Living_mulch_summary - Githubissues

yaoli1haoji commented 5 years ago

Living_mulch_project

The overall goal. To test whether living mulch treatment lead to a more healthy soil microbiome community

Hypothesis to test

The soil microbiome community in living mulch treatment is different from the communities in other treatment
The soil microbiome community in living mulch treatment are more diverse. it has more species and more unique species.

yaoli1haoji commented 5 years ago

How to test the Hypothesis?

First,Alpha diversity

1:Species richness (OTU count) "How many?" How many different species could be detected in a microbial ecosystem? Species richness is the number of different species in a sample. Practically, we count the number of distinguishable taxa (OTU's) in each sample

2:Species diversity (Shannon index) "How different?" How are the microbes balanced to each other? Do we have species evenness (similar abundance level) or do some species dominate others? Shannon index measures how evenly the microbes are distributed in a sample.

yaoli1haoji commented 5 years ago

Second, Beta Diversity

1:How different is the microbial composition in one environment compared to another?

2:diversity in microbial community between different environments (difference in taxonomic abundance profiles from different samples).

Bray–Curtis dissimilarity

based on abundance or read count data
differences in microbial abundances between two samples (e.g., at species level) values are from 0 to 1 0 means both samples share the same species at exact the same abundances 1 means both samples have complete different species abundances

Jaccard distance
based on presence or absence of species (does not include abundance information)
different in microbial composition between two samples 0 means both samples share exact the same species 1 means both samples have no species in common

UniFrac distance
sequence distances (phylogenetic tree)
based on the fraction of branch length that is shared between two samples or unique to one or the other sample unweighted UniFrac: purely based on sequence distances (does not include abundance information) weighted UniFrac: branch lengths are weighted by relative abundances (includes both sequence and abundance information)