plot_subgraph: subnetwork plotting functionality.
In MEGENA: Multiscale Clustering of Geometrical Network
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
A modification of plot_module() function for more general subnetwork plotting purpose.
Usage
1
2
3
4
Arguments
module
A character vector containing gene names to be subsetted.
hub
If provided, genes in hub will be highlighted as triangles in resulting figure.
PFN
igraph object retaining PFN topology.
node.default.color
Default node colors for those that do not intersect with signatures in gene.set.
gene.set
A list object containing signatures for customized coloring of nodes in resulting network plot.
color.code
A character vector with matched length to "gene.set", to specify colors for each signature.
show.legend
TRUE/FALSE for showing node legend on the bottom of the figure.
label.hubs.only
TRUE/FALSE to show labels for significant hub genes only, or all genes. Defauly is TRUE.
hubLabel.col
Label color for hubs. Default is "red"
hubLabel.sizeProp
A multiplicative factor to adjust hub label sizes with respect to node size values. Default is 0.5
show.topn.hubs
Maximal number of hubs to label on module subnetwork. Default is 10.
node.sizeProp
A multiplicative factor to adjust node sizes with respect to 90th percentile degree node size. Default is 13
label.sizeProp
A multiplicative factor to adjust node label sizes with respect to 90th percentile degree node size. Default is 13
label.scaleFactor
Overall scale factor to control the final size of node labels appearing in figure. Default is 10.
layout
Network layout algorithm to apply. Options are: "kamada.kawai", "fruchterman.reingold".
Details
Subnetwork plot functionality with application of "ggrepel" package for node labeling. The most effective way to control overall node label size is through label.scaleFactor.
Value
A list object holding ggplot object and node annotation table.
Author(s)
Won-Min Song
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
## Not run:
rm(list = ls())
library(MEGENA)
data(Sample_Expression)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
output.summary <- MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 5000,
annot.table = NULL,id.col = NULL,symbol.col = NULL,
output.sig = TRUE)
pnet.obj <- plot_subgraph(module = output.summary$modules[[1]],
hub = c("CD3E","CD2"),PFN = g,node.default.color = "black",
gene.set = NULL,color.code = c("grey"),show.legend = TRUE,
label.hubs.only = TRUE,hubLabel.col = "red",hubLabel.sizeProp = 0.5,
show.topn.hubs = 10,node.sizeProp = 13,label.sizeProp = 13,
label.scaleFactor = 10,layout = "kamada.kawai")
# the plot
pnet.obj[[1]]
# the annotation
pnet.obj[[2]]
## End(Not run)
Example output
Loading required package: doParallel
Loading required package: foreach
Loading required package: iterators
Loading required package: parallel
Loading required package: igraph
Attaching package: ‘igraph’
The following objects are masked from ‘package:stats’:
decompose, spectrum
The following object is masked from ‘package:base’:
union
i = 1
i = 2
- outputting correlation results...
####### PFN Calculation commences ########
[1] "PFG is complete."
Commence multiscale clustering....
Calculating distance metric and similarity...
iteration:1
- #. tested:1
- k=2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,
- #. of split:4
- assess improvements over compactness
iteration:2
- #. tested:4
- k=2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,
- #. of split:1
- k=2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,
- #. of split:4
- assess improvements over compactness
- k=2,3,4,5,6,7,
- #. of split:0
- k=2,3,4,5,6,7,8,
- #. of split:0
iteration:3
- #. tested:3
- k=2,3,4,5,6,
- #. of split:0
- k=2,3,4,5,6,7,
- #. of split:0
- k=2,3,4,5,6,7,8,9,
- #. of split:0
Commence MHA...
Calculating hub significance.....
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
Identifying similar scales....
- Calculating within-module degree profiles.....
K.max:8
Cluster scales based on degree profiles...
k = 2,3,4,5,6,7,8,
- identified: 3
Identifying hub genes significant in each scale level...
