sdpapet
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Hi, I can't build a tree using Bray–Curtis dissimilarity, with jackknife values. Also, I can't make jackedknifed nMDS plots based on Bray–Curtis dissimilarity either.
I tried both methods using work flow script "jackknifed_beta_diversity.py" and step by step. For the workflow, I include the following specifies changes in the parameter files
unweighted_unifrac,weighted_unifrac,bray_curtis
multiple_rarefactions_even_depth:num-reps 1000
However, the results don't give me any bray_curtis distance matrix. It still gives me the PCOA plot based on unweighted_unifrac and weighted_unifrac.
I have to used the 1000 subsample OTU tables in the rarefaction fold to do it by hand. Here are the problems.
1> I run beta_diversity.py using 1000 OTU tables as input fold and I get 1000 bray-curtis distance matrices.
2> I run upgma_cluster.py based on the matrices to build a UPGMA tree. I want to build one tree with node supporting information, but I get 1000 trees. I don't know how to build one tree with node supporting information ("master tree")
3> Similarly, I run "nmds.py" with the 1000 matrices. It will give me 1000 nmds coordinates files. I don't know how to combine them into a master file and plot it as the PCOA plot generated from "jackknifed_beta_diversity.py".
4> The last question. I am wondering if the PCOA plot generated from "jackknifed_beta_diversity.py" based on the average coordinates value of all iterations (1000 in this case).