Concurrent de novo inference of cell types and gene regulatory networks with NetMap

Abstract

Existing methods for GRN inference based on single-cell RNA-sequencing (scRNA-seq) data typically require prior cell type calling and then infer GRNs at cell type resolution. This risks propagation of errors from cell annotation to GRN inference. Moreover, it makes existing methods blind to regulatory programs that are shared between different cell types, as well as to subtle differences in regulatory activity between cells of the same type. To address these limitations, we present NetMap, a pipeline that jointly infers cell-level GRNs and clusters cells based on shared regulatory programs, combining state-of-the-art single-cell autoencoders and explainable artificial intelligence methods. NetMap outperforms existing methods on synthetic data with a known ground-truth GRN and is able to reliably detect biologically plausible regulons on real data. Moreover, requiring only a small set of user-provided marker genes, NetMap can be used for GRN structure-informed probabilistic cell type assignment. We demonstrate the utility of this approach using PBMC data, where we achieve near perfect clustering and cell type recovery performance compared with manually created cell type labels. Finally, we applied NetMap to a data set with naive, activated and exhausted T-cells, and were able to verify that NetMap retrieves relevant T-cell state signatures from the data, with two approaches. NetMap can be installed as a python package from https://github.com/bionetslab/netmap and is compatible with the scverse system. Scripts to reproduce our results can be found at https://github.com/bionetslab/netmap-evaluation.

Joint inference of paired dynamical gene regulatory networks reveals distinct cell-state landscapes of neutrophil reprogramming
A. Ren, Y. You, M. Lu
bioRxiv16 Sep 2026PreprintCC BYFull textSystems & synthetic biologybioRxiv
Where single-cell foundation models help: a benchmark of annotation, perturbation and regulatory inference against simple baselines
F. A. Cupaioli, I. Merelli
bioRxiv3 Nov 2026PreprintAll rights reservedFull textBioinformatics & computational biologybioRxiv
ntRoot: Genetic Ancestry Inference Across Diverse Genomic Data Types
Lauren Coombe, Taghrid Aloraini, Inanc Birol +1
Current protocols29 Oct 2026Peer-reviewedCC BYFull textBioinformatics & computational biologyEurope PMCEurope PMCPMC
RiboGRAM learns multi-scale non-coding RNA grammar for structural and regulatory inference
H. Wang, Z. Chen, H. Huang +4
bioRxiv12 Oct 2026PreprintCC BYFull textBioinformatics & computational biologybioRxiv
mTOR disrupts cerebrovascular RNA regulatory networks in Alzheimers disease
H. makhlouf, A. Banh, c. pomilio +5
bioRxiv15 Sep 2026PreprintAll rights reservedFull textNeurosciencebioRxiv

Cite

BibTeX RIS

BibTeX

@misc{hartebrodt2026concurrent,
  title = {{Concurrent de novo inference of cell types and gene regulatory networks with NetMap}},
  author = {A. Hartebrodt and M. Oubounyt and G. P. de Almeida and T. Reim and N. Wenke and J. Baumbach and D. Zehn and D. B. Blumenthal},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.04.756614},
  url = {https://www.biorxiv.org/content/10.64898/2026.10.04.756614v1},
  note = {CC BY},
}

RIS

TY  - UNPB
TI  - Concurrent de novo inference of cell types and gene regulatory networks with NetMap
AU  - A. Hartebrodt
AU  - M. Oubounyt
AU  - G. P. de Almeida
AU  - T. Reim
AU  - N. Wenke
AU  - J. Baumbach
AU  - D. Zehn
AU  - D. B. Blumenthal
PY  - 2026
DA  - 2026/10/04
PB  - bioRxiv
DO  - 10.64898/2026.10.04.756614
UR  - https://www.biorxiv.org/content/10.64898/2026.10.04.756614v1
AB  - Existing methods for GRN inference based on single-cell RNA-sequencing (scRNA-seq) data typically require prior cell type calling and then infer GRNs at cell type resolution. This risks propagation of errors from cell annotation to GRN inference. Moreover, it makes existing methods blind to regulatory programs that are shared between different cell types, as well as to subtle differences in regulatory activity between cells of the same type. To address these limitations, we present NetMap, a pipeline that jointly infers cell-level GRNs and clusters cells based on shared regulatory programs, combining state-of-the-art single-cell autoencoders and explainable artificial intelligence methods. NetMap outperforms existing methods on synthetic data with a known ground-truth GRN and is able to reliably detect biologically plausible regulons on real data. Moreover, requiring only a small set of user-provided marker genes, NetMap can be used for GRN structure-informed probabilistic cell type assignment. We demonstrate the utility of this approach using PBMC data, where we achieve near perfect clustering and cell type recovery performance compared with manually created cell type labels. Finally, we applied NetMap to a data set with naive, activated and exhausted T-cells, and were able to verify that NetMap retrieves relevant T-cell state signatures from the data, with two approaches. NetMap can be installed as a python package from https://github.com/bionetslab/netmap and is compatible with the scverse system. Scripts to reproduce our results can be found at https://github.com/bionetslab/netmap-evaluation.
ER  -

Abstract

Related

Cite