Merge branch 'main' of https://github.com/Core-Bioinformatics/Flufftail

Author: rafkol98

README.md

@@ -1,11 +1,12 @@
-# Flufftail
+# *Flufftail*: Fuzzy Learning and Uncertainty Framework For Transcriptional Trajectories And Interaction Logic. 
+### R Kollyfas, I Mohorianu
 
-## Overview
-Due to recent technological advances, single-cell assays are now firmly accepted as state-of-the-art for a wide range of bio-medical projects; RNA-focused assays represent a cost-effective and quick proxy for both DNA and protein evaluations, as well as containing intrinsic expression signal. Clustering is pivotal in scRNA-seq for partitioning cells into distinct groups, setting the stage for analyses such as trajectory inference of cell-cell interactions. Recently, community detection algorithms emerged as superior for identifying transcriptomic-driven subpopulations.
+## Consensus clustering reveals Gene Regulatory Network Dynamics on Single-Cell assays 
+Thanks to recent, rapid technological advances, single-cell assays are now state-of-the-art for a wide range of biomedical projects; RNA-focused assays represent a cost-effective and quick proxy for both DNA and protein quantifications, in addition to intrinsic expression signal. Clustering is pivotal in scRNA-seq for partitioning cells into distinct groups (putative cell types), setting the stage for analyses such as trajectory inference or cell-cell interactions. Recently, community detection algorithms emerged as superior/efficient for identifying transcriptomic-driven subpopulations.
 
-Despite their enhanced performance, all cutting-edge community detection techniques are inherently stochastic and crisp; iterative runs result in variable partitions and interpretations even when applied to identical inputs. This variability highlights the need of replacing crisp assignation with a probabilistic/fuzzy approach i.e., fuzzy clustering, where cells can concurrently be associated with multiple clusters.
+Despite their enhanced performance, all cutting-edge community detection techniques are inherently stochastic and crisp; iterative runs result in variable partitions (and interpretations) when applied to the same input, and with the same hyper-parameters. This variability highlights the need of replacing crisp assignation with a probabilistic/fuzzy approach i.e., fuzzy clustering, where cells can concurrently be associated with multiple clusters.
 
-Inspired by findings from Gribben et al. on biphenotypic cells in metabolic dysfunction-associated diseases, in this project we introduce *Flufftail*, a comprehensive R package designed for single-cell data analysis through the angle of fuzzy clustering. *Flufftail* exploits the variability of standard clustering approaches by proposing a fuzzy community-detection clustering coupled with the characterization of fuzzy entries (cells/genes). The assessment of membership degrees, characterisation of hard clusters, and evaluation of co-clustering behaviour are summarised in interactive plots, facilitating the information transfer between wet- and dry-lab scientists. Additionally, we developed a new methodology for identifying key genes (major regulatory hubs) that drive biological transitions through fuzzy gene module clustering. Furthermore, *Flufftail* presents a new approach for characterising gene regulatory network (GRN) dynamics and the evolution of regulatory interactions across the pseudotime ordering of cells.
+Inspired by findings from *Gribben et al.* on biphenotypic cells in metabolic dysfunction-associated diseases, we introduce *Flufftail*, a comprehensive R package designed for single-cell datasets (agnostic to modalities), summarising signal through the lens of fuzzy clustering. *Flufftail* exploits the variability of standard clustering approaches by proposing a fuzzy community-detection clustering coupled with the characterization of fuzzy entries (cells/genes). The assessment of membership degrees, characterisation of hard clusters, and evaluation of co-clustering behaviour are summarised in interactive plots, facilitating the information transfer between wet- and dry-lab scientists. Additionally, we developed a new methodology for identifying key genes (major regulatory hubs) that drive biological transitions through fuzzy gene module clustering. Furthermore, *Flufftail* presents a new approach for characterising gene regulatory network (GRN) dynamics and the evolution of regulatory interactions across the pseudotime ordering of cells.
 
 This marks the first exploration/exploitation of GRN dynamics across different points (or bins) of pseudotime in single-cell data, advancing our understanding of the mechanisms controlling cellular plasticity and transdifferentiation.