stefpeschel/NetCoMi
Network Construction and Comparison for Microbiome Data

Package index
Search the stefpeschel/NetCoMi package
Functions
102
Source code
55
Man pages
27
Home
/
GitHub
/
stefpeschel/NetCoMi
/

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

knitr::opts_chunk$set(fig.path="man/figures/readme/")

NetCoMi

DOI install with bioconda

NetCoMi (Network Construction and Comparison for Microbiome Data) provides functionality for constructing, analyzing, and comparing networks suitable for the application on microbial compositional data. The R package implements the workflow proposed in

Stefanie Peschel, Christian L Müller, Erika von Mutius, Anne-Laure Boulesteix, Martin Depner (2020). NetCoMi: network construction and comparison for microbiome data in R. Briefings in Bioinformatics, bbaa290. https://doi.org/10.1093/bib/bbaa290.

NetCoMi allows its users to construct, analyze, and compare microbial association or dissimilarity networks in a fast and reproducible manner. Starting with a read count matrix originating from a sequencing process, the pipeline includes a wide range of existing methods for treating zeros in the data, normalization, computing microbial associations or dissimilarities, and sparsifying the resulting association/ dissimilarity matrix. These methods can be combined in a modular fashion to generate microbial networks. NetCoMi can either be used for constructing, analyzing and visualizing a single network, or for comparing two networks in a graphical as well as a quantitative manner, including statistical tests. The package furthermore offers functionality for constructing differential networks, where only differentially associated taxa are connected.

library(phyloseq)
library(NetCoMi)
data("soilrep")

soil_warm_yes <- phyloseq::subset_samples(soilrep, warmed == "yes")
soil_warm_no  <- phyloseq::subset_samples(soilrep, warmed == "no")

net_seas_p <- netConstruct(soil_warm_yes, soil_warm_no,
                           filtTax = "highestVar",
                           filtTaxPar = list(highestVar = 500),
                           zeroMethod = "pseudo",
                           normMethod = "clr",
                           measure = "pearson",
                           verbose = 0)

netprops1 <- netAnalyze(net_seas_p, clustMethod = "cluster_fast_greedy",
                        gcmHeat = FALSE)

nclust <- as.numeric(max(names(table(netprops1$clustering$clust1))))

col <- c(topo.colors(nclust), rainbow(6))

plot(netprops1, 
     sameLayout = TRUE, 
     layoutGroup = "union", 
     colorVec = col,
     borderCol = "gray40", 
     nodeSize = "degree", 
     cexNodes = 0.9, 
     nodeSizeSpread = 3, 
     edgeTranspLow = 80, 
     edgeTranspHigh = 50,
     groupNames = c("Warming", "Non-warming"), 
     showTitle = TRUE, 
     cexTitle = 2.8,
     mar = c(1,1,3,1), 
     repulsion = 0.9, 
     labels = FALSE, 
     rmSingles = "inboth",
     nodeFilter = "clustMin", 
     nodeFilterPar = 10, 
     nodeTransp = 50, 
     hubTransp = 30)

Exemplary network comparison using soil microbiome data ('soilrep' data from phyloseq package). Microbial associations are compared between the two experimantal settings 'warming' and 'non-warming' using the same layout in both groups.

Table of Contents

  1. Methods included in NetCoMi
  2. Installation
  3. Development version
  4. Usage
  5. References

Methods included in NetCoMi

Here is an overview of methods available for network construction, together with some information on their implementation in R:

Association measures:

Dissimilarity measures:

Methods for zero replacement:

Normalization methods:

TSS, CSS, COM, VST, and the clr transformation are described in [@badri2020shrinkage].

Installation

# Required packages
install.packages("devtools")
install.packages("BiocManager")

# Install NetCoMi
devtools::install_github("stefpeschel/NetCoMi", 
                         dependencies = c("Depends", "Imports", "LinkingTo"),
                         repos = c("https://cloud.r-project.org/",
                                   BiocManager::repositories()))

If there are any errors during installation, please install the missing dependencies manually.

In particular the automatic installation of SPRING and SpiecEasi (only available on GitHub) does sometimes not work. These packages can be installed as follows (the order is important because SPRING depends on SpiecEasi):

devtools::install_github("zdk123/SpiecEasi")
devtools::install_github("GraceYoon/SPRING")

Packages that are optionally required in certain settings are not installed together with NetCoMi. These can be installed automatically using:

installNetCoMiPacks()

# Please check:
?installNetCoMiPacks()

If not installed via installNetCoMiPacks(), the required package is installed by the respective NetCoMi function when needed.

