Seurat protocols

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

Information


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
Requirements
ggplot2, cowplot, Matrix, Java
Source code URL https://cran.r-project.org/web/packages/Seurat/index.html
Maintained Yes

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Documentation


Maintainers


  • person_outline Satija Lab <>
  • person_outline Paul Hoffman <>

Additional information


https://github.com/satijalab/seurat

Publications for Seurat

Seurat in pipelines

 (3)
2018
PMCID: 5908193
PMID: 29630593
DOI: 10.1371/journal.pcbi.1006053

[…] distance; celltree method [] clusters single cells by a detected tree structure outlining the hierarchical relationship between single-cell samples to introduce biological prior knowledge; seurat [] was proposed to infer cellular localization by integrating single-cell rna-seq data with in situ rna patterns; and more recently a consensus clustering approach sc3 [] was proposed […]

2018
PMCID: 5946446
PMID: 29747587
DOI: 10.1186/s12864-018-4738-2

[…] in the dataset, we employed the fpkm values as the input for principal component analysis using the factorminer r package []. the significance of the principal components was obtained with the seurat package [] via a permutation test, after 1000 randomized samplings []., we performed weighted gene correlation network analysis each of the sample types (oocytes, innerccs and outerccs) using […]

2016
PMCID: 5073357
PMID: 27694495
DOI: 10.4049/jimmunol.1600959

[…] and the most highly variable genes were extracted using the sclvm r package (version 0.99.2) (). principal component analysis was applied to the most variable genes using the implementation of seurat r package (version 1.2.1) (). hierarchical clustering using the euclidean distance and the ward’s minimum variance criterion was implemented by means of the pheatmap r package (version 1.0.8) […]


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Seurat in publications

 (23)
PMCID: 5904143
PMID: 29666373
DOI: 10.1038/s41467-018-03933-2

[…] 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, […]

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

[…] distance; celltree method [] clusters single cells by a detected tree structure outlining the hierarchical relationship between single-cell samples to introduce biological prior knowledge; seurat [] was proposed to infer cellular localization by integrating single-cell rna-seq data with in situ rna patterns; and more recently a consensus clustering approach sc3 [] was proposed […]

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

[…] naïve-like h9 datasets show weak batch effect of fluidigm c1 system (additional file : figure s1b). the random differentiation of ebs causes the batch effect (additional file : figure s1c). we used seurat to perform principal component analysis (pca) and t-distributed stochastic neighbor embedding (t-sne) analysis []. seurat divided our samples into four main clusters, including two eb clusters […]

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

[…] 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 http://satijalab.org/seurat/). specifically, genes were considered expressed only if their expression level was ≥ 1. genes expressed in < 3 cells and cells […]

PMCID: 5786505
PMID: 29249358
DOI: 10.1016/j.cell.2017.11.036

[…] from further analysis, leaving 222 cells and 9,271 genes. expression data was log transformed (log2(tpm+1)) before further analysis. clustering and differential expression were performed using the seurat package (version 1.4.0.16) in r (version 3.3.2) (). briefly, we identified 708 variable genes with log-mean expression values greater than 0.05 and dispersion (variance/mean) greater than 0.8. […]


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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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