scbsp 0.0.1

A package that efficiently computes p-values for a given set of genes based on input matrices representing cell coordinates and gene expression data

scBSP

scBSP is a specialized package designed for processing biological data, specifically in the analysis of gene expression and cell coordinates. It efficiently computes p-values for a given set of genes based on input matrices representing cell coordinates and gene expression data.

Installation

pip install "git+https://github.com/YQ-Wang/scBSP.git"

Usage

To use scBSP, you need to provide two primary inputs:

1. Cell Coordinates Matrix (input_sp_mat):

   • Format: Numpy array.
   • Dimensions: N x D, where N is the number of cells and D is the dimension of coordinates.

2. Gene Expression Matrix (input_exp_mat_raw):

   • Format: Numpy array, Pandas DataFrame, or CSR matrix.
   • Dimensions: N x P, where N is the number of cells and P is the number of genes.

Additionally, you must specify the following parameters:

• d1: A floating-point number.
• d2: A floating-point number.

Example

import scbsp

# Example data loading
input_sp_mat = ...
input_exp_mat_raw = ...

# Optional parameters
d1 = ...
d2 = ...

# Calculate p-values
p_values = scbsp.granp(input_sp_mat, input_exp_mat_raw, d1, d2)

Output

The output of scBSP is a list of p-values corresponding to the given genes.