Similarity network

Version Compatibility

Reference examples tested with: SNFtool 2.3+, igraph 2.0+, pheatmap 1.0+.

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('SNFtool') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

SNFtool argument names are easy to misread: affinityMatrix's width argument is sigma (not alpha or mu), dist2 returns SQUARED Euclidean distance (take the square root), and spectralClustering's K is the number of CLUSTERS, a name collision with the K-neighbors argument of affinityMatrix/SNF.

Similarity Network Fusion

"Stratify my patients using multi-omics data" -> Fuse per-omic patient-similarity networks into one graph and spectral-cluster it - because the clustering always returns the number of subtypes requested, so the subtype is a claim to defend, not a discovery.

• R: SNF() to fuse networks, spectralClustering() to partition, then validate

Scope: patient-similarity-space integration - per-omic affinity networks, SNF fusion, spectral clustering into candidate subtypes, cluster-number choice, and the stability/survival/replication defense, plus the integrative-clustering landscape (NEMO, PINS, iCluster, CIMLR, intNMF, consensus). Feature-space latent factors -> mofa-integration. Supervised feature signatures -> mixomics-analysis. Survival mechanics -> clinical-biostatistics/survival-analysis. Single-cell graph clustering -> single-cell/clustering.

The Single Most Important Modern Insight -- Spectral Clustering Always Returns the Requested Number of Clusters, So a Subtype Is a Claim to Defend, Not a Thing Discovered

Ask the spectral step for four clusters and it returns four, whether or not four real patient groups exist; the eigengap suggesting "four" means only that the fused graph has a roughly four-block shape, not that the disease has four subtypes. SNF's fusion is genuinely powerful - its cross-network diffusion reinforces patient pairs that multiple omics agree on and erodes omic-specific noise - but power to draw a boundary is not evidence the boundary is real. Three defenses, none of which the algorithm supplies:

1. Stability. Resample the patients, re-cluster, and measure agreement (consensus index / NMI across subsamples). A real subtype recurs; an artifact does not. Report stability across a fixed (K, sigma) grid rather than tuning those hyperparameters to a target.
2. Survival separation after adjustment. Subtypes should differ in outcome in a Cox model adjusted for known prognostic covariates (stage, age, grade), so a "subtype" is not just re-encoded stage. The discovery-cohort p is supporting evidence, not proof - and a permutation p is safer than the chi-square approximation at small arm sizes.
3. Replication and the fused-versus-best-single-omic check. Multi-omics does not consistently beat the best single omic (Rappoport and Shamir 2018), and SNF's diffusion can dilute a signal carried by one omic. Benchmark the fused clustering against each single-omic clustering, and replicate the subtypes in an independent cohort.

The honest report names the hyperparameters, shows the clusters are not knife-edge-sensitive to them, and treats the discovery survival p as supporting evidence, not the finding.

Tool Taxonomy (Integrative Clustering)

Tool	Citation	Mechanism	When
SNF (SNFtool)	Wang 2014 Nat Methods 11:333	per-omic affinity + cross-network diffusion -> spectral	complete data; non-linear consensus reinforcing multi-omic agreement; the default
NEMO	Rappoport 2019 Bioinformatics 35:3348	per-omic relative similarity, AVERAGED (no iteration) -> spectral	PARTIAL/mosaic data; fast; comparable accuracy on full data
PINS / PINSPlus	Nguyen 2017 Genome Res 27:2025; Nguyen 2019 Bioinformatics 35:2843	perturbation clustering; keeps partitions robust to noise; auto-picks C	when partition stability is the priority
iCluster / iClusterPlus	Shen 2009 Bioinformatics 25:2906; Mo 2013 PNAS 110:4245	model-based joint latent-variable + feature selection	want a generative model and feature selection; tends to pick few clusters
CIMLR	Ramazzotti 2018 Nat Commun 9:4453	multiple-kernel learning per omic -> k-means	one Gaussian kernel per omic too rigid; strong survival results
intNMF	Chalise 2017 PLoS One 12:e0176278	joint non-negative matrix factorization	non-negative data; parts-based factorization + clustering
Consensus / COCA	Monti 2003 Mach Learn 52:91; Hoadley 2014 Cell 158:929	cluster each omic, then cluster the matrix-of-clusters	late integration; want each omic's clustering visible and a consensus

