SCITRAM: a Single-cell Integrated transcription regulation modeler for predicting master transcription factors

Abstract

Single-cell omics technologies have provided unprecedented access to molecular cell heterogeneity, revolutionizing our understanding of complex living systems. Although generating such type of data has become a streamlined process, data processing still relies on command-line driven tools, or commercial solutions. Furthermore, single-cell transcriptomics analysis is usually reduced to stratifying cells on the grounds of gene expression signatures, while deconvolving gene regulatory programs (and their associated master transcription factors) responsible for cell heterogeneity are not classically addressed with the available tools, or performed under different data processing platforms. Herein, we present SCITRAM (Single-Cell Integrated TRAnscription regulation Modeler), a user-friendly stand-alone computational solution for predicting master transcription factors from single-cell transcriptomics data. SCITRAM first reconstructs a primary gene regulatory network and subsequently uses this network as template to model transcriptional cascades driven by the in-silico activation of transcription factors identified within the system. We validated SCITRAM performance across a variety of cell fate transition events including T cell exhaustion during solid tumor treatment, lineage stratification during pancreatic endocrinogenesis, as well as nervous tissue formation in cerebral organoid models. Overall, SCITRAM provides an accessible and integrated platform for processing (large amounts of) single-cell transcriptomics data, enabling the investigation of transcriptional regulatory programs and their master transcription factors without requiring a command-line driven environment.

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Cite

BibTeX RIS

BibTeX

@misc{mendozaparra2026scitram,
  title = {{SCITRAM: a Single-cell Integrated transcription regulation modeler for predicting master transcription factors}},
  author = {M. A. Mendoza-Parra and A. Galindo-Albarra and M. Duvina and P. Barbao and A. Rodriguez-Garcia and S. Guedan and C. Heuser-Loy and L. Gattinoni},
  howpublished = {bioRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.30.755779},
  url = {https://www.biorxiv.org/content/10.64898/2026.09.30.755779v1},
  note = {CC BY-NC},
}

RIS

TY  - UNPB
TI  - SCITRAM: a Single-cell Integrated transcription regulation modeler for predicting master transcription factors
AU  - M. A. Mendoza-Parra
AU  - A. Galindo-Albarra
AU  - M. Duvina
AU  - P. Barbao
AU  - A. Rodriguez-Garcia
AU  - S. Guedan
AU  - C. Heuser-Loy
AU  - L. Gattinoni
PY  - 2026
DA  - 2026/09/30
PB  - bioRxiv
DO  - 10.64898/2026.09.30.755779
UR  - https://www.biorxiv.org/content/10.64898/2026.09.30.755779v1
AB  - Single-cell omics technologies have provided unprecedented access to molecular cell heterogeneity, revolutionizing our understanding of complex living systems. Although generating such type of data has become a streamlined process, data processing still relies on command-line driven tools, or commercial solutions. Furthermore, single-cell transcriptomics analysis is usually reduced to stratifying cells on the grounds of gene expression signatures, while deconvolving gene regulatory programs (and their associated master transcription factors) responsible for cell heterogeneity are not classically addressed with the available tools, or performed under different data processing platforms. Herein, we present SCITRAM (Single-Cell Integrated TRAnscription regulation Modeler), a user-friendly stand-alone computational solution for predicting master transcription factors from single-cell transcriptomics data. SCITRAM first reconstructs a primary gene regulatory network and subsequently uses this network as template to model transcriptional cascades driven by the in-silico activation of transcription factors identified within the system. We validated SCITRAM performance across a variety of cell fate transition events including T cell exhaustion during solid tumor treatment, lineage stratification during pancreatic endocrinogenesis, as well as nervous tissue formation in cerebral organoid models. Overall, SCITRAM provides an accessible and integrated platform for processing (large amounts of) single-cell transcriptomics data, enabling the investigation of transcriptional regulatory programs and their master transcription factors without requiring a command-line driven environment.
ER  -

Abstract

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