Assigning module/KDA membership
Calculating node topological properties
- # of genes: 43
- # of hubs: 2
- generating module subnetwork figure...
node.lab id node.size node.shape X1 X2
CD3E CD3E CD3E 38.505399 hub 1.08196871 0.68793133
CD3D CD3D CD3D 18.598795 gene -0.59820031 1.78460447
CD2 CD2 CD2 37.451167 hub -0.71857815 0.11006972
CXCL11 CXCL11 CXCL11 8.873881 gene -2.80800335 -1.89082777
ITK ITK ITK 19.368649 gene 0.76348184 -1.11081879
ACAP1 ACAP1 ACAP1 15.717805 gene 1.15210236 2.26095347
IL2RG IL2RG IL2RG 18.598795 gene 0.36733038 1.60218017
PTPRCAP PTPRCAP PTPRCAP 11.197591 gene 2.59417336 1.35442226
SLAMF6 SLAMF6 SLAMF6 15.717805 gene 2.24192949 -0.67315919
SH2D1A SH2D1A SH2D1A 16.796386 gene 0.99394723 -0.54646429
SIRPG SIRPG SIRPG 13.000000 gene -1.28293814 1.20820169
CD5 CD5 CD5 11.197591 gene 0.09939443 0.21472777
NKG7 NKG7 NKG7 8.873881 gene 2.25255225 0.53915864
TIGIT TIGIT TIGIT 15.717805 gene -1.97729514 0.44093412
CD96 CD96 CD96 14.472676 gene 0.16917838 -0.91337377
CD247 CD247 CD247 11.197591 gene -1.85510282 1.61603281
CXCR3 CXCR3 CXCR3 14.472676 gene -0.26170585 1.49011281
PTPN7 PTPN7 PTPN7 13.000000 gene 1.82132662 2.09332717
TBC1D10C TBC1D10C TBC1D10C 13.000000 gene 2.42218823 1.83304880
UBASH3A UBASH3A UBASH3A 8.873881 gene -0.38127016 0.90757841
LY9 LY9 LY9 11.197591 gene 1.84689228 -0.80501203
GZMA GZMA GZMA 11.197591 gene 0.77134288 -0.08465634
CD48 CD48 CD48 11.197591 gene 1.09191214 2.60026484
TBX21 TBX21 TBX21 8.873881 gene -2.18598610 1.00502491
CXCL10 CXCL10 CXCL10 13.000000 gene -3.19673967 0.02057735
ICOS ICOS ICOS 11.197591 gene -1.66109807 -0.23938802
CD3G CD3G CD3G 11.197591 gene -0.57709549 -1.72670732
GBP5 GBP5 GBP5 16.796386 gene -2.25683022 -0.58039711
SLAMF1 SLAMF1 SLAMF1 8.873881 gene 1.79028925 -2.24590971
HLA-B HLA-B HLA-B 8.873881 gene -3.41450719 -0.61733258
GIMAP5 GIMAP5 GIMAP5 8.873881 gene 1.57235419 1.58263799
HLA-H HLA-H HLA-H 5.598795 gene -2.88851193 -1.32383245
TMEM176A TMEM176A TMEM176A 8.873881 gene 0.90793077 1.13745597
LCK LCK LCK 11.197591 gene -0.07070277 -0.23816541
ZNF831 ZNF831 ZNF831 8.873881 gene 2.10309341 -1.88143345
SIT1 SIT1 SIT1 8.873881 gene 0.17930021 2.57430663
SLA2 SLA2 SLA2 13.000000 gene -1.46023794 2.01432460
TRAT1 TRAT1 TRAT1 8.873881 gene -0.34060378 -1.45033811
CCL5 CCL5 CCL5 14.472676 gene 0.43121773 0.75318386
GZMK GZMK GZMK 8.873881 gene 2.51778528 0.05452838
ZAP70 ZAP70 ZAP70 8.873881 gene 2.59044179 3.12663638
GPR174 GPR174 GPR174 8.873881 gene 0.46540416 -2.52744789
CXCL9 CXCL9 CXCL9 13.000000 gene -1.78131423 -0.90553579
MEGENA documentation built on May 1, 2019, 8:07 p.m.
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Description Usage Arguments Details Value Author(s) Examples
Description
A modification of plot_module() function for more general subnetwork plotting purpose.
