Allows studying of spatial patterning of gene expression at the single-cell level. Seurat is an R package that enables quality control (QC), analysis, and exploration of single cell RNA-seq data. The software includes three computational methods: (1) unsupervised clustering and discovery of cell types and states, (2) spatial reconstruction of single cell data, and (3) integrated analysis of single cell RNA-seq across conditions, technologies, and species. It can also localize rare subpopulations, and map both spatially restricted and scattered groups.
Allows to perform several low-level analyses on of single-cell RNA-seq data. Scran is a package that provides functions to normalize cell-specific biases, assign cell cycle phase, and detect highly variable and significantly correlated genes.
A linear modeling framework that correlates genotype and phenotype information in scRNA-seq data. SSrGE uses an accumulative ranking approach to select expressed nucleotide variations linked to the expression of a particular gene. SSrGE infers a sparse linear model for each gene and keeps the non-null inferred coefficients. SSrGE can be used as a dimension reduction/feature selection procedure or as a feature ranking. In all the cancer datasets tested, effective and expressed nucleotide variations (eeSNVs) achieve better accuracies and visualization than gene expression for identifying subpopulations
Provides a method for the simultaneous isolation of genomic DNA and total RNA from single cells. SIDR physically isolates total RNA, regardless of polyadenylation, from the single-cell lysate that contains the nucleus by using magnetic microbead capture. It was developed as a platform for revealing and understanding a wide range of unknown correlations between genomic/epigenomic alterations and gene expression patterns.
Characterizes corresponding transcriptomic and epigenetic changes in embryonic stem cells (ESCs). MATCHER gives insight into the sequential changes of genomic information. It allows the use of both single cell gene expression and epigenetic data in the construction of cell trajectories. The tool can be useful for studying a variety of biological processes, such as differentiation, reprogramming, immune cell activation, and tumorigenesis.
Allows quality control (QC) and analysis components of parallel single cell transcriptome and epigenome data. Dr.seq is a quality control (QC) and analysis pipeline that provides both multifaceted QC reports and cell clustering results. Parallel single cell transcriptome data generated by different technologies can be transformed to the standard input with contained functions. Using relevant commands, the software can also be used to report quality measurements based on four aspects and can generate detailed analysis results for scATAC-seq and Drop-ChIP datasets.
An integrated software tool for quality filtering, normalization, feature selection, iterative dimensionality reduction, clustering and the estimation of gene-expression gradients from large ensembles of single-cell RNA-seq datasets. SCell is open source, and implemented with an intuitive graphical interface.
Allows users to remove batch effects. BBKNN offers an alignment approach suitable for a conjunctional use with the Scanpy software. This program can generate neighbor graphs as well as uniform manifold approximation and projection (UMAP) visualization. It also includes features to calculate approximate nearest neighbors. It generates files that can be used for further analysis containing clustering and pseudotime inference.
Enables interrogation of multiple single-cell (scRNA-Seq) datasets. scMerge is an algorithm that removes unwanted variation by combining expressed genes and utilizing pseudo-replicates across datasets. This method performs well in multiple scenarios and enhances biological discovery, including inferring cell developmental trajectories. It corrects specific batch effects within an experiment as well as removing dataset-specific effects across collections of datasets.
Offers multi-dataset integration. Harmony is a package which is able to identify both common and rare cell types using a two-step iterative strategy. The software is able to simultaneously accounts for multiple sources of variation and can handle large datasets by using linear methods. Additionally, this program includes functions for Seurat6 pipeline analyses.
Consists of a single cell RNA-seq imputation method based on the matrix decomposition. scRMD is an approach dedicated to the imputation of single RNA data. This program requires two assumptions for working: the low-rankness assumption and the sparsity assumption. A random matrix theory is used for guiding the selection of different parameters and for avoiding a computationally expensive parameter tuning procedure.
Incorporates and perform a batch-correction for heterogeneous single-cell RNA sequencing (scRNA-seq) datasets. Scanorama is an approach able to distinguish only the scRNA-seq datasets with overlapping cell types which are necessary to its analysis. It can be used to highlight: (i) causes of discrepancies between experiments, (ii) genes which contribute to the alignment of two datasets. This program supplies a modular structure that is suited for integration in scRNA-seq workflows.
Quantifies batch effects in single-cell RNA-sequencing (scRNA-seq) data. kBET allows users to study high-dimensional data without prior assumptions regarding statistical properties. It can be applied to any type of next-generation sequencing (NGS) data given a reasonable sample size per batch. The software was evaluated on simulated data with three different degrees of batch effects.
Serves for explorative analysis of index-sorted, single-cell transcriptomic data. indeXplorer allows users to explore their own datasets and is designed to use index-sorted datasets. This software supports ordinary single-cell transcriptomic data and can display raw and pre-processed data that are stored in a central data folder. It generates a file of raw read counts with single cells and genes for each transcriptomic data set.
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