Bioconda

Thanks to daydream-boost, NetCoMi can also be installed from conda bioconda channel with

# You can install an individual environment firstly with
# conda create -n NetCoMi
# conda activate NetCoMi
conda install -c bioconda -c conda-forge r-netcomi

Development version

Everyone who wants to use new features not included in any releases is invited to install NetCoMi's development version:

devtools::install_github("stefpeschel/NetCoMi", 
                         ref = "develop",
                         dependencies = c("Depends", "Imports", "LinkingTo"),
                         repos = c("https://cloud.r-project.org/",
                                   BiocManager::repositories()))

Please check the NEWS document for features implemented on develop branch.

Usage

We use the American Gut data from SpiecEasi package to look at some examples of how NetCoMi is applied. NetCoMi's main functions are netConstruct() for network construction, netAnalyze() for network analysis, and netCompare() for network comparison. As you will see in the following, these three functions must be executed in the aforementioned order. A further function is diffnet() for constructing a differential association network. diffnet() must be applied to the object returned by netConstruct().

First of all, we load NetCoMi and the data from American Gut Project (provided by SpiecEasi, which is automatically loaded together with NetCoMi).

library(NetCoMi)
data("amgut1.filt")
data("amgut2.filt.phy")

Network with SPRING as association measure

Network construction and analysis

We firstly construct a single association network using SPRING for estimating associations (conditional dependence) between OTUs.

The data are filtered within netConstruct() as follows:

measure defines the association or dissimilarity measure, which is "spring" in our case. Additional arguments are passed to SPRING() via measurePar. nlambda and rep.num are set to 10 for a decreased execution time, but should be higher for real data. Rmethod is set to “approx” to estimate the correlations using a hybrid multi-linear interpolation approach proposed by @yoon2020fast. This method considerably reduces the runtime while controlling the approximation error.

Normalization as well as zero handling is performed internally in SPRING(). Hence, we set normMethod and zeroMethod to "none".

We furthermore set sparsMethod to "none" because SPRING returns a sparse network where no additional sparsification step is necessary.

We use the "signed" method for transforming associations into dissimilarities (argument dissFunc). In doing so, strongly negatively associated taxa have a high dissimilarity and, in turn, a low similarity, which corresponds to edge weights in the network plot.

The verbose argument is set to 3 so that all messages generated by netConstruct() as well as messages of external functions are printed.

net_spring <- netConstruct(amgut1.filt,
                           filtTax = "highestFreq",
                           filtTaxPar = list(highestFreq = 50),
                           filtSamp = "totalReads",
                           filtSampPar = list(totalReads = 1000),
                           measure = "spring",
                           measurePar = list(nlambda=10, 
                                             rep.num=10,
                                             Rmethod = "approx"),
                           normMethod = "none", 
                           zeroMethod = "none",
                           sparsMethod = "none", 
                           dissFunc = "signed",
                           verbose = 2,
                           seed = 123456)

Analyzing the constructed network

NetCoMi's netAnalyze() function is used for analyzing the constructed network(s).

Here, centrLCC is set to TRUE meaning that centralities are calculated only for nodes in the largest connected component (LCC).

Clusters are identified using greedy modularity optimization (by cluster_fast_greedy() from igraph package).

Hubs are nodes with an eigenvector centrality value above the empirical 95% quantile of all eigenvector centralities in the network (argument hubPar).

weightDeg and normDeg are set to FALSE so that the degree of a node is simply defined as number of nodes that are adjacent to the node.

By default, a heatmap of the Graphlet Correlation Matrix (GCM) is returned (with graphlet correlations in the upper triangle and significance codes resulting from Student's t-test in the lower triangle). See ?calcGCM and ?testGCM for details.

props_spring <- netAnalyze(net_spring, 
                           centrLCC = TRUE,
                           clustMethod = "cluster_fast_greedy",
                           hubPar = "eigenvector",
                           weightDeg = FALSE, normDeg = FALSE)

#?summary.microNetProps
summary(props_spring, numbNodes = 5L)

Plotting the GCM heatmap manually

plotHeat(mat = props_spring$graphletLCC$gcm1,
         pmat = props_spring$graphletLCC$pAdjust1,
         type = "mixed",
         title = "GCM", 
         colorLim = c(-1, 1),
         mar = c(2, 0, 2, 0))

# Add rectangles highlighting the four types of orbits
graphics::rect(xleft   = c( 0.5,  1.5, 4.5,  7.5),
               ybottom = c(11.5,  7.5, 4.5,  0.5),
               xright  = c( 1.5,  4.5, 7.5, 11.5),
               ytop    = c(10.5, 10.5, 7.5,  4.5),
               lwd = 2, xpd = NA)

text(6, -0.2, xpd = NA, 
     "Significance codes:  ***: 0.001;  **: 0.01;  *: 0.05")

Visualizing the network

We use the determined clusters as node colors and scale the node sizes according to the node's eigenvector centrality. 

