Type: Package
Title: Biclustering with Missing Data
Version: 0.2.3
Maintainer: John Reisner <johntreisner@gmail.com>
Description: 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.
URL: https://github.com/jreisner/biclustermd
BugReports: https://github.com/jreisner/biclustermd/issues
Depends: ggplot2 (≥ 3.0.0), R (≥ 3.5.0), tidyr (≥ 0.8.1)
Imports: biclust (≥ 2.0.1), doParallel (≥ 1.0.14), dplyr (≥ 0.7.6), foreach (≥ 1.4.4), magrittr (≥ 1.5), nycflights13 (≥ 1.0.0), phyclust (≥ 0.1-24)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-06-17 14:40:03 UTC; johnreisner
Author: John Reisner [cre, aut, cph], Hieu Pham [ctb, cph], Jing Li [ctb, cph]
Repository: CRAN
Date/Publication: 2021-06-17 15:10:06 UTC

biclustermd: A package to bicluster data with missing values

Description

The main function is biclustermd(). Results can be plotted with autoplot() and as.Biclust() converts results to Biclust objects.

Convert a biclustermd object to a Biclust object

Description

Convert a biclustermd object to a Biclust object

Usage

as.Biclust(object)

Arguments

object

The biclustermd object to convert to a Biclust object

Value

Returns an object of class Biclust.

Examples

data("synthetic")

bc <- biclustermd(synthetic, 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)
bc

as.Biclust(bc)

# biclust::drawHeatmap won't work since it doesn't exclude NAs
## Not run: biclust::drawHeatmap(synthetic, as.Biclust(bc), 6)

# bicluster 6 is in the top right-hand corner here:
autoplot(bc)
# compare with bicust::drawHeatmap2:
biclust::drawHeatmap2(synthetic, as.Biclust(bc), 6)

# bicluster 3 is in the bottom right-hand corner here:
autoplot(bc)
# compare with bicust::drawHeatmap2:
biclust::drawHeatmap2(synthetic, as.Biclust(bc), 3)

Make a heatmap of sparse biclustering results

Description

Make a heatmap of sparse biclustering results

Usage

## S3 method for class 'biclustermd'
autoplot(
  object,
  axis.text = NULL,
  reorder = FALSE,
  transform_colors = FALSE,
  c = 1/6,
  cell_alpha = 1/5,
  col_clusts = NULL,
  row_clusts = NULL,
  ...
)

Arguments

object

An object of class "biclustermd".

axis.text

A character vector specifying for which axes text should be drawn. Can be any of "x", "col" for columns, "y", "row" for rows, or any combination of the four. By default this is NULL; no axis text is drawn.

reorder

A logical. If TRUE, heatmap will be sorted according to the cell-average matrix, A.

transform_colors

If equals TRUE then the data is scaled by c and run through a standard normal cdf before plotting. If FALSE (default), raw data values are used in the heat map.

c

Value to scale the data by before running it through a standard normal CDF. Default is 1/6.

cell_alpha

A scalar defining the transparency of shading over a cell and by default this equals 1/5. The color corresponds to the cell mean.

col_clusts

A vector of column cluster indices to display. If NULL (default), all are displayed.

row_clusts

A vector of row cluster indices to display. If NULL (default), all are displayed.

...

Arguments to be passed to geom_vline() and geom_hline().

Value

An object of class ggplot.

Examples

data("synthetic")

bc <- biclustermd(synthetic, 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)
bc
autoplot(bc)

autoplot(bc, axis.text = c('x', 'row')) +
    ggplot2::scale_fill_distiller(palette = "Spectral", na.value = "white")

# Complete shading
autoplot(bc, axis.text = c('col', 'row'), cell_alpha = 1)

# Transformed values and no shading
autoplot(bc, transform_colors = TRUE, c = 1/20, cell_alpha = 0)

# Focus on row cluster 1 and column cluster 2
autoplot(bc, col_clusts = 2, row_clusts = 1)

Plot similarity measures between two consecutive biclusterings.

