1 - 13 of 13 results

FVFC / Functional Virtual Flow Cytometry

Intends to identify distribution patterns in cell populations thanks to single-cell transcriptome study. FVFC uses gene coexpression network analysis (GCNA) to detect modules of genes with similar expression profiles and summarize them into eigengenes, which allows users to explore the distribution of cells interactively, interpret the gene features and generate new hypothesis. It also provides an interactive visualization using a clustering index parameter which helps to highlight interesting 2D patterns in the scatter plot matrix (SPLOM). The method was tested thanks to two large single-cell studies.

SCEED / Single Cell Empirical Experimental Design and analysis

Assists researchers in designing a single cell experiment and optimal analysis procedure. SCEED is an empirical methodology that has functionality to simulate single cell RNA sequencing (scRNA-seq) data with user provided statistical characteristics: total number of cells, genes, groups proportions, marker genes and fold change (fC) of marker genes. The software is completely flexible and any number of single-cell algorithms can be added for testing as per user’s requirements.

SAFE-clustering / Single-cell Aggregated (From Ensemble) Clustering

Provides stable and robust clustering for scRNA-seq data. SAFE-clustering is an unsupervised ensemble method that: (i) performs independent clustering using four state-of-the-art methods, SC3, CIDR, Seurat and t-SNE + k-means; and (ii) combines the four individual solutions into one consolidated solution using one of three hypergraph partitioning algorithms: hypergraph partitioning algorithm (HGPA), meta-clustering algorithm (MCLA) and cluster-based similarity partitioning algorithm (CSPA).

DIMM-SC / DIrichlet Mixture Model for clustering droplet-based Single Cell transcriptomic data

Models both the within-cluster and between-cluster variability of unique molecular identifier (UMI) count data. DIMM-SC is a novel statistical method for clustering droplet-based single cell transcriptomic data. It facilitates rigorous statistical inference of cell population heterogeneity. This tool can be useful for the fast-growing community of large-scale single cell transcriptome analysis.

SCENT / Single Cell ENTropy

Identifies known cell subpopulations of varying potency, enabling reconstruction of cell-lineage trajectories. SCENT is an algorithm that can be used to identify and quantify biologically relevant expression heterogeneity in single-cell populations, as well as to reconstruct cell-lineage trajectories from time-course data. It differs substantially from other single-cell algorithms in that it uses single-cell entropy to independently order single cells in pseudo-time (i.e. differentiation potency), without the need for feature selection or clustering.