MOTIVATION: Several machine learning (ML) algorithms dedicated to the detection of healthy and diseased cell types from single-cell RNA sequencing (scRNA-seq) data have been proposed for biomedical purposes. This raises concerns about their vulnerability to adversarial attacks, exploiting threats causing malicious alterations of the classifiers' output with defective and well-crafted input. RESULTS: With adverSCarial, adversarial attacks of single-cell transcriptomic data can easily be simulated in a range of ways, from expanded but undetectable modifications to aggressive and targeted ones, enabling vulnerability assessment of scRNA-seq classifiers to variations of gene expression, whether technical, biological, or intentional. We exemplify the usefulness and performance with a panel of attack modes proposed in adverSCarial by assessing the robustness of five scRNA-seq classifiers, each belonging to a distinct class of ML algorithm, and explore the potential unlocked by exposing their inner workings and sensitivities on four different datasets. These analyses can guide the development of more reliable models, with improved interpretability, usable in biomedical research and future clinical applications. AVAILABILITY AND IMPLEMENTATION: adverSCarial is a freely available R package accessible from Bioconductor: https://bioconductor.org/packages/adverSCarial/ or https://doi.org/10.18129/B9.bioc.adverSCarial. A development version is available at https://github.com/GhislainFievet/adverSCarial.

In this paper, we present the competition “Real-time Machine Learning Competition on Data Streams a BigData Cup Challenge of the IEEE Big Data 2019 conference. Data streams, such as data originated from sensors, have increasingly gained the interest of researchers and companies and are currently widely studied in data science. Companies in the telecommunication and energy industries are trying to exploit these data and get real-time insights on their services and equipment. In order to extract valuable knowledge from data streams, one must be able to analyze the data as they arrive and make meaningful predictions. For this purpose, we use fast incremental learners. There already exists a great community that is organizing various competitions on machine learning tasks for batch learners. Our goal was to introduce the same approach to engage the whole community in solving essential problems in data stream mining. We performed a new kind of data science competition based on a real-time prediction setting, using a novel competition platform on data streams. The examples to predict were released in real-time, and the predictions had also to be submitted in real-time. To the best of our knowledge, this was the first data science competition conducted in real-time. The task of the competition was to predict network activity, and the data has been provided by one of our partner companies.