Description

Creates a ggplot of the three similarity measures used in biclustermd::bicluster() for both row and column dimensions.

Usage

## S3 method for class 'biclustermd_sim'
autoplot(object, similarity = NULL, facet = TRUE, ncol = NULL, ...)

Arguments

object

Object of class "biclustermd_sim"

similarity

A character vector indicating which similarity measure to plot. Can be any of "Rand", "HA", "Jaccard", or "used". If "used", plot only the measure used as the stopping condition in the algorithm). By default (NULL) all three are plotted. When plotted, the used measure will have an asterisk.

facet

If TRUE (default), each similarity measure will be in its own plot. if FALSE, all three similarity measures for rows and columns are given in one plot.

ncol

If faceting, the number of columns to arrange the plots in.

...

Arguments to pass to ggplot2::geom_point()

Value

A ggplot object.

Examples

data("synthetic")

bc <- biclustermd(synthetic, 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)
bc
autoplot(bc$Similarities, ncol = 1)

Plot sums of squared errors (SSEs) consecutive biclustering iterations.

Description

Creates a ggplot of the decrease in SSE recorded in biclustermd::bicluster().

Usage

## S3 method for class 'biclustermd_sse'
autoplot(object, ...)

Arguments

object

Object of class "biclustermd_sse" with columns "Iteration" and "SSE"

...

Arguments to pass to ggplot2::geom_point()

Value

A ggplot object.

Examples

data("synthetic")

bc <- biclustermd(synthetic, 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)
bc
autoplot(bc$SSE)

Bicluster data with non-random missing values

Description

Bicluster data with non-random missing values

Usage

biclustermd(
  data,
  row_clusters = floor(sqrt(nrow(data))),
  col_clusters = floor(sqrt(ncol(data))),
  miss_val = mean(data, na.rm = TRUE),
  miss_val_sd = 1,
  similarity = "Rand",
  row_min_num = floor(nrow(data)/row_clusters),
  col_min_num = floor(ncol(data)/col_clusters),
  row_num_to_move = 1,
  col_num_to_move = 1,
  row_shuffles = 1,
  col_shuffles = 1,
  max.iter = 100,
  verbose = FALSE
)

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.

row_clusters

The number of clusters to partition the rows into. The default is floor(sqrt(nrow(data))).

col_clusters

The number of clusters to partition the columns into. The default is floor(sqrt(ncol(data))).

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. By default, this equals the mean of data.

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 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 floor(nrow(data) / row_clusters).

col_min_num

Minimum column prototype size in order to be eligible to be chosen when filling an empty row prototype. Default is floor(ncol(data) / col_clusters).

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.

verbose

Logical. If TRUE, will report progress.

Value

A list of class biclustermd:

params

a list of all arguments passed to the function, including defaults.

data

the inputted two way table of data.

P0

the initial column partition matrix.

Q0

the initial row partition matrix.

InitialSSE

the SSE of the original partitioning.

P

the final column partition matrix.

Q

the final row partition matrix.

SSE

a matrix of class biclustermd_sse detailing the SSE recorded at the end of each iteration.

Similarities

a data frame of class biclustermd_sim detailing the value of row and column similarity measures recorded at the end of each iteration. Contains information for all three similarity measures. This carries an attribute "used" which provides the similarity measure used as the stopping condition for the algorithm.

iteration

the number of iterations the algorithm ran for, whether max.iter was reached or convergence was achieved.

A

the final prototype matrix which gives the average of each bicluster.

References

Li, J., Reisner, J., Pham, H., Olafsson, S., and Vardeman, S. (2020) Biclustering with Missing Data. Information Sciences, 510, 304–316.

See Also

rep_biclustermd, tune_biclustermd

Examples

data("synthetic")
# default parameters
bc <- biclustermd(synthetic)
bc
autoplot(bc)

# providing the true number of row and column clusters
bc <- biclustermd(synthetic, col_clusters = 3, row_clusters = 2)
bc
autoplot(bc)

# an example with the nycflights13::flights dataset
library(nycflights13)
data("flights")

library(dplyr)
flights_bcd <- flights %>%
  select(month, dest, arr_delay)

flights_bcd <- flights_bcd %>%
  group_by(month, dest) %>%
  summarise(mean_arr_delay = mean(arr_delay, na.rm = TRUE)) %>%
  spread(dest, mean_arr_delay) %>%
  as.data.frame()

rownames(flights_bcd) <- flights_bcd$month
flights_bcd <- as.matrix(flights_bcd[, -1])

flights_bc <- biclustermd(data = flights_bcd, col_clusters = 6, row_clusters = 4,
                  row_min_num = 3, col_min_num = 5,
                  max.iter = 20, verbose = TRUE)
flights_bc

Make a binary vector with all values equal to zero except for one

Description

Make a binary vector with all values equal to zero except for one

Usage

binary_vector_gen(n, i)

Arguments

n

Desired vector length.

i

Index whose value is one.

Value

A vector

Make a heat map of bicluster cell sizes.

Description

Make a heat map of bicluster cell sizes.

Usage

cell_heatmap(x, ...)

Arguments

x

An object of class biclustermd.

...

Arguments to pass to geom_tile()

Examples

data("synthetic")

bc <- biclustermd(synthetic, 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)

cell_heatmap(bc)

cell_heatmap(bc) + ggplot2::scale_fill_viridis_c()

Make a data frame containing the MSE for each bicluster cell

Description

Make a data frame containing the MSE for each bicluster cell

Usage

cell_mse(x)

Arguments

x

An object of class biclustermd.

Value

A data frame giving the row cluster, column cluster, the number of data points in each row and column cluster, the number of data points missing in the cell, and the cell MSE.

Examples

data("synthetic")
bc <- biclustermd(synthetic, 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)
cell_mse(bc)

Calculate the sum cluster SSE in each iteration

Description

Calculate the sum cluster SSE in each iteration

Usage

cluster_iteration_sum_sse(data, P, Q)

Arguments

data

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

P

Matrix for column prototypes.

Q

Matrix for row prototypes.

Value

The SSE for the parameters specified.

A generic to gather column names

Description

A generic to gather column names

Usage

col.names(x)

Arguments

x

an object to retrieve column names from

Get data matrix column names and their corresponding column cluster membership

Description

Get data matrix column names and their corresponding column cluster membership

Usage

## S3 method for class 'biclustermd'
col.names(x)

Arguments

x

and object of class biclustermd

Value

a data frame with column names of the shuffled matrix and corresponding column cluster names.

Examples

data("synthetic")
# default parameters
bc <- biclustermd(synthetic)
bc
col.names(bc)
# this is a simplified version of the output for gather(bc):
library(dplyr)
gather(bc) %>% distinct(col_cluster, col_name)

Get column names in each column cluster

Description

Get column names in each column cluster

Usage

col_cluster_names(x, data)

Arguments

x

Biclustering object to extract column cluster designation from

data

Data that contains the column names

Value

A data frame with two columns: cluster corresponds to the column cluster and name gives the column names in each cluster.

Examples

data("synthetic")
rownames(synthetic) <- letters[1:nrow(synthetic)]
colnames(synthetic) <- letters[1:ncol(synthetic)]
bc <- biclustermd(synthetic, 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)
bc

Compare two biclusterings or a pair of partition matrices

Description

Compare two biclusterings or a pair of partition matrices

Usage

compare_biclusters(bc1, bc2)

Arguments

bc1

the first biclustering or partition matrix. Must be either of class biclustermd or matrix.

bc2

the second biclustering or partition matrix. Must be either of class biclustermd or matrix.

