SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptomic data for biological research.
• Low RNA capture rates in scRNA-seq lead to sparse data with significant 'dropout' events.
• These dropouts complicate downstream analyses, hindering accurate biological interpretation.

Purpose of the Study:

• To introduce a novel computational method, SCC, for imputing dropout events in scRNA-seq data.
• To evaluate the performance of SCC against existing methods using both simulated and real-world datasets.

Main Methods:

• Developed the SCC (Single-cell Clustering) algorithm specifically for dropout imputation in scRNA-seq.
• Compared SCC's imputation accuracy and downstream analysis performance against two established methods.
• Utilized both simulated scRNA-seq data and empirical datasets for rigorous validation.

Main Results:

• SCC demonstrated competitive imputation performance compared to existing methods.
• SCC significantly reduced intra-class cell distances, indicating improved cell population homogeneity.
• Clustering accuracy was demonstrably enhanced by SCC imputation on both simulated and real scRNA-seq data.

Conclusions:

• SCC is an effective computational tool for mitigating dropout noise in scRNA-seq datasets.
• The method improves the quality of scRNA-seq data, facilitating more reliable downstream analyses.
• The SCC tool and its source code are publicly available for research use.