Table 1.
Representative studies for the tissue-specific combinatorial gene regulation based on TF cooperations.
Fig 1.
Number of tissue-specific TF cooperations identified by the PC-TraFF+ algorithm with different α-values.
The subtracted background grows with α, thus reducing the number of specific cooperations.
Fig 2.
Flowchart of analysis procedures.
(a) Identification of tissue-specific genes from RNA-seq data and extraction of promoter region of genes. (b) Identification of TFs expressed for each tissue in RNA-seq data. (c) Application of PC-TraFF [1]. (d) Application of PC-TraFF+ [26]. (e) Reconstruction of tissue-specific TF-TF cooperation networks.
Table 2.
Numbers of TFs and tissue specific genes under study.
Fig 3.
Occurrence of TFs present in the tissues.
Number of TFs with an expression value ≥ τ and their overlap between ten tissues represented in matrix layouts using the UpSet technique [35]. Purple circles in the matrix layout are related to the tissues that are part of the intersection. For the sake of clarity not all intersections are displayed.
Table 3.
Numbers of cooperative TF pairs identified for each tissue as significant by PC-TraFF and TSG-set specific by PC-TraFF+.
Fig 4.
Occurrence of TSG-set-specific TF cooperations identified by PC-TraFF+ approach in ten tissues.
Number of TF cooperations and their overlap between tissues represented in matrix layouts using the UpSet technique [35]. Lines with purple circles in the matrix layout show the tissues with overlapping TF cooperations. For the sake of clarity not all intersections are displayed.
Table 4.
Top three TSG-set-specific cooperative TF pairs of each tissue.
Fig 5.
Cooperation networks for the TSG-set-specific TF pairs of (a) lung-, (b) kidney- and (c) liver-tissue.