Value

If comparing a pair of biclusterings, a list containing the column similarity indices and the row similarity indices, in that order. If a pair of matrices, a vector of similarity indices.

Examples

data("synthetic")
bc <- biclustermd(synthetic, col_clusters = 3, row_clusters = 2)
bc2 <- biclustermd(synthetic, col_clusters = 3, row_clusters = 2)

# compare the two biclusterings
compare_biclusters(bc, bc2)

# determine the similarity between initial and final row clusterings
compare_biclusters(bc$Q0, bc$Q)

Randomly select a column prototype to fill an empty column prototype with

Description

Randomly select a column prototype to fill an empty column prototype with

Usage

fill_empties_P(data, obj, col_min_num = 10, col_num_to_move = 5)

Arguments

data

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

obj

A matrix for column clusters, typically named P.

col_min_num

Minimum column prototype size in order to be eligible to be chosen when filling an empty column prototype. Default is 10.

col_num_to_move

Number of columns to remove from the sampled prototype to put in the empty column prototype. Default is 5.

Value

A matrix for column clusters, i.e., a P matrix.

Randomly select a row prototype to fill an empty row prototype with

Description

Randomly select a row prototype to fill an empty row prototype with

Usage

fill_empties_Q(data, obj, row_min_num = 10, row_num_to_move = 5)

Arguments

data

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

obj

A matrix for row clusters, typically named Q

row_min_num

Minimum row prototype size in order to be eligible to be chosen when filling an empty row prototype. Default is 10.

row_num_to_move

Number of rows to remove from the sampled prototype to put in the empty row prototype. Default is 5.

Value

A matrix for row clusters, i.e., a Q matrix.

Format a partition matrix

Description

Formats a partition matrix so that subsets in a partition will be ordered by the value of the smallest in each subset

Usage

format_partition(P1)

Arguments

P1

A partition matrix.

Value

A formatted partition matrix.

Gather a biclustermd object

Description

Gather a biclustermd object

Usage

## S3 method for class 'biclustermd'
gather(
  data,
  key = NULL,
  value = NULL,
  ...,
  na.rm = FALSE,
  convert = FALSE,
  factor_key = FALSE
)

Arguments

data

a biclustermd object to gather.

key

unused; included for consistency with tidyr generic

value

unused; included for consistency with tidyr generic

...

unused; included for consistency with tidyr generic

na.rm

unused; included for consistency with tidyr generic

convert

unused; included for consistency with tidyr generic

factor_key

unused; included for consistency with tidyr generic

Value

A data frame containing the row names and column names of both the two-way table of data biclustered and the cell-average matrix.

Examples

data("synthetic")

bc <- biclustermd(synthetic, 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)
gather(bc)

# bicluster 6 is in the top right-hand corner here:
autoplot(bc)

# bicluster 3 is in the bottom right-hand corner here:
autoplot(bc)

Compute the Jaccard similarity coefficient for two clusterings

Description

Compute the Jaccard similarity coefficient for two clusterings

Usage

jaccard_similarity(clus1, clus2)

Arguments

clus1

vector giving the first set of clusters

clus2

vector giving the second set of clusters

Value

a numeric

References

Milligan, G.W. and Cooper, M. C. (1986) A study of the comparability of external criteria for hierarchical cluster analysis. Multivariate Behavioral Research, 21, 441-458.

Make a heatmap of cell MSEs

Description

Make a heatmap of cell MSEs

Usage

mse_heatmap(x, ...)

Arguments

x

An object of class biclustermd.

...

Arguments to pass to geom_tile()

Value

A ggplot object.

Examples

data("synthetic")
bc <- biclustermd(synthetic, 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)

mse_heatmap(bc)

mse_heatmap(bc) + ggplot2::scale_fill_viridis_c()

Convert a partition matrix to a vector

Description

For each row in a partition matrix, this function gets the column index for which the row is equal to one. That is, for row i, this function returns the index of the row entry that is equal to one.

