Ocean Survey dataset to protein families, generating a protein family relative abundance matrix that describes the distribution of each protein family across sites. We then use non-negative matrix factorization (NMF) to approximate these protein family profiles as linear combinations of a small number of ecological components. Each component has a characteristic functional profile and site profile. Our approach identifies common functional signatures within several of the components. We use our method as a filter to estimate functional distance between sites, and find that an NMF-filtered measure of functional distance is more strongly correlated with environmental distance than a comparable PCA-filtered measure. We also find that functional distance is more strongly correlated with environmental distance than with geographic distance, in agreement with prior studies. We identify similar protein functions in several components and suggest that functional co-occurrence across metagenomic samples could lead to future methods for de-novo functional prediction. We conclude by discussing how NMF, and other dimension reduction methods, can help enable a macroscopic functional description of marine ecosystems.</p> </div>", "links"=>[], "tags"=>["biogeography", "microbes", "revealed", "non-negative", "matrix", "factorization"], "article_id"=>120022, "categories"=>["Inorganic Chemistry", "Biological Sciences", "Genetics"], "users"=>["Xingpeng Jiang", "Morgan G. I. Langille", "Russell Y. Neches", "Marie Elliot", "Simon A. Levin", "Jonathan A. Eisen", "Joshua S. Weitz", "Jonathan Dushoff"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043866.s001", "https://dx.doi.org/10.1371/journal.pone.0043866.s002", "https://dx.doi.org/10.1371/journal.pone.0043866.s003", "https://dx.doi.org/10.1371/journal.pone.0043866.s004", "https://dx.doi.org/10.1371/journal.pone.0043866.s005", "https://dx.doi.org/10.1371/journal.pone.0043866.s006", "https://dx.doi.org/10.1371/journal.pone.0043866.s007", "https://dx.doi.org/10.1371/journal.pone.0043866.s008", "https://dx.doi.org/10.1371/journal.pone.0043866.s009", "https://dx.doi.org/10.1371/journal.pone.0043866.s010"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Functional_Biogeography_of_Ocean_Microbes_Revealed_through_Non_Negative_Matrix_Factorization/120022", "title"=>"Functional Biogeography of Ocean Microbes Revealed through Non-Negative Matrix Factorization", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-09-18 00:00:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/574791"], "description"=>"<p>Left: We start with a sample of Pfams across sites, and perform a rank factorization, . In real applications the reduction in rank is more dramatic. Color codes show Pfam relative abundance. Right: The subfigures illustrate different ways of looking at the decomposition using rows and columns.</p>", "links"=>[], "tags"=>["conceptual", "nmf"], "article_id"=>245277, "categories"=>["Inorganic Chemistry", "Biological Sciences", "Genetics"], "users"=>["Xingpeng Jiang", "Morgan G. I. Langille", "Russell Y. Neches", "Marie Elliot", "Simon A. Levin", "Jonathan A. Eisen", "Joshua S. Weitz", "Jonathan Dushoff"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043866.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_conceptual_illustration_of_NMF_decomposition_/245277", "title"=>"A conceptual illustration of NMF decomposition.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-18 01:27:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/574962"], "description"=>"<p>a) Five ecological components identified by using NMF across Pfam functional profiles (rows). Colored arrows roughly indicate the clusters of “characteristic” Pfams corresponding to each of the five components; black arrows roughly indicate the cluster of “ubiquitous” Pfams. b) Pfam profile similarity matrices generated using NMF filtering. The matrices are aligned so that the same row corresponds to the same Pfam in each matrix. Pfams with similar profiles are grouped by applying spectral reordering to the similarity matrix (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043866#s4\" target=\"_blank\">Materials and Methods</a>). Due to visualization and computational limitations, a random subset of 1000 Pfams are used for ordering and display.</p>", "links"=>[], "tags"=>["profiles", "nmf", "generated", "components", "corresponding"], "article_id"=>245444, "categories"=>["Inorganic Chemistry", "Biological Sciences", "Genetics"], "users"=>["Xingpeng Jiang", "Morgan G. I. Langille", "Russell Y. Neches", "Marie Elliot", "Simon A. Levin", "Jonathan A. Eisen", "Joshua S. Weitz", "Jonathan Dushoff"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043866.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Functional_profiles_of_NMF_generated_components_and_the_corresponding_similarity_matrix_/245444", "title"=>"Functional profiles of NMF generated components and the corresponding similarity matrix.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-18 01:30:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/575080"], "description"=>"<p>a) Weight for each of the five components at each of the 45 sites (); b) the site-similarity matrix (); c) environmental variables for the sites. The matrices are aligned so that the same row corresponds to the same site in each matrix. Sites are ordered by applying spectral reordering to the similarity matrix (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043866#s4\" target=\"_blank\">Materials and Methods</a>). Rows are aligned across the three matrices.</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "marine and aquatic sciences"], "article_id"=>245557, "categories"=>["Inorganic Chemistry", "Biological Sciences", "Genetics"], "users"=>["Xingpeng Jiang", "Morgan G. I. Langille", "Russell Y. Neches", "Marie Elliot", "Simon A. Levin", "Jonathan A. Eisen", "Joshua S. Weitz", "Jonathan Dushoff"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043866.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Components_across_sites_/245557", "title"=>"Components across sites.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-18 01:32:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/575182"], "description"=>"<p>a. Environmental distance vs. functional distance (cor = 0.451, , regular Mantel test). b. Logged geographic distance vs. functional distance (cor = 0.127/P = 0.014).</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "marine and aquatic sciences"], "article_id"=>245658, "categories"=>["Inorganic Chemistry", "Biological Sciences", "Genetics"], "users"=>["Xingpeng Jiang", "Morgan G. I. Langille", "Russell Y. Neches", "Marie Elliot", "Simon A. Levin", "Jonathan A. Eisen", "Joshua S. Weitz", "Jonathan Dushoff"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043866.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pairwise_correlation_between_distances_/245658", "title"=>"Pairwise correlation between distances.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-18 01:34:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/575265"], "description"=>"<p>The 120 pairs of sites with highest functional (environmental) similarity are linked in blue (green). Environmental similarity is calculated from the environmental distance matrix using the transformation . A movie showing this pattern over a range of similarity thresholds is available as Movie S1.</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "marine and aquatic sciences"], "article_id"=>245757, "categories"=>["Inorganic Chemistry", "Biological Sciences", "Genetics"], "users"=>["Xingpeng Jiang", "Morgan G. I. Langille", "Russell Y. Neches", "Marie Elliot", "Simon A. Levin", "Jonathan A. Eisen", "Joshua S. Weitz", "Jonathan Dushoff"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043866.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Functional_and_environmental_similarity_on_a_global_map_/245757", "title"=>"Functional and environmental similarity on a global map.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-18 01:35:57"}

PMC Usage Stats | Further Information

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Relative Metric

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