Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data
t -distributed stochastic neighbor embedding (t-SNE) is widely used for visualizing single-cell RNA-sequencing (scRNA-seq) data, but it scales poorly to large datasets. We dramatically accelerate t-SNE, obviating the need for data downsampling, and hence allowing visualization of rare cell populatio...
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| Published in | Nature methods Vol. 16; no. 3; pp. 243 - 245 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.03.2019
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
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| Abstract | t
-distributed stochastic neighbor embedding (t-SNE) is widely used for visualizing single-cell RNA-sequencing (scRNA-seq) data, but it scales poorly to large datasets. We dramatically accelerate t-SNE, obviating the need for data downsampling, and hence allowing visualization of rare cell populations. Furthermore, we implement a heatmap-style visualization for scRNA-seq based on one-dimensional t-SNE for simultaneously visualizing the expression patterns of thousands of genes. Software is available at
https://github.com/KlugerLab/FIt-SNE
and
https://github.com/KlugerLab/t-SNE-Heatmaps
.
FIt-SNE, a sped-up version of t-SNE, enables visualization of rare cell types in large datasets by obviating the need for downsampling. One-dimensional t-SNE heatmaps allow simultaneous visualization of expression patterns from thousands of genes. |
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| AbstractList | t-distributed stochastic neighbor embedding (t-SNE) is widely used for visualizing single-cell RNA-sequencing (scRNA-seq) data, but it scales poorly to large datasets. We dramatically accelerate t-SNE, obviating the need for data downsampling, and hence allowing visualization of rare cell populations. Furthermore, we implement a heatmap-style visualization for scRNA-seq based on one-dimensional t-SNE for simultaneously visualizing the expression patterns of thousands of genes. Software is available at https://github.com/KlugerLab/FIt-SNE and https://github.com/KlugerLab/t-SNE-Heatmaps . t -distributed stochastic neighbor embedding (t-SNE) is widely used for visualizing single-cell RNA-sequencing (scRNA-seq) data, but it scales poorly to large datasets. We dramatically accelerate t-SNE, obviating the need for data downsampling, and hence allowing visualization of rare cell populations. Furthermore, we implement a heatmap-style visualization for scRNA-seq based on one-dimensional t-SNE for simultaneously visualizing the expression patterns of thousands of genes. Software is available at https://github.com/KlugerLab/FIt-SNE and https://github.com/KlugerLab/t-SNE-Heatmaps . FIt-SNE, a sped-up version of t-SNE, enables visualization of rare cell types in large datasets by obviating the need for downsampling. One-dimensional t-SNE heatmaps allow simultaneous visualization of expression patterns from thousands of genes. t-distributed Stochastic Neighborhood Embedding (t-SNE) is widely used for visualizing single-cell RNA-sequencing (scRNA-seq) data, but it scales poorly to large datasets. We dramatically accelerate t-SNE, obviating the need for data downsampling, and hence allowing visualization of rare cell populations. Furthermore, we implement a heatmap-style visualization for scRNA-seq based on one-dimensional t-SNE for simultaneously visualizing the expression patterns of thousands of genes. t-distributed stochastic neighbor embedding (t-SNE) is widely used for visualizing single-cell RNA-sequencing (scRNA-seq) data, but it scales poorly to large datasets. We dramatically accelerate t-SNE, obviating the need for data downsampling, and hence allowing visualization of rare cell populations. Furthermore, we implement a heatmap-style visualization for scRNA-seq based on one-dimensional t-SNE for simultaneously visualizing the expression patterns of thousands of genes. Software is available at https://github.com/KlugerLab/FIt-SNE and https://github.com/KlugerLab/t-SNE-Heatmaps.FIt-SNE, a sped-up version of t-SNE, enables visualization of rare cell types in large datasets by obviating the need for downsampling. One-dimensional