tutorials/TUTORIAL_adapt_labels.md
In stefpeschel/NetCoMi: Network Construction and Comparison for Microbiome Data
Tutorial: How to adapt taxa names in the network plot 
Use genus names instead of ASV names: Method 1
In this method, genus names are passed as label vector to the plot
function, so that genus names are shown in the plot, but not in the
summary outputs.
library(NetCoMi)
library(phyloseq)
data("amgut2.filt.phy")
colnames(amgut2.filt.phy@tax_table@.Data) <- c("Kingdom","Phylum","Class",
"Order", "Family", "Genus",
"Species")
# Agglomerate to genus level
amgut_genus <- tax_glom(amgut2.filt.phy,"Genus")
# Split data set into seasonal allergies = yes/no
levels(phyloseq::get_variable(amgut_genus, "SEASONAL_ALLERGIES"))
## [1] "no" "None" "yes"
amgut_g_yes = subset_samples(amgut_genus, SEASONAL_ALLERGIES == "yes")
amgut_g_no = subset_samples(amgut_genus, SEASONAL_ALLERGIES == "no")
# Network construction
net_season <- netConstruct(data = amgut_g_yes,
data2 = amgut_g_no,
measure = "pearson",
normMethod = "clr",
zeroMethod = "none",
sparsMethod = "threshold",
thresh = 0.4,
verbose = 1,
seed = 123456)
## Infos about changed arguments:
## Zero replacement needed for clr transformation. 'multRepl' used.
## 43 taxa and 120 samples remaining in group 1.
## 43 taxa and 162 samples remaining in group 2.
# Network analysis
props_season <- netAnalyze(net_season,
clustMethod = "cluster_fast_greedy")
summary(props_season, showCentr = c("degree", "eigenvector"))
##
## Component sizes
## ```````````````
## Group 1:
## size: 14 4 2 1
## #: 1 1 3 19
##
## Group 2:
## size: 16 2 1
## #: 1 1 25
## ______________________________
## Global network properties
## `````````````````````````
## Largest connected component (LCC):
## group '1' group '2'
## Relative LCC size 0.32558 0.37209
## Clustering coefficient 0.56856 0.62305
## Moduarity 0.22977 0.15556
## Positive edge percentage 59.25926 70.73171
## Edge density 0.29670 0.34167
## Natural connectivity 0.12468 0.13246
## Vertex connectivity 1.00000 1.00000
## Edge connectivity 1.00000 1.00000
## Average dissimilarity* 0.88722 0.86053
## Average path length** 1.38906 1.27974
##
## Whole network:
## group '1' group '2'
## Number of components 24.00000 27.00000
## Clustering coefficient 0.48109 0.62305
## Moduarity 0.41139 0.18849
## Positive edge percentage 60.60606 71.42857
## Edge density 0.03654 0.04651
## Natural connectivity 0.02994 0.03447
##
## *Dissimilarity = 1 - edge weight
## **Path length: Units with average dissimilarity
##
## ______________________________
## Clusters
## - In the whole network
## - Algorithm: cluster_fast_greedy
## ````````````````````````````````
## group '1':
## name: 0 1 2 3 4 5 6
## #: 19 8 2 2 4 6 2
##
## group '2':
## name: 0 1 2 3
## #: 25 6 10 2
##
## ______________________________
## Hubs
## - In alphabetical/numerical order
## - Based on empirical quantiles of centralities
## ```````````````````````````````````````````````
## group '1' group '2'
## 190307 184983
## 190464 188236
## 301645 190464
##
## ______________________________
## Centrality measures
## - In decreasing order
## - Centrality of disconnected components is zero
## ````````````````````````````````````````````````
## Degree (normalized):
## group '1' group '2'
## 301645 0.19048 0.19048
## 188236 0.16667 0.28571
## 190464 0.14286 0.21429
## 305760 0.11905 0.09524
## 190307 0.11905 0.11905
## ______ ______
## 188236 0.16667 0.28571
## 190464 0.14286 0.21429
## 301645 0.19048 0.19048
## 311477 0 0.16667
## 184983 0.09524 0.16667
##
## Eigenvector centrality (normalized):
## group '1' group '2'
## 190307 1 0.79318
## 190464 0.94119 0.97425
## 301645 0.92965 0.2581
## 188236 0.89014 1
## 305760 0.79044 0.21057
## ______ ______
## 188236 0.89014 1
## 190464 0.94119 0.97425
## 184983 0.76936 0.90943
## 190307 1 0.79318
## 311477 0 0.78083
# Create label vector
labels <- as.vector(tax_table(amgut_genus)[, "Genus"])
names(labels) <- rownames(tax_table(amgut_genus))
# Optional: Shorten labels to a desired length
#labels <- substr(labels, 1, 9)
plot(props_season,
repulsion = 0.9,
labels = labels,
sameLayout = TRUE,
layoutGroup = "union",
rmSingles = "inboth",
shortenLabels = "intelligent",
labelPattern = c(5,"'",3),
labelLength = 10,
nodeSize = "mclr",
labelScale = FALSE,
cexNodes = 1.5,
cexLabels = 0.8,
cexHubLabels = 0.8,
cexTitle = 1.2,
groupNames = c("Wet", "Dry"),
hubBorderCol = "gray40",
mar = c(2, 6, 4, 6))
# With shortened node labels:
labels <- substr(labels, 1, 9)
plot(props_season,
repulsion = 0.9,
labels = labels,
sameLayout = TRUE,
layoutGroup = "union",
rmSingles = "inboth",
shortenLabels = "intelligent",
labelPattern = c(5,"'",3),
labelLength = 10,
nodeSize = "mclr",
labelScale = FALSE,
cexNodes = 1.5,
cexLabels = 0.8,
cexHubLabels = 0.8,
cexTitle = 1.2,
groupNames = c("Wet", "Dry"),
hubBorderCol = "gray40",
mar = c(2, 6, 4, 6))
Use genus names instead of ASV names: Method 2
In this method, row names of the count table (stored as otu_table in
the phyloseq object) are set to genus names.
Advantages:
- Genus names are shown in the summaries
- The functionality for adapting labels provided by NetCoMi’s plot
function can be applied
data("amgut2.filt.phy")
colnames(amgut2.filt.phy@tax_table@.Data) <- c("Kingdom","Phylum","Class",
"Order", "Family", "Genus",
"Species")
# Agglomerate to genus level
amgut_genus <- tax_glom(amgut2.filt.phy,"Genus")
# Split data set into seasonal allergies = yes/no
levels(phyloseq::get_variable(amgut_genus, "SEASONAL_ALLERGIES"))
## [1] "no" "None" "yes"
amgut_g_yes = subset_samples(amgut_genus, SEASONAL_ALLERGIES == "yes")
amgut_g_no = subset_samples(amgut_genus, SEASONAL_ALLERGIES == "no")
# Rename taxa
# (Since NetCoMi uses only the otu table, we just rename it's rownames)
g_names <- as.vector(tax_table(amgut_genus)[, "Genus"])
taxtab <- amgut_genus@tax_table@.Data
# Make labels unique
duplis <- g_names[duplicated(g_names) | duplicated(g_names, fromLast=TRUE)]
while(length(duplis) > 0){
duplis.sel <- duplis[duplis == duplis[1]]
g_names[g_names == duplis.sel[1]] <- paste0(duplis.sel[1], 1:length(duplis.sel))
duplis <- g_names[duplicated(g_names) | duplicated(g_names, fromLast=TRUE)]
}
rownames(amgut_g_yes@otu_table@.Data) <- g_names
rownames(amgut_g_no@otu_table@.Data) <- g_names
# Network construction
net_season <- netConstruct(data = amgut_g_yes,
data2 = amgut_g_no,
measure = "pearson",
normMethod = "clr",
zeroMethod = "none",
sparsMethod = "threshold",
thresh = 0.4,
verbose = 1,
seed = 123456)
