To get the full list of phenotypic variables, please check the menu below. Please click on the scatter plot to access the network view page.
Tool description
PANDA reconstructs a gene regulatory network using TF PPI, TF DNA binding motif as regulation prior, and gene expression samples. PUMA reconstructs a gene regulatory network with miRNA as regulators using gene expression samples and miRNA predicted targets by miRanda or TargetScan as regulation priors. The Regulator-regulator interaction matrix is set to the identity matrix in the algorithm. LIONESS reconstructs patient-specific PANDA networks for each gene expression sample.
BONOBO is a single-sample network inference method, which can be used to estimate patient-specific co-expression matrices, which are then fed as input for PANDA to build patient-specific GRNs. To adapt BONOBO-PANDA networks for patient's sex, and in addition to sample-specific co-expression, PANDA's motif prior networks are constructed for each sex (accounting for sex chromosomes), while the third input (PPI networks) are generic for all patients. To download sample-specific networks, you can check the phenotypic information and select the networks by clinical variables or download all the samples in a single file.
Variable description
PANDA-LIONESS networks are single-sample networks generated by first estimating an aggregate PANDA network then deriving patient-specific LIONESS networks. You can either download all the networks to get a matrix of size the number of samples by the number of edges. The number of edges is 644 * 30243 (number of miRNA/TFs * number of genes) (~ 106). Otherwise, you can specify the sample network to download and you will get a miRNA/TF-by-gene matrix named after the GTEx sample reference.
Sample | Subject | Gender | Age | DTH-HRDY | SMAT-SSCR | SMRIN | SMTS | SMUBRID | Isch. time | Time point | Net. |
---|---|---|---|---|---|---|---|---|---|---|---|
All | All | - | - | - | - | - | - | - | - | - | Edg78 GB |
GTEX-1117F-1326-SM-5EGHH | GTEX-1117F | 2 | 60-69 | 4 | 1 | 5.9 | Adipose Tissue | 10414 | 1277 | Actual Death | |
GTEX-111CU-1026-SM-5EGIL | GTEX-111CU | 1 | 50-59 | 0 | 0 | 7.4 | Adipose Tissue | 10414 | 84 | Actual Death | |
GTEX-111YS-1326-SM-5EGGK | GTEX-111YS | 1 | 60-69 | 0 | 0 | 7.9 | Adipose Tissue | 10414 | 156 | Actual Death | |
GTEX-1122O-0926-SM-5N9C9 | GTEX-1122O | 2 | 60-69 | 0 | 0 | 5.8 | Adipose Tissue | 10414 | 93 | Actual Death | |
GTEX-1128S-0926-SM-5GZZU | GTEX-1128S | 2 | 60-69 | 2 | 1 | 6.5 | Adipose Tissue | 10414 | 844 | Actual Death | |
GTEX-113JC-0726-SM-5GZZR | GTEX-113JC | 2 | 50-59 | 2 | 1 | 7 | Adipose Tissue | 10414 | 639 | Actual Death | |
GTEX-117YW-0826-SM-5H11O | GTEX-117YW | 1 | 50-59 | 3 | 0 | 6.4 | Adipose Tissue | 10414 | 838 | Actual Death | |
GTEX-117YX-0726-SM-5GIET | GTEX-117YX | 1 | 50-59 | 0 | 0 | 8.8 | Adipose Tissue | 10414 | 91 | Actual Death | |
GTEX-1192X-1526-SM-5H11I | GTEX-1192X | 1 | 50-59 | 4 | 1 | 5.8 | Adipose Tissue | 10414 | 910 | Actual Death |