Variant calling enhances the identification of cancer cells in single-cell RNA sequencing data
Fig 3
Relationship between putative driver alteration counts and inferred CNV for normal tissues (left) and tumor (right) dataset cells.
(A) Cancer dataset (at right) cells shown based on primary tumor site, normal cells shown together at left. (B) TNBC cells, grouped by patient, shown in comparison to normal cells, grouped by tissue type. (C) CRC cells, grouped by patient, shown in comparison to normal cells, grouped by tissue type. Higher mean absolute CNV values indicate predicted structural alterations resulting in copy number variation, and lower values suggest limited CNV. Dashed rectangles in (A) indicate regions of interest: groups of cells that might be identified as cancer cells by either CNV inference or putative driver alteration count. Dashed rectangles are bounded at the lower ends by the 99th percentile values derived from the values for 4,415 normal cells amenable to both CopyKAT and variant analysis. Dashed polygon in (B) indicates cells of interest, with either high CNV scores or high putative driver counts, that might be selected for downstream analyses. In (A), first, second, and third quartiles are indicated for tumor cells by dashed lines and bold labels along their respective axes.