Package 'netboost'

Package startup: used to fetch installation path of the own package, as required for executing binary programs delivered with it.

Description

Package startup: used to fetch installation path of the own package, as required for executing binary programs delivered with it.

Usage

.onAttach(libname, pkgname)

Arguments

libname	Path to R installation (base package dir)
pkgname	Package name (should be "netboost")

Value

none

Examples

## Not run: nb_example()

---

Calculate network adjacencies for filter

Description

Calculate network adjacencies for filter

Usage

calculate_adjacency(
  datan,
  filter,
  soft_power = 2,
  method = c("pearson", "kendall", "spearman"),
  cores = getOption("mc.cores", 2L)
)

Arguments

datan	Data frame were rows correspond to samples and columns to features.
filter	Filter-Matrix as generated by the nb_filter function.
soft_power	Integer. Exponent of the transformation. Set automatically based on the scale free topology criterion if unspecified.
method	A character string specifying the method to be used for correlation coefficients.
cores	Integer. Amount of CPU cores used (<=1 : sequential).

Value

Vector with adjacencies for the filter

---

Function to calcutate distance

Description

Function to calcutate distance

Usage

cpp_dist_tom(filter, adjacency)

Arguments

filter	Filter matrix
adjacency	Vector

Details

Steps: 1. - Sequential preparation of index and partner caches per value in filter 2. - Parallel calculation of the distances with cached vectors

Value

numeric vector

---

Initialise boosting with chosen accelerator hardware (x86, AVX, FMA)

Description

Initialise boosting with chosen accelerator hardware (x86, AVX, FMA)

Usage

cpp_filter_base(data, stepno = 20L, mode_ = 2L)

Arguments

data	Matrix
stepno	Amount of steps
mode_	Accelerator mode (0: x86, 1: FMA, 2: AVX)

Value

none

---

Boosting cleanup (required to free memory)

Description

Boosting cleanup (required to free memory)

Usage

cpp_filter_end()

Value

none

---

Single boosting step

Description

Single boosting step

Usage

cpp_filter_step(col_y)

Arguments

col_y

Row in data matrix

Details

Must be initialised before using @seefilter_base

Value

integer vector

---

Tree search.

Description

Constraint: IDs 0 <= x (Integer)

Usage

cpp_tree_search(netboost_forest)

Arguments

netboost_forest

Input-matrix (4 columns, ids in colum 0,1,3)

Value

List

---

Module detection for an individual tree

Description

Module detection for an individual tree

Usage

cut_dendro(
  tree_dendro,
  min_cluster_size = 2L,
  datan,
  ME_diss_thres,
  name_of_tree = "",
  qc_plot = TRUE,
  n_pc = 1,
  robust_PCs = FALSE,
  nb_min_varExpl = 0.5,
  method = c("pearson", "kendall", "spearman")
)

Arguments

tree_dendro	List of tree specific objects including dendrogram, tree data and features originating from the tree_dendro function.
min_cluster_size	Integer. The minimum number of features in one module.
datan	Data frame were rows correspond to samples and columns to features.
ME_diss_thres	Numeric. Module Eigengene Dissimilarity Threshold for merging close modules.
name_of_tree	String. Annotating plots and messages.
qc_plot	Logical. Should plots be created?
n_pc	Number of principal components and variance explained entries to be calculated. The number of returned variance explained entries is currently ‘min(n_pc,10)’. If given ‘n_pc’ is greater than 10, a warning is issued.
robust_PCs	Should PCA be calculated on ranked data (Spearman PCA)? Rotations will not correspond to original data if this is applied.
nb_min_varExpl	Minimum proportion of variance explained for returned module eigengenes. The number of PCs is capped at n_pc.
method	A character string specifying the method to be used for correlation coefficients.

Value

List

---

Module detection for the results from a nb_mcupgma call

Description

Module detection for the results from a nb_mcupgma call

Usage

cut_trees(
  trees,
  datan,
  forest,
  min_cluster_size = 2L,
  ME_diss_thres,
  qc_plot = TRUE,
  n_pc = 1,
  robust_PCs = FALSE,
  nb_min_varExpl = 0.5,
  method = c("pearson", "kendall", "spearman")
)

Arguments

trees	List of trees, where one tree is a list of ids and rows
datan	Data frame were rows correspond to samples and columns to features.
forest	Raw dendrogram-matrix as generated by the nb_mcupgma function.
min_cluster_size	Integer. The minimum number of features in one module.
ME_diss_thres	Numeric. Module Eigengene Dissimilarity Threshold for merging close modules.
qc_plot	Logical. Should plots be created?
n_pc	Number of principal components and variance explained entries to be calculated. The number of returned variance explained entries is currently ‘min(n_pc,10)’. If given ‘n_pc’ is greater than 10, a warning is issued.
robust_PCs	Should PCA be calculated on ranked data (Spearman PCA)? Rotations will not correspond to original data if this is applied.
nb_min_varExpl	Minimum proportion of variance explained for returned module eigengenes. The number of PCs is capped at n_pc.
method	A character string specifying the method to be used for correlation coefficients.

Value

List

Examples

data('tcga_aml_meth_rna_chr18',  package='netboost')
 cores <- as.integer(getOption('mc.cores', 2))
 datan <- as.data.frame(scale(tcga_aml_meth_rna_chr18, center=TRUE,
 scale=TRUE))
 filter <- nb_filter(datan=datan, stepno=20L, until=0L, progress=1000L,
 cores=cores,mode=2L)
 dist <- nb_dist(datan=datan, filter=filter, soft_power=3L, cores=cores)
 max_singleton = dim(tcga_aml_meth_rna_chr18)[2]
 forest <- nb_mcupgma(filter=filter, dist=dist, max_singleton=max_singleton,
 cores=cores)
 trees <- tree_search(forest)
 results <- cut_trees(trees=trees,datan=datan, forest=forest,
 min_cluster_size=10L, ME_diss_thres=0.25, qc_plot=TRUE)

---

Execute a program/script from the installed MCUPGMA suite.

Description

Execute a program/script from the installed MCUPGMA suite.

Usage

mcupgma_exec(exec = NULL, ..., console = TRUE)

Arguments

exec	Name of the file of the executable.
...	Arguments passed to mcupgma executable in order required by program
console	Logical. Print output to R console or fetch for return to caller

Value

console=TRUE: exit code (0: no error). console=FALSE: STDOUT/STDERR output

Examples

mcupgma_exec(exec="cluster.pl", "--help")

---

Netboost clustering step

Description

Netboost clustering step

Usage

nb_clust(
  filter,
  dist,
  datan,
  max_singleton = dim(datan)[2],
  min_cluster_size = 2L,
  ME_diss_thres = 0.25,
  cores = getOption("mc.cores", 2L),
  qc_plot = TRUE,
  n_pc = 1,
  robust_PCs = FALSE,
  nb_min_varExpl = 0.5,
  method = c("pearson", "kendall", "spearman")
)

Arguments

filter	Filter-Matrix as generated by the nb_filter function.
dist	Distance-Matrix as generated by the nb_dist function.
datan	Data frame were rows correspond to samples and columns to features.
max_singleton	Integer. The maximal singleton in the clustering. Usually equals the number of features.
min_cluster_size	Integer. The minimum number of features in one module.
ME_diss_thres	Numeric. Module Eigengene Dissimilarity Threshold for merging close modules.
cores	Integer. Amount of CPU cores used (<=1 : sequential)
qc_plot	Logical. Create plot.
n_pc	Number of principal components and variance explained entries to be calculated. The number of returned variance explained entries is currently ‘min(n_pc,10)’. If given ‘n_pc’ is greater than 10, a warning is issued.
robust_PCs	Should PCA be calculated on ranked data (Spearman PCA)? Rotations will not correspond to original data if this is applied.
nb_min_varExpl	Minimum proportion of variance explained for returned module eigengenes. The number of PCs is capped at n_pc.
method	A character string specifying the method to be used for correlation coefficients.

