Reference-free deconvolution of complex samples based on cross-cell-type differential analysis: Systematic evaluations with various feature selection options.

ABSTRACT:

Introduction

Genomic and epigenomic data from complex samples reflect the average level of multiple cell types. However, differences in cell compositions can introduce bias into many relevant analyses. Consequently, the accurate estimation of cell compositions has been regarded as an important initial step in the analysis of complex samples. A large number of computational methods have been developed for estimating cell compositions; however, their applications are limited due to the absence of reference or prior information. As a result, reference-free deconvolution has the potential to be widely applied due to its flexibility. A previous study emphasized the importance of feature selection for improving estimation accuracy in reference-free deconvolution.

Methods

In this p

SUBMITTER: Zhang W 

PROVIDER: S-EPMC12162504 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature