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Dimensionality reduction methods have found vast application as visualization tools in diverse areas of science. Although many di erent methods exist, their performance is o en insu cient for providing quick insight into many contemporary datasets, and the unsupervised mode of use prevents the users from utilizing the methods for dataset exploration and netuning the details for improved visualization quality. We present BlosSOM, a high-performance semi-supervised dimensionality reduction so ware for interactive user-steerable visualization of high-dimensional datasets with millions of individual data points. BlosSOM builds on a GPUaccelerated implementation of the EmbedSOM algorithm, complemented by several landmarkbased algorithms for interfacing the unsupervised model learning algorithms with the user supervision. We show the application of BlosSOM on realistic datasets, where it helps to produce high-quality visualizations that incorporate user-speci ed layout and focus on certain features. We believe the semi-supervised dimensionality reduction will improve the data visualization possibilities for science areas such as single-cell cytometry, and provide a fast and e cient base methodology for new directions in dataset exploration and annotation.

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