Bio hi c analysis matrix operations skills matrix operations

Version Compatibility

Reference examples tested with: cooler 0.10+, cooltools 0.7+, bioframe 0.7+

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

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

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

cooltools standardized its API around 0.7 (functions take a view_df viewframe; expected_cis defaults to smooth=True, aggregate_smoothed=True). .mcool is multi-resolution: analysis functions take a single-resolution URI (file.mcool::/resolutions/10000), never the bare .mcool. A matrix must be balanced (a stored weight column) before O/E, compartments, insulation, or dots; clr.matrix(balance=True) on an unbalanced cooler returns all-NaN.

Hi-C Matrix Operations

"Make the pixels of my Hi-C matrix comparable to each other." -> Balance (remove per-bin coverage bias under equal-visibility), then divide by distance-matched expected (remove the polymer P(s) background) to get O/E.

• Python: cooler.balance_cooler(clr, cis_only=True, store=True), then cooltools.expected_cis(clr) and divide observed by per-diagonal expected.

The Single Most Important Modern Insight -- Balancing Makes ONE Matrix Self-Consistent; It Does NOT Make Two Matrices Comparable

Balancing (ICE/KR) is a within-matrix operation: it solves for per-bin bias weights so every bin has equal genome-wide visibility, making a single map internally consistent. It does nothing to relate map A to map B. Two balanced matrices at different sequencing depth still differ in absolute magnitude, dynamic range, and noise floor -- and rescale_marginals makes the absolute balanced values arbitrary-scaled anyway. "I balanced both, now I'll subtract/log2-ratio them" is the single most common error in the field: the depth difference is read as biology. Cross-sample comparison requires downsampling to equal valid-pair count, distance-matched O/E, and a replicate-aware differential tool (multiHiCcompare, HiCcompare loess-over-distance, dcHiC) -- route to hic-differential.

Two corollaries that flow from the same equal-visibility model:

1. CNV silently breaks balancing. The premise is that every bin should make the same number of contacts; any deviation is technical bias. That is true for a diploid uniform-copy genome and FALSE for tumors/aneuploids -- a 3-copy region genuinely contacts ~3x more. ICE forces equal marginals and ERASES that real copy-number, then redistributes it perversely (post-ICE high-copy regions go cis-depleted, trans-enriched; Servant 2018). Use raw counts for CNV/SV calling (coverage IS the signal -> copy-number); plain ICE/KR on an aneuploid is a category error (use CNV-aware LOIC/CAIC for 3D structure).
2. A balanced cis map is still dominated by distance-decay. The A/B plaid and focal loops are a faint modulation under the P(s) background. Dividing by distance-matched expected (O/E) before eigendecomposition is mandatory, or the top eigenvector is just the decay curve, not compartments.

Normalization-Method Taxonomy

Method	What it does	Mechanism	When
ICE (cooler native)	true matrix balancing	iterative proportional fitting (Sinkhorn); equalizes all marginals	default; robust, converges on sparse/low-depth where KR fails
KR (juicer)	true matrix balancing	Knight-Ruiz Newton solver; SAME fixed point as ICE	fast (few iterations); fails to converge on sparse/high-res maps
SCALE (juicer)	true matrix balancing	modern KR-family solver, more robust	juicer's default; converges where KR diverges on sparse maps
VC / vanilla coverage	NOT true balancing	single pass: divide by row-coverage * col-coverage (one ICE iteration)	fast robust fallback; leaves residual bias
VC_SQRT	NOT true balancing	divide by sqrt of coverage product (gentler than VC)	very sparse data where full balancing overfits
LOIC / CAIC (Servant 2018)	CNV-aware balancing	condition on copy-number; LOIC keeps the CN effect, CAIC removes it	aneuploid/tumor genomes (plain ICE is wrong here)

KR and ICE reach the same balanced map -- choose by convergence, not quality: KR is faster but blows up on sparse/low-depth/high-resolution matrices; ICE is the robust default; SCALE is the juicer-side answer when KR fails.

