Fig 1.
Left Panel: Given multi-sample gene expression samples for a tissue, and a human protein interaction network, we derive several expression-based and network-based features for every gene. Right Panel: We compile a set of positive and negative gene-tissue pairs by integrating (i) previously curated disease-tissue map, (ii) OMIM database, (iii) HPO database, and (iv) HPA database. Using the compiled gene-tissue pairs we train a XGBOOST model and apply it to prioritize all unlabeled genes in a tissue.
Table 1.
Biological features and their potential to discriminate positive and background gene-tissue pairs.
P-value: Wilcoxon test significance comparing the feature values for the positive and the negative gene-tissue pairs. auROC: Area under the receiver operating characteristic curve. auPRC: Area under the precision recall curve.
Fig 2.
(A) Positive genes in each tissue is ranked based on a model trained on other tissues. Mean rank of positive genes in each tissue (y-axis) is consistently far higher than the negative genes in that tissue. (x-axis). (B) Similar to (A) but here each gene’s rank in Positive tissue (y-axis) is compared with the same gene’s rank in negative tissues (x-axis); the model training excludes the gene of interest. (C) Each feature’s importance (F score) estimated by the model is shown. (D) ROC for overall cross-validation accuracy for various combinations of features.
Fig 3.
FUGUE validation and application to trait-tissue mapping.
(A) In 6 tissues where the trait is unambiguously mapped to the tissue based on mouse knockout, the tissue-relevant genes are ranked significantly higher (y-axis) than the random expectation. (B) Essential genes in human iPSCs are ranked much higher than random expectation; here the model was trained on GTEx and applied to iPSC, minimizing overfitting. (C) Pipeline to map GWAS genes to specific tissues based on the overlap of GWAS genes with the top ranked genes by FUGUE or by Z-score. (D) A few examples of trait-tissue mapping uniquely revealed by FUGUE. The trait oval indicates the number of GWAS genes, the tissue oval indicates the top 10% TRGs and a couple of top ranked genes associated to the trait are highlighted. These genes have direct literature evidence for involvement with the trait.
Fig 4.
Top TRTFs capture developmental as well as structural relations among several mature tissues.
(A) Cluster dendrogram of tissues belonging to the endodermal (purple), mesodermal (green) and the ectodermal (blue) lineages. (B) Cluster dendrogram of primary and secondary reproductive organs and adipose tissue. As an illustration, we have shown for each tissue, two top TFs previously shown to be involved in the tissue development or homeostasis.
Fig 5.
TRGs cluster both on the linear as well as in the 3D genome and are dysregulated in tissue specific cancer.
(A) Z-score distribution for fraction of TRGs contained in clusters (size > = 5 TRGs) compared to a null distribution along the linear genome (top 5% TRGs considered; 5 or more genes in a cluster no more than 500kb apart) (B) Z-score distribution of number of TADs containing more than 5% of TRGs (C) Odds ratio distribution for TRGs enrichment in dysregulated genes in cancer types. Red line denotes the expected mean.