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Jenna M. Lang edited Results Taxonomic Diversity Across Dietary Patterns.md
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##Sequence Processing and Summary Statistics
The number of high-quality sequences per
meal (SAMPLE?) sample (i.e., meal) ranged from 168,669-318,956.
Sequences were clustered and clusters were assigned to a taxonomic group (when possible) using the pick\_open\_reference\_otus.py script with a 97% similarity cutoff and the gg\_13\_8\_otus reference taxonomy provided by the Greengenes Database Consortium (http://greengenes.secondgenome.com.) After OTU
assignment (EXPLAIN THIS HERE), assignment, mitochondrial and chloroplast sequences were filtered out,
and sequences that were observed only once
(globally) (WHAT DOES GLOBALLY MEAN HERE?_ across all samples were
removed. removed, and sequences that were Unassigned at the Domain taxonomic level were removed (these Unassigned sequences were verified via a manual BLAST search to be Chloroplast sequences.) After
filtration (DOES THIS REFER TO THE MITO AND CPST STEP?), this filtration, the range of sequences per sample decreased to 771-244,597. All subsequent diversity analyses were performed on samples that were rarefied to 771 sequences per
sample (THIS SEEMS A BIT CRAZY - WHY LET ONE SAMPLE DRIVE THE NUMBER OF SEQUENCES USED SO MUCH?). sample.
##Taxonomic Composition and Diversity of the Different Dietary Patterns
In terms of taxonomic richness (both measured by number of OTUs and phylogenetic diversity,) there was no significant difference between diet types (Fig) (non-parametric Kruskal-Wallis test with compare\_alpha\_diversity.py, P>0.6). We also tested for the significant variation in frequency of individual OTUs between diet types using the Kruskal-Wallis
test (WHY USE THIS SPECIFIC TEST?) test, as implemented in the group_significance.py
script. script.. This test is appropriate for comparing independent groups, with unequal samples sizes, that may not be normally distributed. None of the OTUs were significantly different between the three diet types.