Effect of imputation on gene network reconstruction from single-cell RNA-seq data

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell transcriptomics advances gene regulatory network (GRN) inference.
• Challenges include high zero counts and lack of standardized preprocessing for single-cell RNA sequencing (scRNA-seq) data.
• Imputation methods can enhance gene-gene correlations but their impact on GRN reconstruction is unclear.

Purpose of the Study:

• To evaluate the effects of imputation on the performance and structure of reconstructed gene regulatory networks from single-cell data.
• To determine if imputation improves or hinders GRN inference.

Main Methods:

• Comparison of GRN reconstruction performance before and after applying imputation methods to scRNA-seq datasets.
• Analysis of changes in gene-gene correlations and network topology.

Main Results:

• Imputation leads to an inflation of gene-gene correlations.
• This inflation affects predicted network structures.
• The performance of GRN reconstruction may decrease generally after imputation.

Conclusions:

• Imputation can negatively impact GRN reconstruction by altering correlation structures.
• A specific combination of algorithms is advisable for imputation-guided network analysis.
• Indiscriminate use of imputation before GRN reconstruction is cautioned against.