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IMAP: Integrated Microbiome Analysis Pipelines: End-to-End Practical User Guides Using Integrated Approaches

8 Exploring QIIME 2 Output: Feature Table, Taxonomy, and Metadata

In this section, we’ll explore key components, including the Feature Table, Taxonomy information, and associated metadata.

8.1 Initial Data Exploration

Before jumping into the analysis, let’s get acquainted with the primary outputs generated by QIIME 2:

  1. Feature Table: This table represents the abundance of different features (microbial taxa or operational taxonomic units) across samples.

  2. Taxonomy Data: Information on the taxonomic classification of each feature, helping us understand the microbial community composition.

  3. Metadata: Additional information associated with each sample, providing context and experimental details.

8.2 Starting the Analysis

  1. Load Data: Begin by loading the Feature Table, Taxonomy, and metadata into your analysis environment.
# Example R code for loading QIIME 2 data
feature_table <- read.table("path/to/feature_table.tsv", sep = "\t", header = TRUE, row.names = 1)
taxonomy_data <- read.table("path/to/taxonomy_data.tsv", sep = "\t", header = TRUE, row.names = 1)
metadata <- read.table("path/to/metadata.tsv", sep = "\t", header = TRUE, row.names = 1)