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
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.
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.
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.
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 ......
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.
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
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
Fang Wang ([email protected]), Qihan Wang ([email protected])
Oct. 30, 2019