t-SNE heatmaps allow simultaneous visualization of expression patterns from thousands of genes. t-distributed stochastic neighbor embedding (t-SNE) is widely used for visualizing single-cell RNA-sequencing (scRNA-seq) data, but it scales poorly to large datasets. We dramatically accelerate t-SNE, obviating the need for data downsampling, and hence allowing visualization of rare cell populations. Furthermore, we implement a heatmap-style visualization for scRNA-seq based on one-dimensional t-SNE for simultaneously visualizing the expression patterns of thousands of genes. Software is available at https://github.com/KlugerLab/FIt-SNE and https://github.com/KlugerLab/t-SNE-Heatmaps .t-distributed stochastic neighbor embedding (t-SNE) is widely used for visualizing single-cell RNA-sequencing (scRNA-seq) data, but it scales poorly to large datasets. We dramatically accelerate t-SNE, obviating the need for data downsampling, and hence allowing visualization of rare cell populations. Furthermore, we implement a heatmap-style visualization for scRNA-seq based on one-dimensional t-SNE for simultaneously visualizing the expression patterns of thousands of genes. Software is available at https://github.com/KlugerLab/FIt-SNE and https://github.com/KlugerLab/t-SNE-Heatmaps . |
| Author | Rachh, Manas Linderman, George C. Steinerberger, Stefan Hoskins, Jeremy G. Kluger, Yuval |
| AuthorAffiliation | 2 Department of Mathematics, Yale University, New Haven, CT 06511, USA 3 Department of Pathology, Yale University School of Medicine, New Haven, CT 06510, USA 1 Applied Mathematics Program, Yale University, New Haven, CT 06511, USA |
| AuthorAffiliation_xml | – name: 3 Department of Pathology, Yale University School of Medicine, New Haven, CT 06510, USA – name: 2 Department of Mathematics, Yale University, New Haven, CT 06511, USA – name: 1 Applied Mathematics Program, Yale University, New Haven, CT 06511, USA |
| Author_xml | – sequence: 1 givenname: George C. orcidid: 0000-0002-0074-0346 surname: Linderman fullname: Linderman, George C. organization: Applied Mathematics Program, Yale University – sequence: 2 givenname: Manas surname: Rachh fullname: Rachh, Manas organization: Applied Mathematics Program, Yale University – sequence: 3 givenname: Jeremy G. surname: Hoskins fullname: Hoskins, Jeremy G. organization: Applied Mathematics Program, Yale University – sequence: 4 givenname: Stefan surname: Steinerberger fullname: Steinerberger, Stefan organization: Department of Mathematics, Yale University – sequence: 5 givenname: Yuval orcidid: 0000-0002-3035-071X surname: Kluger fullname: Kluger, Yuval email: yuval.kluger@yale.edu organization: Applied Mathematics Program, Yale University, Department of Pathology, Yale University School of Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30742040$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Nature America, Inc. 2019 2019© The Author(s), under exclusive licence to Springer Nature America, Inc. 2019 |
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-distributed stochastic neighbor embedding (t-SNE) is widely used for visualizing single-cell RNA-sequencing (scRNA-seq) data, but it scales poorly to large... t-distributed stochastic neighbor embedding (t-SNE) is widely used for visualizing single-cell RNA-sequencing (scRNA-seq) data, but it scales poorly to large... t-distributed Stochastic Neighborhood Embedding (t-SNE) is widely used for visualizing single-cell RNA-sequencing (scRNA-seq) data, but it scales poorly to... |
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| SubjectTerms | 631/114/1305 631/114/1386 631/114/794 Algorithms Animals Base Sequence Bioinformatics Biological Microscopy Biological Techniques Biomedical and Life Sciences Biomedical Engineering/Biotechnology Brief Communication Computational Biology - methods Embedding Gene expression Gene Expression Profiling - methods Gene sequencing Genetic Markers Interpolation Life Sciences Mice Proteomics Ribonucleic acid RNA RNA - genetics Sequence Analysis, RNA - methods Single-Cell Analysis - methods Stochastic Processes Visualization |
| Title | Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data |
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