## Infos about changed arguments:
## Zero replacement needed for clr transformation. 'multRepl' used.
## 43 taxa and 120 samples remaining in group 1.
## 43 taxa and 162 samples remaining in group 2.
# Network analysis
props_season <- netAnalyze(net_season,
clustMethod = "cluster_fast_greedy")
# Network analysis summary (which now shows genus names)
summary(props_season, showCentr = c("degree", "eigenvector"))
##
## Component sizes
## ```````````````
## Group 1:
## size: 14 4 2 1
## #: 1 1 3 19
##
## Group 2:
## size: 16 2 1
## #: 1 1 25
## ______________________________
## Global network properties
## `````````````````````````
## Largest connected component (LCC):
## group '1' group '2'
## Relative LCC size 0.32558 0.37209
## Clustering coefficient 0.56856 0.62305
## Moduarity 0.22977 0.15556
## Positive edge percentage 59.25926 70.73171
## Edge density 0.29670 0.34167
## Natural connectivity 0.12468 0.13246
## Vertex connectivity 1.00000 1.00000
## Edge connectivity 1.00000 1.00000
## Average dissimilarity* 0.88722 0.86053
## Average path length** 1.38906 1.27974
##
## Whole network:
## group '1' group '2'
## Number of components 24.00000 27.00000
## Clustering coefficient 0.48109 0.62305
## Moduarity 0.41139 0.18849
## Positive edge percentage 60.60606 71.42857
## Edge density 0.03654 0.04651
## Natural connectivity 0.02994 0.03447
##
## *Dissimilarity = 1 - edge weight
## **Path length: Units with average dissimilarity
##
## ______________________________
## Clusters
## - In the whole network
## - Algorithm: cluster_fast_greedy
## ````````````````````````````````
## group '1':
## name: 0 1 2 3 4 5 6
## #: 19 8 2 2 4 6 2
##
## group '2':
## name: 0 1 2 3
## #: 25 6 10 2
##
## ______________________________
## Hubs
## - In alphabetical/numerical order
## - Based on empirical quantiles of centralities
## ```````````````````````````````````````````````
## group '1' group '2'
## g__3 g__3
## g__6 g__5
## g__Coprococcus g__Faecalibacterium
##
## ______________________________
## Centrality measures
## - In decreasing order
## - Centrality of disconnected components is zero
## ````````````````````````````````````````````````
## Degree (normalized):
## group '1' group '2'
## g__6 0.19048 0.19048
## g__Faecalibacterium 0.16667 0.28571
## g__3 0.14286 0.21429
## g__Escherichia 0.11905 0.09524
## g__Coprococcus 0.11905 0.11905
## ______ ______
## g__Faecalibacterium 0.16667 0.28571
## g__3 0.14286 0.21429
## g__6 0.19048 0.19048
## g__2 0 0.16667
## g__5 0.09524 0.16667
##
## Eigenvector centrality (normalized):
## group '1' group '2'
## g__Coprococcus 1 0.79318
## g__3 0.94119 0.97425
## g__6 0.92965 0.2581
## g__Faecalibacterium 0.89014 1
## g__Escherichia 0.79044 0.21057
## ______ ______
## g__Faecalibacterium 0.89014 1
## g__3 0.94119 0.97425
## g__5 0.76936 0.90943
## g__Coprococcus 1 0.79318
## g__2 0 0.78083
plot(props_season,
repulsion = 0.9,
sameLayout = TRUE,
layoutGroup = "union",
rmSingles = "inboth",
shortenLabels = "simple",
labelLength = 9,
nodeSize = "mclr",
labelScale = FALSE,
cexNodes = 1.5,
cexLabels = 0.8,
cexHubLabels = 0.8,
cexTitle = 1.2,
groupNames = c("Wet", "Dry"),
hubBorderCol = "gray40",
mar = c(2, 6, 4, 6))
Only hub nodes are labeled
If cexLabels is set to zero, only the hub node’s labels are plotted.
plot(props_season,
repulsion = 0.9,
sameLayout = TRUE,
layoutGroup = "union",
rmSingles = "inboth",
shortenLabels = "simple",
labelLength = 9,
nodeSize = "mclr",
labelScale = FALSE,
cexNodes = 1.5,
cexLabels = 0,
cexHubLabels = 1,
cexTitle = 1.2,
groupNames = c("Wet", "Dry"),
hubBorderCol = "gray40",
mar = c(2, 6, 4, 6))
stefpeschel/NetCoMi documentation built on Feb. 4, 2024, 8:20 a.m.
