arXiv subjects

Ariel Chernomoretz

Publications and source records attributed to Ariel Chernomoretz.

3 recordsLinked to original sources

arXiv2023

scX: A user-friendly tool for scRNA-seq exploration

Single-cell RNA sequencing (scRNA-seq) has transformed our ability to explore biological systems. Nevertheless, proficient expertise is essential for handling and interpreting the data. In this paper, we present scX, an R package built on the Shiny framework that streamlines the analysis, exploration, and visualization of single-cell experiments. With an interactive graphic interface, implemented as a web application, scX provides easy access to key scRNAseq analyses, including marker identification, gene expression profiling, and differential gene expression analysis. Additionally, scX seamlessly integrates with commonly used single-cell Seurat and SingleCellExperiment R objects, resulting in efficient processing and visualization of varied datasets. Overall, scX serves as a valuable and user-friendly tool for effortless exploration and sharing of single-cell data, simplifying some of the complexities inherent in scRNAseq analysis.

q-bio.GN↗

arXiv2017

Ultrasensitivity on signaling cascades revisited: Linking local and global ultrasensitivity estimations

Ultrasensitive response motifs, which are capable of converting graded stimulus in binary responses, are very well-conserved in signal transduction networks. Although it has been shown that a cascade arrangement of multiple ultrasensitive modules can produce an enhancement of the system's ultrasensitivity, how the combination of layers affects the cascade's ultrasensitivity remains an open question for the general case. Here we introduced a methodology that allowed us to determine the presence of sequestration effects and to quantify the relative contribution of each module to the overall cascade's ultrasensitivity. The proposed analysis framework provides a natural link between global and local ultrasensitivity descriptors and is particularly well-suited to characterize and better understand mathematical models used to study real biological systems. As a case study we considered three mathematical models introduced by O'Shaughnessy et al. to study a tunable synthetic MAPK cascade, and showed how our methodology might help modelers to better understand modeling alternatives.

q-bio.MN↗

arXiv2014

Mining the modular structure of protein interaction networks

Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms exist to infer topological network partitions. However, due to respective technical idiosyncrasies they might produce dissimilar modular decompositions of a given network. In this contribution, we aimed to analyze how alternative modular descriptions could condition the outcome of follow-up network biology analysis. We considered a human protein interaction network and two paradigmatic cluster recognition algorithms, namely: the Clauset-Newman-Moore and the infomap procedures. We analyzed at what extent both methodologies yielded different results in terms of granularity and biological congruency. In addition, taking into account Guimera cartographic role characterization of network nodes, we explored how the adoption of a given clustering methodology impinged on the ability to highlight relevant network meso-scale connectivity patterns. As a case study we considered a set of aging related proteins, and showed that only the high-resolution modular description provided by infomap, could unveil statistically significant associations between them and inter-intra modular cartographic features. Besides reporting novel biological insights that could be gained from the discovered associations, our contribution warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies. In particular our results suggested that sub-optimal partitions from the strict point of view of their modularity levels might still be worth being analyzed when meso-scale features were to be explored in connection with external source of biological knowledge.

q-bio.MN↗