This package performs lineage tracing using copy number profile from single cell sequencing technology. It will infer:
- An rooted directed minimal spanning tree (RDMST) to represent aneuploidy evolution of tumor cells.
- The focal and broad copy number alterations associated with lineage expansion.
This package runs on Python 2.7.
It also requires R/3.5 to run and has dependency on the R packages:
igraph and HelloRanges.
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 MEDALT.tar.gz. Type 'tar zxvf MEDALT.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 MEDALT package
-I INPUT, --input=INPUT
the input file is single cell copy number matrix estimated from scDNA-seq or scRNA-seq
-D DATATYPE, --datatype=DATATYPE
the input file type either D (scDNA-seq) or R (scRNA-seq)
-G GENOME, --genome=GENOME
Genome version "hg19" or "hg38"
-O OUTPATH, --outpath=OUTPATH
the output path.
-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.
-R PERMUTATION, --permutation=PERMUTATION
Performing tree reconstruction based on permutation data (T) or not (F) to estimate background distribution.
If T, both permuted copy number matrix and reconstructed tree using permuted data will be used. Otherwise (F), only permuted copy number matrix will be used.
Default value is F due to time cost.
Single cell copy number input files:
Two kinds of input files are allowed in MEDALT:
(1) Integer copy number profile from scDNA-seq
(2) Inferred copy number profile from scRNA-seq
scDNA-seq input
chr pos cell1 cell2 cell3 ......
1 977836 2 3 1 ......
1 1200863 3 3 1 ......
scRNA-seq input
cell1 cell2 cell3 ......
gene1 0.5 1.5 2.1 ......
gene2 1.1 1.8 0.6 ......
For scRNA-seq input, the copy number is inferred relative copy number (relative to normal cells) instead of integer copy number. If value close to 1, it means diploid. Value close to 0.5 means copy number = 1. Value close to 1.5 means copy number = 3. We directly incorporate inferCNV result as input.
Python scTree.py [-O <output path>] [-W <smoothing window size>] [-R <permutation tree reconstruction>] –P <MEDALT package path> –I <input file> -D <input file type> -G <genome version>
[...] contains optional parameters.
The mandatory arguments are -P, -I, -D and -G.
The input file type (-D) is either "D" (DNA) or "R" (RNA).
The genome version (-G) is either "hg19" or "hg38".
By default, we estimate background using by-chromosome permuted single cell copy number matrix rather than reconstructing a tree from permuted matrix due to time cost. You can change the setting by -R T. The default value of smoothing window size (-W) is 30, which defines the smoothing window as 30 adjacent genes for scRNA-seq data.
Try MEDALT in the package directory on the different example datasets
Example 1: Input integer copy number profile from scDNA-seq data
python scTree.py -P ./ -I ./example/scDNA.CNV.txt -D D -G hg19 -O ./example/outputDNA
Example 2: Input inferred relative copy number profile from scRNA-seq data
python scTree.py -P ./ -I ./example/scRNA.CNV.txt -D R -G hg19 -O ./example/outputRNA
In order to save time, we don't reconstruct trees based on permutation data. You can set -R T to reconstruct permuted tree.
Three text files:
(1) CNV.tree.txt which is an rooted directed tree including three columns: parent node, child node and distance
(2) segmental.LSA.txt which includes broad CNAs significantly associated with lineage expansion
(3) gene.LSA.txt which includes focal (gene) CNAs significantly associated with lineage expansion
LSA output
region Score pvalue adjustp cell depth subtreesize CNA
chr10:q26.3 -0.89 0.001 0.007 t4c17 2 38 DEL
chr7:q11 0.58 0.007 0.017 t4c17 2 38 AMP
chr7:p15.3 0.57 0.001 0.005 t4c14 4 14 AMP
chr10:q24.2 -0.85 0.019 0.248 t4c14 4 14 DEL
region: genomic loci which have CNA are associated with lineage expansion;
Score: average cumulative fold level (CFL) in the lineage;
pvalue: emprival p value of LSA;
adjustp: corrected p value after FDR corrected;
cell: the cell node that corresponding associated lineage rooted at;
depth: the depth of cell in MEDALT tree
subtreesize: the size of corresponding lineage
CNA: direction of copy number alteration, amplification (AMP) or deletion (DEL)
If there is parallel evolution event, the results will be saved in a separate file.
Two figures:
(1) singlecell.tree.pdf which is a visualization of MEDALT by igraph. You also can input CNV.tree.txt into Cytoscape to generate preferred visualization.
(2) LSA.tree.pdf which is a visualization of identified CNAs by igraph.
In LSA figure, we only show top 3 events for each lineage. You can check more details in segmental or gene level LSA file.
Fang Wang (fwang9@mdanderson.org), Qihan Wang (Chuck.Wang@rice.du)
April. 06, 2020
