Tutorial gallery

Every tutorial, by data type and analysis step — each runs on real data, from a dataset to its results. Click a card to open the tutorial; the chapters (tracing, seqFISH, single-cell Hi-C, bulk Hi-C) explain the methods around them.

Chromatin tracing

Importing FOF-CT chromatin-tracing data4DN FOF-CT tracing with its cell and RNA tables, read and written backImporting PyHiM chromatin-trace tables (ECSV)PyHiM ECSV traces (Bintu 2018 IMR90) as a ChromDataSpot-to-trace alignment with jieRaw seqFISH+ spots to chromosome traces, against the authors' tracesImputing missing FISH coordinatesFilling missing loci — linear, cubic, SnapFISH-IMPUTE — scored on hidden spotsTAD callingTADs from 3-D distances (ArcFISH), next to Hi-C domains of the same lociSingle-allele domains with FISHnetDomains of single alleles (FISHnet) and how often each boundary is usedLoop calling on chromatin tracing dataLoops from 3-D distances with the axis-wise F-testA/B compartments from chromatin tracingA/B compartments from imaging, against Hi-C of the same cells

Imaging multi-omics: DNA seqFISH+

Importing seqFISH+ multi-omics data (Takei 2025 cerebellum)DNA seqFISH+ with chromatin marks and RNA (Takei 2025 cerebellum)

Single-cell and spatial Hi-C

Pseudo-bulk Hi-C: compartments per cell type, at matched depthCell-type maps summed from an atlas store over HTTP; compartment strength at matched depthSingle-cell 3-D genome reconstruction: NucDynamics and EMberSingle-cell 3-D structures with NucDynamics and EMber on the native engineContact-map cell embedding with FastHigashiCells embedded by their contact maps (FastHigashi)Cell embeddings and clusters across modalitiesCells by RNA, ATAC or contacts (scHiCAR), and imaged cells by chromatin marks

Bulk Hi-C

Calling structures from a bulk Hi-C mapCompartments, insulation domains, HiCCUPS / Mustache loops and APA, against the published callsBulk Hi-C to 3-D: MDS and IGM population deconvolutionA consensus structure (MDS) and a population of structures (IGM) from one mapJoint Hi-C + FISH reconstruction with GEM-FISHHi-C and FISH distances in one model (GEM-FISH)

Common to all data

ChromData: the data modelChromData: bins, spots, tracks, intervals, results, subsettingThe .chromdata.zarr storeThe .chromdata.zarr store — backed and remote reads, streaming, linked and embedded dataPer-locus featuresDistance maps, variance features, peaks and projections along the genomeVisualisation: plots and the web browserPlots with uchrom.pl and the web browser from Python