opt-SNE was developed to solve two fundamental problems with t-SNE. The first is the tendency of large datasets to fail to produce useful embeddings. The second is the need to empirically search the algorithm parameter space to find optimal settings.
It is common to observe the following benefits using the opt-SNE methodology compared to previously conventional strategies for running t-SNE:
To see examples of the benefits listed above and for thorough background and discussion on this work, please read the paper available in Nature Communications.
Omiq supports two ways to run opt-SNE:
In order to build and run opt-SNE on any personal computer, navigate to the Multicore opt-SNE github repository and follow the directions there. Omiq is happy to offer free help with this process. Just reach out using the form below.
2) On the Web
For ease-of-use and performance considerations, opt-SNE is also available in the cloud-based OMIQ Data Science Platform. This requires neither installation of software nor experience with command-line interfaces. To access this service, visit www.omiq.ai.
Some data files referenced within the opt-SNE paper are available for download below. All files are zipped.
18 parameter fluorescent flow cytometry file from Belkina AC, Snyder-Cappione JE. OMIP-037: 16-color panel to measure inhibitory receptor signatures from multiple human immune cell subsets. Cytometry A. 2017 Feb;91(2):175-179. doi: 10.1002/cyto.a.22983.
41 parameter mass cytometry dataset from Bendall SC et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science. 2011 May 6;332(6030):687-96. doi: 10.1126/science.1198704.