Decision Tree by Scenario

Scenario	Recommended	Why
Need to balance a diploid map	cooler.balance_cooler(cis_only=True, store=True) (ICE)	robust default; cis-only is the analysis convention
KR failed to converge (sparse/high-res)	fall back to ICE, or SCALE on the juicer side	same fixed point; ICE/SCALE are the robust solvers
Tumor / aneuploid genome, 3D structure	CNV-aware LOIC/CAIC (Servant 2018)	plain ICE erases real copy-number
CNV / SV calling from Hi-C	RAW counts (no balancing)	coverage is the signal -> copy-number
Compartments at 100kb-1Mb	balance -> expected_cis -> O/E -> Pearson -> eigenvector	O/E removes P(s) so the plaid is visible -> compartment-analysis
Focal loops / TADs	balance -> expected_cis -> O/E	local enrichment needs the distance background removed -> loop-calling, tad-detection
P(s) / polymer-state diagnostic	expected_cis(smooth=True) -> log-derivative	the derivative reads out loop-extrusion machinery
Imported a juicer KR/VC weight column	check divisive_weights before applying	cooler weights are multiplicative, juicer's are divisive
Compare two conditions	downsample to equal depth, then -> hic-differential	balancing is within-matrix, not a cross-sample normalizer
Bare .mcool passed and KeyError	use file.mcool::/resolutions/<bp> URI	.mcool is a container of resolutions

Balance a Matrix (ICE)

Goal: Remove one-dimensional per-bin coverage bias so every bin has equal genome-wide visibility within this single map.

Approach: Mask low-coverage and blacklisted bins FIRST (mad_max on log-marginals + explicit blacklist of centromere/rDNA/unmappable), drop the first two diagonals (ligation chemistry, not 3D contact), then run cis-only ICE; the multiplicative weight vector is stored in the weight column.

import cooler

clr = cooler.Cooler('matrix.mcool::/resolutions/10000')
bias, stats = cooler.balance_cooler(clr, cis_only=True, mad_max=5, ignore_diags=2, blacklist=None, store=True)
print('converged:', stats['converged'], 'scale:', stats['scale'])   # stats also reports var, divisive_weights

clr = cooler.Cooler('matrix.mcool::/resolutions/10000')              # re-open to see the stored weights
balanced = clr.matrix(balance=True).fetch('chr1')                    # raw[i,j] * w[i] * w[j]; masked bins -> NaN

cis_only=True is the convention for compartment/TAD/loop work -- trans signal is weak, noisy ambient ligation that pulls the bias estimates toward trans noise. ignore_diags=2 drops the main diagonal (self-ligation/dangling ends) and first off-diagonal (undigested/religated fragments): huge untrustworthy counts that would dominate the marginals. mad_max=5 filters bins whose log-marginal is >5 MAD below the median; without it a near-empty unmappable/centromeric bin gets a gigantic weight and ICE diverges. Masking (mad_max + blacklist) MUST precede balancing -- balancing cannot rescue a no-signal bin, it amplifies it.

CLI equivalent:

cooler balance --cis-only --mad-max 5 --ignore-diags 2 matrix.mcool::/resolutions/10000

Expected: cis P(s) Curve and trans Scalar

Goal: Build the distance-decay background (the denominator for O/E) and the P(s) curve for diagnostics.

Approach: cis expected is a per-diagonal curve (one value per separation s -- this IS P(s)); trans expected is a single scalar per chromosome-pair block (trans contacts are ~distance-independent). cooltools enforces the split with two functions.

import cooltools
import bioframe

clr = cooler.Cooler('matrix.mcool::/resolutions/10000')
view_df = bioframe.make_viewframe(clr.chromsizes)               # whole-chromosome regions; or arms for acrocentric genomes

cvd = cooltools.expected_cis(clr, view_df=view_df, smooth=True, aggregate_smoothed=True, ignore_diags=2)
# columns include: region1, region2, dist, dist_bp, n_valid, count.avg, balanced.avg, balanced.avg.smoothed.agg

trans_exp = cooltools.expected_trans(clr, view_df=view_df)      # one balanced.avg per region1-region2 block

smooth=True smooths P(s) in log10(distance) space (smooth_sigma=0.1). Pre-0.7.0 cooltools errored on raw smoothing (clr_weight_name=None, smooth=True, issue #456); that was fixed in 0.7.0, and raw smoothing now returns count.avg.smoothed. Regardless of version, balance first: a raw expected still carries per-bin coverage bias, so it is not a clean P(s)/O/E denominator.

Observed/Expected Matrix

Goal: Divide out the polymer distance-decay so enrichment (loops, plaid) stands above the local background.

