scbsp 0.2.1

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

\n# 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

To install scBSP, run the following command:

pip install scbsp

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

Here's a simple example to demonstrate how to compute p-values using scBSP:

import scbsp

# Load your data into these variables
input_sp_mat = ...  # Cell Coordinates Matrix
input_exp_mat_raw = ...  # Gene Expression Matrix

# Set the optional parameters
d1 = 0.5  # Example value
d2 = 0.5  # Example value

# Execute the calculation
p_values = scbsp.granp(input_sp_mat, input_exp_mat_raw, d1, d2)

Output

The function returns a list of p-values, each corresponding to the genes in the provided gene expression matrix. These p-values help in identifying significant differences in gene expression across different cell coordinates, facilitating advanced biological data analysis.