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Method Article
Michelangelo Cordenonsi
Corresponding Author
Silvio Bicciato
Corresponding Author
Stefano Piccolo
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
We describe a multistep computational procedure to identify candidate master Transcriptional Regulators (TRs) of glioblastoma (GBM) from glioblastoma single cell gene expression profiles and tissue-specific transcription factors.
Keywords
gene regulatory network, master Transcriptional Regulators, single-cell RNA-seq analysis
This is a list of supplementary files associated with this preprint. Click to download.
- supplTable1.xlsx
Supplementary Table 1: List of candidate regulators for the network reverse engineering with ARACNe-AP as derived from analysis by the HOMER algorithm of the GBM ATAC-seq peaks of Corces et al. (2018)
- supplTable1.xlsx
Supplementary Table 1: List of candidate regulators for the network reverse engineering with ARACNe-AP as derived from analysis by the HOMER algorithm of the GBM ATAC-seq peaks of Corces et al. (2018)
- supplTable2.xlsx
Supplementary Table 2: mapping of each TR to a set of putative target genes as derived from Data S2 of Corces et al. (2018)
- supplTable2.xlsx
Supplementary Table 2: mapping of each TR to a set of putative target genes as derived from Data S2 of Corces et al. (2018)
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Posted 09 Dec, 2020
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Associated Publications
Single-cell analyses reveal YAP/TAZ as regulators of stemness and cell plasticity in glioblastoma
by Martina Castellan, Alberto Guarnieri, Atsushi Fujimura, +14
Nature Cancer (07 December, 2020)
An open repository of community-contributed protocols sponsored by Nature Research.
Learn more about Protocol Exchange
Method Article
Michelangelo Cordenonsi
Corresponding Author
Silvio Bicciato
Corresponding Author
Stefano Piccolo
Corresponding Author
Version History
CURRENT STATUS: Posted
Version 1
Posted 09 Dec, 2020
No community comments so far
Metrics
Comments: 0
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
We describe a multistep computational procedure to identify candidate master Transcriptional Regulators (TRs) of glioblastoma (GBM) from glioblastoma single cell gene expression profiles and tissue-specific transcription factors.
This is a list of supplementary files associated with this preprint. Click to download.
- supplTable1.xlsx
Supplementary Table 1: List of candidate regulators for the network reverse engineering with ARACNe-AP as derived from analysis by the HOMER algorithm of the GBM ATAC-seq peaks of Corces et al. (2018)
- supplTable1.xlsx
Supplementary Table 1: List of candidate regulators for the network reverse engineering with ARACNe-AP as derived from analysis by the HOMER algorithm of the GBM ATAC-seq peaks of Corces et al. (2018)
- supplTable2.xlsx
Supplementary Table 2: mapping of each TR to a set of putative target genes as derived from Data S2 of Corces et al. (2018)
- supplTable2.xlsx
Supplementary Table 2: mapping of each TR to a set of putative target genes as derived from Data S2 of Corces et al. (2018)
Loading...
Associated Publications
Single-cell analyses reveal YAP/TAZ as regulators of stemness and cell plasticity in glioblastoma
by Martina Castellan, Alberto Guarnieri, Atsushi Fujimura, +14
Nature Cancer (07 December, 2020)