Meta-analysis of gene activity (MAGA) contributions and correlation with gene expression, through GAGAM.
Martini, L.; Bardini, R.; Savino, A.; Di Carlo, S.
Show abstract
It is well-known how sequencing technologies propelled cellular biology research in the latest years, giving an incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with Single-cell ATAC sequencing supporting it and becoming more popular. In this regard, multi-modal technologies play a crucial role, allowing the possibility to perform the mentioned sequencing modalities simultaneously on the same cells. Yet, there still needs to be a clear and dedicated way to analyze this multi-modal data. One of the current methods is to calculate the Gene Activity Matrix, which summarizes the accessibility of the genes at the genomic level, to have a more direct link with the transcriptomic data. However, this concept is not well-defined, and it is unclear how various accessible regions impact the expression of the genes. Therefore, this work presents a meta-analysis of the Gene Activity matrix based on the Genomic-Annotated Gene Activity Matrix model, aiming to investigate the different influences of its contributions on the activity and their correlation with the expression. This allows having a better grasp on how the different functional regions of the genome affect not only the activity but also the expression of the genes.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Fusion of single-cell transcriptome and DNA-binding data, for genomic network inference in cortical development 96%
- Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation 96%
- ELIMINATOR: Essentiality anaLysIs using MultIsystem Networks And inTeger prOgRamming 95%
Similar papers in this journal
Similar papers in this journal
- Mcadet: a feature selection method for fine-resolution single-cell RNA-seq data based on multiple correspondence analysis and community detection 95%
- Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways 95%
- Application of Modular Response Analysis to Medium- to Large-Size Biological Systems 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.