WikiPathways
WikiPathways is an open science platform for biological pathways contributed, updated, and used by the research community.
Seurat is an R package designed for single-cell RNA-seq data analysis, exploration, and integration of diverse single-cell data types.

Seurat is an R package that facilitates the quality control, analysis, and exploration of single-cell RNA-seq data. It aims to enable researchers to identify and interpret sources of heterogeneity from single-cell transcriptomic measurements and to integrate diverse types of single-cell data, including scATAC-seq data. Seurat introduces methods for integrative multimodal analysis, flexible and scalable analysis of large datasets, and analysis of spatial datasets, supporting both sequencing-based and imaging-based approaches. Designed with clear visualizations and interpretable results, Seurat is intended for both dry-lab and wet-lab researchers in the field of single-cell genomics and transcriptomics. It is developed and maintained by the Satija lab and is released under the MIT license.
Seurat is an R package that facilitates the quality control, analysis, and exploration of single-cell RNA-seq data.
Explore all tools that specialize in quality control of single-cell rna-seq data. This domain focus ensures Seurat delivers optimized results for this specific requirement.
Explore all tools that specialize in normalization and scaling of single-cell data. This domain focus ensures Seurat delivers optimized results for this specific requirement.
Explore all tools that specialize in dimensionality reduction (pca, t-sne, umap). This domain focus ensures Seurat delivers optimized results for this specific requirement.
Explore all tools that specialize in clustering of cells based on gene expression profiles. This domain focus ensures Seurat delivers optimized results for this specific requirement.
Explore all tools that specialize in differential expression analysis to identify marker genes. This domain focus ensures Seurat delivers optimized results for this specific requirement.
Explore all tools that specialize in visualization of single-cell data. This domain focus ensures Seurat delivers optimized results for this specific requirement.
Integrates experiments measuring different modalities (scRNA-seq and scATAC-seq) using 'bridge integration,' mapping scATAC-seq datasets onto scRNA-seq datasets.
Enables analysis of large datasets by storing representative subsamples in-memory for rapid and iterative analysis while keeping the full dataset accessible via on-disk storage.
Facilitates high-performance analysis through bit-packing compression techniques, optimized C++ code, and streamlined/lazy operations.
Supports a wide variety of spatially resolved data types (sequencing-based and imaging-based) and provides analytical techniques for scRNA-seq integration, deconvolution, and niche identification.
Provides flexible and streamlined workflows for the integration of multiple scRNA-seq datasets, making it easier to explore the results of different integration methods.
Install R (https://www.r-project.org/).
Install RStudio (https://www.rstudio.com/).
Install Seurat package from CRAN using install.packages('Seurat').
Load the Seurat library in R using library(Seurat).
Import single-cell RNA-seq data into R.
Create a Seurat object using CreateSeuratObject().
Follow the guided tutorials and vignettes available on the Seurat website (https://satijalab.org/seurat/).
All Set
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Verified feedback from other users.
"Seurat is widely used in the single-cell RNA-seq analysis community and is known for its comprehensive functionality and clear visualizations. Its flexibility and support for diverse data types are well-regarded."
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WikiPathways is an open science platform for biological pathways contributed, updated, and used by the research community.

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