# help page
?plot.microNetProps

p <- plot(props_spring, 
          nodeColor = "cluster", 
          nodeSize = "eigenvector",
          title1 = "Network on OTU level with SPRING associations", 
          showTitle = TRUE,
          cexTitle = 2.3)

legend(0.7, 1.1, cex = 2.2, title = "estimated association:",
       legend = c("+","-"), lty = 1, lwd = 3, col = c("#009900","red"), 
       bty = "n", horiz = TRUE)

Note that edge weights are (non-negative) similarities, however, the edges belonging to negative estimated associations are colored in red by default (negDiffCol = TRUE).

By default, a different transparency value is added to edges with an absolute weight below and above the cut value (arguments edgeTranspLow and edgeTranspHigh). The determined cut value can be read out as follows:

p$q1$Arguments$cut

Export to Gephi

Some users may be interested in how to export the network to Gephi. Here's an example:

# For Gephi, we have to generate an edge list with IDs.
# The corresponding labels (and also further node features) are stored as node list.

# Create edge object from the edge list exported by netConstruct()
edges <- dplyr::select(net_spring$edgelist1, v1, v2)

# Add Source and Target variables (as IDs)
edges$Source <- as.numeric(factor(edges$v1))
edges$Target <- as.numeric(factor(edges$v2))
edges$Type <- "Undirected"
edges$Weight <- net_spring$edgelist1$adja

nodes <- unique(edges[,c('v1','Source')])
colnames(nodes) <- c("Label", "Id")

# Add category with clusters (can be used as node colors in Gephi)
nodes$Category <- props_spring$clustering$clust1[nodes$Label]

edges <- dplyr::select(edges, Source, Target, Type, Weight)

write.csv(nodes, file = "nodes.csv", row.names = FALSE)
write.csv(edges, file = "edges.csv", row.names = FALSE)

The exported .csv files can then be imported into Gephi.

Network with Pearson correlation as association measure

Let's construct another network using Pearson's correlation coefficient as association measure. The input is now a phyloseq object.

Since Pearson correlations may lead to compositional effects when applied to sequencing data, we use the clr transformation as normalization method. Zero treatment is necessary in this case.

A threshold of 0.3 is used as sparsification method, so that only OTUs with an absolute correlation greater than or equal to 0.3 are connected.

net_pears <- netConstruct(amgut2.filt.phy,  
                          measure = "pearson",
                          normMethod = "clr",
                          zeroMethod = "multRepl",
                          sparsMethod = "threshold",
                          thresh = 0.3,
                          verbose = 3)

Network analysis and plotting:

props_pears <- netAnalyze(net_pears, 
                          clustMethod = "cluster_fast_greedy")

plot(props_pears, 
     nodeColor = "cluster", 
     nodeSize = "eigenvector",
     title1 = "Network on OTU level with Pearson correlations", 
     showTitle = TRUE,
     cexTitle = 2.3)

legend(0.7, 1.1, cex = 2.2, title = "estimated correlation:", 
       legend = c("+","-"), lty = 1, lwd = 3, col = c("#009900","red"), 
       bty = "n", horiz = TRUE)

Let's improve the visualization by changing the following arguments:

plot(props_pears, 
     nodeColor = "cluster", 
     nodeSize = "eigenvector",
     repulsion = 0.8,
     rmSingles = TRUE,
     labelScale = FALSE,
     cexLabels = 1.6,
     nodeSizeSpread = 3,
     cexNodes = 2,
     hubBorderCol = "darkgray",
     title1 = "Network on OTU level with Pearson correlations", 
     showTitle = TRUE,
     cexTitle = 2.3)

legend(0.7, 1.1, cex = 2.2, title = "estimated correlation:",
       legend = c("+","-"), lty = 1, lwd = 3, col = c("#009900","red"),
       bty = "n", horiz = TRUE)

Edge filtering

The network can be sparsified further using the arguments edgeFilter (edges are filtered before the layout is computed) and edgeInvisFilter (edges are removed after the layout is computed and thus just made "invisible").

plot(props_pears,
     edgeInvisFilter = "threshold",
     edgeInvisPar = 0.4,
     nodeColor = "cluster", 
     nodeSize = "eigenvector",
     repulsion = 0.8,
     rmSingles = TRUE,
     labelScale = FALSE,
     cexLabels = 1.6,
     nodeSizeSpread = 3,
     cexNodes = 2,
     hubBorderCol = "darkgray",
     title1 = paste0("Network on OTU level with Pearson correlations",
                     "\n(edge filter: threshold = 0.4)"),
     showTitle = TRUE,
     cexTitle = 2.3)

legend(0.7, 1.1, cex = 2.2, title = "estimated correlation:",
       legend = c("+","-"), lty = 1, lwd = 3, col = c("#009900","red"),
       bty = "n", horiz = TRUE)

Using the "unsigned" transformation

In the above network, the "signed" transformation was used to transform the estimated associations into dissimilarities. This leads to a network where strongly positive correlated taxa have a high edge weight (1 if the correlation equals 1) and strongly negative correlated taxa have a low edge weight (0 if the correlation equals -1).

