Constructing Networks for Microbiome Data

Constructing microbial association networks and dissimilarity based networks (where nodes are subjects) from compositional count data.

Usage

netConstruct(data,
             data2 = NULL,
             dataType = "counts",
             group = NULL,
             matchDesign = NULL,
             taxRank = NULL,
             
             # Association/dissimilarity measure:
             measure = "spieceasi",
             measurePar = NULL,
             
             # Preprocessing:
             jointPrepro = NULL,
             filtTax = "none",
             filtTaxPar = NULL,
             filtSamp = "none",
             filtSampPar = NULL,
             zeroMethod = "none",
             zeroPar = NULL,
             normMethod = "none",
             normPar = NULL,
             
             # Sparsification:
             sparsMethod = "t-test",
             thresh = 0.3,
             alpha = 0.05,
             adjust = "adaptBH",
             trueNullMethod = "convest",
             lfdrThresh = 0.2,
             nboot = 1000L,
             assoBoot = NULL,
             cores = 1L,
             logFile = "log.txt",
             softThreshType = "signed",
             softThreshPower = NULL,
             softThreshCut = 0.8,
             kNeighbor = 3L,
             knnMutual = FALSE,
             
             # Transformation:
             dissFunc = "signed",
             dissFuncPar = NULL,
             simFunc = NULL,
             simFuncPar = NULL,
             scaleDiss = TRUE,
             weighted = TRUE,
             
             # Further arguments:
             sampleSize = NULL,
             verbose = 2,
             seed = NULL
             )

Arguments

data

numeric matrix. Can be a count matrix (rows are samples, columns are OTUs/taxa), a phyloseq object, or an association/dissimilarity matrix (dataType must be set). a second count matrix/phyloseq object or a second association/dissimilarity matrix.

data2

optional numeric matrix used for constructing a second network (belonging to group 2). Can be either a second count matrix/phyloseq object or a second association/dissimilarity matrix.

dataType

character indicating the data type. Defaults to "counts", which means that data (and data2) is a count matrix or object of class phyloseq. Further options are "correlation", "partialCorr" (partial correlation), "condDependence" (conditional dependence), "proportionality" and "dissimilarity".

group

optional binary vector used for splitting the data into two groups. If group is NULL (default) and data2 is not set, a single network is constructed. See 'Details.'

matchDesign

Numeric vector with two elements specifying an optional matched-group (i.e. matched-pair) design, which is used for the permutation tests in netCompare and diffnet. c(1,1) corresponds to a matched-pair design. A 1:2 matching, for instance, is defined by c(1,2), which means that the first sample of group 1 is matched to the first two samples of group 2 and so on. The appropriate order of samples must be ensured. If NULL, the group memberships are shuffled randomly while group sizes identical to the original data set are ensured.

taxRank

character indicating the taxonomic rank at which the network should be constructed. Only used if data (and data 2) is a phyloseq object. The given rank must match one of the column names of the taxonomic table (the @tax_table slot of the phyloseq object). Taxa names of the chosen taxonomic rank must be unique (consider using the function renameTaxa to make them unique). If a phyloseq object is given and taxRank = NULL, the row names of the OTU table are used as node labels.

measure

character specifying the method used for either computing the associations between taxa or dissimilarities between subjects. Ignored if data is not a count matrix (if dataType is not set to "counts"). Available measures are: "pearson", "spearman", "bicor", "sparcc", "cclasso", "ccrepe", "spieceasi" (default), "spring", "gcoda" and "propr" as association measures, and "euclidean", "bray", "kld", "jeffrey", "jsd", "ckld", and "aitchison" as dissimilarity measures. Parameters are set via measurePar.

measurePar

list with parameters passed to the function for computing associations/dissimilarities. See 'Details' for the respective functions. For SpiecEasi or SPRING as association measure, an additional list element "symBetaMode" is accepted to define the "mode" argument of symBeta.

jointPrepro

logical indicating whether data preprocessing (filtering, zero treatment, normalization) should be done for the combined data sets, or each data set separately. Ignored if a single network is constructed. Defaults to TRUE if group is given, and to FALSE if data2 is given. Joint preprocessing is not possible for dissimilarity networks.

filtTax

character indicating how taxa shall be filtered. Possible options are:

"none"

Default. All taxa are kept.

