Intuitively, two data instances should be co-located in the resulting visualization if their multi-dimensional profiles are similar. Two-dimensional embeddings and their visualizations may assist in the analysis and interpretation of high-dimensional data. The visualizations constructed by our proposed approach are cleared of batch effects, and the cells from secondary data sets correctly co-cluster with cells from the primary data sharing the same cell type.

In these data sets, the batch effects are particularly strong as the data comes from different institutions and was obtained using different experimental protocols. We demonstrate the utility of this approach with an analysis of six recently published single-cell gene expression data sets containing up to tens of thousands of cells and thousands of genes. This prevents any interactions between instances in the secondary data and implicitly mitigates batch effects. The new, secondary data is embedded one data-point at the time. To circumvent these batch effects, we propose an embedding procedure that takes a t-SNE visualization constructed on a reference data set and uses it as a scaffold for embedding new data. When working with multiple data sets, a straightforward application of these methods often fails instead of revealing underlying classes, the resulting visualizations expose data set-specific clusters. Dimensionality reduction techniques, such as t-SNE, can construct informative visualizations of high-dimensional data.

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