Identifies stem cells among all detectable cell types within a population. StemID is an algorithm based on RaceID2 (Rare Cell Type Identification) an algorithm for the identification of rare and abundant cell types from single cell transcriptome data. The method is based on transcript counts obtained with unique molecular identifies. StemID is an algorithm for the derivation of cell lineage trees based on RaceID2 results and predicts multipotent cell identities.
Serves for single-cell data analysis. Granatum is a program that provides biologists with access to single-cell bioinformatics methods, and software developers with the opportunity to promote and combine their tools with various others in customizable pipelines. Its architecture simplifies the incorporation of cutting-edge tools and enables handling of large datasets. Moreover, it can eliminate inter-module incompatibilities by isolating the dependencies of each module.
Offers a method for rare cell type identification into single-cell RNA-seq. GiniClust can perform its detection on both in normal tissues and disease samples. This program is based on a modification of the Gini index which was normalized and defined as bidirectional to allows the identification of genes specifically unexpressed in a rare cell type and the removal of a systematic bias toward lowly expressed genes.
Performs a simultaneous detection of common and rare cell types from single-cell gene expression data. GiniClust2 is a cluster-aware, weighted ensemble clustering method that combines Gini index- and Fano factor-based clustering methods. This software clusters the targeted cells using Gini index-based features followed by a second clustering, using then Fano factor-based features, to lastly combine each result via a cluster-aware, weighted ensemble approach.
Models transcriptional cell fates as mixtures of the Gaussian Process Latent Variable Model and Overlapping Mixtures of Gaussian Processes (OMGP). GPfates is based on first reconstructing the differentiation trajectory from the observed data, thereby establishing an order for the cells. In a second step, GPfates uses the inferred temporal orders as input for a nonparametric time series mixture model. This approach revealed two simultaneous trends emerging during pseudotime, which separated from each other, indicating that a developmental bifurcation occurred. In a third step, GPfates uses a change point model, thereby facilitating to annotate pseudotime after bifurcation. The source code is freely available for download.
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