We now use the "unsigned" transformation so that the edge weight between strongly correlated taxa is high, no matter of the sign. Hence, a correlation of -1 and 1 would lead to an edge weight of 1.

Network construction

We can pass the network object from before to netConstruct() to save runtime.

net_pears_unsigned <- netConstruct(data = net_pears$assoEst1,
                                   dataType = "correlation", 
                                   sparsMethod = "threshold",
                                   thresh = 0.3,
                                   dissFunc = "unsigned",
                                   verbose = 3)

Estimated correlations and adjacency values

The following histograms demonstrate how the estimated correlations are transformed into adjacencies (= sparsified similarities for weighted networks).

Sparsified estimated correlations:

hist(net_pears$assoMat1, 100, xlim = c(-1, 1), ylim = c(0, 400),
     xlab = "Estimated correlation", 
     main = "Estimated correlations after sparsification")

Adjacency values computed using the "signed" transformation (values different from 0 and 1 will be edges in the network):

hist(net_pears$adjaMat1, 100, ylim = c(0, 400),
     xlab = "Adjacency values", 
     main = "Adjacencies (with \"signed\" transformation)")

Adjacency values computed using the "unsigned" transformation:

hist(net_pears_unsigned$adjaMat1, 100, ylim = c(0, 400),
     xlab = "Adjacency values", 
     main = "Adjacencies (with \"unsigned\" transformation)")

Network analysis and plotting

props_pears_unsigned <- netAnalyze(net_pears_unsigned, 
                                   clustMethod = "cluster_fast_greedy",
                                   gcmHeat = FALSE)

plot(props_pears_unsigned, 
     nodeColor = "cluster", 
     nodeSize = "eigenvector",
     repulsion = 0.9,
     rmSingles = TRUE,
     labelScale = FALSE,
     cexLabels = 1.6,
     nodeSizeSpread = 3,
     cexNodes = 2,
     hubBorderCol = "darkgray",
     title1 = "Network with Pearson correlations and \"unsigned\" transformation", 
     showTitle = TRUE,
     cexTitle = 2.3)

legend(0.7, 1.1, cex = 2.2, title = "estimated correlation:",
       legend = c("+","-"), lty = 1, lwd = 3, col = c("#009900","red"),
       bty = "n", horiz = TRUE)

While with the "signed" transformation, positive correlated taxa are likely to belong to the same cluster, with the "unsigned" transformation clusters contain strongly positive and negative correlated taxa.

Network on genus level

We now construct a further network, where OTUs are agglomerated to genera. 

library(phyloseq)
data("amgut2.filt.phy")

# Agglomerate to genus level
amgut_genus <- tax_glom(amgut2.filt.phy, taxrank = "Rank6")

# Taxonomic table
taxtab <- as(tax_table(amgut_genus), "matrix")

# Rename taxonomic table and make Rank6 (genus) unique
amgut_genus_renamed <- renameTaxa(amgut_genus, 
                                  pat = "<name>", 
                                  substPat = "<name>_<subst_name>(<subst_R>)",
                                  numDupli = "Rank6")

# Network construction and analysis
net_genus <- netConstruct(amgut_genus_renamed,
                          taxRank = "Rank6",
                          measure = "pearson",
                          zeroMethod = "multRepl",
                          normMethod = "clr",
                          sparsMethod = "threshold",
                          thresh = 0.3,
                          verbose = 3)

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

Network plots

Modifications:

# Compute layout
graph3 <- igraph::graph_from_adjacency_matrix(net_genus$adjaMat1, 
                                              weighted = TRUE)
set.seed(123456)
lay_fr <- igraph::layout_with_fr(graph3)

# Row names of the layout matrix must match the node names
rownames(lay_fr) <- rownames(net_genus$adjaMat1)

plot(props_genus,
     layout = lay_fr,
     shortenLabels = "intelligent",
     labelLength = 10,
     labelPattern = c(5, "'", 3, "'", 3),
     nodeSize = "fix",
     nodeColor = "gray",
     cexNodes = 0.8,
     cexHubs = 1.1,
     cexLabels = 1.2,
     title1 = "Network on genus level with Pearson correlations", 
     showTitle = TRUE,
     cexTitle = 2.3)

legend(0.7, 1.1, cex = 2.2, title = "estimated correlation:",
       legend = c("+","-"), lty = 1, lwd = 3, col = c("#009900","red"), 
       bty = "n", horiz = TRUE)

