  Dimensionality reduction methods have found vast application as visualization
tools in diverse areas of science. Although many different methods exist, their
performance is often insufficient 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 fine-tuning the details for
improved visualization quality. We present BlosSOM, a high-performance
semi-supervised dimensionality reduction software for interactive
user-steerable visualization of high-dimensional datasets with millions of
individual data points. BlosSOM builds on a GPU-accelerated implementation of
the EmbedSOM algorithm, complemented by several landmark-based 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-specified
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 efficient
base methodology for new directions in dataset exploration and annotation.
