Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex.

ABSTRACT:

Background

Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has been the lack of integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue sample. Deconvolution algorithm performance has not yet been evaluated across different RNA extraction methods (cytosolic, nuclear, or whole cell RNA), differe

SUBMITTER: Huuki-Myers LA 

PROVIDER: S-EPMC10888823 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature