A multistep computational procedure to identify candidate master Transcriptional Regulators (TRs) of glioblastoma (GBM)
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.
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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)
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)
Supplementary Table 2: mapping of each TR to a set of putative target genes as derived from Data S2 of Corces et al. (2018)
Supplementary Table 2: mapping of each TR to a set of putative target genes as derived from Data S2 of Corces et al. (2018)
Posted 09 Dec, 2020
A multistep computational procedure to identify candidate master Transcriptional Regulators (TRs) of glioblastoma (GBM)
Posted 09 Dec, 2020
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.
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