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Seurat
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.
MATCHER / Manifold Alignment To CHaracterize Experimental Relationships
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.
Dr.seq
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.
SSrGE
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
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