Meta-Analysis of cortical inhibitory interneurons markers landscape and their performances in scRNA-seq studies

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

doi:10.1109/bibm52615.2021.9669888

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…In this instance, the unsupervised clustering algorithm has identified 19 clusters, and the evident resemblance among the identified clusters can be easily discerned. Additionally, it is feasible to assess their similarity using metrics such as Adjust Rand Index (ARI) and Adjust Mutual Information (AMI), commonly employed to gauge classification similarities [19] [31].…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, one relevant characteristic of a cell type marker for heterogeneity investigation is its specificity [31] defined as: The concept of “high specificity” refers to a characteristic where a feature utilized to distinguish a particular cell type is exclusively present in cells belonging to that specific type. This study assesses the level of specificity in terms of both the accessibility of GH el and the activity of literature marker genes for various cell types derived from the Seurat integration, subsequently drawing comparisons between them.…”

Section: Methodsmentioning

confidence: 99%

“…Indeed, one relevant characteristic of a cell type marker for heterogeneity investigation is its specificity [31] defined as:…”

Section: E Differentially Accessible Features and Their Specificitymentioning

confidence: 99%

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GRAIGH: Gene Regulation accessibility integrating GeneHancer database

Martini,

Savino,

Bardini

et al. 2023

Preprint

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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: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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GRAIGH: Gene Regulation accessibility integrating GeneHancer database

Martini,

Savino,

Bardini

et al. 2023

Preprint

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“…Cellular heterogeneity can be studied by examining specific marker genes whose expression characterizes and identifies particular cell types. Differential expression analysis performed on scRNA-seq data allows cell types to be labeled in a cluster based on previously known marker genes and discovering new markers [20].…”

Section: (C))mentioning

confidence: 99%

A starting guide on multi-omic single-cell data joint analysis: basic practices and results

Martini,

Bardini,

Di Carlo

2024

Preprint

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Multi-omics single-cell data represent an excellent opportunity to investigate biological complexity in general and generate new insights into the biological complexity of heterogeneous multicellular populations. Considering one omics pool at a time captures partial cellular states, while combining data from different omics collections allows for a better reconstruction of the intricacies of cell regulations at a particular time. However, multi-omics data provide only an opportunity. Computational approaches can leverage such opportunities, given that they raise the challenge of consistent data integration and multi-omics analysis. This work showcases a bioinformatic workflow combining existing methods and packages to analyze transcriptomic and epigenomic single-cell data separately and jointly, generating a new, more complete understanding of cellular heterogeneity.

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“…Widely utilized for profiling thousands of single-cell transcriptional profiles, scRNA-seq enables the investigation of cellular heterogeneity based on gene expression [ 1 , 2 , 3 ]. Simultaneously, the emerging popularity of scATAC-seq proves invaluable.…”

Section: Introductionmentioning

confidence: 99%

Cross-Omic Transcription Factor Analysis: An Insight on Transcription Factor Accessibility and Expression Correlation

Martini,

Bardini,

Savino

et al. 2024

Genes

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It is well known how sequencing technologies propelled cellular biology research in recent years, providing 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 simultaneously perform the mentioned sequencing modalities on the same cells. Yet, there still needs to be a clear and dedicated way to analyze these multi-modal data. One of the current methods is to calculate the Gene Activity Matrix (GAM), 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. Moreover, the transcription process is highly regulated by the transcription factors that bind to the different DNA regions. Therefore, this work presents a continuation of the meta-analysis of Genomic-Annotated Gene Activity Matrix (GAGAM) contributions, aiming to investigate the correlation between the TF expression and motif information in the different functional genomic regions to understand the different Transcription Factors (TFs) dynamics involved in different cell types.

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Meta-Analysis of cortical inhibitory interneurons markers landscape and their performances in scRNA-seq studies

Cited by 8 publications

References 12 publications

GRAIGH: Gene Regulation accessibility integrating GeneHancer database

GRAIGH: Gene Regulation accessibility integrating GeneHancer database

A starting guide on multi-omic single-cell data joint analysis: basic practices and results

Cross-Omic Transcription Factor Analysis: An Insight on Transcription Factor Accessibility and Expression Correlation

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