LEfSe  

LDA Effect Size (LEfSe) (Segata et. al 2010) is An algorithm for HighDimensional biomarker discovery and explanation that identifies genomic features (genes, pathways, or taxa) characterizing the differences between two or more biological conditions (or classes, see figure below). It emphasizes both statistical significance and biological relevance, allowing researchers to identify differentially abundant features that are also consistent with biologically meaningful categories (subclasses). LEfSe first robustly identifies features that are statistically different among biological classes. It then performs additional tests to assess whether these differences are consistent with respect to expected biological behavior. Specifically, we first use the nonparametric factorial KruskalWallis (KW) sumrank test to detect features with significant differential abundance with respect to the class of interest; biological significance is subsequently investigated using a set of pairwise tests among subclasses using the (unpaired) Wilcoxon ranksum test. As a last step, LEfSe uses Linear Discriminant Analysis to estimate the effect size of each differentially abundant feature and, if desired by the investigator, to perform dimension reduction. LEfSe consists of six modules performing the following steps (see the figure below).


Volcano plot  

Volcano plot shows two important indicators (Fold change/ pvalue), which can be used to screen out genes with differential expression between two samples intuitively and reasonably. Using T test analysis of significant differentially expressed genes between the two samples, with log2 (a fold change) as the abscissa, by T test significance test P values of negative logarithm  log10 (P value) as the ordinate, brings the Volcano figure (Volcano Plot), use of certain filter condition (such as more than 1.5 times change and P < 0.05), can filter out significantly differentially expressed genes, for subsequent research. 

PICRUSt  

PICRUSt: Phylogenetic Investigation of Communities by Reconstruction of Unobserved States
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First Step 

Tax4Fun  

Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data. 
