API

Import infercnvpy together with scanpy as

import scanpy as sc
import infercnvpy as cnv

For consistency, the infercnvpy API tries to follow the scanpy API as closely as possible.

Input/Output: io

genomic_position_from_gtf(gtf_file[, adata, ...])	Get genomic gene positions from a GTF file.
read_scevan(adata, scevan_res_dir[, ...])	Load results from SCEVAN [FCS+21] for downstream analysis with infercnvpy.

Preprocessing: pp

neighbors(adata[, use_rep, key_added, inplace])

Compute the neighborhood graph based on the result from infercnvpy.tl.infercnv().

Tools: tl

Tools add an interpretable annotation to the AnnData object which usually can be visualized by a corresponding plotting function.

The tools for embeddings and clustering mirror the scanpy API. However, while the scanpy tools operate on transcriptomics data, the infercnvpy equivalent operates on CNV data.

InferCNV

infercnv(adata, *[, reference_key, ...])	Infer Copy Number Variation (CNV) by averaging gene expression over genomic regions.
copykat(adata[, gene_ids, segmentation_cut, ...])	Inference of genomic copy number and subclonal structure.

CNV scores

cnv_score(adata[, groupby, use_rep, ...])	Assign each cnv cluster a CNV score.
ithcna(adata, groupby, *[, use_rep, ...])	Compute the ITHCNA diversity score based on copy number variation [WFH+21].
ithgex(adata, groupby, *[, use_raw, layer, ...])	Compute the ITHGEX diversity score based on gene expression cite:Wu2021.

Embeddings

pca(adata[, svd_solver, zero_center, ...])	Compute the PCA on the result of infercnvpy.tl.infercnv().
umap(adata[, neighbors_key, key_added, inplace])	Compute the UMAP on the result of infercnvpy.tl.infercnv().
tsne(adata[, use_rep, key_added, inplace])	Compute the t-SNE on the result of infercnvpy.tl.infercnv().

Clustering

leiden(adata[, neighbors_key, key_added, ...])

Perform leiden clustering on the CNV neighborhood graph.

Plotting: pl

InferCNV

chromosome_heatmap(adata, *[, groupby, ...])	Plot a heatmap of smoothed gene expression by chromosome.
chromosome_heatmap_summary(adata, *[, ...])	Plot a heatmap of average of the smoothed gene expression by chromosome per category in groupby.

Embeddings

umap(adata, **kwargs)	Plot the CNV UMAP.
tsne(adata, **kwargs)	Plot the CNV t-SNE.

Datasets: datasets

maynard2020_3k()	Return the dataset from [MMR+20] as AnnData object, downsampled to 3000 cells.
oligodendroglioma()	The original inferCNV example dataset.