Asset Details
Do you wish to reserve the book?
Assessing single-cell transcriptomic variability through density-preserving data visualization
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Assessing single-cell transcriptomic variability through density-preserving data visualization
Do you wish to request the book?
Assessing single-cell transcriptomic variability through density-preserving data visualization
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Assessing single-cell transcriptomic variability through density-preserving data visualization
2021
Overview
Nonlinear data visualization methods, such as
t
-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), summarize the complex transcriptomic landscape of single cells in two dimensions or three dimensions, but they neglect the local density of data points in the original space, often resulting in misleading visualizations where densely populated subsets of cells are given more visual space than warranted by their transcriptional diversity in the dataset. Here we present den-SNE and densMAP, which are density-preserving visualization tools based on t-SNE and UMAP, respectively, and demonstrate their ability to accurately incorporate information about transcriptomic variability into the visual interpretation of single-cell RNA sequencing data. Applied to recently published datasets, our methods reveal significant changes in transcriptomic variability in a range of biological processes, including heterogeneity in transcriptomic variability of immune cells in blood and tumor, human immune cell specialization and the developmental trajectory of
Caenorhabditis elegans
. Our methods are readily applicable to visualizing high-dimensional data in other scientific domains.
den-SNE and densMAP enhance single-cell transcriptomic data visualization by incorporating density information.
Publisher
Nature Publishing Group US,Nature Publishing Group
