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 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.
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.
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.
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.
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