Approach: Map the per-diagonal cis expected (balanced.avg, keyed by dist) onto a dense balanced matrix by diagonal offset -- vectorized with numpy diagonal indexing, NOT an O(n^2) Python loop.

import numpy as np

def oe_matrix(clr, region, cvd):
    obs = clr.matrix(balance=True).fetch(region)
    chrom = region.split(':')[0] if isinstance(region, str) else region[0]
    exp = cvd[cvd['region1'] == chrom].set_index('dist')['balanced.avg']
    exp_by_dist = exp.reindex(range(obs.shape[0])).to_numpy()              # one expected per separation s
    expected = exp_by_dist[np.abs(np.subtract.outer(np.arange(obs.shape[0]), np.arange(obs.shape[0])))]
    return obs / expected                                                  # NaN where expected is NaN (masked diags)

oe = oe_matrix(clr, 'chr1', cvd)
log_oe = np.log2(oe)                                                        # symmetric around 0 for display

cooltools also ships cooltools.lib.numutils.observed_over_expected(matrix, mask) (returns a 4-tuple (OE, dist_bins, sum_pixels, n_pixels)) for a self-contained dense O/E without a precomputed cvd.

P(s) Log-Derivative -- the Polymer-State Diagnostic

Goal: Read out chromatin polymer state and loop-extrusion machinery from the shape of the contact-decay curve.

Approach: Take the slope of P(s) in log-log space; a reference slope near -1 over 0.1-1 Mb is the crumpled-globule background, and a loop-extrusion bump (~100kb interphase) appears as a peak in the derivative. Smooth in logspace FIRST or the derivative is pure noise.

agg = cvd[(cvd['region1'] == cvd['region2']) & (cvd['dist'] > 0)].drop_duplicates('dist_bp')
slope = np.gradient(np.log(agg['balanced.avg.smoothed.agg']), np.log(agg['dist_bp']))
# slope ~ -1 over 0.1-1 Mb; a bump toward 0 near ~100kb flags cohesin loop extrusion (flattens on WAPL/RAD21 loss)

Resolution-vs-Depth Budget

The achievable resolution is a function of depth and genome size, not a free choice. Rule of thumb: a bin needs ~1000 contacts to be reliably populated, and the number of bins scales as N^2 with the number of genomic bins -- so halving bin size quarters per-bin coverage. Coarse features are cheap, focal features are expensive:

Feature	Resolution	Approximate depth (human)	Why
A/B compartments	100kb-1Mb	tens of millions of valid pairs	chromosome-scale, few large bins -> cheap
TADs / insulation	10-50kb	hundreds of millions	sub-Mb domains; window 5-25x the bin
Loops / dots	<=10kb	billions (Rao 2014 in-situ Hi-C ~ billions)	focal kb-scale pixels; coarse bins blur anchors

Calling 10kb loops from a shallow library binned at 50kb is not a resolution choice -- there is no signal there. Choose the finest resolution where median per-bin contacts stay near ~1000.

Per-Method Failure Modes

Cross-sample subtraction of balanced matrices

Trigger: balancing two libraries then log2-ratioing/subtracting. Mechanism: balancing is within-matrix; balanced magnitude still scales with depth and rescale_marginals makes it arbitrary. Symptom: systematic genome-wide "differences" that track sequencing depth. Fix: downsample to equal valid pairs, compare O/E, use a replicate-aware tool -> hic-differential.

Plain ICE on an aneuploid / tumor

Trigger: balance_cooler on a genome with large copy-number swings. Mechanism: equal-visibility forces equal marginals, erasing the real ~CN-fold coverage. Symptom: high-copy regions look cis-depleted/trans-enriched (Servant 2018); CNV vanishes. Fix: raw counts for CNV calling; LOIC/CAIC for 3D structure.

Masking after (not before) balancing

Trigger: low-coverage centromere/rDNA/unmappable bins left in before ICE. Mechanism: a near-empty bin gets a gigantic bias weight. Symptom: ICE fails to converge, or stripe artifacts radiate from a few bins. Fix: set mad_max and pass blacklist; masking precedes balancing.

Eigendecomposition on a balanced (non-O/E) map

Trigger: compartment calling skips the expected/O/E step. Mechanism: the distance-decay dominates the balanced cis map. Symptom: top eigenvector is the P(s) curve, not the A/B plaid. Fix: divide by expected_cis (O/E) before correlating/eigendecomposing.

Crossing multiplicative and divisive weights

Trigger: applying an imported juicer KR/VC vector as if it were a cooler weight. Mechanism: cooler weights are multiplicative (rawww), juicer's are divisive (raw/w/w). Symptom: correction inverts -- high-bias bins get MORE extreme; nothing errors. Fix: check the weight column's divisive_weights attribute before applying.

