A sandbox for prediction and integration of DNA, RNA, and protein data in single cells

Authors

Malte D Luecken, Daniel B Burkhardt, Robrecht Cannoodt, Christopher Lance, Aditi Agrawal, Hananeh Aliee, Ann T Chen, Louise Deconinck, Angela M Detweiler, Alejandro Granados, Shelly Huynh, Laura Isacco, Yang Joon Kim, Sunil Kuppasani, Heiko Lickert, Aaron McGeever, Honey Mekonen, Joaquin Caceres, Maurizio Morri, Michaela Mueller, Norma F Neff, Sheryl Paul, Kaylie Schneider, Scott Steelman, Michael Sterr, Dan J Treacy, Alexander Tong, Alexandra-Chloé Villani, Guilin Wang, Jia Yan, Ce Zhang, Angela O Pisco, Smita Krishnaswamy, Fabian J Theis, Jonathan M Bloom

Date

December 10, 2021

Abstract

The last decade has witnessed a technological arms race to encode the molecular states of cells into DNA libraries, turning DNA sequencers into scalable single-cell microscopes. Single-cell measurement of chromatin accessibility (DNA), gene expression (RNA), and proteins has revealed rich cellular diversity across tissues, organisms, and disease states. However, single-cell data poses a unique set of challenges. A dataset may comprise millions of cells with tens of thousands of sparse features. Identifying biologically relevant signals from the background sources of technical noise requires innovation in predictive and representational learning. Furthermore, unlike in machine vision or natural language processing, biological ground truth is limited. Here we leverage recent advances in multi-modal single-cell technologies which, by simultaneously measuring two layers of cellular processing in each cell, provide ground truth analogous to language translation. We define three key tasks to predict one modality from another and learn integrated representations of cellular state. We also generate a novel dataset of the human bone marrow specifically designed for benchmarking studies. The dataset and tasks are accessible through an open-source framework that facilitates centralized evaluation of community-submitted methods.

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