Bio Atac Seq Co Accessibility

Version Compatibility

Reference examples tested with: Cicero 1.20+, monocle3 1.3+, ArchR 1.0.2+, SCENIC+ 1.0+, pycisTopic 1.0+, Signac 1.13+, GenomicRanges 1.54+, GenomicInteractions 1.36+, BSgenome.Hsapiens.UCSC.hg38 1.4+.

Verify before use:

• R: packageVersion('') then ?function_name to verify parameters
• Python: pip show then help(module.function) to check signatures

If code throws unexpected errors, introspect the installed package and adapt rather than retrying.

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.

• R: cicero::run_cicero(input_cds, genomic_coords) -> peak-pair connection scores
• R: ArchR::addCoAccessibility(proj) -> ArchR-internal Cicero wrapper
• Python: pycisTopic + SCENIC+ for network-level inference combining ATAC + RNA + motifs

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 | Tied to ArchR; same biology as Cicero | | SCENIC+ (Bravo 2023) | Multi-step: co-accessibility + motif scoring + RNA correlation | Multiome (ATAC + RNA) or paired | TF-driven enhancer-gene networks | Most comprehensive; multi-modal | Multiome data required; computationally heavy | | LinkPeaks (Signac) | 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:

1. Reduce dimensionality (UMAP from input).
2. Build k-NN graph of cells.
3. Aggregate k cells into metacells (default k = 50).
4. Compute correlation in accessibility across metacells, restricted to peak pairs within genomic_distance_max (default 500 kb cis).
5. Apply graphical lasso with regularization alpha to sparsify the correlation matrix.
6. Output: per-pair connection score; positive = co-variation, negative = anti-co-variation.

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 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).

Cicero -- metacell aggregation hides cell-type-specific connections

Trigger: Running Cicero on heterogeneous dataset spanning multiple cell types.

Mechanism: Metacells aggregate across cell types; connections that exist only in one cell type get diluted.

Fix: Run Cicero per-cluster separately; combine results with cluster annotations. Cell-type-specific connections often differ.

Cicero -- distance assumption

Trigger: Default genomic_distance_max=500000 (500 kb cis only).

Mechanism: Distal connections beyond 500 kb cis are excluded; trans-chromosomal entirely missed.

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  0.25, ]
cat(sprintf('Total conns: %d; strong (>0.25): %d\n', nrow(conns), nrow(strong)))

ArchR getCoAccessibility

library(ArchR)
proj  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()` fits a regression of pairwise correlation versus genomic distance for nearby peaks; the slope gives the expected co-accessibility decay with distance. Cicero's alpha penalty is set proportional to this slope so the graph sparsifies at biologically appropriate distance scales.

**Implementation:** Cicero internally calls `estimate_distance_parameter(cicero_cds, window=window, maxit=100, sample_num=100)` -- the function bootstraps 100 windows, fits per-window distance-correlation regression, and averages the alpha estimates. Default value is robust at typical peak densities; manual override is rarely needed.

**When manual tuning helps:** Very dense peaksets (>200k peaks) may need higher alpha to control false positives; very sparse (= 5) primary; ABC + HiChIP intersection is high-confidence |
| Have ATAC + H3K27ac, no 3D | 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 |
|  0
cat(sprintf('Cicero connections overlapping HiCCUPS loops: %.1f%%\n',
            100 * mean(overlap)))

Reconciliation

| Pattern | Likely cause | Action | |---------|--------------|--------| | Cicero many weak connections; ArchR few strong | Different alpha or aggregation | Standardize parameters | | LinkPeaks (Multiome) finds connections Cicero misses | 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 (chrstartend 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)

Related Skills

• atac-seq/single-cell-atac - scATAC preprocessing (input)
• atac-seq/consensus-peakset - Peak set used for connection inference
• atac-seq/motif-deviation - chromVAR for TF activity (complement)
• atac-seq/enhancer-gene-linking - ABC, ENCODE-rE2G, CRISPRi-FlowFISH validation when Hi-C is available
• atac-seq/deep-learning-atac - chromBPNet variant effect at predicted enhancers
• gene-regulatory-networks/scenic-regulons - Standalone SCENIC for TF networks
• hi-c-analysis/loop-calling - Physical contacts from Hi-C
• hi-c-analysis/contact-pairs - Hi-C / Micro-C contact pairs
• single-cell/multimodal-integration - Multiome integration
• chip-seq/peak-annotation - Cross-validate with TF ChIP
• pathway-analysis/gsea - Downstream gene-level enrichment

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: GPTomics
• Source: GPTomics/bioSkills
• License: MIT

Install and usage instructions live in the source repository linked above.