Value

List

Examples

data('tcga_aml_meth_rna_chr18',  package='netboost')
 cores <- as.integer(getOption('mc.cores', 2))
 datan <- as.data.frame(scale(tcga_aml_meth_rna_chr18, center=TRUE, 
 scale=TRUE))
 filter <- nb_filter(datan=datan, stepno=20L, until=0L, progress=1000L,
 cores=cores,mode=2L)
 dist <- nb_dist(datan=datan, filter=filter, soft_power=3L, cores=cores)
 max_singleton = dim(tcga_aml_meth_rna_chr18)[2]
 pdf("test.pdf",width=30)
 sum_res <- nb_clust(filter=filter, dist=dist, datan=datan,
 max_singleton=max_singleton, min_cluster_size=10L, ME_diss_thres=0.25,
 cores=cores, qc_plot=TRUE, n_pc=2L, nb_min_varExpl=0.5)
 dev.off()

---

Calculate distance (external wrapper for internal C++ function) Parallelisation inside C++ program with RcppParallel.

Description

Calculate distance (external wrapper for internal C++ function) Parallelisation inside C++ program with RcppParallel.

Usage

nb_dist(
  filter,
  datan,
  soft_power = 2,
  cores = getOption("mc.cores", 2L),
  verbose = getOption("verbose"),
  method = c("pearson", "kendall", "spearman")
)

Arguments

filter	Filter-Matrix as generated by the nb_filter function.
datan	Data frame were rows correspond to samples and columns to features.
soft_power	Integer. Exponent of the transformation. Set automatically based on the scale free topology criterion if unspecified.
cores	Integer. Amount of CPU cores used (<=1 : sequential).
verbose	Additional diagnostic messages.
method	A character string specifying the method to be used for correlation coefficients.

Value

Vector with distances.

Examples

data('tcga_aml_meth_rna_chr18', package='netboost')
 cores <- as.integer(getOption('mc.cores', 2))
 datan <- as.data.frame(scale(tcga_aml_meth_rna_chr18, center=TRUE,
 scale=TRUE))
 filter <- nb_filter(datan=datan, stepno=20L, until=0L, progress=1000L,
 cores=cores,mode=2L)
 dist <- nb_dist(datan=datan, filter=filter, soft_power=3L, cores=cores)
 summary(dist)

---

Boosting via C++ function. Parallelisation by R-package parallel with forking (overhead of this method does not fall into account as single steps are ~10s).

Description

Parallelisation via multicore (via 'parallel'-package). So *nix only atm.

Usage

nb_filter(
  datan,
  stepno = 20L,
  until = 0L,
  progress = 1000L,
  filter_method = c("spearman", "skip", "kendall", "boosting", "pearson"),
  cores = getOption("mc.cores", 2L),
  mode = 2L,
  verbose = getOption("verbose")
)

Arguments

datan	Data frame were rows correspond to samples and columns to features.
stepno	Integer amount of boosting steps
until	Stop at index/column (if 0: iterate through all columns)
progress	Integer. If > 0, print progress after every X steps (mind: parallel!)
filter_method	The following filtering methods are supported: "boosting" (non-zero coefficients in likelihood based boosting), "skip" (no filter), "kendall" (stats::cor.test), "spearman" (stats::cor.test), "pearson" (stats::cor.test)
cores	Integer. Amount of CPU cores used (<=1 : sequential)
mode	Integer. Mode (0: x86, 1: FMA, 2: AVX). Features are only available if compiled accordingly and available on the hardware.
verbose	Additional diagnostic messages.

Value

matrix n times 2 matrix with the indicies of the n unique entrees of the filter

Examples

data('tcga_aml_meth_rna_chr18',  package='netboost')
 cores <- as.integer(getOption('mc.cores', 2))
 datan <- as.data.frame(scale(tcga_aml_meth_rna_chr18, center=TRUE,
 scale=TRUE))
 filter <- nb_filter(datan=datan, stepno=20L, until=0L, progress=1000L,
 cores=cores,mode=2L)
 head(filter)
 nrow(filter)/(ncol(datan)*(ncol(datan)-1)/2) # proportion of potential undirected edges

---

Calculate dendrogram for a sparse distance matrix (external wrapper MC-UPGMA clustering package Loewenstein et al.

Description

Calculate dendrogram for a sparse distance matrix (external wrapper MC-UPGMA clustering package Loewenstein et al.

Usage

nb_mcupgma(
  filter,
  dist,
  max_singleton,
  cores = getOption("mc.cores", 2L),
  verbose = getOption("verbose")
)

Arguments

filter	Filter-Matrix as generated by the nb_filter function.
dist	Distance-Matrix as generated by the nb_dist function.
max_singleton	Integer The maximal singleton in the clustering. Usually equals the number of features.
cores	Integer Amount of CPU cores used (<=1 : sequential)
verbose	Logical Additional diagnostic messages.