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Tutorial: How to adapt taxa names in the network plot
Use genus names instead of ASV names: Method 1
In this method, genus names are passed as label vector to the plot function, so that genus names are shown in the plot, but not in the summary outputs.
library(NetCoMi)
library(phyloseq)
data("amgut2.filt.phy")
colnames(amgut2.filt.phy@tax_table@.Data) <- c("Kingdom","Phylum","Class",
"Order", "Family", "Genus",
"Species")
# Agglomerate to genus level
amgut_genus <- tax_glom(amgut2.filt.phy,"Genus")
# Split data set into seasonal allergies = yes/no
levels(phyloseq::get_variable(amgut_genus, "SEASONAL_ALLERGIES"))
## [1] "no" "None" "yes"
amgut_g_yes = subset_samples(amgut_genus, SEASONAL_ALLERGIES == "yes")
amgut_g_no = subset_samples(amgut_genus, SEASONAL_ALLERGIES == "no")
# Network construction
net_season <- netConstruct(data = amgut_g_yes,
data2 = amgut_g_no,
measure = "pearson",
normMethod = "clr",
zeroMethod = "none",
sparsMethod = "threshold",
thresh = 0.4,
verbose = 1,
seed = 123456)
## Infos about changed arguments:
## Zero replacement needed for clr transformation. 'multRepl' used.
## 43 taxa and 120 samples remaining in group 1.
## 43 taxa and 162 samples remaining in group 2.
# Network analysis
props_season <- netAnalyze(net_season,
clustMethod = "cluster_fast_greedy")
summary(props_season, showCentr = c("degree", "eigenvector"))
##
## Component sizes
## ```````````````
## Group 1:
## size: 14 4 2 1
## #: 1 1 3 19
##
## Group 2:
## size: 16 2 1
## #: 1 1 25
## ______________________________
## Global network properties
## `````````````````````````
## Largest connected component (LCC):
## group '1' group '2'
## Relative LCC size 0.32558 0.37209
## Clustering coefficient 0.56856 0.62305
## Moduarity 0.22977 0.15556
## Positive edge percentage 59.25926 70.73171
## Edge density 0.29670 0.34167
## Natural connectivity 0.12468 0.13246
## Vertex connectivity 1.00000 1.00000
## Edge connectivity 1.00000 1.00000
## Average dissimilarity* 0.88722 0.86053
## Average path length** 1.38906 1.27974
##
## Whole network:
## group '1' group '2'
## Number of components 24.00000 27.00000
## Clustering coefficient 0.48109 0.62305
## Moduarity 0.41139 0.18849
## Positive edge percentage 60.60606 71.42857
## Edge density 0.03654 0.04651
## Natural connectivity 0.02994 0.03447
##
## *Dissimilarity = 1 - edge weight
## **Path length: Units with average dissimilarity
##
## ______________________________
## Clusters
## - In the whole network
## - Algorithm: cluster_fast_greedy
## ````````````````````````````````
## group '1':
## name: 0 1 2 3 4 5 6
## #: 19 8 2 2 4 6 2
##
## group '2':
## name: 0 1 2 3
## #: 25 6 10 2
##
## ______________________________
## Hubs
## - In alphabetical/numerical order
## - Based on empirical quantiles of centralities
## ```````````````````````````````````````````````
## group '1' group '2'
## 190307 184983
## 190464 188236
## 301645 190464
##
## ______________________________
## Centrality measures
## - In decreasing order
## - Centrality of disconnected components is zero
## ````````````````````````````````````````````````
## Degree (normalized):
## group '1' group '2'
## 301645 0.19048 