Coabundance Analysis
correlate(data, method = "sparcc", ...)
| data | matrix or data frame with abundance count data |
|---|---|
| method | character of coabundance method. One of 'sparcc', 'mb', 'pearson', or 'spearman' |
| ... | arguments passed to selected function (one of correlate_sparcc, correlate_mb, correlate_spearman, or correlate_pearson) |
The option method (sparcc by default) defines the method use to calculate the interaction between pairs of coabundant taxa:
pearson: Pearson correlation coefficient
spearman: Spearman's rank correlation coefficient
mb: Inverse Covariance based on (Meinshausen et al. 2006)
as implemented in (Kurtz et al. 2015)
sparcc: SparCC correlation based on (Friedman and Alm 2012)
as implemented in (Kurtz et al. 2015)
or (Watts et al. 2019)
Meinshausen N, Bühlmann P, others (2006). “High-dimensional graphs and variable selection with the lasso.” The annals of statistics, 34(3), 1436--1462.
Kurtz ZD, M?ller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA (2015). “Sparse and compositionally robust inference of microbial ecological networks.” PLoS Comput Biol, 11(5), e1004226.
Friedman J, Alm EJ (2012). “Inferring correlation networks from genomic survey data.” PLoS Comput Biol, 8(9), e1002687.
Watts SC, Ritchie SC, Inouye M, Holt KE (2019). “FastSpar: rapid and scalable correlation estimation for compositional data.” Bioinformatics, 35(6), 1064--1066.