Decision Tree by Scenario

Scenario	Recommended	Why
Complete multi-omics, want a non-linear consensus partition	SNF + spectralClustering	cross-network diffusion reinforces multi-omic agreement
Some patients missing an omic (mosaic/partial)	NEMO	built for partial data; no imputation, no patient loss
Partition robustness/auto cluster number is the priority	PINSPlus	perturbation clustering builds stability in
Want a generative model and integrated feature selection	iCluster / iClusterBayes	model-based joint latent variable
Need feature-level interpretation (which genes define a subtype)	-> mofa-integration / mixomics-analysis	SNF has no feature model; feature-space tools live there
Validate subtypes against outcome	-> clinical-biostatistics/survival-analysis	Cox / log-rank / KM mechanics
SNF underperforms on survival in a benchmark	MCCA (survival) or rMKL-LPP (clinical enrichment)	topped those criteria in Rappoport and Shamir 2018
Which method at all / paired vs mosaic	-> integration-design	the correspondence and method decision

Build the Per-Omic Affinity Networks

Goal: Turn each omic into a patient-by-patient similarity network on a common scale, collapsing the high-dimensional feature space into an n-by-n object so feature count buys no votes.

Approach: Standardize each continuous omic per feature, compute the (square-rooted) Euclidean distance, then apply the local-scaled Gaussian kernel. SNF requires every patient to have every omic, so intersect to common samples first and report how many that drops.

library(SNFtool)

K <- 20        # neighbors defining the local kernel bandwidth; SNFtool guidance 10-30; changes cluster count
sigma <- 0.5   # kernel width multiplier (affinityMatrix's third arg, named sigma not alpha); guidance 0.3-0.8
t_iter <- 20   # cross-diffusion iterations; converges by ~10-20

norm_views <- lapply(list(rna=rna, meth=meth, mirna=mirna), standardNormalization)   # per-feature z-score before distance
dists <- lapply(norm_views, function(x) dist2(x, x)^(1/2))                            # dist2 returns SQUARED distance
affinities <- lapply(dists, function(d) affinityMatrix(d, K, sigma))

Fuse and Cluster

Goal: Fuse the per-omic networks into one graph and partition it, treating the cluster number as the central claim rather than a nuisance parameter.

Approach: Run SNF's cross-diffusion, read the four cluster-number estimates the package returns (not as truth but as plausibility), then spectral-cluster. The package itself warns the estimates cannot guarantee accuracy.

fused <- SNF(affinities, K, t_iter)
estimateNumberOfClustersGivenGraph(fused, NUMC=2:8)   # returns FOUR estimates: K1/K12 (eigengap), K2/K22 (rotation cost)
clusters <- spectralClustering(fused, K=4, type=3)    # here K is the CLUSTER COUNT (not neighbors); type 3 = Ng-Jordan-Weiss default

Defend the Subtypes

Goal: Show the clusters are stable, separate outcome, and are not just the best single omic before calling them subtypes.

Approach: Compare the fused clustering against each single-omic clustering, assess stability under resampling, and rank the post-hoc feature attribution with the package function rather than a hand-rolled test. Survival mechanics are routed out.

concordanceNetworkNMI(c(affinities, list(fused)), C=4)   # NMI among per-omic and fused clusterings: did fusion beat the best single omic?
feat_rank <- rankFeaturesByNMI(norm_views, fused)        # POST-HOC attribution: features that track the clusters, not a model that made them

Validate survival separation in a covariate-adjusted Cox model and report events per arm (clinical-biostatistics/survival-analysis owns the mechanics); assess stability by resampling patients and re-clustering, reporting agreement across subsamples. To assign a new patient to an existing subtype without re-clustering, use groupPredict(train_views, test_views, groups, K=20, method=1) (label propagation). For a mosaic cohort, switch to NEMO rather than dropping the incomplete patients.

Per-Method Failure Modes

Subtype count reported as a discovery

Trigger: "I found 4 subtypes" with no defense. Mechanism: spectral clustering returns the requested C regardless of structure. Symptom: a clean-looking partition that does not replicate. Fix: require eigengap plausibility, resampling stability, covariate-adjusted survival, and external replication before claiming a subtype count.

Hyperparameters tuned to a label, then claimed

Trigger: grid-searching K and sigma to maximize NMI against known labels. Mechanism: in real discovery there are no labels, so tuning to NMI is circular. Symptom: a result that only holds at the chosen (K, sigma). Fix: fix or pre-register a small (K, sigma) grid and show the clustering is stable across it; report sensitivity as a result.

Complete-data requirement ignored

Trigger: Reduce(intersect, ...) silently dropping patients missing an omic. Mechanism: SNF's cross-diffusion multiplies aligned n-by-n matrices, so it needs complete data. Symptom: a decimated, biased cohort. Fix: report the dropped count and bias; use NEMO for partial data; do not impute a whole omic to keep a patient.

Fusion assumed to beat the best single omic

Trigger: reporting the fused clustering without a single-omic comparison. Mechanism: fusion's diffusion can dilute a signal carried by one omic; multi-omics is not consistently better (Rappoport and Shamir). Symptom: a fused result no better than the best layer. Fix: benchmark fused vs each single omic with concordanceNetworkNMI and per-omic survival.

