Describes a platform for 3D in situ transcriptomics, enabled by DNA library preparation/sequencing and novel hydrogel-tissue chemistry. STARmap can map 10s-1000s of RNAs simultaneously in millimetre-scale volumes. This software has been tested for the study of molecularly-defined cell types and activity-regulated gene expression in mouse cortex and was able to be adapted to larger 3D tissues blocks to apprehend short- and long- range spatial organization of cortical neurons on a specific volumetric scale.
Provides a stochastic gene expression model that allows for transcriptional switching between two states. This package implements a two-state stochastic switch model for the population of mRNA molecules in single cells, where transcription in the so called OFF state is less active than in the ON state, but may occur at a positive rate. This approach can serve for studying transcription in single cells from the kind of data arising in flow cytometry experiments.
Provides an interface for data access from R matrix representations. Beachmat serves for the implementation of computationally intensive algorithms in C++ that can be immediately applied to a wide range of R matrix classes. It allows researchers to investigate a large single-cell RNA sequencing (scRNA-seq) data set. This tool is useful for the study of high-throughput biological data stored in large matrices.
Evaluates and compares two or more single cell RNA-Seq samples. ClusterMap proceeds first by exploiting the marker genes for each sub-group of each sample as the basic input to perform the matching. It then visualizes the matching results through several views. Ultimately, this software defines the property modifications across samples for each matched group.
Enables systematic comparison of computational tools and straightforward cross-study data integration. matchSCore is a Jaccard index-based scoring system that quantifies clustering and marker accuracy in a combined score. It can also be used to integrate cluster identitied across different data sets. This method can be applied to the comparative analysis of phenotypes across data sets, thus providing a straightforward solution to annotate single-cell projects.
Allows comparative analysis of single-cell RNA-seq data. scQuery is a web application that supports the analysis of new, large scale single cell RNA (scRNA)-seq datasets. It includes features for processing all scRNA-seq experiments in public databases and for associating different profiles with cell type based on a constrained ontology. It also aligns the raw read data, assigns them to a pre-defined set of genes, and quantifies their expression.
Models single cell read count data in a hierarchical manner. SCHiRM is a model that can be applied to detect dependencies in single cell RNA sequencing (scRNA-seq) data. The software accounts for uncertainty in both input and output variables and can be extended in several ways due to its modular design. It was tested on both simulated and experimental scRNA-seq data.
Summarizes cell populations by adding features’ measures of dispersion and covariances to population averages, for morphological profiling. This method computes the cell population’s dispersion, such as standard deviation or median absolute deviation (MAD) for each feature and concatenate these values with the average profile. The capture of cell-to-cell heterogeneity can assist the enhancement of image-based profiling of cell populations.
Aligns two manifolds such that related points in each measurement space are aligned together. MAGAN is a generative adversarial network (GAN) that discovers relationships between domains by aligning their manifolds rather than just superimposing them. The algorithm can be used when one system is measured in two different ways and thus forms two different manifolds. It facilitates the integration of datasets from multiple biological modalities.
Elucidates branching developmental pathways and mechanisms from single cell profiles. tSpace is an algorithm that can determine developmental relations and reveal branch points. This method is able to perform across different biological systems and platforms. It was applied to published single cell RNA-seq (scRNAseq) data from mouse intestinal epithelial cells. tSpace can serve in the field of singe cell analysis.
Assigns single cell transcriptomes to clones in human dermal fibroblasts. Cardelino is an R package and a container that includes two principal functions: (i) clone identification with clonal genotype configuration and (ii) donor identification with or without genotypes. In addition, vignettes for the donor recognition and clone detection use cases are provided.
Assists users in learning a molecular signature from the RNAsc data. DropLasso expands the regularization method of abandonment to the estimation of sparse linear models. This R package was developed as an extension of the abandonment regularization, obtained by adding a spacing inducing normalization to the objective function of the abandonment regularization.
Consists of a method for molecular sampling. This approach uses computations performed by molecular ensembles to encode the abundance of each species in a sample before measurement. It can quantify each of a large number of species of molecules in a pool. It is useful for measuring massive single-cell RNA profiles. This algorithm enables logarithmic or even sub-logarithmic sampling for precision desired in ubiquitous sequencing applications.
0 - 0 of 0
0 - 0 of 0