Usage
1 2 3 4 |
Arguments
module |
A character vector containing gene names to be subsetted. |
hub |
If provided, genes in hub will be highlighted as triangles in resulting figure. |
PFN |
igraph object retaining PFN topology. |
node.default.color |
Default node colors for those that do not intersect with signatures in gene.set. |
gene.set |
A list object containing signatures for customized coloring of nodes in resulting network plot. |
color.code |
A character vector with matched length to "gene.set", to specify colors for each signature. |
show.legend |
TRUE/FALSE for showing node legend on the bottom of the figure. |
label.hubs.only |
TRUE/FALSE to show labels for significant hub genes only, or all genes. Defauly is TRUE. |
hubLabel.col |
Label color for hubs. Default is "red" |
hubLabel.sizeProp |
A multiplicative factor to adjust hub label sizes with respect to node size values. Default is 0.5 |
show.topn.hubs |
Maximal number of hubs to label on module subnetwork. Default is 10. |
node.sizeProp |
A multiplicative factor to adjust node sizes with respect to 90th percentile degree node size. Default is 13 |
label.sizeProp |
A multiplicative factor to adjust node label sizes with respect to 90th percentile degree node size. Default is 13 |
label.scaleFactor |
Overall scale factor to control the final size of node labels appearing in figure. Default is 10. |
layout |
Network layout algorithm to apply. Options are: "kamada.kawai", "fruchterman.reingold". |
Details
Subnetwork plot functionality with application of "ggrepel" package for node labeling. The most effective way to control overall node label size is through label.scaleFactor.
Value
A list object holding ggplot object and node annotation table.
Author(s)
Won-Min Song
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ## Not run:
rm(list = ls())
library(MEGENA)
data(Sample_Expression)
ijw <- calculate.correlation(datExpr[1:100,],doPerm = 2)
el <- calculate.PFN(ijw[,1:3])
g <- graph.data.frame(el,directed = FALSE)
MEGENA.output <- do.MEGENA(g = g,remove.unsig = FALSE,doPar = FALSE,n.perm = 10)
output.summary <- MEGENA.ModuleSummary(MEGENA.output,
mod.pvalue = 0.05,hub.pvalue = 0.05,
min.size = 10,max.size = 5000,
annot.table = NULL,id.col = NULL,symbol.col = NULL,
output.sig = TRUE)
pnet.obj <- plot_subgraph(module = output.summary$modules[[1]],
hub = c("CD3E","CD2"),PFN = g,node.default.color = "black",
gene.set = NULL,color.code = c("grey"),show.legend = TRUE,
label.hubs.only = TRUE,hubLabel.col = "red",hubLabel.sizeProp = 0.5,
show.topn.hubs = 10,node.sizeProp = 13,label.sizeProp = 13,
label.scaleFactor = 10,layout = "kamada.kawai")
# the plot
pnet.obj[[1]]
# the annotation
pnet.obj[[2]]
## End(Not run)
|
Example output
Loading required package: doParallel
Loading required package: foreach
Loading required package: iterators
Loading required package: parallel
Loading required package: igraph
Attaching package: ‘igraph’
The following objects are masked from ‘package:stats’:
decompose, spectrum
The following object is masked from ‘package:base’:
union
i = 1
i = 2
- outputting correlation results...
####### PFN Calculation commences ########
[1] "PFG is complete."
Commence multiscale clustering....
Calculating distance metric and similarity...
iteration:1
- #. tested:1
- k=2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,
- #. of split:4
- assess improvements over compactness
iteration:2
- #. tested:4
- k=2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,
- #. of split:1
- k=2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,
- #. of split:4
- assess improvements over compactness
- k=2,3,4,5,6,7,
- #. of split:0
- k=2,3,4,5,6,7,8,
- #. of split:0
iteration:3
- #. tested:3
- k=2,3,4,5,6,
- #. of split:0
- k=2,3,4,5,6,7,
- #. of split:0
- k=2,3,4,5,6,7,8,9,
- #. of split:0
Commence MHA...
Calculating hub significance.....
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
permutation no.:1,2,3,4,5,6,7,8,9,10,
Identifying similar scales....
- Calculating within-module degree profiles.....
K.max:8
Cluster scales based on degree profiles...
k = 2,3,4,5,6,7,8,
- identified: 3
Identifying hub genes significant in each scale level...
