semvec/pypi-packages huggingface dataset:
CellCover 0.1.9
CellCover
Keywords: python,single-cell,marker gene


# CellCover

This is the python version of CellCover. To run CellCover, Gurobi installation is necessary. Please follow the README.md in [lanlanji/CoveringPackage (github.com)](https://github.com/lanlanji/CoveringPackage) to acquire and install the Gurobi academic liscence.

### Installing CellCover:

```python
pip install CellCover
```

### Using CellCover to obtain marker panel

To run the CelCover, the following python variables need to be define first: 

- **data**: a numpy array of your single cell RNA-seq data with shape (N,G) where N is the number of cells and G is the size of the gene portfolio.

- **gene**: a numpy array of gene names with shape (G,)

- **CellTypeLabels**: a numpy array of cell types with shape (N,)

- **CellTypeNames**: a numpy array of distinct cell type names (string)

- **ct**: a single string of the cell type name that user want to find covering markers for, e.g. "CD4"

What is more, there is a list of hyperparameters that need to be defined before running the covering:

- **binarization_threshold**: the threshold above which we binarize the gene expression to 1, below which we binarize the gene expression to 0 

- **minSize**: the depth of covering

- **alpha**: 1 - covering rate. The default is $0.05$

- **te**: This is a parameter for pruning the data. For each cell type, the gene expressing more than te * 100 percent of time are selected for finding the covering markers. The default is $0.1$.

- **top_num_gene**: This is another pruning parameter. In each class, **top_num_gene** number of genes with the highest margin score will be selected for marker selection. The default is $6000$.

The pipeline of getting the covering marker panel of the user defined cell type **ct** is

```python
from CellCover import binarization
from CellCover import SensList
from CellCover import weight
from CellCover import covering
from CellCover import getCoveringVariables
data = binarization(mat = data, binarization_threshold =  binarization_threshold)
sens = SensList(mat = data, CellTypeLabels =  CellTypeLabels, CellTypeNames=CellTypeNames)
X,w,g = weight(mat = data,sens =sens, CellTypeLabels = CellTypeLabels, CellTypeNames = CellTypeNames, ct = ct,gene = gene)
cov = covering(Z=X, minSize = minSize, alpha = alpha,weights = w)
marker = getCoveringVariables(cov, ngenes = len(g), geneNames = g, nlevels = 1)

```