Optimized Network Inference for Immune Diseased single cells (ONIDsc)

Elena Merino Tejero¹Dwain Jude Vaz¹Guillermo Barturen^2,3,4María Rivas-Torrubia³Marta E Alarcon-Riquelme^5,6Walter Kolch^1,7David Matallanas^1*

• ¹Systems Biology Ireland, School of Medicine, College of Health and Agricultural Sciences, University College Dublin, Dublin, Ireland
• ²Department of Genetics, Faculty of Science, University of Granada, 18071, Granada, Spain., Granada, Spain
• ³Pfizer–University of Granada–Junta de Andalucía Centre for Genomics and Oncological Research, Granada, Spain, Granada, Spain
• ⁴Bioinformatics Laboratory, Centro de Investigación Biomédica, Biotechnology Institute, PTS, Avda del Conocimiento S/N, 18100, Granada, Spain., Granada, Spain
• ⁵GENYO, Centre for Genomics and Oncological Research Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Granada, Spain., Granada, Spain
• ⁶Institute of Environmental Medicine, Karolinska Institutet (KI), Stockholm, Stockholm, Sweden
• ⁷Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland, Dublin, Ireland

Mathematical models are powerful tools that can be used to advance our understanding of complex diseases. Autoimmune disorders such as Systemic lupus erythematosus (SLE) are highly heterogeneous and require high-resolution mechanistic approaches. In this work, we present ONIDsc, a single-cell regulatory network inference model designed to elucidate immune-related disease mechanisms in SLE. ONIDsc enhances the GLG causality model used in SINGE by finding the optimal lambda penalty with cyclical coordinate descent. We benchmarked ONIDsc against existing models and found it consistently outperforms SINGE and other methods when gold standards are generated from ChIP-seq and ChIP-chip experiments. We then applied ONIDsc to three large-scale datasets, one from control patients and the two from SLE patients, to reconstruct networks common to different immune cell types. ONIDsc identified four gene transcripts: MXRA8, NADK, POLR3GL and UBXN11 in CD4TCs, CD8TREGs, CD8TC1s and LDGs that were present in SLE patients but absent in controls in controls. These genes were significantly related to nicotinate metabolism, RNA transcription, protein phosphorylation and the RND 1-3 signaling pathways, previously associated with immune regulation. Our results highlight ONIDsc's potential as a powerful tool for dissecting physiological and pathological processes in immune cells using high-dimensional single-cell data.