API Reference¶

iscc's Python API mirrors the CLI pipeline — grow → sample → assay, with treatment applied during growth and optional inference of parameters from real data. Import the package as import iscc; see the Overview for how the stages connect.

Tumor¶

Grow a selection-driven tumor on a grid of demes — small tissue patches, each holding up to a carrying capacity of cells. Both engines are spatially explicit at the deme level and simulate the same process (birth, death, mutation, and dispersal under a CINner fitness model); they differ only in how the cell population is represented.


`iscc.tumor.GenotypeTumor`	Represents each deme as genotype counts. Fast and scalable — the default.
`iscc.tumor.GlandularTumor`	Represents each cell as its own object. Exact, but does not scale to large tumors.
`iscc.tumor.Tumor`	Shared base of the two engines — the common construction parameters.

Treatment¶

Therapies applied to the tumor during growth.


`iscc.treatment.Chemotherapy`	Cytotoxic therapy acting on dividing cells.
`iscc.treatment.TargetedTherapy`	Therapy against a driver-defined subclone.
`iscc.treatment.Immunotherapy`	Immune-cell–mediated killing in the microenvironment.
`iscc.treatment.Surgery`	Resect a compartment (e.g. the primary).
`iscc.treatment.Treatment`	Shared base of the therapies — dosing schedule + per-cell effect.

Sample¶

Draw cells from the grown tumor before assaying.


`iscc.sample.Biopsy`	Spatially-localized sample of cells.
`iscc.sample.Dissociation`	Whole-tumor dissociation into a cell suspension.

Data¶

Generate single-cell and bulk molecular data from the sampled cells.


`iscc.data.bulkDNA`	Bulk DNA-seq.
`iscc.data.scDNA`	Single-cell DNA (copy number + SNV).
`iscc.data.scRNA`	Single-cell RNA expression.
`iscc.data.Visium`	10x Visium spatial transcriptomics.
`iscc.data.DNA`	Shared base of the DNA assays — breadth + coverage core.

Each assay page also lists technology presets — the parameter settings that approximate named platforms (MALBAC / DLP / Tapestri for scDNA, WGS / WES / panel for bulk DNA, and 10x / Smart-seq3 for scRNA). The assays' technical defaults live in these hyper-parameter containers (the targets of the Calibration estimators, overridable per assay):


`iscc.data.DNABatchHyperParams`	DNA technical parameters.
`iscc.data.RNABatchHyperParams`	scRNA technical parameters.
`iscc.data.VisiumBatchHyperParams`	Visium technical parameters.

Reads¶

Generate sequencing reads (FASTQ, optionally aligned BAM) from the assayed counts. DNA reads use DWGSIM/ART; RNA reads use a synthetic 10x transcriptome or the scReadSim template. These call external read simulators, so the corresponding binaries must be installed.


`iscc.data.reads.emit_dna_reads`	Bulk / single-cell DNA reads (→ FASTQ, optional BAM).
`iscc.data.reads.emit_scrna_reads`	Mutation-aware single-cell RNA reads.
`iscc.data.reads.emit_visium_reads`	Spatial (Visium) reads.

Inference¶

Fit the tumor's evolutionary parameters (division / mutation / dispersal / selection rates) to real data by Approximate Bayesian Computation over the growth simulator.


`iscc.inference.ABC`	Approximate Bayesian computation over evolutionary rates.
`iscc.inference.Prior`	Product prior over the named parameters to infer.
`iscc.inference.Posterior`	Result of an ABC run — samples and point estimates.

Calibration¶

Fit each assay's technical parameters (library size, dispersion, dropout, capture field, …) to a real reference dataset, so simulated data matches a target platform.


`iscc.data.estimate_dna`	Calibrate the DNA assay from coverage / allele statistics.
`iscc.data.estimate_rna`	Calibrate the scRNA assay from a real count matrix.
`iscc.data.estimate_visium`	Calibrate the Visium assay from a real section.