Usage

part_matrix_to_vector(P0)

Arguments

P0

A partition matrix

Value

An integer vector

Generate an intial, random partition matrix with N objects into K subsets/groups.

Description

This function is used to randomly generate a partition matrix and assign rows or columns to prototypes. Must be the case that N > K.

Usage

partition_gen(N, K)

Arguments

N

Number of objects/rows in a partition matrix

K

Desired number of partitions

Value

A partition matrix.

Create a partition matrix with a partition vector p

Description

Create a partition matrix with a partition vector p

Usage

partition_gen_by_p(N, K, p)

Arguments

N

Rows in a partition matrix

K

Number of prototypes to create

p

Integer vector containing the cluster each row in a partition matrix is to be assigned to.

Value

A partition matrix.

Find the index of the first nonzero value in a vector

Description

Find the index of the first nonzero value in a vector

Usage

position_finder(vec)

Arguments

vec

A binary vector

Value

Position of the first nonzero value in a vector.

Print an object of class biclustermd

Description

Print an object of class biclustermd

Usage

## S3 method for class 'biclustermd'
print(x, ...)

Arguments

x

a biclustermd object.

...

arguments passed to or from other methods

Reorder a bicluster object for making a heat map

Description

Reorder a bicluster object for making a heat map

Usage

reorder_biclust(x)

Arguments

x

A bicluster object.

Value

A list containing the two partition matrices used by gg_bicluster.

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

See Also

biclustermd, tune_biclustermd

Examples

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

Make a heatmap of sparse biclustering results

Description

Make a heatmap of sparse biclustering results

Usage

results_heatmap(
  x,
  reorder = FALSE,
  transform_colors = FALSE,
  c = 1/6,
  cell_alpha = 1/5,
  col_clusts = NULL,
  row_clusts = NULL,
  ...
)

Arguments

x

A biclustermd object.

reorder

A logical. If TRUE, heatmap will be sorted according to the cell-average matrix, A.

transform_colors

If equals TRUE then the data is scaled by c and run through a standard normal cdf before plotting. If FALSE (default), raw data values are used in the heat map.

c

Value to scale the data by before running it through a standard normal CDF. Default is 1/6.

cell_alpha

A scalar defining the transparency of shading over a cell and by default this equals 1/5. The color corresponds to the cell mean.

col_clusts

A vector of column cluster indices to display. If NULL (default), all are displayed.

row_clusts

A vector of row cluster indices to display. If NULL (default), all are displayed.

...

Arguments to be passed to geom_vline() and geom_hline().

Value

An object of class ggplot.

Get data matrix row names and their corresponding row cluster membership

Description

Get data matrix row names and their corresponding row cluster membership

Usage

## S3 method for class 'biclustermd'
row.names(x)

Arguments

x

and object of class biclustermd

Value

a data frame with row names of the shuffled matrix and corresponding row cluster names.

Examples

data("synthetic")
# default parameters
bc <- biclustermd(synthetic)
bc
row.names(bc)
# this is a simplified version of the output for gather(bc):
library(dplyr)
gather(bc) %>% distinct(row_cluster, row_name)

Get row names in each row cluster

Description

Get row names in each row cluster

Usage

row_cluster_names(x, data)

Arguments

x

Biclustering object to extract row cluster designation from

data

Data that contains the row names

Value

A data frame with two columns: cluster corresponds to the row cluster and name gives the row names in each cluster.

Examples

data("synthetic")
rownames(synthetic) <- letters[1:nrow(synthetic)]
colnames(synthetic) <- letters[1:ncol(synthetic)]
bc <- biclustermd(synthetic, 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)
bc

Algorithm run time data

Description

This dataset stems from the R journal article introducing biclustermd to R users. It describes the data attributes and run time for varying data sizes and structures.

Usage

runtimes

Format

An object of class data.frame with 2400 rows and 13 columns.

