I'm in Rob Knight's lab and excited to see (and use) your DivNet package — congrats on the in press acceptance! I have a question regarding the use of the covariate matrix ("X") in the divnet() function. The example given in your tutorial uses the Lee dataset, picking "char" (basalts of different characteristics) as the covariate. I understand that this is useful when sampling from different environments, but I'm not entirely if it would include samples from the same environment that are affected by external factors. A few examples:
A plot of soil sampled in winter versus that same soil sampled in summer
The gut microbiome under the influence of antibiotics versus a gut microbiome without antibiotics
Liver tissue samples of tumors that originate in the liver versus those that metastasize to the liver (e.g. Bullman et al., 2017. Science.)
Is it permissible to use these differences as covariates ("X") for the divnet() function (e.g. antibiotic vs. non-antibiotic gut samples) even though they're technically sampled from the same environment?
Hi Amy,
I'm in Rob Knight's lab and excited to see (and use) your DivNet package — congrats on the in press acceptance! I have a question regarding the use of the covariate matrix ("X") in the divnet() function. The example given in your tutorial uses the Lee dataset, picking "char" (basalts of different characteristics) as the covariate. I understand that this is useful when sampling from different environments, but I'm not entirely if it would include samples from the same environment that are affected by external factors. A few examples:
Is it permissible to use these differences as covariates ("X") for the divnet() function (e.g. antibiotic vs. non-antibiotic gut samples) even though they're technically sampled from the same environment?
Thanks a ton!