Value

Raw dendrogram to be processed by tree_search and tree_dendro.

Examples

data('tcga_aml_meth_rna_chr18', package='netboost')
   cores <- as.integer(getOption('mc.cores', 2))
   datan <- as.data.frame(scale(tcga_aml_meth_rna_chr18,
   center=TRUE, scale=TRUE))
   filter <- nb_filter(datan=datan, stepno=20L, until=0L,
                       progress=1000L, cores=cores, mode=2L)
   dist <- nb_dist(datan=datan, filter=filter, soft_power=3L, cores=cores)
   max_singleton = dim(tcga_aml_meth_rna_chr18)[2]
   forest <- nb_mcupgma(filter=filter, dist=dist,
                        max_singleton=max_singleton, cores=cores)
 head(forest)

---

Netboost module aggregate extraction.

Description

This is a modification of WGCNA::moduleEigengenes() (version WGCNA_1.66) to include more than the first principal component. For details see WGCNA::moduleEigengenes().

Usage

nb_moduleEigengenes(
  expr,
  colors,
  n_pc = 1,
  align = "along average",
  exclude_grey = FALSE,
  grey = if (is.numeric(colors)) 0 else "grey",
  subHubs = TRUE,
  robust = FALSE,
  trapErrors = FALSE,
  return_valid_only = trapErrors,
  soft_power = 6,
  scale = TRUE,
  verbose = 0,
  indent = 0,
  nb_min_varExpl = 0.5
)

Arguments

expr	Expression data for a single set in the form of a data frame where rows are samples and columns are genes (probes).
colors	A vector of the same length as the number of probes in ‘expr’, giving module color for all probes (genes). Color ‘'grey'’ is reserved for unassigned genes. Expression
n_pc	Number of principal components and variance explained entries to be calculated. The number of returned variance explained entries is currently ‘min(n_pc,10)’. If given ‘n_pc’ is greater than 10, a warning is issued.
align	Controls whether eigengenes, whose orientation is undetermined, should be aligned with average expression (‘align = 'along average'’, the default) or left as they are (‘align = ”’). Any other value will trigger an error.
exclude_grey	Should the improper module consisting of 'grey' genes be excluded from the eigengenes?
grey	Value of ‘colors’ designating the improper module. Note that if ‘colors’ is a factor of numbers, the default value will be incorrect.
subHubs	Controls whether hub genes should be substituted for missing eigengenes. If ‘TRUE’, each missing eigengene (i.e., eigengene whose calculation failed and the error was trapped) will be replaced by a weighted average of the most connected hub genes in the corresponding module. If this calculation fails, or if ‘subHubs==FALSE’, the value of ‘trapErrors’ will determine whether the offending module will be removed or whether the function will issue an error and stop.
robust	Should PCA be calculated on ranked data (Spearman PCA)? Rotations will not correspond to original data if this is applied.
trapErrors	Controls handling of errors from that may arise when there are too many ‘NA’ entries in expression data. If ‘TRUE’, errors from calling these functions will be trapped without abnormal exit. If ‘FALSE’, errors will cause the function to stop. Note, however, that ‘subHubs’ takes precedence in the sense that if ‘subHubs==TRUE’ and ‘trapErrors==FALSE’, an error will be issued only if both the principal component and the hubgene calculations have failed.
return_valid_only	logical; controls whether the returned data frame of module eigengenes contains columns corresponding only to modules whose eigengenes or hub genes could be calculated correctly (‘TRUE’), or whether the data frame should have columns for each of the input color labels (‘FALSE’).
soft_power	The power used in soft-thresholding the adjacency matrix. Only used when the hubgene approximation is necessary because the principal component calculation failed. It must be non-negative. The default value should only be changed if there is a clear indication that it leads to incorrect results.
scale	logical; can be used to turn off scaling of the expression data before calculating the singular value decomposition. The scaling should only be turned off if the data has been scaled previously, in which case the function can run a bit faster. Note however that the function first imputes, then scales the expression data in each module. If the expression contain missing data, scaling outside of the function and letting the function impute missing data may lead to slightly different results than if the data is scaled within the function.
verbose	Controls verbosity of printed progress messages. 0 means silent, up to (about) 5 the verbosity gradually increases.
indent	A single non-negative integer controlling indentation of printed messages. 0 means no indentation, each unit above that adds two spaces.
nb_min_varExpl	Minimum proportion of variance explained for returned module eigengenes. Is capped at n_pc.

