The cnr object is a list of four relational matrices. The bins, genes, annotation, qc, chromInfo, and gene.index. The structure is inspired by Scanpy's AnnData to which cleverly integrates complex data into a simple architecture.
An object class list containg a rounded CNR
X, An integer matrix of bins x n.cells containing copy number estimations for each bin[i] and cell[j] Where bins represent a common genomic segment across all cells (either a fixed with, variable binning, or .seg data). This data can be constructed using a variable length bin and CBS (Varbin algorithm) (Baslan et al 2012.), and implemented on Ginkgo, or from hmmCopy. These are upstream analyses to the package.
For mutations in single-cells, the cnq can be a binary incidence (0,1) matrix representing presence or absence of specific mutations, or a ternary (0,1,2) representing genotypes as the number of alternate allele copies
Unlike a standard rounding, we noticed that estimates for deletions and losses don't follow standard numeric rounding. We set a the thresholds of < 0.2 (average quantal estimate of the Y chromosome in females) for deletions (i.e. 0 copies); between 0.2 and 1.2 for losses (i.e. 1 copy); between 1.2 and 2.5 for 2 copies, and everything else standard numeric rounding.
genes, gene copy number interpolation from bins. The genes matrix is an interpolated, transposed, expansion of bins. The expansion is constructed internally using the expand2genes function.
Y, phenotypic data of single-cells, contains cells as rows, and phentypes in columns. Phenotypes can be information about individual samples, or if same-cell methods were used, the RNA expression from the same cell. #'
qc, quality control metrics. This matrix contains additional metadata that is technical, e.g. number of reads, MAPD estimates, and the PASS/FAIL qc.status for individual cells. contains cells as rows and metadata as columns
chromInfo, ordered chromosome information for the bins
gene.index, table to map bins to genes
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