Hi all,
I am using LEfSe web version.
My whole database includes 899 features (in rows) and 8 observations (in columns, 4 observations per group). I used LEfSe to identify significantly discriminating features between the groups but I used the data in 2 different ways:
Way 1: I used the whole dataset, i.e. I included 899 features for the 8 observations, and the result was this:
Way 2: I used a subset of my data, i.e. I included only 13 features for the same 8 observations, and the result was this:
I have 2 questions:
Q1: Why is the LDA score for Actinomyces around 3 in way 1 but it’s 4.8 in way 2?
Q2: Why did I find 6 significantly discriminative features in way 1 but 8 in way 2?
While creating this post, I also noticed that step B results differ, if this helps:
Way 1:
Way 2:
Since I don’t have subclass, I am also wondering about the results from step B way 1, why do I have a different number of significantly discriminative features before and after the wilcoxon test? I am asking this because I have seen solutions for people using LEfSe in python or R, but I didn’t see anything about the web version. I am supposing that in the absence of subclass, the wilcoxon text is made between the observations, am I correct?
Many thanks,
Joana