The Regulatory Peaks Search Module allows users to detect all potential regulatory DNA regions for a specific gene . When given an input gene and search scale , Linkage automatically performs canonical correlation analysis between the quantitative expression level of the input gene and each quantitative chromatin accessibility measure in the region across all samples. Users can easily adjust the search scale and correlation analysis algorithm ( spearman / pearson / kendall ). Then, all the statistically significant results are listed in the Potential Cis-regulatory Regions panel . By clicking on a specific entry of this panel, users can view the scatter plot of quantitative chromatin accessibility and gene expression from the Correlation Plot panel . The corresponding rho and FDR for the correlation analysis will also be shown on the scatter plot.

The Regulatory Peaks Visualization Module allows users to visualize the coverage of mapped ATAC-seq reads around a given specific regulatory peak, as well as the corresponding quantitative expression of the target gene of this regulatory peak . Users initially select a regulatory peak that is obtained from the Regulatory Peaks Search Module. Linkage then categorizes samples into five groups based on the quantitative chromatin accessibility of the specific regulatory peak, ranging from low to high for each sample. The coverage track of mapped ATAC-seq reads and the expression boxplot of the target gene for each group will be shown simultaneously.

The Regulatory Peaks Annotation Module allows users to visualize the annotation of the predicational regulatory peaks for genes that are given in the previous module . Once users click 'Annotate Regulatory Peaks' , Linkage will perform the annotation of all predicational regulatory peaks in terms of genomic location features, including whether the peaks are in the TSS, Exon, 5' UTR, 3' UTR, Intronic, Intergenic, and the position and strand information of the nearest gene of the peaks. The corresponding information is deposited in the Regulatory Peaks Annotation Table . Meanwhile, Linkage also produces the upsetplot for effectively visualizing the overlaps and distribution in the annotation of peaks.

The Cis-Regulatory Elements Detection Module supports users to visualize the enriched TFBS within potential regulatory peaks . By clicking on a specific regulatory peak, users can view the location and binding score information of each enriched TFBS of this DNA region from the Motif Scanning Table . Once users select one TFBS of this table, the corresponding sequence logo of this CREs will appear in the Sequence-logo Plot panel .

The Gene Regulatory Network Module helps users visualize GRNs whose nodes are represented by genes and their corresponding CREs that were inferred from previous analysis of Linkage . First, users input a list of interested genes and TFs which was obtained from the previous analysis. Then users can customize a series of parameters in association with building the GRN, including types of gene symbols, calculation methods, and thresholds of interactions between the nodes (edges of the GRNs). Once users click the 'Build the GRN' button, Linkage will perform a canonical correlation analysis of the quantitative expression level between each interested gene and their potential CREs . The significant calculation results of correlation analysis are shown in the Gene-TF Table panel . Meanwhile, Linkage produces the corresponding informatic and interactive GRNs. Users can further easily change network layouts, select subnetworks, and save the GRNs as spreadsheets with interaction score or plots.

The Pathway Enrichment Module supports users to visualize tabular and graphical pathway enrichment results of the interested genes/TFs that were previously produced from other modules of Linkage. The pathway enrichment analysis can link them with underlying molecular pathways and functional categories such as gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) . Within this module, users can input a list of interested genes/TFs and set four key parameters (i.e., adjusted p-value cutoff, q-value cutoff, minimal size of annotated genes for testing, and maximal size of annotated genes for testing) for pathway enrichment analysis. Then once users click the 'Build the Pathway' button, Linkage automatically performs GO and KEGG enrichment analysis. The corresponding enrichment categories will be returned in the GO/KEGG Enrichment Table . Finally, Linkage implements several different visualization methods to interpret the functional results in the GO/KEGG Enrichment Table from multiple perspectives.