The primary plan of my editorial for this month was to highlight and comment on the special issue of this month: “Machine Learning for Single Cell Data”. I wish to emphasize and thank the Guest Editors of this special issue, Yvan Saeys and Greg Finak, for their outstanding success and hard work to assemble excellent manuscripts for this issue. I am referring to their guest editorial giving you more details on aims and scopes and elaborating on specific articles.. . .
Across single‐cell technologies, including flow and mass cytometry, as well as scRNA‐seq, unsupervised clustering algorithms have become a staple of data analysis and are often hailed as a replacement for manual gating with the promise of an unbiased interrogation of the data. There is no shortage of software for the purpose and many tools are produced with user friendly graphical interfaces for the less programming inclined part of the community. . . .
Deep learning methods developed by the computer vision community are successfully being adapted for use in biomedical image analysis and synthesis applications with some delay. Also in cell image synthesis, we can observe significant improvements in the quality of generated results brought about by deep learning. The typical task is to generate isolated cell images based on training image examples with cropped, centered, and aligned individual cells.. . .
There was long time ago a saying by someone whose name I cannot recall at the moment: “Trust is good but control is better” (Or in other words: Доверие это хорошо. Контроль лучше). This is particularly true for quantitative
science and I have...