Data Preparation

Data Preparation#

The Atacama soil microbiome dataset [D1] contains:

  • \(N = 50\) samples from Atacama Desert soil

  • \(p = 13\) microbial taxa (ASVs)

  • \(q = 5\) environmental covariates: pH, elevation, temperature, humidity, and vegetation

Data Overview describes the dataset in more detail.

Transforming the counts#

Microbiome counts represent relative abundances constrained to sum to a constant. Transform them before estimating a covariance.

# Transform compositional data using mCLR transformation
qiime gglasso transform-features \
     --p-transformation mclr \
     --p-add-metadata False \
     --p-scale-metadata False \
     --i-table data/atacama-counts.qza \
     --i-taxonomy data/classification.qza \
     --m-sample-metadata-file data/selected-atacama-sample-metadata.tsv \
     --o-transformed-table data/atacama-table-mclr.qza

The modified centred log-ratio moves the table into an unconstrained space, handles zeros without adding pseudo-counts, and preserves the relative information between taxa. For the standard centred log-ratio instead, pass --p-transformation clr.

Building the input correlation#

qiime gglasso calculate-covariance \
     --p-method scaled \
     --i-table data/atacama-table-mclr.qza \
     --o-covariance-matrix data/atacama-table-corr.qza

A scaled covariance is the Pearson correlation. For the covariance itself, pass --p-method unscaled.

The input to the graphical lasso problem must be a positive semi-definite matrix.