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

Weiwei Zhang^*Zhonghe TianLing Peng

• School of Mathematical Information, Shaoxing University, Shaoxing, China

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. In this paper, we systematically evaluated five feature selection options and developed an optimal feature-selection-based referencefree deconvolution method. Our proposal iteratively searches for cell-type specific features by integrating cross-cell type differential analysis between one cell type and the other cell types, as well as between two cell types and the other cell types, and performs composition estimation. Comprehensive simulation studies and analyses of seven real datasets show the excellent performance of the proposed method. The proposed method RFdecd is implemented as an R package at https://github.com/wwzhang-study/RFdecd.