I am working with single-cell multi-omics data (RNA-seq, EM-seq, and ATAC-seq) from a single sample (either normal or disease group). My goal is to analyze the interactions among these three omics datasets at either the single-cell level or the cluster level. I have a few specific questions:
1)N-Integration Method Applicability:
Can the N-Integration method be used to analyze interactions within a single state (e.g., only normal group or only disease group)?
2)Data Structure for Integration:
My dataset consists of 6,000 cells grouped into 12 cell types. I am unsure whether the input matrix should have 6,000 rows (single-cell level) or 12 rows (cluster-level averages).
If using 12 rows, there are no replicates, causing perf() to report an error.
If using 6,000 rows, the matrix is highly sparse, and correlation coefficients (calculated via cor()) appear unreliable (either too high or too low).
What would be the recommended approach?
3)Parameter Settings and Method Selection:
Based on the second article, how should the folds parameter in perf() be set for cluster-level analysis?For analyzing interactions at the cluster level, which method is more appropriate: sPLS or SPLS-DA? I understand the former but would like confirmation.
I would greatly appreciate your guidance on these questions. Thank you for your time and assistance!