O/E from a raw (unbalanced) expected

Trigger: computing P(s)/O/E from expected_cis(clr_weight_name=None). Mechanism: raw expected still carries per-bin coverage bias, so dividing by it does not cleanly remove the polymer background; pre-0.7.0 cooltools additionally errored on smooth=True with raw (issue #456, fixed in 0.7.0). Symptom: O/E still shows coverage stripes; on old cooltools, an error demanding balanced data. Fix: balance first, then expected_cis on the balanced weight column.

Quantitative Thresholds

Threshold	Source	Rationale
ignore_diags=2	cooler default; ICE convention	drops self-ligation/dangling (diag 0) + undigested/religated (diag 1); ligation chemistry, not 3D contact
mad_max=5	cooler default	drops bins >5 MAD below median LOG-marginal; near-empty bins otherwise get exploding weights
min_nnz=10	cooler default	<10 nonzero pixels per row is too sparse to estimate a bias reliably
tol=1e-5, max_iters=200	cooler defaults	convergence = variance of balanced marginals < tol; non-convergence usually = a masking problem, not a tol problem
smooth_sigma=0.1	cooltools default	Gaussian std in log10(distance) units for P(s) smoothing
~1000 contacts/bin	depth-budget convention	per-bin coverage floor for a reliably populated bin; bins scale ~N^2
Compartment res 100kb-1Mb	compartment scale	finer bins mix in TAD/loop structure
TAD/insulation res 10-50kb	domain scale	sub-Mb domains; window 5-25x the bin
Loop res <=10kb (needs ~billions of pairs)	Rao 2014 in-situ Hi-C	focal kb-scale; coarse bins blur loop anchors
P(s) slope ~ -1 over 0.1-1 Mb	crumpled/fractal globule	reference background; deviations/derivative read out polymer state

Common Errors

Error / symptom	Cause	Solution
clr.matrix(balance=True) all NaN	cooler not balanced	run cooler.balance_cooler(..., store=True) first
Empty / wrong-resolution result on .mcool	bare .mcool passed	use file.mcool::/resolutions/<bp> URI
ICE not converging	low-coverage bins not masked	raise/set mad_max, pass blacklist; do not just raise max_iters
expected_cis(smooth=True) errors on raw (pre-0.7.0 only)	clr_weight_name=None on old cooltools (issue #456, fixed 0.7.0)	upgrade to cooltools 0.7+, or balance first / smooth=False
O/E enrichment inverted on a tumor	ICE applied to an aneuploid	use raw counts / LOIC/CAIC; equal-visibility is violated
Imported weight makes bias worse	divisive juicer weight applied as multiplicative	check divisive_weights; reciprocate if needed
Cross-condition "difference" tracks depth	subtracting balanced matrices	downsample + O/E + replicate-aware test -> hic-differential

References

• Imakaev et al. 2012 Nat Methods 9:999-1003 -- ICE iterative correction.
• Knight & Ruiz 2013 IMA J Numer Anal 33(3):1029-1047 -- KR matrix-balancing algorithm.
• Rao et al. 2014 Cell 159(7):1665-1680 -- in-situ Hi-C, KR norm, VC/VC_SQRT, kilobase loops (depth budget).
• Cournac et al. 2012 BMC Genomics 13:436 -- sequential/vanilla-coverage (SCN/VC) normalization.
• Servant et al. 2018 BMC Bioinformatics 19:313 -- CNV-aware normalization (LOIC/CAIC); ICE erases copy-number.
• Abdennur & Mirny 2020 Bioinformatics 36(1):311-316 -- cooler.
• Open2C, Abdennur et al. 2024 PLoS Comput Biol 20(5):e1012067 -- cooltools.
• Open2C, Abdennur et al. 2024 Bioinformatics 40(2):btae088 -- bioframe.

Related Skills

• hic-data-io - Load the cooler files this skill balances; divisive-vs-multiplicative weight naming
• compartment-analysis - Consumes the O/E this skill produces for eigenvector calling
• tad-detection - Insulation needs a cis-balanced matrix
• loop-calling - Dots need balanced + expected as prerequisites
• hic-differential - Cross-sample comparison; the right home for subtracting/ratioing conditions
• hic-visualization - Render balanced/O/E/log matrices
• copy-number/cnv-visualization - Raw-count CNV from Hi-C when balancing would erase copy-number
• genome-intervals/bigwig-tracks - Export the P(s)/expected or eigenvector as a bigWig