Since the above visualization is obviously not optimal, we make further adjustments:

set.seed(123456)

plot(props_genus,
     layout = "layout_with_fr",
     shortenLabels = "intelligent",
     labelLength = 10,
     labelPattern = c(5, "'", 3, "'", 3),
     labelScale = FALSE,
     rmSingles = TRUE,
     nodeSize = "clr",
     nodeColor = "cluster",
     hubBorderCol = "darkgray",
     cexNodes = 2,
     cexLabels = 1.5,
     cexHubLabels = 2,
     title1 = "Network on genus level with Pearson correlations", 
     showTitle = TRUE,
     cexTitle = 2.3)

legend(0.7, 1.1, cex = 2.2, title = "estimated correlation:",
       legend = c("+","-"), lty = 1, lwd = 3, col = c("#009900","red"), 
       bty = "n", horiz = TRUE)

Let's check whether the largest nodes are actually those with highest column sums in the matrix with normalized counts returned by netConstruct().

sort(colSums(net_genus$normCounts1), decreasing = TRUE)[1:10]

In order to further improve our plot, we use the following modifications:

# Get phyla names
taxtab <- as(tax_table(amgut_genus_renamed), "matrix")
phyla <- as.factor(gsub("p__", "", taxtab[, "Rank2"]))
names(phyla) <- taxtab[, "Rank6"]
#table(phyla)

# Define phylum colors
phylcol <- c("cyan", "blue3", "red", "lawngreen", "yellow", "deeppink")

plot(props_genus,
     layout = "spring",
     repulsion = 0.84,
     shortenLabels = "none",
     charToRm = "g__",
     labelScale = FALSE,
     rmSingles = TRUE,
     nodeSize = "clr",
     nodeSizeSpread = 4,
     nodeColor = "feature", 
     featVecCol = phyla, 
     colorVec =  phylcol,
     posCol = "darkturquoise", 
     negCol = "orange",
     edgeTranspLow = 0,
     edgeTranspHigh = 40,
     cexNodes = 2,
     cexLabels = 2,
     cexHubLabels = 2.5,
     title1 = "Network on genus level with Pearson correlations", 
     showTitle = TRUE,
     cexTitle = 2.3)

# Colors used in the legend should be equally transparent as in the plot
phylcol_transp <- colToTransp(phylcol, 60)

legend(-1.2, 1.2, cex = 2, pt.cex = 2.5, title = "Phylum:", 
       legend=levels(phyla), col = phylcol_transp, bty = "n", pch = 16) 

legend(0.7, 1.1, cex = 2.2, title = "estimated correlation:",
       legend = c("+","-"), lty = 1, lwd = 3, col = c("darkturquoise","orange"), 
       bty = "n", horiz = TRUE)

Using an association matrix as input

The QMP data set provided by the SPRING package is used to demonstrate how NetCoMi is used to analyze a precomputed network (given as association matrix).

The data set contains quantitative count data (true absolute values), which SPRING can deal with. See ?QMP for details.

nlambda and rep.num are set to 10 for a decreased execution time, but should be higher for real data.

library(SPRING)

# Load the QMP data set
data("QMP") 

# Run SPRING for association estimation
fit_spring <- SPRING(QMP, 
                     quantitative = TRUE, 
                     lambdaseq = "data-specific",
                     nlambda = 10, 
                     rep.num = 10,
                     seed = 123456, 
                     ncores = 1,
                     Rmethod = "approx",
                     verbose = FALSE)

# Optimal lambda
opt.K <- fit_spring$output$stars$opt.index

# Association matrix
assoMat <- as.matrix(SpiecEasi::symBeta(fit_spring$output$est$beta[[opt.K]],
                                        mode = "ave"))
rownames(assoMat) <- colnames(assoMat) <- colnames(QMP)

The association matrix is now passed to netConstruct to start the usual NetCoMi workflow. Note that the dataType argument must be set appropriately.

# Network construction and analysis
net_asso <- netConstruct(data = assoMat,
                         dataType = "condDependence",
                         sparsMethod = "none",
                         verbose = 0)

props_asso <- netAnalyze(net_asso, clustMethod = "hierarchical")

plot(props_asso,
     layout = "spring",
     repulsion = 1.2,
     shortenLabels = "none",
     labelScale = TRUE,
     rmSingles = TRUE,
     nodeSize = "eigenvector",
     nodeSizeSpread = 2,
     nodeColor = "cluster",
     hubBorderCol = "gray60",
     cexNodes = 1.8,
     cexLabels = 2,
     cexHubLabels = 2.2,
     title1 = "Network for QMP data", 
     showTitle = TRUE,
     cexTitle = 2.3)

legend(0.7, 1.1, cex = 2.2, title = "estimated association:",
       legend = c("+","-"), lty = 1, lwd = 3, col = c("#009900","red"), 
       bty = "n", horiz = TRUE)

Network comparison

Now let's look how NetCoMi is used to compare two networks.