"totalReads"

Keep taxa with a total number of reads of at least x.

"relFreq"

Keep taxa whose number of reads is at least x% of the total number of reads.

"numbSamp"

Keep taxa observed in at least x samples.

"highestVar"

Keep the x taxa with highest variance.

"highestFreq"

Keep the x taxa with highest frequency.

Except for "highestVar" and "highestFreq", different filter methods can be combined. The values x are set via filtTaxPar.

filtTaxPar

list with parameters for the filter methods given by filtTax. Possible list entries are: "totalReads" (int), "relFreq" (value in [0,1]), "numbSamp" (int), "highestVar" (int), "highestFreq" (int).

filtSamp

character indicating how samples shall be filtered. Possible options are:

"none"

Default. All samples are kept.

"totalReads"

Keep samples with a total number of reads of at least x.

"numbTaxa"

Keep samples for which at least x taxa are observed.

"highestFreq"

Keep the x samples with highest frequency.

Except for "highestFreq", different filter methods can be combined. The values x are set via filtSampPar.

filtSampPar

list with parameters for the filter methods given by filtSamp. Possible list entries are: "totalReads" (int), "numbTaxa" (int), "highestFreq" (int).

zeroMethod

character indicating the method used for zero replacement. Possible values are: "none" (default), "pseudo", "pseudoZO", "multRepl", "alrEM", "bayesMult". See 'Details'. The corresponding parameters are set via zeroPar. zeroMethod is ignored if the approach for calculating the associations/dissimilarity includes zero handling. Defaults to "multRepl" or "pseudo" (depending on the expected input of the normalization function and measure) if zero replacement is required.

zeroPar

list with parameters passed to the function for zero replacement (zeroMethod). See the help page of the respective function for details. If zeroMethod is "pseudo" or "pseudoZO", the pseudo count can be specified via zeroPar = list(pseudocount = x) (where x is numeric).

normMethod

character indicating the normalization method (to make counts of different samples comparable). Possible options are: "none" (default), "TSS" (or "fractions"), "CSS", "COM", "rarefy", "VST", "clr", and "mclr". See 'Details'. The corresponding parameters are set via normPar.

normPar

list with parameters passed to the function for normalization (defined by normMethod).

sparsMethod

character indicating the method used for sparsification (selected edges that are connected in the network). Available methods are:

"none"

Leads to a fully connected network

"t-test"

Default. Associations being significantly different from zero are selected using Student's t-test. Significance level and multiple testing adjustment is specified via alpha and adjust. sampleSize must be set if dataType is not "counts".

"bootstrap"

Bootstrap procedure as described in Friedman and Alm (2012). Corresponding arguments are nboot, cores, and logFile. Data type must be "counts".

"threshold"

Selected are taxa pairs with an absolute association/dissimilarity greater than or equal to the threshold defined via thresh.

"softThreshold"

Soft thresholding method according to Zhang and Horvath (2005) available in the WGCNA package. Corresponding arguments are softThreshType, softThreshPower, and softThreshCut.

"knn"

Construct a k-nearest neighbor or mutual k-nearest neighbor graph using nng. Corresponding arguments are kNeighbor, and knnMutual. Available for dissimilarity networks only.

thresh

numeric vector with one or two elements defining the threshold used for sparsification if sparsMethod is set to "threshold". If two networks are constructed and one value is given, it is used for both groups. Defaults to 0.3.

alpha

numeric vector with one or two elements indicating the significance level. Only used if Student's t-test or bootstrap procedure is used as sparsification method. If two networks are constructed and one value is given, it is used for both groups. Defaults to 0.05.

adjust

character indicating the method used for multiple testing adjustment (if Student's t-test or bootstrap procedure is used for edge selection). 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 (see p.adjust.methods().

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).

lfdrThresh

numeric vector with one or two elements defining the threshold(s) for local FDR correction (if adjust = "locfdr"). Defaults to 0.2 meaning that associations with a corresponding local FDR less than or equal to 0.2 are identified as significant. If two networks are constructed and one value is given, it is used for both groups.

nboot

integer indicating the number of bootstrap samples, if bootstrapping is used as sparsification method.

assoBoot

logical or list. Only relevant for bootstrapping. Set to TRUE if a list (assoBoot) with bootstrap association matrices should be returned. Can also be a list with bootstrap association matrices, which are used for sparsification. See the example.

cores

integer indicating the number of CPU cores used for bootstrapping. If cores > 1, bootstrapping is performed parallel. cores is limited to the number of available CPU cores determined by detectCores. Then, core arguments of the function used for association estimation (if provided) should be set to 1.

logFile

character defining a log file to which the iteration numbers are stored if bootstrapping is used for sparsification. The file is written to the current working directory. Defaults to "log.txt". If NULL, no log file is created.

softThreshType

character indicating the method used for transforming correlations into similarities if soft thresholding is used as sparsification method (sparsMethod = "softThreshold"). Possible values are "signed", "unsigned", and "signed hybrid" (according to the available options for the argument type of adjacency from WGCNA package).

softThreshPower

numeric vector with one or two elements defining the power for soft thresholding. Only used if edgeSelect = "softThreshold". If two networks are constructed and one value is given, it is used for both groups. If no power is set, it is computed using pickSoftThreshold, where the argument softThreshCut is needed in addition.

softThreshCut

numeric vector with one or two elements (each between 0 and 1) indicating the desired minimum scale free topology fitting index (corresponds to the argument "RsquaredCut" in pickSoftThreshold). Defaults to 0.8. If two networks are constructed and one value is given, it is used for both groups.

kNeighbor

integer specifying the number of neighbors if the k-nearest neighbor method is used for sparsification. Defaults to 3L.

knnMutual

logical used for k-nearest neighbor sparsification. If TRUE, the neighbors must be mutual. Defaults to FALSE.

dissFunc

method used for transforming associations into dissimilarities. Can be a character with one of the following values: "signed"(default), "unsigned", "signedPos", "TOMdiss". Alternatively, a function is accepted with the association matrix as first argument and optional further arguments, which can be set via dissFuncPar. Ignored for dissimilarity measures. See 'Details.'

dissFuncPar

optional list with parameters if a function is passed to dissFunc.

simFunc

function for transforming dissimilarities into similarities. Defaults to f(x)=1-x for dissimilarities in [0,1], and f(x)=1/(1 + x) otherwise.

simFuncPar

optional list with parameters for the function passed to simFunc.

scaleDiss

logical. Indicates whether dissimilarity values should be scaled to [0,1] by (x - min(dissEst)) / (max(dissEst) - min(dissEst)), where dissEst is the matrix with estimated dissimilarities. Defaults to TRUE.

weighted

logical. If TRUE, similarity values are used as adjacencies. FALSE leads to a binary adjacency matrix whose entries equal 1 for (sparsified) similarity values > 0, and 0 otherwise.

sampleSize

numeric vector with one or two elements giving the number of samples that have been used for computing the association matrix. Only needed if an association matrix is given instead of a count matrix and if, in addition, Student's t-test is used for edge selection. If two networks are constructed and one value is given, it is used for both groups.

verbose

integer indicating the level of verbosity. Possible values: "0": no messages, "1": only important messages, "2"(default): all progress messages, "3" messages returned by external functions are shown in addition. Can also be logical.

seed

integer giving a seed for reproducibility of the results.

Value

An object of class microNet containing the following elements:

edgelist1, edgelist2	Edge list with the following columns: v1, v2: names of adjacent nodes/vertices asso: estimated association (only for association networks) diss: dissimilarity sim: similarity (only for unweighted networks) adja: adjacency (equals similarity for weighted networks)
assoMat1, assoMat2	Sparsified associations (NULL for dissimilarity based networks)
dissMat1, dissMat2	Sparsified dissimilarities (for association networks, these are the sparsified associations transformed into dissimilarities)
simMat1, simMat2	Sparsified similarities
adjaMat1, adjaMat2	Adjacency matrices
assoEst1, assoEst2	Estimated associations (NULL for dissimilarity based networks)
dissEst1, dissEst2	Estimated dissimilarities (NULL for association networks)
dissScale1, dissScale2	Scaled dissimilarities (NULL for association networks)
assoBoot1, assoBoot2	List with association matrices for the bootstrap samples. Returned if bootstrapping is used for sparsification and assoBoot is TRUE.
countMat1, countMat2	Count matrices after filtering but before zero replacement and normalization. Only returned if jointPrepro is FALSE or for a single network.
countsJoint	Joint count matrix after filtering but before zero replacement and normalization. Only returned if jointPrepro is TRUE.
normCounts1, normCounts2	Count matrices after zero handling and normalization
groups	Names of the factor levels according to which the groups have been built
softThreshPower	Determined (or given) power for soft-thresholding.
assoType	Data type (either given by dataType or determined from measure)
twoNets	Indicates whether two networks have been constructed
parameters	Parameters used for network construction

Details

The object returned by netConstruct can either be passed to netAnalyze for network analysis, or to diffnet to construct a differential network from the estimated associations.
The function enables the construction of either a single network or two networks. The latter can be compared using the function netCompare.

The network(s) can either be based on associations (correlation, partial correlation / conditional dependence, proportionality) or dissimilarities. Several measures are available, respectively, to estimate associations or dissimilarities using netConstruct. Alternatively, a pre-generated association or dissimilarity matrix is accepted as input to start the workflow (argument dataType must be set appropriately). Depending on the measure, network nodes are either taxa or subjects: In association-based networks nodes are taxa, whereas in dissimilarity-based networks nodes are subjects.

In order to perform a network comparison, the following options for constructing two networks are available:

1. Passing the combined count matrix to data and a group vector to group (of length nrow(data) for association networks and of length ncol(data) for dissimilarity-based networks).

2. Passing the count data for group 1 to data (matrix or phyloseq object) and the count data for group 2 to data2 (matrix or phyloseq object). For association networks, the column names must match, and for dissimilarity networks the row names.

3. Passing an association/dissimilarity matrix for group 1 to data and an association/dissimilarity matrix for group 2 to data2.

Group labeling:
If two networks are generated, the network belonging to data is always denoted by "group 1" and the network belonging to data2 by "group 2".
If a group vector is used for splitting the data into two groups, the group names are assigned according to the order of group levels. If group contains the levels 0 and 1, for instance, "group 1" is assigned to level 0 and "group 2" is assigned to level 1.
In the network plot, group 1 is shown on the left and group 2 on the right if not defined otherwise (see plot.microNetProps).

Association measures

Argument	Function
"pearson"	cor
"spearman"	cor
"bicor"	bicor
"sparcc"	sparcc
"cclasso"	cclasso
"ccrepe"	ccrepe
"spieceasi"	spiec.easi
"spring"	SPRING
"gcoda"	gcoda
"propr"	propr

Dissimilarity measures

Argument	Function	Measure
"euclidean"	vegdist	Euclidean distance
"bray"	vegdist	Bray-Curtis dissimilarity
"kld"	KLD	Kullback-Leibler divergence
"jeffrey"	KLD	Jeffrey divergence
"jsd"	KLD	Jensen-Shannon divergence
"ckld"	log	Compositional Kullback-Leibler divergence
"aitchison"	vegdist, cenLR	Aitchison distance

Definitions:

Kullback-Leibler divergence:

Since KLD is not symmetric, 0.5 * (KLD(p(x)||p(y)) + KLD(p(y)||p(x))) is returned.

Jeffrey divergence:

Jeff = KLD(p(x)||p(y)) + KLD(p(y)||p(x))

Jensen-Shannon divergence:

JSD = 0.5 KLD(P||M) + 0.5 KLD(Q||M), where P=p(x), Q=p(y), and M=0.5(P+Q).

Compositional Kullback-Leibler divergence:

cKLD(x,y) = p/2 * log(A(x/y) * A(y/x)), where A(x/y) is the arithmetic mean of the vector of ratios x/y.

Aitchison distance:

Euclidean distance of the clr-transformed data.

