Analysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition
Trang Le, Casper F. Winsnes, Ulrika Axelsson, Hao Xu, Jayasankar Mohanakrishnan Kaimal, Diana Mahdessian, Shubin Dai, Ilya S. Makarov, Vladislav Ostankovich, Yang Xu, Eric Benhamou, Christof Henkel, Roman Solovyev, Nikola Banić, Vito Bošnjak, A. Mihovilović Bošnjak, Andrija Miličević, Wei Ouyang, Emma Lundberg
Abstract
While spatial proteomics by fluorescence imaging has quickly become an essential discovery tool for researchers, fast and scalable methods to classify and embed single-cell protein distributions in such images are lacking. Here, we present the design and analysis of the results from the competition Human Protein Atlas - Single-Cell Classification hosted on the Kaggle platform. This represents a crowd-sourced competition to develop machine learning models trained on limited annotations to label single-cell protein patterns in fluorescent images. The particular challenges of this competition include class imbalance, weak labels and multi-label classification, prompting competitors to apply a wide range of approaches in their solutions. The winning models serve as the first subcellular omics tools that can annotate single-cell locations, extract single-cell features and capture cellular dynamics.