Single cell RNA sequencing (scRNA-seq) has become an important new tool for studying gene expression in individual cells of heterogenous samples. While this technology is still maturing, it is already providing powerful new insights into normal and diseased tissue types [1, 2]. In particular, single cell technology has resulted in great strides in cancer research. A hallmark of cancer cells is aneuploidy and chromosomal copy number variations (CNVs), which often correlate with tumor aggressiveness [3,4,5,6]. CNVs can be used to identify subclones of tumor cells and to infer tumor evolution, which can have important clinical implications [7]. Single cell sequencing can be used to analyze subclonal tumor architecture at unprecedented resolution [1, 8]. While single cell DNA sequencing (scDNA-seq) is an emerging technique for this type of analysis, it is very expensive and yet to be optimized. Alternatively, CNVs can be inferred from scRNA-seq and bulk RNA-seq using applications such as inferCNV [9], HoneyBadger [10], and CaSpER [11]. Following, these applications cluster the inferred CNV patterns, allowing to define discrete subclones and infer tumor evolution. This approach for studying tumor clonality and evolution has been used successfully by our group and others [8, 12]. Tumor evolution is commonly visualized with phylogenetic plots, where the length of tree branches is proportional to the number of cells in each subclone. This, in contrast to plotting the dendrogram files, allows for a simple and intuitive representation of tumor evolution. Until now, such visualization required time-consuming and error-prone manual curation. Here we describe a new tool called Uphyloplot2. This program uses inferCNV output files to generate phylogenetic plots depicting tumor evolution, and also works with any other Newick formatted dendrogram files such as those derived from HoneyBADGER and CaSpER (Fig. 1).
Implementation
Uphyloplot2 was written entirely in Python 3 to enable pipeline integration, customization, and platform independence.
Availability and requirements
Project name: Uphyloplot2. Project home page: https://github.com/harbourlab/UPhyloplot2/. Operating system(s): Platform independent. Programming language: Python. Other requirements: None. License: GNU General Public License v3.0. Any restrictions to use by non-academics: No.