Details

A data frame of 2400 rows and 13 variables (defined range, inclusive):

combination_no

Unique identifier of a combination of parameters.

rows

Number of rows in the data matrix. (50, 1500)

cols

Number of columns in the data matrix. (50, 1500)

N

Product of the dimensions of the data. (2500, 2250000)

row_clusts

Number of clusters to partition the rows into. (4, 300)

col_clusts

Number of clusters to partition the columns into. (4, 300)

avg_row_clust_size

Average row cluster size. rows / row_clusts

avg_col_clust_size

Average column cluster size. cols / col_clusts

sparsity

Percent of data values which are missing.

user.self

CPU time used executing instructions to calls (from ?proc.time.

sys.self

CPU time used executing calls (from ?proc.time.

elapsed

Amount of time in seconds it took the algorithm to converge.

iterations

Number of iterations to convergence.

Synthetic data for examples.

Description

This simple dataset allows users to use data that are easy to understand while learning biclustermd. This is a matrix with 6 rows and 12 columns. 50% of values are missing.

Usage

synthetic

Format

An object of class matrix with 6 rows and 12 columns.

Bicluster data over a grid of tuning parameters

Description

Bicluster data over a grid of tuning parameters

Usage

tune_biclustermd(
  data,
  nrep = 10,
  parallel = FALSE,
  ncores = 2,
  tune_grid = NULL
)

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 for each set of parameters. 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.

tune_grid

A data frame of parameters to tune over. The column names of this must match the arguments passed to biclustermd().

Value

A list of:

best_combn

The best combination of parameters,

best_bc

The minimum SSE biclustering using the parameters in best_combn,

grid

tune_grid with columns giving the minimum, mean, and standard deviation of the final SSE for each parameter combination, and

runtime

CPU runtime & elapsed time.

References

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

See Also

biclustermd, rep_biclustermd

Examples

library(dplyr)
library(ggplot2)
data("synthetic")
tg <- expand.grid(
miss_val = fivenum(synthetic),
similarity = c("Rand", "HA", "Jaccard"),
col_min_num = 2,
row_min_num = 2,
col_clusters = 3:5,
row_clusters = 2
)
tg

# in parallel: two cores:
tbc <- tune_biclustermd(synthetic, nrep = 2, parallel = TRUE, ncores = 2, tune_grid = tg)
tbc

tbc$grid %>%
  group_by(miss_val, col_clusters) %>%
  summarise(avg_sd = mean(sd_sse)) %>%
  ggplot(aes(miss_val, avg_sd, color = col_clusters, group = col_clusters)) +
  geom_line() +
  geom_point()

tbc <- tune_biclustermd(synthetic, nrep = 2, tune_grid = tg)
tbc

boxplot(tbc$grid$mean_sse ~ tbc$grid$similarity)
boxplot(tbc$grid$sd_sse ~ tbc$grid$similarity)

# nycflights13::flights dataset

library(nycflights13)
data("flights")

library(dplyr)
flights_bcd <- flights %>%
  select(month, dest, arr_delay)

flights_bcd <- flights_bcd %>%
  group_by(month, dest) %>%
  summarise(mean_arr_delay = mean(arr_delay, na.rm = TRUE)) %>%
  spread(dest, mean_arr_delay) %>%
  as.data.frame()

# months as rows
rownames(flights_bcd) <- flights_bcd$month
flights_bcd <- as.matrix(flights_bcd[, -1])

flights_grid <- expand.grid(
row_clusters = 4,
col_clusters = c(6, 9, 12),
miss_val = fivenum(flights_bcd),
similarity = c("Rand", "Jaccard")
)

# RUN TIME: approximately 40 seconds across two cores.
flights_tune <- tune_biclustermd(
  flights_bcd,
  nrep = 10,
  parallel = TRUE,
  ncores = 2,
  tune_grid = flights_grid
)
flights_tune