Network construction

The data set is split by "SEASONAL_ALLERGIES" leading to two subsets of samples (with and without seasonal allergies). We ignore the "None" group. 

# Split the phyloseq object into two groups
amgut_season_yes <- phyloseq::subset_samples(amgut2.filt.phy, 
                                             SEASONAL_ALLERGIES == "yes")
amgut_season_no <- phyloseq::subset_samples(amgut2.filt.phy, 
                                            SEASONAL_ALLERGIES == "no")

amgut_season_yes
amgut_season_no

The 50 nodes with highest variance are selected for network construction to get smaller networks.

We filter the 121 samples (sample size of the smaller group) with highest frequency to make the sample sizes equal and thus ensure comparability.

n_yes <- phyloseq::nsamples(amgut_season_yes)

# Network construction
net_season <- netConstruct(data = amgut_season_no, 
                           data2 = amgut_season_yes,  
                           filtTax = "highestVar",
                           filtTaxPar = list(highestVar = 50),
                           filtSamp = "highestFreq",
                           filtSampPar = list(highestFreq = n_yes),
                           measure = "spring",
                           measurePar = list(nlambda = 10, 
                                             rep.num = 10,
                                             Rmethod = "approx"),
                           normMethod = "none", 
                           zeroMethod = "none",
                           sparsMethod = "none", 
                           dissFunc = "signed",
                           verbose = 2,
                           seed = 123456)

Alternatively, a group vector could be passed to group, according to which the data set is split into two groups:

# Get count table
countMat <- phyloseq::otu_table(amgut2.filt.phy)

# netConstruct() expects samples in rows
countMat <- t(as(countMat, "matrix"))

group_vec <- phyloseq::get_variable(amgut2.filt.phy, "SEASONAL_ALLERGIES")

# Select the two groups of interest (level "none" is excluded)
sel <- which(group_vec %in% c("no", "yes"))
group_vec <- group_vec[sel]
countMat <- countMat[sel, ]

net_season <- netConstruct(countMat, 
                           group = group_vec, 
                           filtTax = "highestVar",
                           filtTaxPar = list(highestVar = 50),
                           filtSamp = "highestFreq",
                           filtSampPar = list(highestFreq = n_yes),
                           measure = "spring",
                           measurePar = list(nlambda=10, 
                                             rep.num=10,
                                             Rmethod = "approx"),
                           normMethod = "none", 
                           zeroMethod = "none",
                           sparsMethod = "none", 
                           dissFunc = "signed",
                           verbose = 3,
                           seed = 123456)

Network analysis

The object returned by netConstruct() containing both networks is again passed to netAnalyze(). Network properties are computed for both networks simultaneously.

To demonstrate further functionalities of netAnalyze(), we play around with the available arguments, even if the chosen setting might not be optimal.

Note! The arguments must be set carefully, depending on the research questions. NetCoMi's default values are not generally preferable in all practical cases!

props_season <- netAnalyze(net_season, 
                           centrLCC = FALSE,
                           avDissIgnoreInf = TRUE,
                           sPathNorm = FALSE,
                           clustMethod = "cluster_fast_greedy",
                           hubPar = c("degree", "eigenvector"),
                           hubQuant = 0.9,
                           lnormFit = TRUE,
                           normDeg = FALSE,
                           normBetw = FALSE,
                           normClose = FALSE,
                           normEigen = FALSE)

summary(props_season)

Visual network comparison

First, the layout is computed separately in both groups (qgraph's "spring" layout in this case).

Node sizes are scaled according to the mclr-transformed data since SPRING uses the mclr transformation as normalization method.

Node colors represent clusters. Note that by default, two clusters have the same color in both groups if they have at least two nodes in common (sameColThresh = 2). Set sameClustCol to FALSE to get different cluster colors.

plot(props_season, 
     sameLayout = FALSE, 
     nodeColor = "cluster",
     nodeSize = "mclr",
     labelScale = FALSE,
     cexNodes = 1.5, 
     cexLabels = 2.5,
     cexHubLabels = 3,
     cexTitle = 3.7,
     groupNames = c("No seasonal allergies", "Seasonal allergies"),
     hubBorderCol  = "gray40")

legend("bottom", title = "estimated association:", legend = c("+","-"), 
       col = c("#009900","red"), inset = 0.02, cex = 4, lty = 1, lwd = 4, 
       bty = "n", horiz = TRUE)

Using different layouts leads to a "nice-looking" network plot for each group, however, it is difficult to identify group differences at first glance.

Thus, we now use the same layout in both groups. In the following, the layout is computed for group 1 (the left network) and taken over for group 2.

rmSingles is set to "inboth" because only nodes that are unconnected in both groups can be removed if the same layout is used.

plot(props_season, 
     sameLayout = TRUE, 
     layoutGroup = 1,
     rmSingles = "inboth", 
     nodeSize = "mclr", 
     labelScale = FALSE,
     cexNodes = 1.5, 
     cexLabels = 2.5,
     cexHubLabels = 3,
     cexTitle = 3.8,
     groupNames = c("No seasonal allergies", "Seasonal allergies"),
     hubBorderCol  = "gray40")

legend("bottom", title = "estimated association:", legend = c("+","-"), 
       col = c("#009900","red"), inset = 0.02, cex = 4, lty = 1, lwd = 4, 
       bty = "n", horiz = TRUE)

In the above plot, we can see clear differences between the groups. The OTU "322235", for instance, is more strongly connected in the "Seasonal allergies" group than in the group without seasonal allergies, which is why it is a hub on the right, but not on the left.

However, if the layout of one group is simply taken over to the other, one of the networks (here the "seasonal allergies" group) is usually not that nice-looking due to the long edges. Therefore, NetCoMi (>= 1.0.2) offers a further option (layoutGroup = "union"), where a union of the two layouts is used in both groups. In doing so, the nodes are placed as optimal as possible equally for both networks.

The idea and R code for this functionality were provided by Christian L. Müller and Alice Sommer

plot(props_season, 
     sameLayout = TRUE, 
     repulsion = 0.95,
     layoutGroup = "union",
     rmSingles = "inboth", 
     nodeSize = "mclr", 
     labelScale = FALSE,
     cexNodes = 1.5, 
     cexLabels = 2.5,
     cexHubLabels = 3,
     cexTitle = 3.8,
     groupNames = c("No seasonal allergies", "Seasonal allergies"),
     hubBorderCol  = "gray40")

legend("bottom", title = "estimated association:", legend = c("+","-"), 
       col = c("#009900","red"), inset = 0.02, cex = 4, lty = 1, lwd = 4, 
       bty = "n", horiz = TRUE)

Quantitative network comparison

Since runtime is considerably increased if permutation tests are performed, we set the permTest parameter to FALSE. See the tutorial_createAssoPerm file for a network comparison including permutation tests.

Since permutation tests are still conducted for the Adjusted Rand Index, a seed should be set for reproducibility.

comp_season <- netCompare(props_season, 
                          permTest = FALSE, 
                          verbose = FALSE,
                          seed = 123456)

summary(comp_season, 
        groupNames = c("No allergies", "Allergies"),
        showCentr = c("degree", "between", "closeness"), 
        numbNodes = 5)

Differential networks

We now build a differential association network, where two nodes are connected if they are differentially associated between the two groups.

Due to its very short execution time, we use Pearson's correlations for estimating associations between OTUs.

Fisher's z-test is applied for identifying differentially correlated OTUs. Multiple testing adjustment is done by controlling the local false discovery rate.

Note: sparsMethod is set to "none", just to be able to include all differential associations in the association network plot (see below). However, the differential network is always based on the estimated association matrices before sparsification (the assoEst1 and assoEst2 matrices returned by netConstruct()).

net_season_pears <- netConstruct(data = amgut_season_no, 
                                 data2 = amgut_season_yes, 
                                 filtTax = "highestVar",
                                 filtTaxPar = list(highestVar = 50),
                                 measure = "pearson", 
                                 normMethod = "clr",
                                 sparsMethod = "none", 
                                 thresh = 0.2,
                                 verbose = 3)

# Differential network construction
diff_season <- diffnet(net_season_pears,
                       diffMethod = "fisherTest", 
                       adjust = "lfdr")

# Differential network plot
plot(diff_season, 
     cexNodes = 0.8, 
     cexLegend = 3,
     cexTitle = 4,
     mar = c(2,2,8,5),
     legendGroupnames = c("group 'no'", "group 'yes'"),
     legendPos = c(0.7,1.6))

In the differential network shown above, edge colors represent the direction of associations in the two groups. If, for instance, two OTUs are positively associated in group 1 and negatively associated in group 2 (such as '191541' and '188236'), the respective edge is colored in cyan.