Value

eigengenes Module eigengenes in a dataframe, with each column corresponding to one eigengene. The columns are named by the corresponding color with an ‘'ME'’ prepended, e.g., ‘MEturquoise’ etc. If ‘return_valid_only==FALSE’, module eigengenes whose calculation failed have all components set to ‘NA’.

averageExpr If ‘align == 'along average'’, a dataframe containing average normalized expression in each module. The columns are named by the corresponding color with an ‘'AE'’ prepended, e.g., ‘AEturquoise’ etc.

var_explained A dataframe in which each column corresponds to a module, with the component ‘var_explained[PC, module]’ giving the variance of module ‘module’ explained by the principal component no. ‘PC’. The calculation is exact irrespective of the number of computed principal components. At most 10 variance explained values are recorded in this dataframe.

n_pc A copy of the input ‘n_pc’.

validMEs A boolean vector. Each component (corresponding to the columns in ‘data’) is ‘TRUE’ if the corresponding eigengene is valid, and ‘FALSE’ if it is invalid. Valid eigengenes include both principal components and their hubgene approximations. When ‘return_valid_only==FALSE’, by definition all returned eigengenes are valid and the entries of ‘validMEs’ are all ‘TRUE’.

validColors A copy of the input colors with entries corresponding to invalid modules set to ‘grey’ if given, otherwise 0 if ‘colors’ is numeric and 'grey' otherwise.

allOK Boolean flag signalling whether all eigengenes have been calculated correctly, either as principal components or as the hubgene average approximation.

allPC Boolean flag signalling whether all returned eigengenes are principal components.

isPC Boolean vector. Each component (corresponding to the columns in ‘eigengenes’) is ‘TRUE’ if the corresponding eigengene is the first principal component and ‘FALSE’ if it is the hubgene approximation or is invalid.

isHub Boolean vector. Each component (corresponding to the columns in ‘eigengenes’) is ‘TRUE’ if the corresponding eigengene is the hubgene approximation and ‘FALSE’ if it is the first principal component or is invalid.

validAEs Boolean vector. Each component (corresponding to the columns in ‘eigengenes’) is ‘TRUE’ if the corresponding module average expression is valid.

allAEOK Boolean flag signalling whether all returned module average expressions contain valid data. Note that ‘return_valid_only==TRUE’ does not imply ‘allAEOK==TRUE’: some invalid average expressions may be returned if their corresponding eigengenes have been calculated correctly.

---

Plot dendrogram from Netboost output.

Description

Plot dendrogram from Netboost output.

Usage

nb_plot_dendro(
  nb_summary = NULL,
  labels = FALSE,
  main = "",
  colorsrandom = FALSE
)

Arguments

nb_summary	Netboost results as generated by the nb_summary function.
labels	Boolean flag whether labels should be attached to the leafs.
main	Plot title.
colorsrandom	Boolean flag whether module colors should be shuffeled.

Value

invisible null

Examples

data('tcga_aml_meth_rna_chr18',  package='netboost')
results <- netboost(datan = tcga_aml_meth_rna_chr18, stepno = 20L,
soft_power = 3L, min_cluster_size = 10L, n_pc = 2, scale=TRUE,
ME_diss_thres = 0.25, qc_plot = FALSE)
set.seed(1234) # reproducible but shuffled color-module matching
nb_plot_dendro(nb_summary = results, labels = FALSE, main = 'Test',
colorsrandom = TRUE)

---

Summarize results from a forest. Plot trees together.

Description

Summarize results from a forest. Plot trees together.

Usage

nb_summary(clust_res)

Arguments

clust_res

Clustering results from cut_trees call.