Survival p taken at face value

Trigger: a log-rank p on discovery clusters. Mechanism: C, K, sigma were chosen partly to get separable groups, and arms are small. Symptom: an over-optimistic p that does not replicate. Fix: adjust for prognostic covariates, report events per arm, use a permutation p, and require replication.

Feature attribution treated as the model

Trigger: presenting ranked features as what generated the clusters. Mechanism: SNF has no feature-level model; attribution is post-hoc. Symptom: causal claims SNF cannot support. Fix: use rankFeaturesByNMI and present it as post-hoc characterization; for a feature model use MOFA/DIABLO.

SNFtool API misread

Trigger: treating dist2 output as Euclidean, passing alpha to affinityMatrix, or reading spectralClustering's K as neighbors. Mechanism: dist2 is squared, the width arg is sigma, and that K is the cluster count. Symptom: distorted affinities or the wrong number of clusters. Fix: take dist2(...)^(1/2), pass sigma, and read the K name in context.

Quantitative Thresholds

Threshold	Source	Rationale
K neighbors ~20 (range 10-30)	Wang 2014 Nat Methods 11:333; SNFtool docs	sets the local kernel bandwidth; small K fragments, large K over-smooths and changes cluster count
sigma ~0.5 (range 0.3-0.8)	SNFtool docs	kernel width multiplier; wider blurs clusters, narrower sharpens noise
t iterations ~10-20	Wang 2014 Nat Methods 11:333	cross-diffusion converges; more iterations do little past convergence
estimateNumberOfClustersGivenGraph returns FOUR estimates	SNFtool docs	eigengap (K1/K12) and rotation cost (K2/K22); plausibility not proof
Fused must beat the best single omic to justify fusion	Rappoport and Shamir 2018 Nucleic Acids Res 46:10546	multi-omics is not consistently better; check explicitly
Stability across a fixed (K, sigma) grid + permutation survival p	Monti 2003 Mach Learn 52:91	resampling stability and a permutation p guard against artifact subtypes

Common Errors

Error / symptom	Cause	Solution
Distorted affinities / wrong-scale distances	dist2 output used as Euclidean	take dist2(...)^(1/2)
Clusters change unexpectedly	affinityMatrix width passed as alpha or wrong arg	the third argument is sigma
Wrong number of clusters	spectralClustering's K read as neighbors	that K is the cluster count
Function dist2 not found after first call	a variable named dist2 shadowing the function	name distance variables differently (d1, d2)
Many patients silently dropped	complete-data intersection on a mosaic cohort	report the drop; use NEMO for partial data
Hand-rolled feature ranking	reimplementing attribution with aov	use rankFeaturesByNMI(list_of_views, fused)

References

• Wang B, Mezlini AM, Demir F, et al. 2014. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods 11:333-337.
• Rappoport N, Shamir R. 2018. Multi-omic and multi-view clustering algorithms: review and cancer benchmark. Nucleic Acids Res 46:10546-10562.
• Rappoport N, Shamir R. 2019. NEMO: cancer subtyping by integration of partial multi-omic data. Bioinformatics 35:3348-3356.
• Nguyen T, Tagett R, Diaz D, Draghici S. 2017. A novel approach for data integration and disease subtyping. Genome Res 27:2025-2039.
• Nguyen H, Shrestha S, Draghici S, Nguyen T. 2019. PINSPlus: a tool for tumor subtype discovery in integrated genomic data. Bioinformatics 35:2843-2846.
• Shen R, Olshen AB, Ladanyi M. 2009. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 25:2906-2912.
• Mo Q, Wang S, Seshan VE, et al. 2013. Pattern discovery and cancer gene identification in integrated cancer genomic data. PNAS 110:4245-4250.
• Ramazzotti D, Lal A, Wang B, Batzoglou S, Sidow A. 2018. Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival. Nat Commun 9:4453.
• Chalise P, Fridley BL. 2017. Integrative clustering of multi-level 'omic data based on non-negative matrix factorization algorithm. PLoS One 12:e0176278.
• Monti S, Tamayo P, Mesirov J, Golub T. 2003. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn 52:91-118.
• von Luxburg U. 2007. A tutorial on spectral clustering. Stat Comput 17:395-416.

Related Skills

• integration-design - The method-selection and paired-vs-mosaic decision
• mofa-integration - Feature-space latent factors (which molecular axes matter)
• mixomics-analysis - Supervised feature signatures for known classes
• data-harmonization - Per-omic scaling before building distances
• clinical-biostatistics/survival-analysis - Survival validation of discovered subtypes
• single-cell/clustering - Graph clustering of cells (different object)
• pathway-analysis/go-enrichment - Enrichment of subtype-associated features
• workflows/multi-omics-pipeline - End-to-end multi-omics integration pipeline