pegasus.calc_infercnv

pegasus.calc_infercnv(data, genome, reference_key=None, reference_cat=None, mat_key=None, run_log_norm=True, noise=0.2, window_size=100, res_key='cnv', exclude_chromosomes=['chrM', 'chrX', 'chrY'], chunk_size=5000, n_jobs=-1)[source]

Infer the Copy Number Variant (CNV) from single-cell data [Tirosh16]. This implementation is a variant of InferCNA algorithm. It has 2 steps:

  • Step 1: Calculate initial CNV scores by window smoothing the log-norm counts of genes along genomic regions for each cell, and center the results by cell medians.

  • Step 2: Calculate final CNV scores by correcting the initial CNV scores using the group means of reference cells (non-malignant cell groups specified by users).

Notice that if the input count matrix is already log-normalized, you don’t need to apply log-norm operation again. Set run_log_norm=False for this case. The log-normalization is TP100K (i.e. number of counts is 1e5 for each cell after normalization).

Parameters

  • data (MultimodalData or UnimodalData) – Single-cell count matrix.

  • genome (str) – Specify which genome’s gene order to use. Available keywords: GRCh38_2020_A for human genome GRCh38-2020-A, mm10_2020_A for mouse genome mm10-2020-A.

  • reference_key (str, optional, default: None) – The cell attribute key used for specifying non-malignant cell groups as reference. Must be a column in data.obs field. If None, which is the default, no cell group is used as reference, so the initial CNV scores is the final scores to return.

  • reference_cat (str, optional, default: None) – Specify which cell groups in data.obs[reference_key] as reference. If None, which is the default, no cell group is used as reference, so the initial CNV scores is the final scores to return.

  • mat_key (str, optional, default: None) – Specify the count matrix to use for calculating CNV scores. If None, which is the default, use the current matrix data.X.

  • run_log_norm (bool, optional, default: True) – If applying log-normalization to the input count matrix. If the input matrix is raw counts, then must apply log-normalization; otherwise, if the input matrix is already log-normed, you can set this parameter to False.

  • noise (float, optional, default: 0.2) – The noise term used in the correction step to get final CNV scores corrected by reference cells. It does not take effects if reference_key or reference_cat is None.

  • window_size (int, optional, default: 100) – The window size used for window smoothing average gene expressions along genome positions.

  • res_key (str, optional, default: cnv) – Specify the key for storing results back to the input data object.

  • exclude_chromosomes (List[str], optional, default: ["chrM", "chrX", "chrY"]) – Specify chromosomes to exclude from calculating CNV scores. By default, exlude genes on chrM, chrX and chrY genomes. When using the mouse genome, you’ll need to explicitly specify which chromosomes to exclude.

  • chunk_size (int, optional, default: 5000) – The chunk size of cells that runs per thread. Ths is useful for speeding up the calculation via multi-threading.

  • n_jobs (int, optional, default: -1) – Specify number of threads for the calculation. By default, use all the vCPUs of the machine.

Return type

None

Returns

  • None

  • Update data.obsm –

    • data.obsm["X_"+res_key]: The matrix of final CNV scores calculated, with rows for cells and columns for genes ordered by their positions on genomic regions.

    • data.uns[res_key]: A dictionary of starting positions of all chromosomes used for calculating CNV scores. This is used for plotting the InferCNV plot.

Example

>>> pg.calc_infercnv(data, "GRCh38_2020_A", reference_key="anno", reference_cat=["Macrophage", "T", "B", "Plasma"])