| name | sparcc |
| description | SparCC — Sparse Correlations for Compositional data; infer taxon–taxon correlations from relative abundances without naive Pearson on compositions. Use for correlation networks on microbiome tables. Original: https://bitbucket.org/yonatanf/sparcc; convenient R implementation inside SpiecEasi (sparcc()). Route via research-analysis; conditional networks → spieceasi.
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| license | MIT |
| category | evaluation |
| tags | ["SparCC","network","correlation","compositionality","microbiome"] |
| upstream | https://bitbucket.org/yonatanf/sparcc |
| stage | report |
SparCC
Upstream (original): https://bitbucket.org/yonatanf/sparcc ·
R helper often via SpiecEasi sparcc()
Citation
Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey
data. PLoS Comput. Biol. 8, e1002687 (2012).
https://doi.org/10.1371/journal.pcbi.1002687
See also docs/references.md.
Analytical thinking
Relative abundances are compositional — Pearson/Spearman on proportions
yields spurious correlations. SparCC estimates correlations under a sparse
assumption. Correlation ≠ direct ecological interaction; for conditional
independence graphs prefer spieceasi. Filter rare taxa; report bootstrap /
thresholding if used.
How to run
library(SpiecEasi)
Or follow the original SparCC Python/bitbucket pipeline for the installed tag.
Decision tree
Microbial association network?
├─ Compositional correlations → sparcc
├─ Sparse conditional networks → spieceasi
├─ DA between groups → ancombc / maaslin2
└─ Plot networks in R suite → microeco (± igraph)
Related skills
spieceasi · research-analysis · microeco · phyloseq · maaslin2 ·
tool-selection