Value

List

Examples

data('tcga_aml_meth_rna_chr18',  package='netboost')
 cores <- as.integer(getOption('mc.cores', 2))
 datan <- as.data.frame(scale(tcga_aml_meth_rna_chr18, center=TRUE,
 scale=TRUE))
 filter <- nb_filter(datan=datan, stepno=20L, until=0L, progress=1000L,
 cores=cores,mode=2L)
 dist <- nb_dist(datan=datan, filter=filter, soft_power=3L, cores=cores)
 max_singleton = dim(tcga_aml_meth_rna_chr18)[2]
 forest <- nb_mcupgma(filter=filter,dist=dist,max_singleton=max_singleton,
 cores=cores)
 trees <- tree_search(forest)
 results <- cut_trees(trees=trees,datan=datan, forest=forest,
 min_cluster_size=10L, ME_diss_thres=0.25, qc_plot=FALSE)
 sum_res <- nb_summary(clust_res=results)

---

Transfer of Netboost clustering to new data.

Description

Transfer of Netboost clustering to new data.

Usage

nb_transfer(
  nb_summary = NULL,
  new_data = NULL,
  scale = FALSE,
  robust_PCs = FALSE,
  only_module_membership = FALSE
)

Arguments

nb_summary	Netboost results as generated by the nb_summary function.
new_data	Data frame were rows correspond to samples and columns to features.
scale	Logical. Should data be scaled and centered?
robust_PCs	Should PCA be calculated on ranked data (Spearman PCA)? Rotations will not correspond to original data if this is applied.
only_module_membership	Logical. Should only module memberships be transfered and PCs be newly computed?

Value

List

Examples

data('tcga_aml_meth_rna_chr18',  package='netboost')
results <- netboost(datan = tcga_aml_meth_rna_chr18, stepno = 20L,
    soft_power = 3L, min_cluster_size = 10L, n_pc = 2, scale=TRUE,
    ME_diss_thres = 0.25, qc_plot=FALSE)
ME_transfer <- nb_transfer(nb_summary = results,
    new_data = tcga_aml_meth_rna_chr18,
    scale = TRUE)
all(round(results[["MEs"]], 12) == round(ME_transfer, 12))

---

Netboost clustering.

Description

The Netboost clustering is performed in three subsequent steps. First, a filter of important edges in the network is calculated. Next, pairwise distances are calculated. Last, clustering is performed. For details see Schlosser et al. doi...

Usage

netboost(
  datan = NULL,
  stepno = 20L,
  filter_method = c("boosting", "skip", "kendall", "spearman", "pearson"),
  until = 0L,
  progress = 1000L,
  mode = 2L,
  soft_power = NULL,
  max_singleton = ncol(datan),
  qc_plot = TRUE,
  min_cluster_size = 2L,
  ME_diss_thres = 0.25,
  n_pc = 1,
  robust_PCs = FALSE,
  nb_min_varExpl = 0.5,
  cores = as.integer(getOption("mc.cores", 2)),
  scale = TRUE,
  method = c("pearson", "kendall", "spearman"),
  verbose = getOption("verbose")
)

Arguments

datan	Data frame were rows correspond to samples and columns to features.
stepno	Integer amount of boosting steps applied in the filtering step
filter_method	The following filtering methods are supported: "boosting" (non-zero coefficients in likelihood based boosting), "skip" (no filter), "kendall" (stats::cor.test), "spearman" (stats::cor.test), "pearson" (stats::cor.test)
until	Stop at index/column (if 0: iterate through all columns). For testing purposes in large datasets.
progress	Integer. If > 0, print progress after every X steps ( Progress might not be reported completely accurate due to parallel execution)
mode	Integer. Mode (0: x86, 1: FMA, 2: AVX). Features are only available if compiled accordingly and available on the hardware.
soft_power	Integer. Exponent of the transformation. Set automatically based on the scale free topology criterion if unspecified.
max_singleton	Integer. The maximal singleton in the clustering. Usually equals the number of features.
qc_plot	Logical. Should plots be created?
min_cluster_size	Integer. The minimum number of features in one module.
ME_diss_thres	Numeric. Module Eigengene Dissimilarity Threshold for merging close modules.
n_pc	Number of principal components and variance explained entries to be calculated. The number of returned variance explained entries is currently ‘min(n_pc,10)’. If given ‘n_pc’ is greater than 10, a warning is issued.
robust_PCs	Should PCA be calculated on ranked data (Spearman PCA)? Rotations will not correspond to original data if this is applied.
nb_min_varExpl	Minimum proportion of variance explained for returned module eigengenes. The number of PCs is capped at n_pc.
cores	Integer. Amount of CPU cores used (<=1 : sequential)
scale	Logical. Should data be scaled and centered?
method	A character string specifying the method to be used for correlation coefficients.
verbose	Additional diagnostic messages.

