diffnet: Constructing Differential Networks for Microbiome Data

In stefpeschel/NetCoMi: Network Construction and Comparison for Microbiome Data

Constructing Differential Networks for Microbiome Data

Description

Constructs a differential network for objects of class microNet. Three methods for identifying differentially associated taxa are provided: Fisher's z-test, permutation test, discordant method.

Usage

diffnet(
  x,
  diffMethod = "permute",
  discordThresh = 0.8,
  n1 = NULL,
  n2 = NULL,
  fisherTrans = TRUE,
  nPerm = 1000L,
  permPvalsMethod = "pseudo",
  cores = 1L,
  verbose = TRUE,
  logFile = NULL,
  seed = NULL,
  alpha = 0.05,
  adjust = "lfdr",
  lfdrThresh = 0.2,
  trueNullMethod = "convest",
  pvalsVec = NULL,
  fileLoadAssoPerm = NULL,
  fileLoadCountsPerm = NULL,
  storeAssoPerm = FALSE,
  fileStoreAssoPerm = "assoPerm",
  storeCountsPerm = FALSE,
  fileStoreCountsPerm = c("countsPerm1", "countsPerm2"),
  assoPerm = NULL
)

Arguments

x	an object of class microNet (returned by netConstruct).
diffMethod	character string indicating the method used for determining differential associations. Possible values are "permute" (default) for performing permutation tests according to Gill et al. (2010), "discordant", which calls discordantRun (Siska and Kechris, 2016), and "fisherTest" for Fisher's z-test (Fisher , 1992).
discordThresh	numeric value in [0,1]. Only used for the discordant method. Specifies a threshold for the posterior probability that a pair of taxa is differentially correlated between the groups. Taxa pairs with a posterior above this threshold are connected in the network. Defaults to 0.8.
n1, n2	integer giving the sample sizes of the two data sets used for network construction. Needed for Fisher's z-test if association matrices instead of count matrices were used for network construction.
fisherTrans	logical. If TRUE (default), Fisher-transformed correlations are used for permutation tests.
nPerm	integer giving the number of permutations for the permutation tests. Defaults to 1000L.
permPvalsMethod	character indicating the method used for determining p-values for permutation tests. Currently, "pseudo" is the only available option (see details).
cores	integer indicating the number of CPU cores used for permutation tests. If cores > 1, the tests are performed parallel. Is limited to the number of available CPU cores determined by detectCores. Defaults to 1L (no parallelization).
verbose	logical. If TRUE (default), progress messages are shown.
logFile	a character string defining the name of a log file, which is created when permutation tests are conducted (therein the current iteration numbers are stored). Defaults to NULL so that no file is created.
seed	integer giving a seed for reproducibility of the results.
alpha	numeric value between 0 and 1 giving the significance level. Significantly different correlations are connected in the network. Defaults to 0.05.
adjust	character indicating the method used for multiple testing adjustment for the tests for differentially correlated pairs of taxa. Possible values are "lfdr" (default) for local false discovery rate correction (via fdrtool), "adaptBH" for the adaptive Benjamini-Hochberg method (Benjamini and Hochberg, 2000), or one of the methods provided by p.adjust.
lfdrThresh	defines a threshold for the local fdr if "lfdr" is chosen as method for multiple testing correction. Defaults to 0.2 meaning that correlations with a corresponding local fdr less than or equal to 0.2 are identified as significant.
trueNullMethod	character indicating the method used for estimating the proportion of true null hypotheses from a vector of p-values. Used for the adaptive Benjamini-Hochberg method for multiple testing adjustment (chosen by adjust = "adaptBH"). Accepts the provided options of the method argument of propTrueNull: "convest" (default), "lfdr", "mean", and "hist". Can alternatively be "farco" for the "iterative plug-in method" proposed by Farcomeni (2007).
pvalsVec	vector with p-values used for permutation tests. Can be used for performing another method for multiple testing adjustment without executing the complete permutation process again. See the example.
fileLoadAssoPerm	character giving the name (without extension) or path of the file storing the "permuted" association/dissimilarity matrices that have been exported by setting storeAssoPerm to TRUE. Only used for permutation tests. Set to NULL if no existing associations should be used.
fileLoadCountsPerm	character giving the name (without extension) or path of the file storing the "permuted" count matrices that have been exported by setting storeCountsPerm to TRUE. Only used for permutation tests, and if fileLoadAssoPerm = NULL. Set to NULL if no existing count matrices should be used.
storeAssoPerm	logical indicating whether the association (or dissimilarity) matrices for the permuted data should be stored in a file. The filename is given via fileStoreAssoPerm. If TRUE, the computed "permutation" association/dissimilarity matrices can be reused via fileLoadAssoPerm to save runtime. Defaults to FALSE.
fileStoreAssoPerm	character giving the file name to store a matrix containing a matrix with associations/dissimilarities for the permuted data. Can also be a path.
storeCountsPerm	logical indicating whether the permuted count matrices should be stored in an external file. Defaults to FALSE.
fileStoreCountsPerm	character vector with two elements giving the names of two files storing the permuted count matrices belonging to the two groups.
assoPerm	only needed for output generated with NetCoMi v1.0.1! A list with two elements used for the permutation procedure. Each entry must contain association matrices for "nPerm" permutations. This can be either the "assoPerm" value as part of the output returned by diffnet or netCompare. See the example.