0.19048
## 188236 0.16667 0.28571
## 190464 0.14286 0.21429
## 305760 0.11905 0.09524
## 190307 0.11905 0.11905
## ______ ______
## 188236 0.16667 0.28571
## 190464 0.14286 0.21429
## 301645 0.19048 0.19048
## 311477 0 0.16667
## 184983 0.09524 0.16667
##
## Eigenvector centrality (normalized):
## group '1' group '2'
## 190307 1 0.79318
## 190464 0.94119 0.97425
## 301645 0.92965 0.2581
## 188236 0.89014 1
## 305760 0.79044 0.21057
## ______ ______
## 188236 0.89014 1
## 190464 0.94119 0.97425
## 184983 0.76936 0.90943
## 190307 1 0.79318
## 311477 0 0.78083
# Create label vector
labels <- as.vector(tax_table(amgut_genus)[, "Genus"])
names(labels) <- rownames(tax_table(amgut_genus))
# Optional: Shorten labels to a desired length
#labels <- substr(labels, 1, 9)
plot(props_season,
repulsion = 0.9,
labels = labels,
sameLayout = TRUE,
layoutGroup = "union",
rmSingles = "inboth",
shortenLabels = "intelligent",
labelPattern = c(5,"'",3),
labelLength = 10,
nodeSize = "mclr",
labelScale = FALSE,
cexNodes = 1.5,
cexLabels = 0.8,
cexHubLabels = 0.8,
cexTitle = 1.2,
groupNames = c("Wet", "Dry"),
hubBorderCol = "gray40",
mar = c(2, 6, 4, 6))
# With shortened node labels:
labels <- substr(labels, 1, 9)
plot(props_season,
repulsion = 0.9,
labels = labels,
sameLayout = TRUE,
layoutGroup = "union",
rmSingles = "inboth",
shortenLabels = "intelligent",
labelPattern = c(5,"'",3),
labelLength = 10,
nodeSize = "mclr",
labelScale = FALSE,
cexNodes = 1.5,
cexLabels = 0.8,
cexHubLabels = 0.8,
cexTitle = 1.2,
groupNames = c("Wet", "Dry"),
hubBorderCol = "gray40",
mar = c(2, 6, 4, 6))
Use genus names instead of ASV names: Method 2
In this method, row names of the count table (stored as otu_table in
the phyloseq object) are set to genus names.
Advantages:
- Genus names are shown in the summaries
- The functionality for adapting labels provided by NetCoMi’s plot function can be applied
data("amgut2.filt.phy")
colnames(amgut2.filt.phy@tax_table@.Data) <- c("Kingdom","Phylum","Class",
"Order", "Family", "Genus",
"Species")
# Agglomerate to genus level
amgut_genus <- tax_glom(amgut2.filt.phy,"Genus")
# Split data set into seasonal allergies = yes/no
levels(phyloseq::get_variable(amgut_genus, "SEASONAL_ALLERGIES"))
## [1] "no" "None" "yes"
amgut_g_yes = subset_samples(amgut_genus, SEASONAL_ALLERGIES == "yes")
amgut_g_no = subset_samples(amgut_genus, SEASONAL_ALLERGIES == "no")
# Rename taxa
# (Since NetCoMi uses only the otu table, we just rename it's rownames)
g_names <- as.vector(tax_table(amgut_genus)[, "Genus"])
taxtab <- amgut_genus@tax_table@.Data
# Make labels unique
duplis <- g_names[duplicated(g_names) | duplicated(g_names, fromLast=TRUE)]
while(length(duplis) > 0){
duplis.sel <- duplis[duplis == duplis[1]]
g_names[g_names == duplis.sel[1]] <- paste0(duplis.sel[1], 1:length(duplis.sel))
duplis <- g_names[duplicated(g_names) | duplicated(g_names, fromLast=TRUE)]
}
rownames(amgut_g_yes@otu_table@.Data) <- g_names
rownames(amgut_g_no@otu_table@.Data) <- g_names
# Network construction
net_season <- netConstruct(data = amgut_g_yes,
data2 = amgut_g_no,
measure = "pearson",
normMethod = "clr",
zeroMethod = "none",
sparsMethod = "threshold",
thresh = 0.4,
verbose = 1,
seed = 123456)
