Using empirical biological knowledge to infer regulatory networks from multi-omics data

BMC Bioinformatics

Table 2 Summary of all used data sets

Dataset	Investigated gene set	Samples	Details
GSE127960 ZIC5 WT	16GE / 0CNV / 0METH	2	HCT116 colon cancer cell line
GSE127960 ZIC5 KO	16GE / 0CNV / 0METH	4	HCT116 colon cancer cell line
GSE127960 ZIC5 KO	16GE / 0CNV / 0METH	4	ZIC5 knockout
TCGA-COAD MSS	24GE / 24CNV / 4METH	27	Primary tumour stage II/III
TCGA-COAD MSS	24GE / 24CNV / 4METH	27	With MSS phenotype
TCGA-COAD MSI	24GE / 24CNV / 3METH	69	Primary tumour
TCGA-COAD MSI	24GE / 24CNV / 3METH	69	With MSI phenotype
TCGA-COAD NAT	24GE / 24CNV / 16METH	19	Histologically normal tissue
TCGA-COAD NAT	24GE / 24CNV / 16METH	19	Adjacent to the tumour
DREAM4 1-5	10GE / 0CNV / 0METH	1	Five independent in silico networks
DREAM4 1-5	10GE / 0CNV / 0METH	1	To assess the consistency of prediction
TCGA-AML	25GE / 25CNV / 25METH	173	Acute myeloid leukemia
PETACC-3 MSS	39GE / 23CNV / 0METH	176	Primary tumour stage III
PETACC-3 MSS	39GE / 23CNV / 0METH	176	With MSS phenotype

MSS microsatellite stability, MSI microsatellite instability, NAT histologically normal tissue adjacent to the tumour, GE gene expression, CNV copy number variation, METH methylation probe, KO knockout, WT wild-type, AML acute myeloid leukemia

ISSN: 1471-2105