Abstract
Single-cell transcriptomics yields ever growing data sets containing RNA expression levels for thousands of genes from up to millions of cells. Common data analysis pipelines include a dimensionality reduction step for visualising the data in two dimensions, most frequently performed using t-distributed stochastic neighbour embedding (t-SNE). It excels at revealing local structure in high-dimensional data, but naive applications often suffer from severe shortcomings, e.g. the global structure of the data is not represented accurately. Here we describe how to circumvent such pitfalls, and develop a protocol for creating more faithful t-SNE visualisations. It includes PCA initialisation, a high learning rate, and multi-scale similarity kernels; for very large data sets, we additionally use exaggeration and downsampling-based initialisation. We use published single-cell RNA-seq data sets to demonstrate that this protocol yields superior results compared to the naive application of t-SNE.
Highlights
Single-cell transcriptomics yields ever growing data sets containing RNA expression levels for thousands of genes from up to millions of cells
Our analysis indicates that UMAP does not necessarily solve t-distributed stochastic neighbour embedding (t-SNE)’s problems out of the box and might require as many careful parameter and/ or initialisation choices as t-SNE does
We showed that using informative initialisation can substantially improve the global structure of the final embedding because it survives through the optimisation process
Summary
Single-cell transcriptomics yields ever growing data sets containing RNA expression levels for thousands of genes from up to millions of cells. Common data analysis pipelines include a dimensionality reduction step for visualising the data in two dimensions, most frequently performed using t-distributed stochastic neighbour embedding (t-SNE). We describe how to circumvent such pitfalls, and develop a protocol for creating more faithful t-SNE visualisations It includes PCA initialisation, a high learning rate, and multi-scale similarity kernels; for very large data sets, we use exaggeration and downsampling-based initialisation. Through improved experimental techniques it has become possible to obtain gene expression data from thousands or even millions of cells[3,4,5,6,7,8] Computational analysis of such data sets often entails unsupervised, exploratory steps including dimensionality reduction for visualisation. We use FIt-SNE16, a recently developed fast t-SNE implementation, for all experiments
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