CNAViz: An interactive webtool for user-guided segmentation of tumor DNA sequencing data
Fig 5
Manual editing using CNAViz results in more accurate identification of CNA status of breast cancer driver genes compared to an existing segmentation algorithm.
The DNA sequencing data of two tumor samples (DCIS and INV) obtained from each of three breast cancer patients (P5, P6, and P10) analyzed by [27]. (b) The number of correctly identified CNAs for breast cancer driver genes (y-axis) is reported across all samples of the three patients when using either the existing segmentation algorithm HATCHet (yellow) or after manual refinement of the HATCHet results with CNAViz (green). The number of correct driver genes is listed above each bar. (c) The number of breast-cancer driver genes with different types of CNAs inferred by either HATCHet (columns in top table) or HATCHet + CNAViz (columns in bottom table) is compared with the high-resolution CNAs measured by the matched classification in single-cell sequencing data (rows in both tables). (d) The CNAs (y-axis) inferred by HATCHet + CNAViz for two distinct sub-populations of cancer cells identified in Patient 10 are shown in orange and purple, with 0.15 separation for visual clarity.