Robust reconstruction of single-cell RNA-seq data with iterative gene weight updates.

ABSTRACT:

Motivation

Single-cell RNA-sequencing technologies have greatly enhanced our understanding of heterogeneous cell populations and underlying regulatory processes. However, structural (spatial or temporal) relations between cells are lost during cell dissociation. These relations are crucial for identifying associated biological processes. Many existing tissue-reconstruction algorithms use prior information about subsets of genes that are informative with respect to the structure or process to be reconstructed. When such information is not available, and in the general case when the input genes code for multiple processes, including being susceptible to noise, biological reconstruction is often computationally challenging.

Results

We propose an algorithm that iteratively ident

SUBMITTER: Sheng Y 

PROVIDER: S-EPMC10311330 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature