Package 'ccml' reference manual

Title:	Consensus Clustering for Different Sample Coverage Data
Description:	Consensus clustering, also called meta-clustering or cluster ensembles, has been increasingly used in clinical data. Current consensus clustering methods tend to ensemble a number of different clusters from mathematical replicates with similar sample coverage. As the fact of common variety of sample coverage in the real-world data, a new consensus clustering strategy dealing with such biological replicates is required. This is a two-step consensus clustering package, which is used to input multiple predictive labels with different sample coverage (missing labels).
Authors:	Chuanxing Li [aut, cre], Meng Zhou [aut]
Maintainer:	Chuanxing Li <[email protected]>
License:	GPL-2
Version:	1.4.0
Built:	2024-11-12 06:13:32 UTC
Source:	https://github.com/pulmonomics-lab/ccml

Calculate normalized consensus weight(NCW) matrix based on permutation.

Description

Calculate normalized consensus weight(NCW) matrix based on permutation.

Usage

callNCW(
  title = "",
  label,
  nperm = 10,
  ncore = 1,
  seedn = 100,
  stability = TRUE,
  plot = NULL
)
callNCW(
  title = "",
  label,
  nperm = 10,
  ncore = 1,
  seedn = 100,
  stability = TRUE,
  plot = NULL
)

Arguments

`title`	A character value for output directory. Directory is created only if not existed. This title can be an abosulte or relative path.
`label`	A matrix or data frame of input labels, columns=different clustering results and rows are samples.
`nperm`	A integer value of the permutation numbers, or nperm=10(default), which means `nperm`*1000 times of permutation.
`ncore`	A integer value of cores to use, or ncore=1 (default). It's the input core numbers for the parallel computation in this package `parallel`.
`seedn`	A numerical value to set the start random seed for reproducible results, or seedn=100 (default). For every 1000 iteration, the seed will +1 to get repeat results.
`stability`	A logical value. Should estimate the stability of normalized consensus weight based on permutation numbers (default stability=TRUE), or not?
`plot`	character value. NULL(default) - print to screen, 'pdf', 'png', 'pngBMP' for bitmap png, helpful for large datasets, or 'pdf'. Input for `randConsensusMatrix`.

Value

A matrix of normalized consensus weights.

Examples


# load data
data(example_data)
label=example_data

# if plot is not NULL, results will be saved in "result_output" directory
title="result_output"


# run ncw
ncw<-callNCW(title=title,label=label,stability=TRUE,nperm=4,ncore=1)


# load data
data(example_data)
label=example_data

# if plot is not NULL, results will be saved in "result_output" directory
title="result_output"


# run ncw
ncw<-callNCW(title=title,label=label,stability=TRUE,nperm=4,ncore=1)

A two-step consensus clustering inputing multiple predictive labels with different sample coverages (missing labels)

Description

A two-step consensus clustering inputing multiple predictive labels with different sample coverages (missing labels)

Usage

ccml(
  title,
  label,
  output = "rdata",
  nperm = 10,
  ncore = 1,
  seedn = 100,
  stability = TRUE,
  maxK = 15,
  reps = 1000,
  pItem = 0.9,
  plot = NULL,
  clusterAlg = "spectralClusteringAffinity",
  innerLinkage = "complete",
  ...
)
ccml(
  title,
  label,
  output = "rdata",
  nperm = 10,
  ncore = 1,
  seedn = 100,
  stability = TRUE,
  maxK = 15,
  reps = 1000,
  pItem = 0.9,
  plot = NULL,
  clusterAlg = "spectralClusteringAffinity",
  innerLinkage = "complete",
  ...
)

Arguments

`title`	A character value for output directory. Directory is created only if not existed. This title can be an abosulte or relative path. Input for `callNCW, plotCompareCW, ConsensusClusterPlus::ConsensusClusterPlus, ConsensusClusterPlus::calcICL`
`label`	A matrix or data frame of input labels or a character value of input file name, columns=different clustering results and rows are samples. `label` could be import as '.rdata', '.rda', or '.csv'. Input for `callNCW, plotCompareCW`
`output`	A character value for output format, or "rdata"(default) as save to .rdata when both output and plot are not NULL, others will return to workspace.
`nperm`	A integer value of the permutation numbers, or nperm=10(default), which means `nperm`*1000 times of permutation. Input for `callNCW`
`ncore`	A integer value of cores to use, or ncore=1 (default). It's the input core numbers for the parallel computation in this package `parallel`. Input for `callNCW`
`seedn`	A numerical value to set the start random seed for reproducible results, or seedn=100 (default). For every 1000 iteration, the seed will +1 to get repeat results. Input for `callNCW, ConsensusClusterPlus::ConsensusClusterPlus`
`stability`	A logical value. Should estimate the stability of normalized consensus weight based on permutation numbers (default stability=TRUE), or not? Input for `callNCW`
`maxK`	integer value. maximum cluster number to evaluate. Input for `ConsensusClusterPlus::ConsensusClusterPlus` for the consensus clustering based on normalized consensus weights.
`reps`	integer value. number of subsamples. Input for `ConsensusClusterPlus::ConsensusClusterPlus`
`pItem`	numerical value. proportion of items to sample. Input for `ConsensusClusterPlus::ConsensusClusterPlus`
`plot`	character value. NULL(default) - print to screen, 'pdf', 'png', 'pngBMP' for bitmap png, helpful for large datasets. Input for `ConsensusClusterPlus::ConsensusClusterPlus, ConsensusClusterPlus::calcICL,callNCW,plotCompareCW`
`clusterAlg`	character value. cluster algorithm. 'spectralClusteringAffinity' for spectral clustering of similarity/affinity matrix(default), other methods for clustering of distance matrix, 'hc' heirarchical (hclust), 'pam' for paritioning around medoids, 'km' for k-means upon data matrix, 'kmdist' for k-means upon distance matrices (former km option), or a function that returns a clustering. Input for `ConsensusClusterPlus::ConsensusClusterPlus`.
`innerLinkage`	heirarchical linkage method for subsampling, or "complete"(default). Input for `ConsensusClusterPlus::ConsensusClusterPlus`
`...`	Other input arguments for `ConsensusClusterPlus::ConsensusClusterPlus`

Value

A list of three items

ncw - A matrix of normalized consensus weights. Output from callNCW.
fcluster - A list of length maxK. Each element is a list containing consensusMatrix (numerical matrix), consensusTree (hclust), consensusClass (consensus class asssignments). ConsensusClusterPlus also produces images. Output from ConsensusClusterPlus::ConsensusClusterPlus
icl a list of two elements clusterConsensus and itemConsensus corresponding to cluster-consensus and item-consensus. Output from ConsensusClusterPlus::ConsensusClusterPlus

Examples


# load data
data(example_data)
label=example_data

# if plot is not NULL, results will be saved in "result_output" directory
title="result_output"


# not estimate stability of permutation numbers.
res_1=ccml(title=title,label=label,nperm = 3,ncore=1,stability=FALSE,maxK=5,pItem=0.8)

# other methods for clustering of distance matrix
res_2<-ccml(title=title,label=label,nperm = 10,ncore=1,stability=TRUE,maxK=3,
            pItem=0.9,clusterAlg = "hc")

# set the start random seed
res_3<-ccml(title=title,label=label,output=FALSE,nperm = 5,ncore=1,seedn=150,stability=TRUE,maxK=3,
           pItem=0.9)


# load data
data(example_data)
label=example_data

# if plot is not NULL, results will be saved in "result_output" directory
title="result_output"


# not estimate stability of permutation numbers.
res_1=ccml(title=title,label=label,nperm = 3,ncore=1,stability=FALSE,maxK=5,pItem=0.8)

# other methods for clustering of distance matrix
res_2<-ccml(title=title,label=label,nperm = 10,ncore=1,stability=TRUE,maxK=3,
            pItem=0.9,clusterAlg = "hc")

# set the start random seed
res_3<-ccml(title=title,label=label,output=FALSE,nperm = 5,ncore=1,seedn=150,stability=TRUE,maxK=3,
           pItem=0.9)

The input data for example

Description

In this matrix, columns represent the results of different clustering results and rows are samples.

Usage

example_data
example_data

Format

A matrix with 10 rows and 5 columns.

Plot of original consensus weights vs. normalized consensus weights grouping by the number of co-appeared percent of clustering(non-missing).

Description

Plot of original consensus weights vs. normalized consensus weights grouping by the number of co-appeared percent of clustering(non-missing).

