Liquid Association for Network Dynamics Detection

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Documentation for package ‘LANDD’ version 1.1.0

Help Pages

get.W Record genes W
getgobp Create a table to record Gene Ontology Biological Process mapping results. Every gene W's community takes a row.
graph.kd Find weights based on kernel density on the graph. There are three common ways to invoke 'graph.kd': • 'graph.kd(relate_matrix, graph, smoothing.normalize=c('one'))' • 'graph.kd(relate_matrix, graph, smoothing.normalize=c('squareM'))' • 'graph.kd(relate_matrix, graph, smoothing.normalize=c('none'))' The first method is used when the total weight of all genes z is set to 'one'. In this way, those genes surrounded by more genes z will not take advantages over those surrounded by fewer genes. In contrast, the second method takes the number of genes around into consideration, the result of the first method will multiply the square of the number of genes around. The third method does not normalize the data. Thus genes with more neighbors are more likely to receive higher weights.
lascouting Find the liquid association scouting genes.
normalizeInputMatrix Normalize the input Matrix
simulateLANDD Simulate LANDD
visualize Visualize: Generate a graph which vividly displays the gene X, Y and W.
visualize.community visualize ego gene X, its k step neighbours, and the W gene communities: Generate a graph with different community in different colors. 'visualize.community()'is used to create a graph to display the layout of genes X, X's k-step neighborhood, W and their corresponding community.
xw.distance Create a table to record the distance between gene x and gene w.