Do a couple of essential compositional data analysis things on giant data sets.
- CMake 3.15
- C++17
Download the source code, then run
mkdir build
cd build
cmake ..
make
make install
This program assumes that OTUs (or whatever) are on rows, and samples are on columns. Also, it assumes that there are fewer samples than OTUs/clusters/seqs.
Note: seed is the random seed used for Randomized SVD.
coda <seed> <counts> <clr_out.tsv> <aitchison_dist_out.tsv> <sample_projection_out.tsv>
If you have OpenMP, then you can set number of threads like this:
OMP_NUM_THREADS=n coda ...
Where coda ... is the command you would normally run.
- CLR transformed OTU table
- Aitchison distance matrix for the samples
- Projection of samples into PC space based on the CLR transformation (like doing a compositional biplot)