ccsf 1.0.0

Leveraging cell-cell similarity from gene expression data for high-performance spatial and temporal cellular mappings.

ccsf

Leveraging cell-cell similarity for high-performance spatial and temporal cellular mappings from gene expression data (Cell Patterns, 2023)

CCSF is a replacement of PCA for gene expression and other tabular data analysis

CCSF is a cell-cell similarity-driven framework of genomic data analysis for high-fidelity dimensionality reduction, clustering, visualization, and spatial and temporal cellular mappings. The approach exploits the similarity features of the cells for the discovery of discriminative patterns in the data. For a wide variety of datasets, the proposed approach drastically improves the accuracies of visualization and spatial and temporal mapping analyses as compared to PCA and state-of-the-art techniques. Computationally, the method is about 15 times faster than the existing ones and thus provides an urgently needed technique for reliable and efficient analysis of genomic data.