Statistics with R for Microbiome Analysis: From Raw Reads to Relative Abundance
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This book covers the necessary analysis steps for dealing with microbiome data. The microbial samples are sequenced into raw reads or fastq files. These raw reads should be quality checked to assure their quality per base or per read. Then, after removing the errors, we can infer their exact amplicon sequence variants (ASVs). After that, we taxonomically classify these sequences to represent the different taxonomy levels of species, genus, family, order, class, and phylum and generate a phylogenetic tree. Finally, the sequence data, the taxonomy table, phylogenetic tree, and sample data are co...
This book covers the necessary analysis steps for dealing with microbiome data. The microbial samples are sequenced into raw reads or fastq files. These raw reads should be quality checked to assure their quality per base or per read. Then, after removing the errors, we can infer their exact amplicon sequence variants (ASVs). After that, we taxonomically classify these sequences to represent the different taxonomy levels of species, genus, family, order, class, and phylum and generate a phylogenetic tree. Finally, the sequence data, the taxonomy table, phylogenetic tree, and sample data are combined in a single phyloseq object for ease of plotting, manipulation, and analysis of the different components of microbiome data. The final phyloseq object can also be cleaned for certain low prevalent sequences. All these steps are explained in R codes using a freely available microbiome data of 360 fecal samples.
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