Methods for zero replacement

Argument	Method	Function
"none"	No zero replacement (only available if no zero replacement is needed for the chosen normalization method and association/dissimilarity measure).	-
"pseudo"	A pseudo count (defined by pseudocount as optional element of zeroPar) is added to all counts. A unit zero count is used by default.	-
"pseudoZO"	A pseudo count (defined by pseudocount as optional element of zeroPar) is added to zero counts only. A unit zero count is used by default.	-
"multRepl"	Multiplicative simple replacement	multRepl
"alrEM"	Modified EM alr-algorithm	lrEM
"bayesMult"	Bayesian-multiplicative replacement	cmultRepl

Normalization methods

Argument	Method	Function
"TSS"	Total sum scaling	t(apply(countMat, 1, function(x) x/sum(x)))
"CSS"	Cumulative sum scaling	cumNormMat
"COM"	Common sum scaling	t(apply(countMat, 1, function(x) x * min(rowSums(countMat)) / sum(x)))
"rarefy"	Rarefying	rrarefy
"VST"	Variance stabilizing transformation	varianceStabilizingTransformation
"clr"	Centered log-ratio transformation	clr
"mclr"	Modified central log ratio transformation	mclr

These methods (except for rarefying) are described in Badri et al.(2020).

Transformation methods
Functions used for transforming associations into dissimilarities:

Argument	Function
"signed"	sqrt(0.5 * (1 - x))
"unsigned"	sqrt(1 - x^2)
"signedPos"	diss <- sqrt(0.5 * (1-x))
	diss[x < 0] <- 0
"TOMdiss"	TOMdist

References

badri2020normalizationNetCoMi

benjamini2000adaptiveNetCoMi

farcomeni2007someNetCoMi

friedman2012inferringNetCoMi

WGCNApackageNetCoMi

zhang2005generalNetCoMi

See also

netAnalyze for analyzing the constructed network(s), netCompare for network comparison, diffnet for constructing differential networks.

Examples

# Load data sets from American Gut Project (from SpiecEasi package)
data("amgut1.filt")
data("amgut2.filt.phy")

# Single network with the following specifications:
# - Association measure: SpiecEasi
# - SpiecEasi parameters are defined via 'measurePar' 
#   (check ?SpiecEasi::spiec.easi for available options)
# - Note: 'rep.num' should be higher for real data sets
# - Taxa filtering: Keep the 50 taxa with highest variance
# - Sample filtering: Keep samples with a total number of reads 
#   of at least 1000

net1 <- netConstruct(amgut2.filt.phy, 
                     measure = "spieceasi",
                     measurePar = list(method = "mb",
                                       pulsar.params = list(rep.num = 10),
                                       symBetaMode = "ave"),
                     filtTax = "highestVar",
                     filtTaxPar = list(highestVar = 50),
                     filtSamp = "totalReads",
                     filtSampPar = list(totalReads = 1000),
                     sparsMethod = "none",
                     normMethod = "none",
                     verbose = 3)
#> Checking input arguments ... 
#> Done.
#> Data filtering ...
#> 35 samples removed.
#> 88 taxa removed.
#> 50 taxa and 261 samples remaining.
#> 
#> Calculate 'spieceasi' associations ... 
#> 
#> Applying data transformations...
#> Selecting model with pulsar using stars...
#> Fitting final estimate with mb...
#> done
#> Done.

# Network analysis (see ?netAnalyze for details)
props1 <- netAnalyze(net1, clustMethod = "cluster_fast_greedy")


# Network plot (see ?plot.microNetProps for details)
plot(props1)


#----------------------------------------------------------------------------
# Same network as before but on genus level and without taxa filtering

amgut.genus.phy <- phyloseq::tax_glom(amgut2.filt.phy, taxrank = "Rank6")

dim(phyloseq::otu_table(amgut.genus.phy))
#> [1]  43 296

# Rename taxonomic table and make Rank6 (genus) unique
amgut.genus.renamed <- renameTaxa(amgut.genus.phy, pat = "<name>", 
                                  substPat = "<name>_<subst_name>(<subst_R>)",
                                  numDupli = "Rank6")
#> Column 7 contains NAs only and is ignored.
                                  
net_genus <- netConstruct(amgut.genus.renamed, 
                          taxRank = "Rank6",
                          measure = "spieceasi",
                          measurePar = list(method = "mb",
                                            pulsar.params = list(rep.num = 10),
                                            symBetaMode = "ave"),
                          filtSamp = "totalReads",
                          filtSampPar = list(totalReads = 1000),
                          sparsMethod = "none",
                          normMethod = "none",
                          verbose = 3)
#> Checking input arguments ... 
#> Done.
#> Data filtering ...
#> 35 samples removed.
#> 43 taxa and 261 samples remaining.
#> 
#> Calculate 'spieceasi' associations ... 
#> 
#> Applying data transformations...
#> Selecting model with pulsar using stars...
#> Fitting final estimate with mb...
#> done
#> Done.

# Network analysis
props_genus <- netAnalyze(net_genus, clustMethod = "cluster_fast_greedy")


# Network plot (with some modifications)
plot(props_genus, 
     shortenLabels = "none",
     labelScale = FALSE,
     cexLabels = 0.8)


#----------------------------------------------------------------------------
# Single network with the following specifications:
# - Association measure: Pearson correlation
# - Taxa filtering: Keep the 50 taxa with highest frequency
# - Sample filtering: Keep samples with a total number of reads of at least 
#  1000 and with at least 10 taxa with a non-zero count
# - Zero replacement: A pseudo count of 0.5 is added to all counts
# - Normalization: clr transformation
# - Sparsification: Threshold = 0.3 
#  (an edge exists between taxa with an estimated association >= 0.3)

net2 <- netConstruct(amgut2.filt.phy, 
                     measure = "pearson",
                     filtTax = "highestFreq",
                     filtTaxPar = list(highestFreq = 50),
                     filtSamp = c("numbTaxa", "totalReads"),
                     filtSampPar = list(totalReads = 1000, numbTaxa = 10),
                     zeroMethod = "pseudo", 
                     zeroPar = list(pseudocount = 0.5),
                     normMethod = "clr",
                     sparsMethod = "threshold",
                     thresh = 0.3,
                     verbose = 3)
#> Checking input arguments ... 
#> Done.
#> Data filtering ...
#> 35 samples removed.
#> 88 taxa removed.
#> 50 taxa and 261 samples remaining.
#> 
#> Zero treatment:
#> Pseudo count of 0.5 added.
#> 
#> Normalization:
#> Execute clr(){SpiecEasi} ... 
#> Done.
#> 
#> Calculate 'pearson' associations ... 
#> Done.
#> 
#> Sparsify associations via 'threshold' ... 
#> Done.

# Network analysis
props2 <- netAnalyze(net2, clustMethod = "cluster_fast_greedy")


plot(props2)


#----------------------------------------------------------------------------
# Constructing and analyzing two networks 
# - A random group variable is used for splitting the data into two groups

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

# Option 1: Use the count matrix and group vector as input:
net3 <- netConstruct(amgut1.filt,  
                     group = group,
                     measure = "pearson",
                     filtTax = "highestVar",
                     filtTaxPar = list(highestVar = 50),
                     filtSamp = "totalReads",
                     filtSampPar = list(totalReads = 1000),
                     zeroMethod = "multRepl", 
                     normMethod = "clr",
                     sparsMethod = "t-test")
#> Checking input arguments ... 
#> Done.
#> Data filtering ...
#> 77 taxa removed.
#> 50 taxa and 289 samples remaining.
#> 
#> Zero treatment:
#> Execute multRepl() ... 
#> Done.
#> 
#> Normalization:
#> Execute clr(){SpiecEasi} ... 
#> Done.
#> 
#> Calculate 'pearson' associations ... 
#> Done.
#> 
#> Calculate associations in group 2 ... 
#> Done.
#> 
#> Sparsify associations via 't-test' ... 
#> 
#> Adjust for multiple testing via 'adaptBH' ... 
#> Done.
#> Done.
#> 
#> Sparsify associations in group 2 ... 
#> 
#> Adjust for multiple testing via 'adaptBH' ... 
#> Done.
#> Done.
                     
