Single-cell transcriptomes reveal cell-type-specific and sample-specific gene function in human cancer.

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Accurate gene function annotation is vital for understanding cancer pathogenesis.
• Single-cell RNA sequencing (scRNA-seq) offers high resolution for gene function studies.

Purpose of the Study:

• To assess scRNA-seq's contribution to understanding gene function in cancer.
• To compare scRNA-seq with bulk transcriptome data for gene function prediction.

Main Methods:

• Constructed an assessment framework using co-expression networks and neighbor-voting.
• Analyzed 116,814 cells using scRNA-seq data.
• Compared prediction performance of bulk and scRNA-seq data.

Main Results:

• scRNA-seq recalled more experimentally verified gene functions than bulk transcriptome.
• scRNA-seq identified cell-type-specific functions, particularly in immune cells.
• scRNA-seq revealed sample-specific functions, highlighting its utility for personalized insights.
• Optimal prediction requires at least 500 cells; 10x Genomics platform outperformed Smart-seq2.

Conclusions:

• scRNA-seq plays an irreplaceable role in decoding biological processes in cancer.
• scRNA-seq provides unique insights into cell-type and sample-specific gene functions.
• This study establishes a framework for evaluating gene function prediction using scRNA-seq.