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Co-accessibility (cis-Regulatory Linkage)
"Which enhancers connect to which promoters in my scATAC data?" -> Use cell-to-cell variability in joint accessibility of nearby peaks to infer cis-regulatory connections without explicit RNA expression. Output is a peak-pair graph with co-accessibility scores; thresholding produces enhancer-gene candidate pairs.
Co-accessibility is NOT 3D contact; it's a statistical association based on cell-to-cell co-variation. Strong co-accessibility correlates with Hi-C/Micro-C contacts (~30-50% concordance) but is not equivalent.
What Co-accessibility Captures vs What It Doesn't
Captures
Misses
Peak pairs that vary together across cell states
3D physical contacts that don't vary in accessibility
Cis-regulatory grammar within a cell type
Trans-chromosomal interactions
Active enhancer-promoter pairs
Constitutive structural contacts
Lineage-specific regulation
Developmental contacts that opened before scATAC sample
Distance-decay biology of enhancer-promoter
Hub enhancers that contact many distal targets
For physical contact, use Hi-C, Micro-C, or PCHi-C. Co-accessibility is the chromatin-only proxy.
Algorithmic Taxonomy
Tool
Method
Input
Output
Strength
Fails when
Cicero (Pliner 2018)
Graphical lasso on aggregated cell metacells
scATAC peak-cell matrix + cell trajectory
Peak-pair connection score (0-1)
Original, well-validated; integrates with Monocle3
Slow on >50K cells; sensitive to alpha tuning
ArchR getCoAccessibility
Cicero-based; uses ArchR's metacell aggregation
ArchR project
Same as Cicero
Built-in to ArchR pipeline; faster on large datasets
Pearson correlation of accessibility with paired gene expression
Multiome
Peak-gene linkage score
Direct enhancer-gene from RNA correlation
Multiome-only; not pure ATAC
GeneHancer / FANTOM5 / EpiMap
Bulk-derived enhancer-gene reference
None (database lookup)
Pre-computed enhancer-gene pairs
Comprehensive; published references
Cell-type-agnostic; may not match the biology of interest
Methodology evolves; verify against Pliner 2018 (Cicero), Bravo 2023 (SCENIC+), Nasser 2021 (ABC model alternative for enhancer-gene), and current Hi-C concordance benchmarks.
How Cicero Works (Conceptually)
Cell-to-cell variability is too sparse for direct correlation. Cicero solves this via metacells:
Reduce dimensionality (UMAP from input).
Build k-NN graph of cells.
Aggregate k cells into metacells (default k = 50).
Compute correlation in accessibility across metacells, restricted to peak pairs within genomic_distance_max (default 500 kb cis).
Apply graphical lasso with regularization alpha to sparsify the correlation matrix.
Connection thresholds typically 0.05-0.5; > 0.25 is high-confidence.
Per-Tool Failure Modes
Cicero -- alpha tuning shifts results
Trigger: Default alpha (sometimes computed automatically from data); custom alpha < 0.5 or > 5.
Mechanism: Alpha controls graphical lasso regularization. Too low: dense graph with many spurious connections; too high: sparse with biology missing.
Symptom: Connection count varies 10-100x across alpha sweeps.
Fix: Use Cicero's estimate_distance_parameter() to get data-driven alpha; verify connection count is biologically plausible (~10-50% of peaks have at least one strong connection).
Fix: For specific use cases (e.g., gene desertless TADs), increase genomic_distance_max to 1 Mb or more. Trans connections require Hi-C, not co-accessibility.
SCENIC+ -- RNA scaling
Trigger: RNA-side dropouts in Multiome data.
Mechanism: SCENIC+ requires reasonable RNA quantification per cell. Sparse Multiome RNA with many zero genes causes correlation degradation.
Fix: Filter cells with insufficient RNA; aggregate cells if necessary. Multiome RNA should look comparable to standalone scRNA-seq.
LinkPeaks (Signac) -- Distance default
Trigger: Default LinkPeaks(..., distance=5e+05).
Mechanism: Same as Cicero; 500 kb cis only by default.
Fix: Same; widen if needed but trans not supported.
Decision Tree by Goal
Goal
Tool
ATAC-only enhancer-promoter inference
Cicero
ATAC-only inside ArchR ecosystem
ArchR getCoAccessibility
Multiome (RNA + ATAC) enhancer-gene inference
LinkPeaks (Signac) for direct correlation; SCENIC+ for TF network
TF-driven regulatory networks
SCENIC+ (requires Multiome)
Comparison against Hi-C / Micro-C
Cicero output -> overlap with HiCCUPS loops
Published reference enhancer-gene pairs
GeneHancer, FANTOM5, EpiMap (pre-computed lookup)
Gene desertless distal regulation
Cicero with widened distance; or H3K27ac HiChIP
Cicero Standard Workflow
Goal: Infer cis-regulatory peak-peak connections from a scATAC peak-cell matrix.
Approach: Build a Monocle3 CellDataSet, reduce dimensions via LSI + UMAP, aggregate cells into metacells, then run Cicero's graphical-lasso correlation across the cis window and threshold on connection score.
library(cicero); library(monocle3); library(GenomicRanges)# Input: peak-cell binary matrix from Signac/ArchR (rows = peaks, cols = cells)# Convert peaks to "chrN_start_end" format
peak_names <- paste0(seqnames(peaks),'_', start(peaks),'_', end(peaks))
input_cds <- new_cell_data_set(peak_matrix, cell_metadata=metadata,
gene_metadata=peak_metadata)# Reduce dimensionality (UMAP from input)
input_cds <- detect_genes(input_cds)
input_cds <- estimate_size_factors(input_cds)
input_cds <- preprocess_cds(input_cds, method='LSI')
input_cds <- reduce_dimension(input_cds, reduction_method='UMAP',
preprocess_method='LSI')# Build metacell-aggregated CDS
umap_coords <- reducedDims(input_cds)$UMAP
cicero_cds <- make_cicero_cds(input_cds, reduced_coordinates=umap_coords, k=50)# Run Cicero with hg38 chrom sizes
genome_df <- data.frame(chr=seqnames(seqinfo(BSgenome.Hsapiens.UCSC.hg38)),length=seqlengths(seqinfo(BSgenome.Hsapiens.UCSC.hg38)))
conns <- run_cicero(cicero_cds, genomic_coords=genome_df,
window=500000, sample_num=100)# Filter to high-confidence connections.# Threshold 0.25 is a Cicero-documentation working default; the optimal cutoff# is dataset-dependent and is best calibrated against orthogonal Hi-C / HiChIP.
strong <- conns[conns$coaccess >0.25,]
cat(sprintf('Total conns: %d; strong (>0.25): %d\n', nrow(conns), nrow(strong)))
ArchR getCoAccessibility
library(ArchR)
proj <- loadArchRProject('ArchR_out')
proj <- addCoAccessibility(proj, reducedDims='IterativeLSI',
k=100, knnIteration=500,
maxDist=250000)# 250 kb cis (wider than the 100 kb default)
co_acc <- getCoAccessibility(proj, corCutOff=0.5,# Default 0.5 in ArchR; lower for more (calibrate against Hi-C/HiChIP)
returnLoops=TRUE)# TRUE (default) -> GRanges loops object; FALSE -> DataFrame of peak-pair correlations
With returnLoops=TRUE (the default) ArchR returns the connections as a GRanges loops object compatible with GenomicInteractions for direct overlap with Hi-C loops; returnLoops=FALSE instead returns a DataFrame of peak-pair correlations.
Visualizing Connections
# As arc plot at a locus of interest
library(Gviz); library(GenomicInteractions)# Cicero Peak1/Peak2 are chr_start_end strings; convert to chr:start-end for GRanges()
gi <- GenomicInteractions(anchor1=GRanges(sub('_(\\d+)_(\\d+)$',':\\1-\\2', strong$Peak1)),
anchor2=GRanges(sub('_(\\d+)_(\\d+)$',':\\1-\\2', strong$Peak2)),
counts=as.integer(strong$coaccess *100))
track <- InteractionTrack(gi, name='co-accessibility')
plotTracks(track)
For genome-browser visualization with ArchR: plotPeak2GeneHeatmap() shows the peak-gene linkage matrix; plotBrowserTrack() overlays connections on tracks.
SCENIC+ TF-Driven Networks
SCENIC+ 1.0 runs as a Snakemake pipeline (CLI), not a single monolithic Python call. Prepare the inputs first (a pycisTopic cisTopic object, motif-enrichment results, and paired RNA AnnData), then scaffold and run the workflow:
# Scaffold the pipeline, then edit its config.yaml to point at the cisTopic object,# motif-enrichment results, and GEX AnnData
scenicplus init_snakemake --out_dir scplus_pipeline/
snakemake --cores 16 --snakefile scplus_pipeline/Snakemake/workflow/Snakefile
# eRegulons (TF + target genes + linked enhancers) are written to the output MuData (scplusmdata.h5mu)
SCENIC+ is significantly more complex than Cicero; budget 1-2 days for setup. The benefit is that outputs are TF -> enhancer -> gene triples, not just peak-peak co-accessibility.
Cicero Alpha Mathematics
Trigger: Tuning Cicero's regularization parameter for the graphical lasso step.
Mechanism:estimate_distance_parameter() searches for the smallest distance-penalty scaling (Cicero's distance_parameter, called "alpha" here) such that, across random genomic windows, no more than ~5% of peak pairs beyond distance_constraint retain non-zero graphical-lasso entries and fewer than 80% of all entries are non-zero. This penalizes long-range co-accessibility so the graph sparsifies at biologically appropriate distance scales -- it is not a correlation-vs-distance regression slope.
Implementation: Cicero calls estimate_distance_parameter(cicero_cds, window=window, maxit=100, sample_num=100, genomic_coords=genome_df) over sample_num random windows and returns one distance_parameter per window; take the mean and pass it to generate_cicero_models(cicero_cds, distance_parameter=mean(...)). Supply genomic_coords explicitly -- its default is cicero::human.hg19.genome, wrong for an hg38 analysis.
When manual tuning helps: Very dense peaksets (>200k peaks) may need a higher distance_parameter to control false positives; very sparse (<10k peaks) may need a lower one to recover signal. Verify by running on a permutation / cell-label-shuffle negative control -- the expected outcome is ~0 strong connections (technical replicates should instead reproduce connections).
ABC Model Cross-Reference
For enhancer-to-gene linking with paired Hi-C/Micro-C, the canonical method is the ABC model (Fulco 2019, Nasser 2021), not Cicero. ABC computes ABC = (Activity_E * Contact_E,G) / sum_e(Activity_e * Contact_e,G); standardizes on combined ATAC + H3K27ac activity and Hi-C contact frequencies. ENCODE-rE2G (Gschwind et al 2023, bioRxiv) is the modern logistic-regression enhancer-gene link predictor.
See atac-seq/enhancer-gene-linking for full ABC and ENCODE-rE2G coverage. Cicero is the ATAC-only fallback when no Hi-C is available.
HiChIP H3K27ac as Orthogonal Anchor
Decision
Action
Have Hi-C / Micro-C
Use ABC (atac-seq/enhancer-gene-linking) primary; Cicero as ATAC-only sanity check
ABC with average HiC fallback (Fulco 2019); document degraded performance
Have only ATAC
Cicero (this skill); known concordance with Hi-C ~30-50%
Cicero is appropriate when no 3D data exists; do not use Cicero in lieu of ABC when Hi-C/Micro-C are available.
Hi-C / Micro-C Concordance
Hi-C concordance
Action
> 50% of strong Cicero connections overlap Hi-C loops
High-confidence; Cicero captures real 3D structure
30-50%
Standard; some 3D contacts don't vary in accessibility
< 20%
Co-accessibility may not reflect contacts; lineage-specific contacts may be missing
Goal: Quantify what fraction of strong Cicero connections are supported by Hi-C loop calls.
Approach: Import HiCCUPS loops as GenomicInteractions, build a parallel object from Cicero connections, then count anchor-anchor overlaps and report the percentage.
# Compare Cicero against published Hi-C loops
library(GenomicInteractions)
hic_loops <- makeGenomicInteractionsFromFile('hiccups_loops.bedpe', type='bedpe',
experiment_name='hiccups', description='HiCCUPS loops')
ci <- GenomicInteractions(anchor1=GRanges(sub('_(\\d+)_(\\d+)$',':\\1-\\2', strong$Peak1)),
anchor2=GRanges(sub('_(\\d+)_(\\d+)$',':\\1-\\2', strong$Peak2)))
overlap <- countOverlaps(ci, hic_loops)>0# anchor-anchor 'any' overlap; 'equal' is too stringent at loop bin resolution
cat(sprintf('Cicero connections overlapping HiCCUPS loops: %.1f%%\n',100* mean(overlap)))
LinkPeaks uses RNA expression as the anchor; Cicero is ATAC-only
Both valid; report intersection as high-confidence
Co-accessibility doesn't match Hi-C in heterochromatin
Heterochromatic contacts are constitutive; co-accessibility needs variation
Expected; co-accessibility complements Hi-C
SCENIC+ network has ENCODE-validated TFs but missing some
Motif database limited or RNA imputation missed
Expand motif database; integrate paired ChIP-seq if available
Operational rule: Co-accessibility is a hypothesis generator. Validate with Hi-C, ChIP-seq, or experimental enhancer-promoter interaction (CRISPRi-FlowFISH).
Common Errors
Error / symptom
Cause
Solution
Cicero make_cicero_cds slow / crashes
k too high or cell count too large
Reduce k or subsample cells
All connections near zero
alpha set too high
Use estimate_distance_parameter()
Connection score > 1 reported
Bug in older Cicero versions
Update; check as.numeric(coaccess) for outliers
ArchR getCoAccessibility "TileMatrix" error
Need PeakMatrix not TileMatrix
addPeakMatrix() first
SCENIC+ install fails
Many heavy dependencies
Use the published Docker image
Connection count varies wildly per run
Stochastic metacell aggregation
Set seed; or aggregate at higher k for stability
LinkPeaks all NaN
RNA expression has too many zeros
Re-filter cells with sufficient RNA
Peak names not matching
format mismatch (chr_start_end vs chr:start-end)
Standardize naming convention
References
Pliner HA et al 2018 Mol Cell 71:858 (Cicero)
Granja JM et al 2021 Nat Genet 53:403 (ArchR getCoAccessibility)
Bravo Gonzalez-Blas C et al 2023 Nat Methods 20:1355 (SCENIC+)
Stuart T et al 2021 Nat Methods 18:1333 (Signac LinkPeaks)
Nasser J et al 2021 Nature 593:238 (ABC model; alternative enhancer-gene)
Fulco CP et al 2019 Nat Genet 51:1664 (CRISPRi-FlowFISH; gold-standard validation)
Mumbach MR et al 2017 Nat Genet 49:1602 (HiChIP H3K27ac for enhancer-promoter)
Boix CA et al 2021 Nature 590:300 (EpiMap; bulk enhancer-gene reference)
