| Type: | Package |
| Title: | Analyzing and Visualizing Differential Correlation Networks in Biological Data |
| Version: | 0.4.5 |
| Date: | 2025-06-08 |
| Description: | A method for identifying pattern changes between 2 experimental conditions in correlation networks (e.g., gene co-expression networks), which builds on a commonly used association measure, such as Pearson's correlation coefficient. This package includes functions to calculate correlation matrices for high-dimensional dataset and to test differential correlation, which means the changes in the correlation relationship among variables (e.g., genes and metabolites) between 2 experimental conditions. |
| License: | GPL-3 |
| Depends: | R (≥ 3.5.0), fdrtool, igraph, multtest, pcaMethods |
| Imports: | graphics, grDevices, stats, utils |
| Suggests: | knitr, prettydoc, rmarkdown, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Encoding: | UTF-8 |
| Language: | en-US |
| Config/testthat/edition: | 3 |
| Date/Publication: | 2025-06-08 04:40:02 UTC |
| NeedsCompilation: | no |
| Packaged: | 2025-06-08 04:29:12 UTC; fuku |
| Repository: | CRAN |
| RoxygenNote: | 7.3.2 |
| Author: | Atsushi Fukushima [aut, cre], Kozo Nishida [aut] |
| Maintainer: | Atsushi Fukushima <afukushima@gmail.com> |
Differential correlations in omics datasets
Description
Analyze and visualize differential correlations in biological networks
Author(s)
Maintainer: Atsushi Fukushima afukushima@gmail.com
Authors:
Kozo Nishida kozo.nishida@gmail.com
A metabolite data set from Arabidopsis leaves by GC-TOF/MS
Description
A metabolite data set. The Arabidopsis metabolome of the aerial regions of individual WT plants and the mto1 and tt4 mutants were analyzed by GC-TOF/MS.
Details
50 samples (WT, n = 17; mto1, n = 13; and tt4, n = 20, biological replicates).
A matrix containing 59 metabolites (rows) and 50 observations (columns).
MetaboLights accession no.: MTBLS40
For comparisons with data from roots (Fukushima et al. 2011) we selected 59 commonly-detected metabolites in both datasets using MetMask http://metmask.sourceforge.net.
Author(s)
Atsushi Fukushima
References
Miyako Kusano, Atsushi Fukushima et al. BMC Syst Biol 2007 1:53
A metabolite data set from Arabidopsis roots by GC-TOF/MS
Description
A metabolite data set. The Arabidopsis metabolome of the roots of individual WT plants and the mto1 and tt4 mutants were analyzed by GC-TOF/MS.
Details
53 root samples (WT, n = 17; mto1 n = 16; and tt4, n = 20, biological replicates).
A matrix containing 59 metabolites (rows) and 53 observations (columns).
MetaboLights accession no.: MTBLS45
For comparisons with data from aerial parts (Kusano, Fukushima et al. 2007) we selected 59 commonly-detected metabolites in both datasets using MetMask http://metmask.sourceforge.net.
Author(s)
Atsushi Fukushima
References
Atsushi Fukushima et al. BMC Syst Biol 2011 5:1.
Hierarchical clustering of molecules
Description
Cluster molecules
Usage
cluster.molecule(
data,
method = "pearson",
linkage = "average",
absolute = FALSE
)
Arguments
data |
matrix or data frame |
method |
c("pearson", "spearman", "kendall", "euclidean", "maximum", "manhattan", "canberra", "binary", or "minkowski") |
linkage |
c("average", "ward", "single", "complete", "mcquitty", "median", "centroid") |
absolute |
if TRUE, then 1-|COR| else 1-COR, default is FALSE |
Value
an object of class 'hclust'
Author(s)
Atsushi Fukushima
Examples
cluster.molecule(as.matrix(t(iris[,1:4])), "pearson", "average")
Export differential correlations between two conditions
Description
Export differential correlations of comparison of two correlation matrices
Usage
comp.2.cc.fdr(
output.file = "res.txt",
data1,
data2,
method = "pearson",
p.adjust.methods = "local",
threshold = 0.05,
save = FALSE
)
Arguments
output.file |
can specify file name of the results exported |
data1 |
data matrix under condition 1 |
data2 |
data matrix under condition 2 |
method |
c("pearson", "spearman", "kendall") |
p.adjust.methods |
c("local", holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none") |
threshold |
a threshold of significance levels of differential correlation |
save |
exports the results as a text if TRUE (default: FALSE) |
Value
data.frame
Author(s)
Atsushi Fukushima
References
Fukushima, A. Gene (2013) 518, 209-214
Examples
## Not run:
data(AraMetRoots)
AraMetRoots[AraMetRoots==0] <- NA
AraMetRootsImp <- completeObs(pca(log2(AraMetRoots), nPcs=3, method="ppca"))
comp.2.cc.fdr(output.file="res.txt", AraMetRootsImp[,1:17], method="spearman",
AraMetRootsImp[,18:37], threshold=0.05)
## End(Not run)
Compare two correlation coefficients
Description
Compare two correlation coefficients using Fisher's Z-transformation
Usage
compcorr(n1, r1, n2, r2)
Arguments
n1 |
sample size under condition 1 |
r1 |
correlation coefficient under condition 1 |
n2 |
sample size under condition 2 |
r2 |
correlation coefficient under condition 1 |
Value
list of result (diff and p-value)
Author(s)
Atsushi Fukushima
References
http://www.fon.hum.uva.nl/Service/Statistics/Two_Correlations.html http://support.sas.com/ctx/samples/index.jsp?sid=494 http://support.sas.com/ctx/samples/index.jsp?sid=494
Examples
compcorr(10, 0.1, 10, 0.9)
Additional distance functions correlation distance (1-r)
Description
Additional distance functions Correlation distance (1-r)
Usage
cor.dist(data, methods = "pearson", absolute = FALSE)
Arguments
data |
a data matrix ([data.frame object] row: metabolites, col: samples or replicates) |
methods |
a character string indicating which correlation coefficient is to be calculated. One of "pearson" (default), "spearman", or "kendall" can be abbreviated. |
absolute |
TRUE means that absolute value of the correlation coefficient is used (Default: FALSE). |
Details
These functions were originally from 'hybridHclust' package. We modified the functions slightly. See also the reference manual in detail.
Value
the resulting correlation matrix
Author(s)
Atsushi Fukushima
Examples
cor.dist(as.matrix(t(iris[,1:4])))
Correlation Test
Description
Correlation Test
Usage
cor2.test(n, r, method = c("pearson", "kendall", "spearman"))
Arguments
n |
the number of samples |
r |
the correlation coefficient |
method |
"pearson" and "spearman" can be used. |
Value
p-value
Author(s)
Atsushi Fukushima
References
http://aoki2.si.gunma-u.ac.jp/R/cor2.html
Examples
cor2.test(30, 0.6)
Generating graph from data matrix
Description
Generating graph from data matrix
Usage
generate_g(
data,
method = "pearson",
cor.thr = 0.6,
neg.flag = 1,
node.col = "red",
node.size = 7,
edge.col = "blue",
edge.width = 3
)
Arguments
data |
data matrix or data frame |
method |
c("Pearson", "Spearman", "Kendall") |
cor.thr |
a threshold of correlation coefficient (default: r >= 0.6) |
neg.flag |
flag where uses or not negative correlations |
node.col |
specifies color of nodes in a graph (default: red) |
node.size |
specifies size of nodes in a graph (default: 7) |
edge.col |
specifies color of edges in a graph (default: blue) |
edge.width |
specifies width of edges in a graph (default: 3) |
Value
igraph object
Author(s)
Atsushi Fukushima
Examples
library(igraph)
mat <- matrix(runif(100), nr=10)
rownames(mat) <- as.character(1:10)
generate_g(mat)
Get eigen molecule
Description
Get eigen molecule
Usage
get.eigen.molecule(data, groups, whichgroups = NULL, methods = "svd", n = 10)
Arguments
data |
a data matrix ([data.frame object] row: molecules, col: samples or replicates) |
groups |
a vector of group memberships as returned by cutree |
whichgroups |
a vector of group numbers to examine |
methods |
c("svd", "nipals", "rnipals", "bpca", "ppca"). See also pca() function in pcaMethods package |
n |
top n principal components |
Value
the resulting vector.