We also take a look at the corresponding associations by constructing association networks that include only the differentially associated OTUs.

props_season_pears <- netAnalyze(net_season_pears, 
                                 clustMethod = "cluster_fast_greedy",
                                 weightDeg = TRUE,
                                 normDeg = FALSE,
                                 gcmHeat = FALSE)

# Identify the differentially associated OTUs
diffmat_sums <- rowSums(diff_season$diffAdjustMat)
diff_asso_names <- names(diffmat_sums[diffmat_sums > 0])

plot(props_season_pears, 
     nodeFilter = "names",
     nodeFilterPar = diff_asso_names,
     nodeColor = "gray",
     highlightHubs = FALSE,
     sameLayout = TRUE, 
     layoutGroup = "union",
     rmSingles = FALSE, 
     nodeSize = "clr",
     edgeTranspHigh = 20,
     labelScale = FALSE,
     cexNodes = 1.5, 
     cexLabels = 3,
     cexTitle = 3.8,
     groupNames = c("No seasonal allergies", "Seasonal allergies"),
     hubBorderCol  = "gray40")

legend(-0.15,-0.7, title = "estimated correlation:", legend = c("+","-"), 
       col = c("#009900","red"), inset = 0.05, cex = 4, lty = 1, lwd = 4, 
       bty = "n", horiz = TRUE)

We can see that the correlation between the aforementioned OTUs '191541' and '188236' is strongly positive in the left group and negative in the right group.

Dissimilarity-based Networks

If a dissimilarity measure is used for network construction, nodes are subjects instead of OTUs. The estimated dissimilarities are transformed into similarities, which are used as edge weights so that subjects with a similar microbial composition are placed close together in the network plot.

We construct a single network using Aitchison's distance being suitable for the application on compositional data.

Since the Aitchison distance is based on the clr-transformation, zeros in the data need to be replaced.

The network is sparsified using the k-nearest neighbor (knn) algorithm.

net_diss <- netConstruct(amgut1.filt,
                         measure = "aitchison",
                         zeroMethod = "multRepl",
                         sparsMethod = "knn",
                         kNeighbor = 3,
                         verbose = 3)

For cluster detection, we use hierarchical clustering with average linkage. Internally, k=3 is passed to cutree() from stats package so that the tree is cut into 3 clusters.

props_diss <- netAnalyze(net_diss,
                         clustMethod = "hierarchical",
                         clustPar = list(method = "average", k = 3),
                         hubPar = "eigenvector")

plot(props_diss, 
     nodeColor = "cluster", 
     nodeSize = "eigenvector",
     hubTransp = 40,
     edgeTranspLow = 60,
     charToRm = "00000",
     shortenLabels = "simple",
     labelLength = 6,
     mar = c(1, 3, 3, 5))

# get green color with 50% transparency
green2 <- colToTransp("#009900", 40)

legend(0.4, 1.1,
       cex = 2.2,
       legend = c("high similarity (low Aitchison distance)",
                  "low similarity (high Aitchison distance)"), 
       lty = 1, 
       lwd = c(3, 1),
       col = c("darkgreen", green2),
       bty = "n")

In this dissimilarity-based network, hubs are interpreted as samples with a microbial composition similar to that of many other samples in the data set.

Soil microbiome example

Here is the code for reproducing the network plot shown at the beginning.

data("soilrep")

soil_warm_yes <- phyloseq::subset_samples(soilrep, warmed == "yes")
soil_warm_no  <- phyloseq::subset_samples(soilrep, warmed == "no")

net_seas_p <- netConstruct(soil_warm_yes, soil_warm_no,
                           filtTax = "highestVar",
                           filtTaxPar = list(highestVar = 500),
                           zeroMethod = "pseudo",
                           normMethod = "clr",
                           measure = "pearson",
                           verbose = 0)

netprops1 <- netAnalyze(net_seas_p, clustMethod = "cluster_fast_greedy")

nclust <- as.numeric(max(names(table(netprops1$clustering$clust1))))

col <- c(topo.colors(nclust), rainbow(6))

plot(netprops1, 
     sameLayout = TRUE, 
     layoutGroup = "union", 
     colorVec = col,
     borderCol = "gray40", 
     nodeSize = "degree", 
     cexNodes = 0.9, 
     nodeSizeSpread = 3, 
     edgeTranspLow = 80, 
     edgeTranspHigh = 50,
     groupNames = c("Warming", "Non-warming"), 
     showTitle = TRUE, 
     cexTitle = 2.8,
     mar = c(1,1,3,1), 
     repulsion = 0.9, 
     labels = FALSE, 
     rmSingles = "inboth",
     nodeFilter = "clustMin", 
     nodeFilterPar = 10, 
     nodeTransp = 50, 
     hubTransp = 30)

References



stefpeschel/NetCoMi documentation built on Feb. 4, 2024, 8:20 a.m.

R Package Documentation

rdrr.io home R language documentation Run R code online

Browse R Packages

CRAN packages Bioconductor packages R-Forge packages GitHub packages

We want your feedback!

Note that we can't provide technical support on individual packages. You should contact the package authors for that.
GitHub issue tracker
ian@mutexlabs.com
Personal blog

 
Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.

Close