The inferCNV method

Essentially, this package is a Python reimplementation of infercnv. It mostly follows the computation steps outlined here, with minor modifications. The computation steps are outlined below. By making use of numpy, scipy, and sparse matrices, it is a lot more computationally efficient.

Computation steps

The function parameters are documented at infercnvpy.tl.infercnv().

1. Subtract the reference gene expression from all cells. Since the data is in log space, this effectively computes the log fold change. If references for multiple categories are available (i.e. multiple values are specified to reference_cat), the log fold change is “bounded”:

   • Compute the mean gene expression for each category separately.

   • Values that are within the minimum and the maximum of the mean of all references, receive a log fold change of 0, since they are not considered different from the background.

   • From values smaller than the minimum of the mean of all references, subtract that minimum.

   • From values larger than the maximum of the mean of all references, subtract that maximum.

   This procedure avoids calling false positive CNV regions due to cell-type specific expression of clustered gene regions (e.g. Immunoglobulin- or HLA genes in different immune cell types).

2. Clip the fold changes at -lfc_cap and +lfc_cap.

3. Smooth the gene expression by genomic position. Computes the average over a running window of length window_size. Compute only every nth window to save time & space, where n = step.

4. Center the smoothed gene expression by cell, by subtracting the median of each cell from each cell.

5. Perform noise filtering. Values < dynamic_theshold * STDDEV are set to 0, where STDDEV is the standard deviation of the smoothed gene expression

6. Smooth the final result using a median filter.

Preparing input data

The anndata.AnnData object should already be filtered for low-quality cells. adata.X needs to be normalized and log-transformed. The method should be fairly robust to different normalization methods (scanpy.pp.normalize_total(), scran, etc.).

The method requires a “reference” value to which the expression of genomic regions is compared. If your dataset contains different cell types and includes both tumor and normal cells, the average of all cells can be used as reference. This is the default.

If you already know which cells are “normal”, you can provide a column from adata.obs to reference_key that contains the annotation. reference_cat specifies one or multiple values in reference_key that refer to normal cells.

Alternatively, you can specify a numpy array with average gene expression values (for instance, derived from a different dataset), to reference which will be used as reference instead. This array needs to align to the var axis of adata.