Author(s)
Atsushi Fukushima
Examples
library(pcaMethods)
data(golub, package = "multtest")
hc.mol1 <- cluster.molecule(golub[1:100, 1:27], "pearson", "average")
g1 <- cutree(hc.mol1, h=0.6)
res1 <- get.eigen.molecule(golub[1:100,], g1)
Getting graph from eigengene module list
Description
Getting graph from eigengene module list
Usage
get.eigen.molecule.graph(eigen.list, label = "Module")
Arguments
eigen.list |
the resulting vector from get.eigen.molecule |
label |
a label of module extracted (default: "Module") |
Value
igraph object
Author(s)
Atsushi Fukushima
Examples
library(pcaMethods)
library(igraph)
data(golub, package = "multtest")
hc.mol1 <- cluster.molecule(golub[, 1:27], "pearson", "average")
g1 <- cutree(hc.mol1, h=0.4)
res1 <- get.eigen.molecule(golub, g1)
g1.eigen <- get.eigen.molecule.graph(res1)
Getting local false discovery rate (lfdr)
Description
Getting local false discovery rate (lfdr) using 'fdrtool' package
Usage
get.lfdr(r)
Arguments
r |
a vector of correlation coefficient under condition |
Value
list of lfdr
Author(s)
Atsushi Fukushima
References
Strimmer, K. Bioinformatics (2008) 24, 1461-1462
Examples
library("fdrtool")
data(pvalues)
get.lfdr(pvalues)
Get minimum and maximum
Description
Get minimum and maximum
Usage
get.min.max(d)
Arguments
d |
data matrix or data frame |
Value
list object of minimum value or maximum value in a data
Author(s)
Atsushi Fukushima
Examples
get.min.max(iris[,1:2])
A compiled expression data from Golub et al. (1999)
Description
Golub data set from CoXpress paper by Michael Watson.
Details
This is a data.frame of AML/ALL leukaemia microarray study, originally from Golub et al Science 286:531 (1999). It comprises gene expression measurements from 38 tumor mRNA samples, including 27 cases of acute lymphoblastic leukemia (ALL) and 11 cases of acute myeloid leukemia (AML). The HU6800 Affymetrix array, which contains 6,800 probe sets, was used. The dataset was filtered to remove genes with negative values in any sample, resulting in a final set of 2,568 genes.
References
Michael Watson, BMC Bioinformatics 2006 7:509.
Plot cluster molecules
Description
Plot cluster molecules
Usage
plotClusterMolecules(
data,
groups = NULL,
group.no = NULL,
title = NULL,
ylim = NULL,
order = NULL,
scale.center = FALSE,
scale.scale = FALSE,
frame = "white",
col = NULL,
bottom.mar = 5,
xlab = "Samples",
ylab = "Relative abundance"
)
Arguments
data |
data matrix or data frame |
groups |
a vector of group memberships as returned by cutree |
group.no |
the group number to be plotted |
title |
a title for the graph |
ylim |
a vector indicating the upper and lower limit for the y-axis |
order |
whether or not to order the columns of the data matrix |
scale.center |
unless NULL, each row is scaled using scale |
scale.scale |
unless NULL, each row is scaled using scale. |
frame |
the color of the frame that is drawn as the background of the plot |
col |
If NULL, all genes will be drawn in the default color (blue). If the text "random" is given, then a set of colors will be generated by |
bottom.mar |
The size of the bottom margin of the plots as sent in par(mar=c(...)) |
xlab |
a lalel of x axis (defalt: "Samples") |
ylab |
a lalel of y axis (defalt: "Relative abundance") |
Value
a graph
Author(s)
Atsushi Fukushima
References
this function was originally from Watson M (2005) BMC Bioinformatics 7:509
Examples
library(pcaMethods)
data(golub, package = "multtest")
hc.mol1 <- cluster.molecule(golub[, 1:27], "pearson", "average")
g1 <- cutree(hc.mol1, h=0.4)
plotClusterMolecules(golub[,1:27], g1, 3)
Plot DiffCorr group
Description
Plot DiffCorr group
Usage
plotDiffCorrGroup(
data,
groups1 = NULL,
groups2 = NULL,
group1.no = NULL,
group2.no = NULL,
g1,
g2,
g1.order = NULL,
g2.order = NULL,
title1 = NULL,
title2 = NULL,
...
