TY  - JOUR
AU  - Sommeria-Klein, G.
AU  - Zinger, L.
AU  - Coissac, E.
AU  - Iribar, A.
AU  - Schimann, H.
AU  - Taberlet, P.
AU  - Chave, J.
PY  - 2020//
TI  - Latent Dirichlet Allocation reveals spatial and taxonomic structure in a DNA-based census of soil biodiversity from a tropical forest
T2  - Mol. Ecol. Resour.
JO  - Molecular Ecology Resources
SP  - 371
EP  - 386
VL  - 20
IS  - 2
PB  - Blackwell Publishing Ltd
KW  - community ecology
KW  - environmental DNA
KW  - metabarcoding
KW  - OTU presence–absence
KW  - soil microbiome
KW  - topic modelling
KW  - bacterium
KW  - biodiversity
KW  - biology
KW  - classification
KW  - eukaryote
KW  - fungus
KW  - genetics
KW  - high throughput sequencing
KW  - isolation and purification
KW  - microbiology
KW  - parasitology
KW  - procedures
KW  - soil
KW  - Bacteria
KW  - Computational Biology
KW  - Eukaryota
KW  - Fungi
KW  - High-Throughput Nucleotide Sequencing
KW  - Soil Microbiology
N2  - High-throughput sequencing of amplicons from environmental DNA samples permits rapid, standardized and comprehensive biodiversity assessments. However, retrieving and interpreting the structure of such data sets requires efficient methods for dimensionality reduction. Latent Dirichlet Allocation (LDA) can be used to decompose environmental DNA samples into overlapping assemblages of co-occurring taxa. It is a flexible model-based method adapted to uneven sample sizes and to large and sparse data sets. Here, we compare LDA performance on abundance and occurrence data, and we quantify the robustness of the LDA decomposition by measuring its stability with respect to the algorithm's initialization. We then apply LDA to a survey of 1,131 soil DNA samples that were collected in a 12-ha plot of primary tropical forest and amplified using standard primers for bacteria, protists, fungi and metazoans. The analysis reveals that bacteria, protists and fungi exhibit a strong spatial structure, which matches the topographical features of the plot, while metazoans do not, confirming that microbial diversity is primarily controlled by environmental variation at the studied scale. We conclude that LDA is a sensitive, robust and computationally efficient method to detect and interpret the structure of large DNA-based biodiversity data sets. We finally discuss the possible future applications of this approach for the study of biodiversity. © 2019 John Wiley & Sons Ltd
SN  - 1755098x (Issn)
UR  - http://dx.doi.org/10.1111/1755-0998.13109
N1  - exported from refbase (http://php.ecofog.gf/refbase/show.php?record=981), last updated on Mon, 08 Feb 2021 14:18:24 -0300
ID  - Sommeria-Klein_etal2020
ER  -