Differential abundance analysis for microbial marker-gene surveys.
We introduce a methodology to assess differential abundance in sparse high-throughput microbial marker-gene survey data. Our approach, implemented in the metagenomeSeq Bioconductor package, relies on a novel normalization technique and a statistical model that accounts for undersampling-a common feature of large-scale marker-gene studies. Using simulated data and several published microbiota data sets, we show that metagenomeSeq outperforms the tools currently used in this field.
1] Graduate Program in Applied Mathematics & Statistics, and Scientific Computation, University of Maryland, College Park, Maryland, USA. [2] Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
Joseph N
JN
Paulson
O Colin
OC
Stine
Héctor Corrada
HC
Bravo
Mihai
0000000196175304
M
Pop
D000465:Algorithms; D000818:Animals; D019540:Area Under Curve; D016000:Cluster Analysis; D003198:Computer Simulation; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods; D005819:Genetic Markers; D014644:Genetic Variation; D006801:Humans; D007422:Intestines / Q000382:microbiology; D056186:Metagenomics / Q000379:methods*; D051379:Mice; D064307:Microbiota; D008957:Models, Genetic; D015233:Models, Statistical; D016011:Normal Distribution; D010641:Phenotype; D012336:RNA, Ribosomal, 16S / Q000235:genetics*; D017422:Sequence Analysis, DNA; D012984:Software
D005819:Genetic Markers; D012336:RNA, Ribosomal, 16S