Value

dendros A list of dendrograms. For each fully separate part of the network an individual dendrogram.

names A vector of feature names.

colors A vector of numeric color coding in matching order of names and module eigengene names (color = 3 -> variable in ME3).

MEs Aggregated module measures (Module eigengenes).

var_explained Proportion of variance explained per module eigengene per principal component (max n_pc principal components are listed).

rotation Matrix of variable loadings divided by their singular values. datan

filter Filter-Matrix as generated by the nb_filter function.

Examples

data('tcga_aml_meth_rna_chr18',  package='netboost')
results <- netboost(datan=tcga_aml_meth_rna_chr18, stepno=20L,
   soft_power=3L, min_cluster_size=10L, n_pc=2, scale=TRUE,
   ME_diss_thres=0.25, qc_plot=TRUE)

---

Returns the absolute path to folder with mcupgma executables and scripts.

Description

Returns the absolute path to folder with mcupgma executables and scripts.

Usage

netboostMCUPGMAPath()

Value

Absolute path for "mcupgma" folder

---

Returns the absolute path to "exec" folder in the package.

Description

Returns the absolute path to "exec" folder in the package.

Usage

netboostPackagePath()

Value

Absolute path of installed package

---

Cleans the netboost temporary folder. This can be useful during the session as mcupgma creates vast directory structures (for iterations). Creates the own folder (all netboost temporary data is stored in netboostTmpPath(), which is equal to tempdir()/netboost). Also used for first time setup of folder.

Description

Cleans the netboost temporary folder. This can be useful during the session as mcupgma creates vast directory structures (for iterations). Creates the own folder (all netboost temporary data is stored in netboostTmpPath(), which is equal to tempdir()/netboost). Also used for first time setup of folder.

Usage

netboostTmpCleanup(verbose = FALSE)

Arguments

verbose

Flag verbose

Value

none

---

Returns the absolute path to temporary folder of the package. To change temporary path, use normal R variables (TEMPDIR etc).

Description

Returns the absolute path to temporary folder of the package. To change temporary path, use normal R variables (TEMPDIR etc).

Usage

netboostTmpPath()

Value

Absolute path for "exec" folder

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TCGA RNA and methylation measurement on a subset of chromosome 18 for 80 AML patients.

Description

TCGA RNA and methylation measurement on a subset of chromosome 18 for 80 AML patients.

Usage

tcga_aml_meth_rna_chr18

Format

A data frame with 80 rows (patients) and 500 variables (features).

Source

https://portal.gdc.cancer.gov/

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Calculate the dendrogram for an individual tree

Description

Calculate the dendrogram for an individual tree

Usage

tree_dendro(tree, datan, forest)

Arguments

tree	A list with two elements. ids, which is an integer vector of feature identifiers and rows, which is an integer vector of selected rows in the corresponding forest
datan	Data frame were rows correspond to samples and columns to features.
forest	Raw dendrogram-matrix as generated by the nb_mcupgma function.

Value

List of tree specific objects including dendrogram, tree data and features.

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Extracts independent trees from nb_mcupgma results (external wrapper for internal C++ function)

Description

Extracts independent trees from nb_mcupgma results (external wrapper for internal C++ function)

Usage

tree_search(forest = NULL)

Arguments

forest

Raw dendrogram-matrix as generated by the nb_mcupgma function.

Value

List

Examples

data('tcga_aml_meth_rna_chr18',  package='netboost')
   cores <- as.integer(getOption('mc.cores', 2))
   datan <- as.data.frame(scale(tcga_aml_meth_rna_chr18, center=TRUE,
                                scale=TRUE))
   filter <- nb_filter(datan=datan, stepno=20L, until=0L, progress=1000L,
                       cores=cores,mode=2L)
   dist <- nb_dist(datan=datan, filter=filter, soft_power=3L, cores=cores)
   max_singleton = dim(tcga_aml_meth_rna_chr18)[2]
   forest <- nb_mcupgma(filter=filter, dist=dist,
                        max_singleton=max_singleton, cores=cores)
   trees <- tree_search(forest)
   str(trees[[length(trees)]])

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