title: Female 22wk  Mouse 1 Choline + FMC
design: Cells were maintained over the duration of the experiment within mice until ex vivo sampling occurred. Immediately after collection, cecal samples were subjected to flash-freezing and stored at -80C until total community DNA extractions could be performed. Microbial DNA was extracted using a MagAttract HMW DNA Kit (Qiagen, Hilden, Germany). The 16S rRNA V4 region was amplified and sequenced on Illumina Iseq 100 platform following the manufacturers instructions. Raw 16S amplicon sequence (forward reads only) and metadata were demultiplexed using the split_libraries_fastq.py script implemented in QIIME1.9.1.  The demultiplexed fastq file was split into sample-specific fastq files using split_sequence_file_on_sample_ids.py script from Qiime1.9.1.  Individual fastq files without nonbiological nucleotides were processed using Divisive Amplicon Denoising Algorithm pipeline.  The output of the dada2 pipeline (feature table of amplicon sequence variants) was processed for alpha and beta diversity analysis using phyloseq and microbiomeSeq (http://www.github.com/umerijaz/microbiomeSeq) packages in R.  Alpha diversity estimates were analysed within group categories using estimate_richness function of the phyloseq package.  Nonmultidimensional scaling (NMDS) was performed using Bray-Curtis dissimilarity matrix between groups and visualized by using ggplot2 package.  We assessed the statistical significance (P<0.05) throughout and whenever necessary adjusted P values for multiple comparisons according to the Benjamini and Hochberg method to control False Discovery Rate, while performing multiple testing on taxa abundance according to sample categories.  We performed an ANOVA among sample categories while measuring -diversity using the plot_anova_diversity function in microbiomeSeq package (http://www.github.com/umerijaz/microbiomeSeq). Permutational multivariate ANOVA with 999 permutations was performed to test the statistical significance of the non-multidimensional scaling patterns with the ordination function of the microbiomeSeq package.  Linear regression was performed on taxa abundances against metadata variables using lm function implemented in dplyr package in R.