## Infos about changed arguments:
## Zero replacement needed for clr transformation. 'multRepl' used.
## 43 taxa and 120 samples remaining in group 1.
## 43 taxa and 162 samples remaining in group 2.
# Network analysis
props_season <- netAnalyze(net_season,
clustMethod = "cluster_fast_greedy")
# Network analysis summary (which now shows genus names)
summary(props_season, showCentr = c("degree", "eigenvector"))
##
## Component sizes
## ```````````````
## Group 1:
## size: 14 4 2 1
## #: 1 1 3 19
##
## Group 2:
## size: 16 2 1
## #: 1 1 25
## ______________________________
## Global network properties
## `````````````````````````
## Largest connected component (LCC):
## group '1' group '2'
## Relative LCC size 0.32558 0.37209
## Clustering coefficient 0.56856 0.62305
## Moduarity 0.22977 0.15556
## Positive edge percentage 59.25926 70.73171
## Edge density 0.29670 0.34167
## Natural connectivity 0.12468 0.13246
## Vertex connectivity 1.00000 1.00000
## Edge connectivity 1.00000 1.00000
## Average dissimilarity* 0.88722 0.86053
## Average path length** 1.38906 1.27974
##
## Whole network:
## group '1' group '2'
## Number of components 24.00000 27.00000
## Clustering coefficient 0.48109 0.62305
## Moduarity 0.41139 0.18849
## Positive edge percentage 60.60606 71.42857
## Edge density 0.03654 0.04651
## Natural connectivity 0.02994 0.03447
##
## *Dissimilarity = 1 - edge weight
## **Path length: Units with average dissimilarity
##
## ______________________________
## Clusters
## - In the whole network
## - Algorithm: cluster_fast_greedy
## ````````````````````````````````
## group '1':
## name: 0 1 2 3 4 5 6
## #: 19 8 2 2 4 6 2
##
## group '2':
## name: 0 1 2 3
## #: 25 6 10 2
##
## ______________________________
## Hubs
## - In alphabetical/numerical order
## - Based on empirical quantiles of centralities
## ```````````````````````````````````````````````
## group '1' group '2'
## g__3 g__3
## g__6 g__5
## g__Coprococcus g__Faecalibacterium
##
## ______________________________
## Centrality measures
## - In decreasing order
## - Centrality of disconnected components is zero
## ````````````````````````````````````````````````
## Degree (normalized):
## group '1' group '2'
## g__6 0.19048 0.19048
## g__Faecalibacterium 0.16667 0.28571
## g__3 0.14286 0.21429
## g__Escherichia 0.11905 0.09524
## g__Coprococcus 0.11905 0.11905
## ______ ______
## g__Faecalibacterium 0.16667 0.28571
## g__3 0.14286 0.21429
## g__6 0.19048 0.19048
## g__2 0 0.16667
## g__5 0.09524 0.16667
##
## Eigenvector centrality (normalized):
## group '1' group '2'
## g__Coprococcus 1 0.79318
## g__3 0.94119 0.97425
## g__6 0.92965 0.2581
## g__Faecalibacterium 0.89014 1
## g__Escherichia 0.79044 0.21057
## ______ ______
## g__Faecalibacterium 0.89014 1
## g__3 0.94119 0.97425
## g__5 0.76936 0.90943
## g__Coprococcus 1 0.79318
## g__2 0 0.78083
plot(props_season,
repulsion = 0.9,
sameLayout = TRUE,
layoutGroup = "union",
rmSingles = "inboth",
shortenLabels = "simple",
labelLength = 9,
nodeSize = "mclr",
labelScale = FALSE,
cexNodes = 1.5,
cexLabels = 0.8,
cexHubLabels = 0.8,
cexTitle = 1.2,
groupNames = c("Wet", "Dry"),
hubBorderCol = "gray40",
mar = c(2, 6, 4, 6))
Only hub nodes are labeled
If cexLabels is set to zero, only the hub node’s labels are plotted.
plot(props_season,
repulsion = 0.9,
sameLayout = TRUE,
layoutGroup = "union",
rmSingles = "inboth",
shortenLabels = "simple",
labelLength = 9,
nodeSize = "mclr",
labelScale = FALSE,
cexNodes = 1.5,
cexLabels = 0,
cexHubLabels = 1,
cexTitle = 1.2,
groupNames = c("Wet", "Dry"),
hubBorderCol = "gray40",
mar = c(2, 6, 4, 6))
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