Fig. 1

The seven steps in CNA detection in single-cell sequencing. a Binning. The number of reads within each bin (bottom) is computed from the pileup of the reads according to where they align (top). b GC correction. Scatter plot of read count per bin with respect to the GC content of the bin. The red curve represents the corresponding regression. c Mappability correction. Scatter plot of read count per bin with respect to the mappability of the bin. The red curve represents the corresponding regression. d Removal of outlier bins. Scatter plot of read count per bin with respect to the genomic position is shown. Outlier bins are shown in red, in contrast with the rest of the genome which are in green. e Removal of outlier cells. A Lorenz curve for the read count at all bins is shown. Gini coefficient is twice the highlighted area between the Lorenz curve and the diagonal line. The higher the Gini coefficient, the more likely the cell is an outlier. f Segmentation. Scatter plot of read count per bin with respect to the genomic position is shown. Dotted vertical lines correspond to the segments’ boundaries. g Calling the absolute copy numbers. The copy number—a non-negative integer—for each segment is determined