Details

Permutation procedure:
The null hypothesis of these tests is defined as

H_0: a1_ij - a2_ij = 0,

where a1_ij and a2_ij denote the association between taxon i and j in group 1 and 2, respectively.
To generate a sampling distribution of the differences under H_0, the group labels are randomly reassigned to the samples while the group sizes are kept. The associations are then re-estimated for each permuted data set. The p-values are calculated as the proportion of "permutation-differences" being larger than the observed difference. A pseudo-count is added to the numerator and denominator in order to avoid zero p-values. The p-values should be adjusted for multiple testing.

Value

The function returns an object of class diffnet. Depending on the performed test method, the output contains the following elements:

Permutation tests:

diffMat	matrix with absolute differences of associations that are significantly different from zero; optional adjacency matrix
diffAdjustMat	matrix with absolute differences of associations that are significantly different from zero (after multiple testing correction); optional adjacency matrix
pvalsVec	vector with p-values
pAdjustVec	vector with adjusted p-values
pvalsMat	matrix with p-values
pAdjustMat	matrix with adjusted p-values
testStatData	vector with test statistics (absolute differences of associations) for the original data
testStatPerm	matrix with test statistics (absolute differences of associations) for the permuted data
assoMat1,assoMat2	matrices with estimated associations (of the original data)

Discordant:

assoMat1,assoMat2	matrices with estimated correlations
diffMat	adjacency matrix (absolute difference of correlations)
classMat	matrix with classes assigned to a taxa pair
diffProbs	matrix with posterior probabilities that a taxa pair is differentially correlated between the groups

Fisher's z-test:

diffMat	matrix with absolute differences of associations that are significantly different from zero; optional adjacency matrix
diffAdjustMat	matrix with absolute differences of associations that are significantly different from zero (after multiple testing correction); optional adjacency matrix
pvalsVec	vector with p-values
pAdjustVec	vector with adjusted p-values
pvalsMat	matrix with p-values
pAdjustMat	matrix with adjusted p-values
assoMat1,assoMat2	matrices with estimated associations (of the original data)

References

Benjamini Y, Hochberg Y (2000). “On the adaptive control of the false discovery rate in multiple testing with independent statistics.” Journal of Educational and Behavioral Statistics, 25(1), 60–83.

Siska C, Kechris K (2016). discordant: The Discordant Method: A Novel Approach for Differential Correlation. R package version 1.2.0, https://github.com/siskac/discordant.

Farcomeni A (2007). “Some results on the control of the false discovery rate under dependence.” Scandinavian Journal of Statistics, 34(2), 275–297.

Fisher RA (1992). “Statistical methods for research workers.” Breakthroughs in Statistics, 66–70. Springer.

Gill R, Datta S, Datta S (2010). “A statistical framework for differential network analysis from microarray data.” BMC Bioinformatics, 11, 95.

See Also

plot.diffnet

Examples

knitr::opts_chunk$set(fig.width = 10, fig.height = 6)

# Load data sets from American Gut Project (from SpiecEasi package)
data("amgut1.filt")

# Generate a random group vector
set.seed(123456)
group <- sample(1:2, nrow(amgut1.filt), replace = TRUE)

# Network construction:
amgut_net <- netConstruct(amgut1.filt, group = group,
                          measure = "pearson",
                          filtTax = "highestVar",
                          filtTaxPar = list(highestVar = 30),
                          zeroMethod = "pseudoZO", normMethod = "clr")

#---------------------
# Differential network

# Fisher's z-test
amgut_diff1 <- diffnet(amgut_net, diffMethod = "fisherTest")

# Network contains no differentially correlated taxa:
## Not run: 
  plot(amgut_diff1)

## End(Not run)

# Without multiple testing correction (statistically not correct!)
amgut_diff2 <- diffnet(amgut_net, diffMethod = "fisherTest", adjust = "none")
plot(amgut_diff2)

## Not run: 
  # Permutation test (permutation matrices are stored)
  amgut_diff3 <- diffnet(amgut_net, 
                         diffMethod = "permute", 
                         nPerm = 1000L,
                         cores = 4L, 
                         adjust = "lfdr",
                         storeCountsPerm = TRUE,
                         fileStoreCountsPerm = c("countsPerm1", "countsPerm2"),
                         storeAssoPerm = TRUE,
                         fileStoreAssoPerm = "assoPerm",
                         seed = 123456)
  
  # Use the p-values again (different adjustment method possible), but without
  # re-estimating the associations
  amgut_diff4 <- diffnet(amgut_net, 
                         diffMethod = "permute", 
                         nPerm = 1000L,
                         adjust = "none", 
                         pvalsVec = amgut_diff3$pvalsVec)
  x11()
  plot(amgut_diff4)
  
  # Use the permutation associations again (same result as amgut_diff4)
  amgut_diff5 <- diffnet(amgut_net, 
                         diffMethod = "permute", 
                         nPerm = 1000L,
                         adjust = "none",
                         fileLoadAssoPerm = "assoPerm")
  x11()
  plot(amgut_diff5)
  
  # Use the permuted count matrices again (same result as amgut_diff4)
  amgut_diff6 <- diffnet(amgut_net, 
                         diffMethod = "permute", 
                         nPerm = 1000L,
                         adjust = "none",
                         fileLoadCountsPerm = c("countsPerm1", "countsPerm2"),
                         seed = 123456)
  x11()
  plot(amgut_diff6)

## End(Not run)

stefpeschel/NetCoMi documentation built on June 14, 2025, 1:15 p.m.