Biclustering with Missing Data
Convert a biclustermd object to a Biclust object
Make a heatmap of sparse biclustering results
Plot similarity measures between two consecutive biclusterings.
Plot sums of squared errors (SSEs) consecutive biclustering iterations...
biclustermd: A package to bicluster data with missing values
Bicluster data with non-random missing values
Make a binary vector with all values equal to zero except for one
Make a heat map of bicluster cell sizes.
Make a data frame containing the MSE for each bicluster cell
Calculate the sum cluster SSE in each iteration
Get data matrix column names and their corresponding column cluster me...
A generic to gather column names
Get column names in each column cluster
Compare two biclusterings or a pair of partition matrices
Randomly select a column prototype to fill an empty column prototype w...
Randomly select a row prototype to fill an empty row prototype with
Format a partition matrix
Gather a biclustermd object
Compute the Jaccard similarity coefficient for two clusterings
Make a heatmap of cell MSEs
Convert a partition matrix to a vector
Generate an intial, random partition matrix with N objects into K subs...
Create a partition matrix with a partition vector p
Find the index of the first nonzero value in a vector
Print an object of class biclustermd
Reorder a bicluster object for making a heat map
Repeat a biclustering to achieve a minimum SSE solution
Make a heatmap of sparse biclustering results
Get data matrix row names and their corresponding row cluster membersh...
Get row names in each row cluster
Bicluster data over a grid of tuning parameters
Biclustering is a statistical learning technique that simultaneously partitions and clusters rows and columns of a data matrix. Since the solution space of biclustering is in infeasible to completely search with current computational mechanisms, this package uses a greedy heuristic. The algorithm featured in this package is, to the best our knowledge, the first biclustering algorithm to work on data with missing values. Li, J., Reisner, J., Pham, H., Olafsson, S., and Vardeman, S. (2020) Biclustering with Missing Data. Information Sciences, 510, 304–316.