# Option 2: Pass the count matrix of group 1 to 'data' 
#           and that of group 2 to 'data2'
# Note: Argument 'jointPrepro' is set to FALSE by default (the data sets 
# are filtered separately and the intersect of filtered taxa is kept, 
# which leads to less than 50 taxa in this example).

amgut1 <- amgut1.filt[group == 1, ]
amgut2 <- amgut1.filt[group == 2, ]

net3 <- netConstruct(data = amgut1, 
                     data2 = amgut2,
                     measure = "pearson",
                     filtTax = "highestVar",
                     filtTaxPar = list(highestVar = 50),
                     filtSamp = "totalReads",
                     filtSampPar = list(totalReads = 1000),
                     zeroMethod = "multRepl", 
                     normMethod = "clr",
                     sparsMethod = "t-test")
#> Checking input arguments ... 
#> Done.
#> Data filtering ...
#> 85 taxa removed in each data set.
#> 42 taxa and 138 samples remaining in group 1.
#> 42 taxa and 151 samples remaining in group 2.
#> 
#> Zero treatment in group 1:
#> Execute multRepl() ... 
#> Done.
#> 
#> Zero treatment in group 2:
#> Execute multRepl() ... 
#> Done.
#> 
#> Normalization in group 1:
#> Execute clr(){SpiecEasi} ... 
#> Done.
#> 
#> Normalization in group 2:
#> Execute clr(){SpiecEasi} ... 
#> Done.
#> 
#> Calculate 'pearson' associations ... 
#> Done.
#> 
#> Calculate associations in group 2 ... 
#> Done.
#> 
#> Sparsify associations via 't-test' ... 
#> 
#> Adjust for multiple testing via 'adaptBH' ... 
#> Done.
#> Done.
#> 
#> Sparsify associations in group 2 ... 
#> 
#> Adjust for multiple testing via 'adaptBH' ... 
#> Done.
#> Done.
                           
# Network analysis
# Note: Please zoom into the GCM plot or open a new window using:
# x11(width = 10, height = 10)
props3 <- netAnalyze(net3, clustMethod = "cluster_fast_greedy")


# Network plot (same layout is used in both groups)
plot(props3, sameLayout = TRUE)


# The two networks can be compared with NetCoMi's function netCompare().

#----------------------------------------------------------------------------
# Example of using the argument "assoBoot"

# This functionality is useful for splitting up a large number of bootstrap 
# replicates and run the bootstrapping procedure iteratively.

niter <- 5
nboot <- 1000
# Overall number of bootstrap replicates: 5000

# Use a different seed for each iteration
seeds <- sample.int(1e8, size = niter)

# List where all bootstrap association matrices are stored
assoList <- list()

for (i in 1:niter) {
  # assoBoot is set to TRUE to return the bootstrap association matrices
  net <- netConstruct(amgut1.filt,
                      filtTax = "highestFreq",
                      filtTaxPar = list(highestFreq = 50),
                      filtSamp = "totalReads",
                      filtSampPar = list(totalReads = 0),
                      measure = "pearson",
                      normMethod = "clr",    
                      zeroMethod = "pseudoZO",
                      sparsMethod = "bootstrap",
                      cores = 1,
                      nboot = nboot,
                      assoBoot = TRUE,
                      verbose = 3,
                      seed = seeds[i])
  
  assoList[(1:nboot) + (i - 1) * nboot] <- net$assoBoot1
}
#> Checking input arguments ... 
#> Done.
#> Data filtering ...
#> 77 taxa removed.
#> 50 taxa and 289 samples remaining.
#> 
#> Zero treatment:
#> Zero counts replaced by 1
#> 
#> Normalization:
#> Execute clr(){SpiecEasi} ... 
#> Done.
#> 
#> Calculate 'pearson' associations ... 
#> Done.
#> 
#> Sparsify associations via 'bootstrap' ... 
#> 
#> 
  |                                                                            
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#> 
#> Attaching package: ‘gtools’
#> The following objects are masked from ‘package:LaplacesDemon’:
#> 
#>     ddirichlet, logit, rdirichlet
#> The following object is masked from ‘package:permute’:
#> 
#>     permute
#> 
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#> 
#> Adjust for multiple testing via 'adaptBH' ... 
#> 
#>  Proportion of true null hypotheses: 0.46
#> Done.
#> Done.
#> Checking input arguments ... 
#> Done.
#> Data filtering ...
#> 77 taxa removed.
#> 50 taxa and 289 samples remaining.
#> 
#> Zero treatment:
#> Zero counts replaced by 1
#> 
#> Normalization:
#> Execute clr(){SpiecEasi} ... 
#> Done.
#> 
#> Calculate 'pearson' associations ... 
#> Done.
#> 
#> Sparsify associations via 'bootstrap' ... 
#> 
#> 
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#> 
#> Adjust for multiple testing via 'adaptBH' ... 
#> 
#>  Proportion of true null hypotheses: 0.46
#> Done.
#> Done.
#> Checking input arguments ... 
#> Done.
#> Data filtering ...
#> 77 taxa removed.
#> 50 taxa and 289 samples remaining.
#> 
#> Zero treatment:
#> Zero counts replaced by 1
#> 
#> Normalization:
#> Execute clr(){SpiecEasi} ... 
#> Done.
#> 
#> Calculate 'pearson' associations ... 
#> Done.
#> 
#> Sparsify associations via 'bootstrap' ... 
#> 
#> 
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#> 
#> Adjust for multiple testing via 'adaptBH' ... 
#> 
#>  Proportion of true null hypotheses: 0.46
#> Done.
#> Done.
#> Checking input arguments ... 
#> Done.
#> Data filtering ...
#> 77 taxa removed.
#> 50 taxa and 289 samples remaining.
#> 
#> Zero treatment:
#> Zero counts replaced by 1
#> 
#> Normalization:
#> Execute clr(){SpiecEasi} ... 
#> Done.
#> 
#> Calculate 'pearson' associations ... 
#> Done.
#> 
#> Sparsify associations via 'bootstrap' ... 
#> 
#> 
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#> 
#> Adjust for multiple testing via 'adaptBH' ... 
#> 
#>  Proportion of true null hypotheses: 0.46
#> Done.
#> Done.
#> Checking input arguments ... 
#> Done.
#> Data filtering ...
#> 77 taxa removed.
#> 50 taxa and 289 samples remaining.
#> 
#> Zero treatment:
#> Zero counts replaced by 1
#> 
#> Normalization:
#> Execute clr(){SpiecEasi} ... 
#> Done.
#> 
#> Calculate 'pearson' associations ... 
#> Done.
#> 
#> Sparsify associations via 'bootstrap' ... 
#> 
#> 
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#> 
#> Adjust for multiple testing via 'adaptBH' ... 
#> 
#>  Proportion of true null hypotheses: 0.46
#> Done.
#> Done.

# Construct the actual network with all 5000 bootstrap association matrices
net_final <- netConstruct(amgut1.filt,
                          filtTax = "highestFreq",
                          filtTaxPar = list(highestFreq = 50),
                          filtSamp = "totalReads",
                          filtSampPar = list(totalReads = 0),
                          measure = "pearson",
                          normMethod = "clr",    
                          zeroMethod = "pseudoZO",
                          sparsMethod = "bootstrap",
                          cores = 1,
                          nboot = nboot * niter,
                          assoBoot = assoList,
                          verbose = 3)
#> Checking input arguments ... 
#> Done.
#> Data filtering ...
#> 77 taxa removed.
#> 50 taxa and 289 samples remaining.
#> 
#> Zero treatment:
#> Zero counts replaced by 1
#> 
#> Normalization:
#> Execute clr(){SpiecEasi} ... 
#> Done.
#> 
#> Calculate 'pearson' associations ... 
#> Done.
#> 
#> Sparsify associations via 'bootstrap' ... 
#> 
#> Adjust for multiple testing via 'adaptBH' ... 
#> 
#>  Proportion of true null hypotheses: 0.46
#> Done.
#> Done.

# Network analysis
props <- netAnalyze(net_final, clustMethod = "cluster_fast_greedy")


# Network plot
plot(props)