Usage

plotCompareCW(title, label, ncw, plot = NULL)
plotCompareCW(title, label, ncw, plot = NULL)

Arguments

`title`	A character value for output directory.
`label`	A matrix or data frame of input labels, columns=different clustering results and rows are samples.
`ncw`	A matrix of normalized consensus weights with sample-by-sample as the order of sample(rows) in `label`.
`plot`	character value. NULL(default) - print to screen, 'pdf', 'png', 'pngBMP' for bitmap png, helpful for large datasets, or 'pdf'.

Value

A ggplot point in PDF format with x-axis: original consensus weights; y-axis: normalized consensus weights; color: percent of co-appeared of clustering; size: number of duplicates sample .

Examples


# load data
data(example_data)
label=example_data

# if plot is not NULL, results will be saved in "result_output" directory
title="result_output"


ncw<-callNCW(title=title,label=label,stability=TRUE)
plotCompareCW(title=title,label=label,ncw=ncw)

# load data
data(example_data)
label=example_data

# if plot is not NULL, results will be saved in "result_output" directory
title="result_output"


ncw<-callNCW(title=title,label=label,stability=TRUE)
plotCompareCW(title=title,label=label,ncw=ncw)

Calculate consensus weight matrix based on the permuted input label matrix. Internal function used by `callNCW`

Description

Calculate consensus weight matrix based on the permuted input label matrix. Internal function used by callNCW

Usage

randConsensusMatrix(
  l.seed,
  l.label = label,
  l.ns = ns,
  l.nc = nc,
  l.nv = nv,
  l.index = index,
  l.pair.ind = pair.ind,
  l.ppath = ppath,
  l.plot = plot
)
randConsensusMatrix(
  l.seed,
  l.label = label,
  l.ns = ns,
  l.nc = nc,
  l.nv = nv,
  l.index = index,
  l.pair.ind = pair.ind,
  l.ppath = ppath,
  l.plot = plot
)

Arguments

`l.seed`	A numerical value to set the random seed for reproducible results, 1000 random label matrix will be generated based on this seed number.
`l.label`	A matrix or data frame of input labels, columns=different clustering results and rows are samples.
`l.ns`	A integer value of number of samples, =`nrow(l.label)`
`l.nc`	A integer value of number of samples, =`ncol(l.label)`
`l.nv`	A integer vector of the number of non missing values for each column in `l.label`
`l.index`	A list of index with length of `l.nc` of non missing values for each column in `l.label`
`l.pair.ind`	A n-by-2 index matrix of array indices of upper triangular of `l.label` with non missing values
`l.ppath`	A character value for output directory.
`l.plot`	character value. NULL(default) - print to screen, 'pdf', 'png', 'pngBMP' for bitmap png, helpful for large datasets, or 'pdf'.

Value

A character of finished seed.

Write a binary file of 1000 random consensus weight matrix(as a vector n-by-1, n= nrow(l.pair.ind)) with the seed l.seed, output file name: paste0("s",l.seed,"rcw").

Perform spectral clustering algorithms for an affinity matrix, using SNFtool::spectralClustering.

Description

Perform spectral clustering algorithms for an affinity matrix, using SNFtool::spectralClustering.

Usage

spectralClusteringAffinity(affi_matrix, k, type = 3)
spectralClusteringAffinity(affi_matrix, k, type = 3)

Arguments

`affi_matrix`	A numerical similarity or affinity matrix.
`k`	A number value of clusters
`type`	The variants of spectral clustering to use. See `SNFtool::spectralClustering`

Value

A vector consisting of cluster labels of each sample.

Package 'ccml'

Help Index

Calculate normalized consensus weight(NCW) matrix based on permutation.

Description

Usage

Arguments

Value

Examples

A two-step consensus clustering inputing multiple predictive labels with different sample coverages (missing labels)

Description

Usage

Arguments

Value

Examples

The input data for example

Description

Usage

Format

Plot of original consensus weights vs. normalized consensus weights grouping by the number of co-appeared percent of clustering(non-missing).

Description

Usage

Arguments

Value

Examples

Calculate consensus weight matrix based on the permuted input label matrix. Internal function used by callNCW

Description

Usage

Arguments

Value

Perform spectral clustering algorithms for an affinity matrix, using SNFtool::spectralClustering.

Description

Usage

Arguments

Value

Calculate consensus weight matrix based on the permuted input label matrix. Internal function used by `callNCW`