Single-cell transcriptomics has revolutionized our understanding of cellular heterogeneity and drug perturbation effects. However, its high cost and the vast chemical space of potential drugs present barriers to experimentally characterizing the effect of chemical perturbations in all the myriad cell types of the human body. To overcome these limitations, several groups have proposed using machine learning methods to directly predict the effect of chemical perturbations either across cell contexts or chemical space. However, advances in this field have been hindered by a lack of well-designed evaluation datasets and benchmarks. To drive innovation in perturbation modeling, the Open Problems Perturbation Prediction (OP3) benchmark introduces a framework for predicting the effects of small molecule perturbations on cell type-specific gene expression. OP3 leverages the Open Problems in Single-cell Analysis benchmarking infrastructure and is enabled by a new single-cell perturbation dataset, encompassing 146 compounds tested on human blood cells. The benchmark includes diverse data representations, evaluation metrics, and winning methods from our "Single-cell perturbation prediction: generalizing experimental interventions to unseen contexts" competition at NeurIPS 2023. We envision that the OP3 benchmark and competition will drive innovation in single-cell perturbation prediction by improving the accessibility, visibility, and feasibility of this challenge, thereby promoting the impact of machine learning in drug discovery.

MOTIVATION: During the last decade, trajectory inference (TI) methods have emerged as a novel framework to model cell developmental dynamics, most notably in the area of single-cell transcriptomics. At present, more than 70 TI methods have been published, and recent benchmarks showed that even state-of-the-art methods only perform well for certain trajectory types but not others. RESULTS: In this work, we present TinGa, a new TI model that is fast and flexible, and that is based on Growing Neural Graphs. We performed an extensive comparison of TinGa to five state-of-the-art methods for TI on a set of 250 datasets, including both synthetic as well as real datasets. Overall, TinGa improves the state-of-the-art by producing accurate models (comparable to or an improvement on the state-of-the-art) on the whole spectrum of data complexity, from the simplest linear datasets to the most complex disconnected graphs. In addition, TinGa obtained the fastest execution times, showing that our method is thus one of the most versatile methods up to date. AVAILABILITY AND IMPLEMENTATION: R scripts for running TinGa, comparing it to top existing methods and generating the figures of this article are available at https://github.com/Helena-todd/TinGa.