FGFICA: Independent Component Analysis of Fusion Genomic Features for Mining Epi-Transcriptome Profiling Data

Existing studies indicate that in-depth studies of the N⁶-methyladenosine (m⁶A) co-methylation patterns in epi-transcriptome profiling data may contribute to understanding its complex regulatory mechanisms. In order to fully utilize the potential features of epi-transcriptome data and consider the advantages of independent component analysis (ICA) in local pattern mining tasks, we propose an ICA algorithm that fuses genomic features (FGFICA) to discover potential functional patterns. FGFICA first extracts and fuses the confidence information, homologous information, and genomic features implied in epi-transcriptome profiling data and then solves the model based on negative entropy maximization. Finally, to mine m⁶A co-methylation patterns, the probability density of the extracted independent components is estimated. In the experiment, FGFICA extracted 64 m⁶A co-methylation patterns from our collected MeRIP-seq high-throughput data. Further analysis of some selected patterns revealed that the m⁶A sites involved in these patterns were highly correlated with four m⁶A methylases, and these patterns were significantly enriched in some pathways known to be regulated by m⁶A.