Bulk Hi-C: methods¶

MDS: one consensus structure¶

Input: .hic / .mcool / .cool. Output: .chromdata.zarr (or per-chromosome for inter mode).

# Single chromosome
python -m uchrom.recon.bulk.mds data.mcool out.chromdata.zarr \
    --resolution=100000 --chrom=chr1 --device=auto

# Whole genome with inter-chromosomal contacts
python -m uchrom.recon.bulk.mds data.mcool ./outdir/ \
    --resolution=100000 --resolution-inter=1000000 \
    --inter=True --device=auto

The solver is SMACOF with CMDS initialisation, implemented in PyTorch so it runs on CUDA / MPS / CPU. Inter-chromosomal mode runs a low-resolution whole-genome scaffold MDS then Procrustes-aligns each chromosome’s high-resolution structure to its scaffold position.

Additional flags:

Flag	Default	Meaning
`--alpha`	4.0	Contact → distance exponent
`--weight`	0.05	Distance-decay prior weight
`--n_iter`	1000	SMACOF iterations
`--partitioned`	False	Split by TAD for better parallelism
`--tad_bed`	None	TAD regions BED when `--partitioned`

IGM: a population of structures¶

IGM (Integrative Genome Modeling; Boninsegna et al. 2022, Nat Methods 19:938; alberlab/igm) fits a population of structures so that, together, they reproduce the contact probabilities of the map: each pair of loci in contact with probability p is placed in contact in a fraction p of the structures (assignment), the structures are relaxed under those restraints (modelling), and the two steps alternate over decreasing probability thresholds. U-Chrom runs the modelling on the native engine (protocol "igm"); the defaults follow the IGM 1.0 protocol, with options from IGM-Plus / IGM 2.0 as fields of IgmParams.

from uchrom.recon.bulk.deconv import deconvolve
from uchrom.recon.bulk.deconv.preprocess import preprocess_hic

prob = preprocess_hic("map.mcool", chroms=["chr21"])          # Hi-C -> contact probabilities
pop = deconvolve(prob.probabilities, bins=prob.bins, method="igm", n_structures=50, seed=1)

The result is one cell per structure and one trace per chromosome copy; out="pop.chromdata.zarr" streams a large population to disk. The tutorial checks it against the input map and against chromatin tracing of the same cell line.

GEM-FISH: Hi-C and chromatin tracing together¶

GEM-FISH (Abbas et al. 2019, Nat Commun 10:2049) builds one model of a chromosome from its Hi-C map and the distances measured by chromatin tracing in the same cell type: first the TADs as beads, placed by Hi-C contacts and the FISH distances between TAD centres, then each TAD at bin resolution with its FISH radius of gyration. U-Chrom’s re-implementation runs on PyTorch.

from uchrom.recon.fish import reconstruct_gem_fish, GEMFISHParams

model = reconstruct_gem_fish("map.mcool", "chr21", resolution=30_000, fish_cd=tracing)

Without fish_cd the FISH terms are dropped and the result is a Hi-C-only model — the comparison the tutorial makes. Hi-C and imaging must come from the same cell type (and the same genome assembly).