By Mohammadamin Edrisi, Xiru Huang, Huw A. Ogilvie and Luay Nakhleh
Department of Computer Science, Rice University
MaCroDNA (Matching Cross-Domain Nucleic Acids) is a correlation-based method to perform mapping between scRNA-seq gene expression and scDNA-seq copy number values. This repository contains the source code for MaCroDNA described in the paper "Accurate integration of single-cell DNA and RNA for analyzing intratumor heterogeneity using MaCroDNA".
Package Requirements
Python >= 3.7
numpy
pandas
scipy
math
gurobipy
Here, only gurobipy need to be installed manually before installing MaCroDNA. Other packages can be installed automatically while installing MaCroDNA
gurobipy Installation
First, Gurobi needs to be installed. The academic license is free. The installation instructions for Gurobi are
| OS | Instruction |
|---|---|
| Linux | Gurobi Installation Guide: Linux |
| Windows | Gurobi Installation Guide: Windows |
| macOS | Gurobi Installation Guide: macOS |
After the installation of Gurobi, gurobipy can be installed using pip
python -m pip install gurobipy
Other installation methods can be found How do I install Gurobi for Python
MaCroDNA Installation
MaCroDNA is under the directory src/MaCroDNA/macrodna.py
Installation Test
You can test the installation using the following code in Python
$ python
> from MaCroDNA import MaCroDNA
> test_macrodna = MaCroDNA()
> test_macrodna.tiny_test()
And the output should be like
******Test DNA data is:
cell1 cell2 cell3 cell4
gene
g1 2 2 1 2
g2 2 2 1 2
g3 3 2 2 2
g4 1 2 2 2
g5 6 2 2 2
g6 2 2 3 6
******Test RNA data is:
cell1 cell2 cell3 cell4
gene
g1 0 2 0 1
g2 0 2 0 1
g3 10 2 2 1
g4 0 2 2 1
g5 20 2 0 1
g6 0 2 5 20
g7 0 0 0 0
******Clone id for each DNA cell is:
clone cell
0 0 cell1
1 1 cell2
2 2 cell3
3 3 cell4
**********
Start Mapping RNA cells to DNA clones
**********
number of cells in dna data 4
number of cells in rna data 4
number of genes in dna data 6
number of genes in rna data 6
1 0
MaCroDNA will be run for 1 steps
the smallest set has 4 number of cells
Set parameter Username
Academic license - for non-commercial use only - expires 2023-04-18
Gurobi Optimizer version 9.5.1 build v9.5.1rc2 (linux64)
Thread count: 10 physical cores, 20 logical processors, using up to 20 threads
Optimize a model with 9 rows, 16 columns and 48 nonzeros
Model fingerprint: 0xd5e76473
Variable types: 0 continuous, 16 integer (16 binary)
Coefficient statistics:
Matrix range [1e+00, 1e+00]
Objective range [2e-01, 1e+00]
Bounds range [1e+00, 1e+00]
RHS range [1e+00, 4e+00]
Found heuristic solution: objective 2.8300077
Presolve removed 9 rows and 16 columns
Presolve time: 0.00s
Presolve: All rows and columns removed
Explored 0 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units)
Thread count was 1 (of 20 available processors)
Solution count 1: 2.83001
Optimal solution found (tolerance 1.00e-04)
Best objective 2.830007718086e+00, best bound 2.830007718086e+00, gap 0.0000%
IsMIP: 1
Solved with MIPFocus: 0
The model has been optimized
Obj: 2.83001
the number of associations in the correspondence matrix 4.0
**********
Finish Mapping
Test Success
**********
Process finished with exit code 0
For more complicated test, you can use test_macrodna.py under test/ directory
cell-to-cell mapping
For mapping RNA cells to DNA cells, the input should be two dataframes dna_df and
rna_df. In these two dataframes, the row ids should be the genes and the column ids
should be the cells. The genes in RNA data and those in DNA data can be different because MaCroDNA can select the same genes
from the data by itself.
To get the cell-to-cell mapping
$ python
> from MaCroDNA import MaCroDNA
> macrodna = MaCroDNA(rna_df, dna_df)
> cell2cell, cell2cell_step = macrodna.cell2cell_assignment()
The first output cell2cell is also a dataframe.
The index ids are the RNA cell ids.
And it only has on column "predict_cell", which is the DNA cell assigned to the corresponding RNA cell.
The second output cell2cell_step is also a dataframe.
It has two columns "predict_cell" and "step".
"predict_cell" is the same as that in cell2cell.
"step" indicates at which step this result is inferred. And the smaller the step number is, the more confident the result is.
cell-to-clone mapping
For mapping RNA cells to DNA clones, the input needs three dataframes dna_df,
rna_df and dna_label. dna_df and rna_df are the same as cell-to-cell mapping.
dna_label has two columns "clone" and "cell", where "cell" is the DNA cells and "clone" is the corresponding clone id
for that DNA cell.
To get the cell-to-clone mapping
$ python
> from MaCroDNA import MaCroDNA
> macrodna = MaCroDNA(rna_df, dna_df, dna_label)
> cell2clone = macrodna.cell2clone_assignment()
The output cell2clone is also a dataframe.
The index ids are the RNA cell ids.
It has two columns. One is "predict_cell", which is the corresponding DNA cell for that RNA cell.
The other column is "predict_clone", which is the predicted clone id for that RNA cell.
If you have any questions, please contact us via edrisi@rice.edu or xiru.huang@rice.edu
This project is covered under MIT License