Comparative single-cell trajectory network enrichment identifies pseudo-temporal systems biology patterns in hematopoiesis and CD8 T-cell development
Groenning, A. G. B.; Oubounyt, M.; Kanev, K.; Lund, J. B.; Kacprowski, T.; Zehn, D.; Rottger, R.; Baumbach, J.
Show abstract
Single cell transcriptomics (scRNA-seq) technologies allow for investigating cellular processes on an unprecedented resolution. While software packages for scRNA-seq raw data analysis exist, no method for the extraction of systems biology signatures that drive different pseudo-time trajectories exists. Hence, pseudo-temporal molecular sub-network expression profiles remain undetermined, thus, hampering our understanding of the molecular control of cellular development on a single cell resolution. We have developed Scellnetor, the first network-constraint time-series clustering algorithm implemented as interactive webtool to identify modules of genes connected in a molecular interaction network that show differentiating temporal expression patterns. Scellnetor allows selecting two differentiation courses or two developmental trajectories for comparison on a systems biology level. Scellnetor identifies mechanisms driving hematopoiesis in mouse and mechanistically interpretable subnetworks driving dysfunctional CD8 T-cell development in chronic infections. Scellnetor is the first method to allow for single cell trajectory network enrichment for systems level hypotheses generation, thus lifting scRNA-seq data analysis to a systems biology level. It is available as an interactive online tool at https://exbio.wzw.tum.de/scellnetor/.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Meta-analysis reveals consistent immune response patterns in COVID-19 infected patients at single-cell resolution 96%
- Pseudotime analysis reveals novel regulatory factors for multigenic onset and monogenic transition of odorant receptor expression 95%
- Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia 95%
Similar papers in this journal
- Latent factor modelling of scRNA-seq data uncovers novel pathways dysregulated in cell subsets of autoimmune disease patients 95%
- Comparing the impact of sample multiplexing approaches for single-cell RNA-sequencing on downstream analysis using cerebellar organoids 95%
- Self-Collected Finger-Prick Blood for Gene Expression Profiling: Unveiling Early Immune Responses in Mild COVID-19 94%
Similar papers in this journal
- Gene Function Revealed at the Moment of Stochastic Gene Silencing 96%
- Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA 95%
- Multiplexed Imaging Analysis of the Tumor-Immune Microenvironment Reveals Predictors of Outcome in Triple-Negative Breast Cancer 93%
Similar papers in this journal
- A novel Boolean network inference strategy to model early hematopoiesis aging 96%
- DeepCORE: An interpretable multi-view deep neural network model to detect co-operative regulatory elements 92%
- A rapid CRISPR competitive assay for in vitro and in vivo discovery of potential drug targets affecting the hematopoietic system 92%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.