)
Arguments
data |
a data matrix or data frame |
groups1 |
a vector of row group membership as produced by cutree under condition 1 |
groups2 |
a vector of row group membership as produced by cutree under condition 2 |
group1.no |
the group number to be plotted (condition 1) |
group2.no |
the group number to be plotted (condition 2) |
g1 |
a vector describing the columns of the data belonging to condition 1 |
g2 |
a vector describing the columns of the data belonging to condition 2 |
g1.order |
whether or not to order the columns of the data matrix for condition 1. If "average", then the columns are ordered by the average expression value. If the name of a gene (row), then the columns are orderd according to the expression levels of that gene. If NULL, columns remain in their original order. |
g2.order |
See g1.order |
title1 |
A title for the left hand graph |
title2 |
A title for the right hand graph |
... |
other parameters to be passed to this function |
Value
a graph
Author(s)
Atsushi Fukushima
Examples
library(pcaMethods)
data(golub, package = "multtest")
hc.mol1 <- cluster.molecule(golub[, 1:27], "pearson", "average")
hc.mol2 <- cluster.molecule(golub[, 28:38], "pearson", "average")
g1 <- cutree(hc.mol1, h=0.4)
g2 <- cutree(hc.mol2, h=0.4)
##
plotDiffCorrGroup(golub, g1, g2, 21, 24, 1:27, 28:38,
scale.center=TRUE, scale.scale=TRUE,
ylim=c(-5,5))
scalingMethods
Description
The pre-treatment methods
Usage
scalingMethods(
data,
methods = c("auto", "range", "pareto", "vast", "level", "power")
)
Arguments
data |
a data matrix ([data.frame object] row: molecules, col: samples or replicates) |
methods |
the chosen methods. |
Value
the resulting data frame (or scaled data matrix)
Author(s)
Atsushi Fukushima
Examples
scalingMethods(iris[,1:4], "level")
Additional distance functions correlation distance (uncentered)
Description
Additional distance functions correlation distance (uncentered)
Usage
uncent.cor2dist(data, i, absolute = FALSE)
Arguments
data |
a data matrix ([data.frame object] row: metabolites, col: samples or replicates) |
i |
i-th row of data |
absolute |
TRUE means that absolute value of the correlation coefficient is used (Default: FALSE). |
Details
These functions were originally from 'hybridHclust' package. We modified the functions slightly. See also the reference manual in detail.
Value
the resulting correlation matrix
Author(s)
Atsushi Fukushima
Examples
uncent.cor2dist(as.matrix(t(iris[,1:4])), 1) ## NOT RUN!
Calculating all pairwise distances using correlation distance
Description
Calculating all pairwise distances using correlation distance
Usage
uncent.cordist(data, absolute = FALSE)
Arguments
data |
a data matrix ([data.frame object] row: metabolites, col: samples or replicates) |
absolute |
TRUE means that absolute value of the correlation coefficient is used (Default: FALSE). |
Details
These functions were originally from 'hybridHclust' package. We modified the functions slightly. See also the reference manual in detail.
Value
the resulting correlation matrix
Author(s)
Atsushi Fukushima
Examples
uncent.cordist(as.matrix(t(iris[,1:4]))) ## NOT RUN!
Writing modules into a text file
Description
Writing modules into a text file
Usage
write.modules(cutree.res, mod.list, outfile = "module_list.txt")
Arguments
cutree.res |
the result of cutree function |
mod.list |
the result of get.eigen.molecule |
outfile |
file name of output |
Value
a text file
Author(s)
Atsushi Fukushima
Examples
## Not run:
data(golub, package = "multtest")
hc.mol1 <- cluster.molecule(golub[, 1:27], "pearson", "average")
g1 <- cutree(hc.mol1, h=0.4)
res1 <- get.eigen.molecule(golub, g1)
write.modules(g1, res1)
## End(Not run)