Assigning module/KDA membership
Calculating node topological properties
- # of genes: 43
- # of hubs: 2
- generating module subnetwork figure...
node.lab id node.size node.shape X1 X2
CD3E CD3E CD3E 38.505399 hub 1.08196871 0.68793133
CD3D CD3D CD3D 18.598795 gene -0.59820031 1.78460447
CD2 CD2 CD2 37.451167 hub -0.71857815 0.11006972
CXCL11 CXCL11 CXCL11 8.873881 gene -2.80800335 -1.89082777
ITK ITK ITK 19.368649 gene 0.76348184 -1.11081879
ACAP1 ACAP1 ACAP1 15.717805 gene 1.15210236 2.26095347
IL2RG IL2RG IL2RG 18.598795 gene 0.36733038 1.60218017
PTPRCAP PTPRCAP PTPRCAP 11.197591 gene 2.59417336 1.35442226
SLAMF6 SLAMF6 SLAMF6 15.717805 gene 2.24192949 -0.67315919
SH2D1A SH2D1A SH2D1A 16.796386 gene 0.99394723 -0.54646429
SIRPG SIRPG SIRPG 13.000000 gene -1.28293814 1.20820169
CD5 CD5 CD5 11.197591 gene 0.09939443 0.21472777
NKG7 NKG7 NKG7 8.873881 gene 2.25255225 0.53915864
TIGIT TIGIT TIGIT 15.717805 gene -1.97729514 0.44093412
CD96 CD96 CD96 14.472676 gene 0.16917838 -0.91337377
CD247 CD247 CD247 11.197591 gene -1.85510282 1.61603281
CXCR3 CXCR3 CXCR3 14.472676 gene -0.26170585 1.49011281
PTPN7 PTPN7 PTPN7 13.000000 gene 1.82132662 2.09332717
TBC1D10C TBC1D10C TBC1D10C 13.000000 gene 2.42218823 1.83304880
UBASH3A UBASH3A UBASH3A 8.873881 gene -0.38127016 0.90757841
LY9 LY9 LY9 11.197591 gene 1.84689228 -0.80501203
GZMA GZMA GZMA 11.197591 gene 0.77134288 -0.08465634
CD48 CD48 CD48 11.197591 gene 1.09191214 2.60026484
TBX21 TBX21 TBX21 8.873881 gene -2.18598610 1.00502491
CXCL10 CXCL10 CXCL10 13.000000 gene -3.19673967 0.02057735
ICOS ICOS ICOS 11.197591 gene -1.66109807 -0.23938802
CD3G CD3G CD3G 11.197591 gene -0.57709549 -1.72670732
GBP5 GBP5 GBP5 16.796386 gene -2.25683022 -0.58039711
SLAMF1 SLAMF1 SLAMF1 8.873881 gene 1.79028925 -2.24590971
HLA-B HLA-B HLA-B 8.873881 gene -3.41450719 -0.61733258
GIMAP5 GIMAP5 GIMAP5 8.873881 gene 1.57235419 1.58263799
HLA-H HLA-H HLA-H 5.598795 gene -2.88851193 -1.32383245
TMEM176A TMEM176A TMEM176A 8.873881 gene 0.90793077 1.13745597
LCK LCK LCK 11.197591 gene -0.07070277 -0.23816541
ZNF831 ZNF831 ZNF831 8.873881 gene 2.10309341 -1.88143345
SIT1 SIT1 SIT1 8.873881 gene 0.17930021 2.57430663
SLA2 SLA2 SLA2 13.000000 gene -1.46023794 2.01432460
TRAT1 TRAT1 TRAT1 8.873881 gene -0.34060378 -1.45033811
CCL5 CCL5 CCL5 14.472676 gene 0.43121773 0.75318386
GZMK GZMK GZMK 8.873881 gene 2.51778528 0.05452838
ZAP70 ZAP70 ZAP70 8.873881 gene 2.59044179 3.12663638
GPR174 GPR174 GPR174 8.873881 gene 0.46540416 -2.52744789
CXCL9 CXCL9 CXCL9 13.000000 gene -1.78131423 -0.90553579
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