fang0828/rdmst

Inferring rooted directed minimal spanning tree to trace tumor cell evolution based on single cell copy number profile

Description

This package is to infer evolutionary tree of tumor cells thourgh integer copy number profile from single cell sequencing technology. It will estimate:

• An rooted directed minimal spanning tree to represent evolution of tumor cells
• The segmental (gene) copy number events associated with specific lineage

System requirements and dependency

This package runs on Python 2.7.

It also requires R 3.5 to run and has dependency on the R packages:

igraph, HelloRanger and DescTools.

Installation

Please download and copy the distribution to your specific location. If you are cloning from github, ensure that you have git-lfs installed.

For example, if the downloaded distribuition is RDMST.tar.gz. Type 'tar zxvf RDMST.tar.gz'

Then, run scTree.py in the resulting folder.

Usage

Options:
  --version             show program's version number and exit
  -h, --help            Show this help message and exit.
  -P PATH, --PATH=PATH
                        the path of RDMST package
  -I INPUT, --Input=INPUT
                        the input file is integer copy number profile estimated from scDNA-seq or scRNA-seq
  -O OUTPATH, --outpath=OUTPATH
                        the output path.
  -D DATATYPE, --DATATYPE=DATATYPE     the input file type either D (scDNA-seq) or R (scRNA-seq)
  -W WINDOWS, --WINDOWS=WINDOWS
                        The size of smoothing windows if your inputfile is from scRNA-seq.
                        The value is the number of genes which will be merge. Default value is 30.

Input files

DNA input files:

Two kinds of input files are allowed in RDMST:

(1) Integer copy number profile from scDNA-seq

(2) Infered copy number profile from scRNA-seq;

scDNA-seq input

chr	pos	cell1  cell2 cell3 ......
chr1	977836	2  3 1 ......
chr1	1200863	3 3 1	......

scRNA-seq input

	cell1  cell2 cell3 ......
gene1	2  3 1 ......
gene2	3 3 1	......

Run RDMST package

Python scTree.py [-O <output path>] [-W <smoothing window size>] –P <RDMST package path> –I <input file> -D <input file type>
[...] contains optional parameters. The mandatory arguments are -P, -I and -D. The input file type is either "D" or "R".

By default, -W is 30, which defines the smoothing window as 30 adjacent gene.

Examples

Try RDMST in the package directory on the different example datasets

Example 1: Input from scDNA-seq data

python scTree.py -P ./ -I ./example/scDNA.CNV.txt -D D -O ./example/output

Example 2: Input from scRNA-seq data

python scTree.py -P ./ -I ./example/scRNA.inferCNV.txt -D R -O ./example/output1 -W 30

Output files

Three text files:

(1) CNV.tree.txt file: an rooted directed tree and the visualization of the tree

(2) segmental.LSA.txt file: significant lineage-specific CNAs at segment level

(3) gene.LSA.txt file: significant lineage-secific CNAs at gene level. RDMST includes
                      more than 400 cancer associated genes which are from 11 oncigenic pathways:
                      DDR, Notch, PI3K, Hippo, RTK/RAS, MYC, p53, Nrf2, TGFB, Wnt and cellcycle.

Two figures:

(1) singlecell.tree.pdf: the RDMST of cells visualized by igraph

(2) LSA.tree.pdf: the RDMST of lineage-specific CNAs visualized by igraph

Developer

Fang Wang ([email protected]), Qihan Wang ([email protected])

Draft date

Oct. 30, 2019