Figure 5

Proliferation trees. (A) Pipeline for tree reconstruction. Mean cluster frequencies were used to identify the root and to enumerate the external nodes (leaves) descending from each cluster. Once the combination of nodes (N _tot ) was identified for each tumor, the tree was rebuilt using a recursive algorithm. As explained in the text, the algorithm was based on the parent-descent relationship between nodes of a full binary tree, which resembles the parent-descent relationship between cells, and implies that each parent node led to two descending nodes. The algorithm started from the root of the tree and progressed down to the leaves by generating pairs of nodes according to the combination found in N _tot . In the shown example, the first two nodes that directly descended from root A are node B, which led to two leaves, and node C, which leads to one leaf E and to node D. Node D, in turn, produces two leaves E. (B) Proliferation trees of the four samples. Each circle represents one node of the tree. In the dominant branch, mutations can be assigned to a given node (red) and the circle size is proportional to the number of mutations. Filled circles identify nodes supported by the gold sets (mutations with frequency ≥4% and in >6 different read positions). Of the four highly similar trees of sample A3 that were compatible with the obtained combination of nodes (Figure S6 in Additional file 2), only the one that makes no a priori assumption on the proliferation history is shown.