Microbiome & multi-omics / Cross-omics correlation network
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Two data sets matched by sample (for example taxa abundance and metabolites or environmental variables) are examined together: each network node is a variable, coloured by data set; edges are correlations passing the thresholds: blue for negative and grey for positive, thicker for larger |r|; full r and P values are in the table.
The heatmaps below show the cross-omics matrix (omics 1 × omics 2) and the within-omics matrices; rows and columns are ordered by average-linkage clustering on correlation so blocks of related variables stand out.
Spearman and Kendall use ranks and are more robust to outliers and monotone non-linear relations, a common choice for abundance data. P values are adjusted separately within the cross-omics, within-omics-1 and within-omics-2 families.
With few samples even a large |r| may not be significant, and correlation does not imply causation. With many variables, keep only the N most variable ones. With a single table only within-table correlations are analysed.
Correlations and P values match scipy.stats.spearmanr / pearsonr / kendalltau (pairwise complete observations); multiple-testing correction matches R p.adjust.
Up to 5,000 samples per table; with more than 200 variables, keep the top N.
Send us your data and research question and you will receive a written plan within 1 working day: analysis steps, parameter rationale, deliverables and timeline. Quoted per project.