GAGAM: A Genomic Annotation-Based Enrichment of scATAC-seq Data for Gene Activity Matrix

Martini, Lorenzo; Bardini, Roberta; Savino, Alessandro; Carlo, Stefano Di

doi:10.1007/978-3-031-07802-6_2

Cited by 7 publications

(18 citation statements)

References 26 publications

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“…Moreover, correlating the trancriptomic and epigenomic levels by linking genes (and their transcription) to specific peaks is not trivial. One of the most known methods to achieve this goal is to employ the concept of Gene Activity Matrix (GAM) [18] [19] [20].…”

Section: Introductionmentioning

confidence: 99%

“…With this approach, all remaining epigenetic information is lost. Most of the lost information traces back to imputed enhancer regions [23], but since they are not well-defined, they can not be linked to their affecting genes.…”

Section: Introductionmentioning

confidence: 99%

“…However, the peaks overlapping gene bodies and promoters are less than 20% of the total [23], meaning that only looking at them is limiting. With this approach, all remaining epigenetic information is lost.…”

Section: Introductionmentioning

confidence: 99%

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GRAIGH: Gene Regulation accessibility integrating GeneHancer database

Martini,

Savino,

Bardini

et al. 2023

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Single-cell assays for transposase-accessible chromatin sequencing data are one of the most powerful tools for studying the epigenetic heterogeneity of cell populations. However, the chromatin accessibility landscape is not well understood and lacks a proper way to interpret it. This work proposes (GRAIGH), a novel approach to the interpretation of genome accessibility through the integration of the GeneHancer database information, which describes genome-wide enhancer-to-gene associations. Firstly, this paper presents the methods for integrating GeneHancer with scATAC-seq data, creating a new matrix where the features are the GeneHancer elements IDs instead of the accessibility peaks. Secondly, it investigates its capability to analyze the data and detect cellular heterogeneity. In particular, this work shows that the GeneHancer elements are selectively accessible for distinct cell types, and more importantly, their connected genes are precisely known marker genes. Moreover, it investigates the specificity of GeneHancer elements accessibility, demonstrating their high selectivity against the gene activity.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GRAIGH: Gene Regulation accessibility integrating GeneHancer database

Martini,

Savino,

Bardini

et al. 2023

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“…Multimodal single-cell analysis provides multiple layers of data to explore neuronal states and their transcriptional profiles across cell types [47, 46, 28, 27, 78]. Indeed, understanding cellular heterogeneity requires profiling thousands of cells in multiple dimensions to accurately identify and characterize neuronal types and subtypes [80, 59, 8].…”

Section: Introductionmentioning

confidence: 99%

Neuronal Spike Shapes (NSS): A Straightforward Approach to Investigate Heterogeneity in Neuronal Excitability States

Martini

Amprimo

Carlo

et al. 2023

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The mammalian brain exhibits a remarkable diversity of neurons, contributing to its intricate architecture and functional complexity. The analysis of multi-modal single-cell datasets enables the investigation of this heterogeneity. In this study, we introduce the Neuronal Spike Shapes (NSS), a straightforward approach for analyzing the electrophysiological profiles of cells based on their Action Potential (AP) waveforms. The NSS method holds the potential to effectively explore the heterogeneity of cell types and states by summarizing the AP waveform into a triangular representation complemented by a set of derived electrophysiological (EP) features. To support this hypothesis, we validate the proposed approach using two datasets of murine cortical interneurons. The validation process involves a combination of NSS-based clustering analysis, Differential Expression (DE), and Gene Ontology (GO) enrichment analysis. The results demonstrate that the NSS-based analysis captures distinct partitions of cells that possess biological relevance independent of cell subtype, suggesting them as potential cellular states. Furthermore, the analysis reveals the emergence of voltage-gated K+ channels as transcriptomic markers associated with the identified electrophysiological partitions. This finding is particularly significant as the expression of voltage-gated K+ channels regulates the hyperpolarization phase of the AP, providing further support for the biological significance of the NSS-based clustering approach.

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“…A promising approach to solve this problem is the Genomic-Annotated Gene Activity Matrix (GAGAM) approach [15] [16]. In GAGAM, the association between genomic regions and accessible genes relies on a genomic model based on genomic annotations.…”

Section: Introductionmentioning

confidence: 99%

Meta-analysis of gene activity (MAGA) contributions and correlation with gene expression, through GAGAM

Martini

Bardini

Savino

et al. 2023

Preprint

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It is well-known how sequencing technologies propelled cel- lular 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 sequenc- ing 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 Ac- tivity 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

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GAGAM: A Genomic Annotation-Based Enrichment of scATAC-seq Data for Gene Activity Matrix

Cited by 7 publications

References 26 publications

GRAIGH: Gene Regulation accessibility integrating GeneHancer database

GRAIGH: Gene Regulation accessibility integrating GeneHancer database

Neuronal Spike Shapes (NSS): A Straightforward Approach to Investigate Heterogeneity in Neuronal Excitability States

Meta-analysis of gene activity (MAGA) contributions and correlation with gene expression, through GAGAM

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