The number of PCs from ordination of DEICODE distance matrix

[SoilRotifer (Mike Robeson)]

Hi @11132,

The following threads may help:

Help understanding DEICODE

Hi Cameron, Sorry, one other question. When I use the qiime plugin I now I understand that the --p-rank parameter defines the output rank of the matrix which means the total variance of your axes in PCA will be defined by that number (rank # = number of PCA vectors to sum to 100% variance). When I analyze my own dataset I find that if I use the default rank setting of 3 that the axes explain 47%,27%,26% of the variance respectively. When I rerun this analysis with --p-rank set to 5 the axes exp…

when things look good, I wonder where I've gone wrong...

Hi @devonorourke, You are correct in lowering the minimum count sum perameters. In this case lowering them to 1 or zero may even be warranted. The DIECODE percent variance explained is a relative percentage across each axis (in this case only three). For example, if the rank is two there will only be two axis and they will sum to 100% of the variance. This can make the % variance increase dramatically over other methods with many PCs. So I would not compare it between methods (like PCoA) and o…

-Mike