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Citations per year

Number of citations per year for the bioinformatics software tool Seurat

Tool usage distribution map

This map represents all the scientific publications referring to Seurat per scientific context
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Associated diseases


Popular tool citations

chevron_left Normalization Cell lineage and pseudotime inference Clustering Marker gene detection Single-cell imputation Dimensionality reduction Differential expression detection Gene filtering scRNA-seq data integration Variable gene detection chevron_right
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Seurat specifications


Unique identifier OMICS_10888
Name Seurat
Software type Package/Module
Interface Command line interface, Graphical user interface
Restrictions to use None
Input data A gene expression matrix, where the rows are genes and the columns are single cells.
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0
Computer skills Advanced
Version 2.2.0
Stability Stable
ggplot2, cowplot, Matrix, Java
Source code URL
Maintained Yes




No version available



  • person_outline Satija Lab
  • person_outline Paul Hoffman

Additional information

Publications for Seurat

Seurat citations


A geometric approach to characterize the functional identity of single cells

Nat Commun
PMCID: 5904143
PMID: 29666373
DOI: 10.1038/s41467-018-03933-2

[…] om the primary functions identified using ACTION, we assigned each cell to a single dominant function, as determined by its closest archetype. We compare our method to five recently proposed methods: Seurat (v2.2), SNNCliq, BackSPIN, single-cell ParTI,, and TSCAN (Supplementary Note ) to predict annotated cell types on the same four datasets (see Methods, Datasets). For the Melanoma dataset, SNNCl […]


A multitask clustering approach for single cell RNA seq analysis in Recessive Dystrophic Epidermolysis Bullosa

PLoS Comput Biol
PMCID: 5908193
PMID: 29630593
DOI: 10.1371/journal.pcbi.1006053

[…] r all cells. Then we ran hierarchical clustering with four different methods for computing cluster distance (‘ward’, ‘complete’, ‘single’, ‘average’) and selected the best clustering results.To apply Seurat [], Seurat v2.0 R package was downloaded from SATIJA LAB. The scRNA-seq data were converted into the required format (gene index | cell index | gene expression) as the input. The parameter “Res […]


Mapping human pluripotent stem cell differentiation pathways using high throughput single cell RNA sequencing

Genome Biol
PMCID: 5887227
PMID: 29622030
DOI: 10.1186/s13059-018-1426-0

[…] The sequenced reads were mapped against the reference GRCh38 using STAR v2.5.2a []. scRNA-seq expression data, quantified by counts via featureCounts v1.5.1 [], were analyzed with Seurat v2.0.1 (PCA, Cluster, t-SNE and cluster) []. In brief, the Seurat object was generated from digital gene expression matrices. The parameter of “Filtercells” is nGene (2000 to 8800) and transcri […]


Molecular transitions in early progenitors during human cord blood hematopoiesis

Mol Syst Biol
PMCID: 5852373
PMID: 29545397
DOI: 10.15252/msb.20178041

[…] man bone marrow CD34+ Quartz‐seq cells were downloaded from NCBI GEO (GSE75478). To integrate this dataset with our Drop‐seq micro‐clusters, we ran the scRNA‐seq integration procedure as described in Seurat 2.0 (Satija et al, ; preprint: Butler & Satija, ). Briefly, the procedure aims to identify potentially shared subpopulations between two datasets, based on shared sources of variation. We ident […]


Single cell RNA seq analysis unveils a prevalent epithelial/mesenchymal hybrid state during mouse organogenesis

Genome Biol
PMCID: 5853091
PMID: 29540203
DOI: 10.1186/s13059-018-1416-2

[…] out nonlinear dimensional reduction (t-SNE) through the Rtsne package in R.For the expression matrix, we analyzed our 1819 single-cell data in the form of log2(TPM/10 + 1) expression values using the Seurat method [] (for details, see Specifically, genes were considered expressed only if their expression level was ≥ 1. Genes expressed in < 3 cells and cells with ≤ 20 […]


Single cell transcriptomics of the developing lateral geniculate nucleus reveals insights into circuit assembly and refinement

Proc Natl Acad Sci U S A
PMCID: 5798372
PMID: 29343640
DOI: 10.1073/pnas.1717871115

[…] edures). After removing these, we proceeded to analyze the remaining 35,326 cells that passed QC (7,499 cells from P5, 7,596 cells from P10, 13,091 cells from P16, and 7,140 cells from P21).Using the Seurat software package for R, we next identified highly variable genes by calculating the average expression and distribution of each gene across all cells (). Genes with high cell-to-cell variabilit […]

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Seurat institution(s)
Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA; Center for Brain Science, Harvard University, Cambridge, MA, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA, USA; Center for Systems Biology, Harvard University, Cambridge, MA, USA; Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
Seurat funding source(s)
Supported by F32 HD075541, the Jane Coffin Childs Memorial Fund for Medical Research, the NIH, NHGRI CEGS 1P50HG006193, the Klarman Cell Observatory, and HHMI.

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