A package that efficiently computes p-values for a given set of genes based on input matrices representing cell coordinates and gene expression data
Project description
\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 "git+https://github.com/YQ-Wang/scBSP.git"
Usage
To use scBSP, you need to provide two primary inputs:
-
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.
-
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.
Optionally, you can specify the following parameters:
leaf_size
: An integer that determines the number of points at which the algorithm switches to brute-force search. Adjusting `leaf_size`` affects the query's speed and the memory required for the tree structure.use_cache
: A boolean (True by default) that controls whether to use caching. Disabling cache (False) reduces memory usage slightly.
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
leaf_size = 80 # Example value
use_cache = True # Default is True
# Execute the calculation
p_values = scbsp.granp(input_sp_mat, input_exp_mat_raw, d1, d2, leaf_size, use_cache)
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.
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