rep_biclustermd: Repeat a biclustering to achieve a minimum SSE solution

Description

Repeat a biclustering to achieve a minimum SSE solution

Usage

rep_biclustermd(
  data,
  nrep = 10,
  parallel = FALSE,
  ncores = 2,
  col_clusters = floor(sqrt(ncol(data))),
  row_clusters = floor(sqrt(nrow(data))),
  miss_val = mean(data, na.rm = TRUE),
  miss_val_sd = 1,
  similarity = "Rand",
  row_min_num = 5,
  col_min_num = 5,
  row_num_to_move = 1,
  col_num_to_move = 1,
  row_shuffles = 1,
  col_shuffles = 1,
  max.iter = 100
)

Arguments

data

Dataset to bicluster. Must to be a data matrix with only numbers and missing values in the data set. It should have row names and column names.

nrep

The number of times to repeat the biclustering. Default 10.

parallel

Logical indicating if the user would like to utilize the foreach parallel backend. Default is FALSE.

ncores

The number of cores to use if parallel computing. Default 2.

col_clusters

The number of clusters to partition the columns into.

row_clusters

The number of clusters to partition the rows into.

miss_val

Value or function to put in empty cells of the prototype matrix. If a value, a random normal variable with sd = miss_val_sd is used each iteration.

miss_val_sd

Standard deviation of the normal distribution miss_val follows if miss_val is a number. By default this equals 1.

similarity

The metric used to compare two successive clusterings. Can be "Rand" (default), "HA" for the Hubert and Arabie adjusted Rand index or "Jaccard". See RRand and for details.

row_min_num

Minimum row prototype size in order to be eligible to be chosen when filling an empty row prototype. Default is 5.

col_min_num

Minimum column prototype size in order to be eligible to be chosen when filling an empty row prototype. Default is 5.

row_num_to_move

Number of rows to remove from the sampled prototype to put in the empty row prototype. Default is 1.

col_num_to_move

Number of columns to remove from the sampled prototype to put in the empty column prototype. Default is 1.

row_shuffles

Number of times to shuffle rows in each iteration. Default is 1.

col_shuffles

Number of times to shuffle columns in each iteration. Default is 1.

max.iter

Maximum number of iterations to let the algorithm run for.

Value

A list of the minimum SSE biclustering, a vector containing the final SSE of each repeat, and the time it took the function to run.

References

Li, J., Reisner, J., Pham, H., Olafsson, S., and Vardeman, S. (2019) Biclustering for Missing Data. Information Sciences, Submitted

Examples

Run this code

# NOT RUN {
data("synthetic")

# 20 repeats without parallelization
repeat_bc <- rep_biclustermd(synthetic, nrep = 20,
                             col_clusters = 3, row_clusters = 2,
                             miss_val = mean(synthetic, na.rm = TRUE),
                             miss_val_sd = sd(synthetic, na.rm = TRUE),
                             col_min_num = 2, row_min_num = 2,
                             col_num_to_move = 1, row_num_to_move = 1,
                             max.iter = 10)
repeat_bc
autoplot(repeat_bc$best_bc)
plot(repeat_bc$rep_sse, type = 'b', pch = 20)
repeat_bc$runtime

# 20 repeats with parallelization over 2 cores
repeat_bc <- rep_biclustermd(synthetic, nrep = 20, parallel = TRUE, ncores = 2,
                             col_clusters = 3, row_clusters = 2,
                             miss_val = mean(synthetic, na.rm = TRUE),
                             miss_val_sd = sd(synthetic, na.rm = TRUE),
                             col_min_num = 2, row_min_num = 2,
                             col_num_to_move = 1, row_num_to_move = 1,
                             max.iter = 10)
repeat_bc$runtime
# }