Generate factor scores, principal component scores, or projection scores of latent, composite, and unmeasured variable modules, respectively, and fit them with an exogenous group effect.
Arguments
- graph
An igraph object.
- data
A matrix or data.frame. Rows correspond to subjects, and columns to graph nodes.
- group
A binary vector. This vector must be as long as the number of subjects. Each vector element must be 1 for cases and 0 for control subjects.
- HM
Hidden model type. For each defined hidden module: (i) if
HM = "LV", a latent variable (LV) will be defined as common unknown cause acting on cluster nodes; (ii) ifHM = "CV", cluster nodes will be considered as regressors of a latent composite variable (CV); (iii) ifHM = "UV", an unmeasured variable (UV) model will be generated for each module, where source nodes (i.e., in-degree = 0) act as common regressors influencing the other nodes via an unmeasured variable. By default, HM is set to "LV" (i.e., the latent variable model).- type
Graph clustering method. If
type = "tahc", network modules are generated using the tree agglomerative hierarchical clustering method (Yu et al., 2015). Other non-tree clustering methods from igraph package include: "wtc" (default value; walktrap community structure with short random walks), "ebc" (edge betweenness clustering), "fgc" (fast greedy method), "lbc" (label propagation method), "lec" (leading eigenvector method), "loc" (multi-level optimization), "opc" (optimal communiy structure), "sgc" (spinglass statistical mechanics). By default, the "wtc" method is used.- size
Minimum number of nodes per hidden module. By default, a minimum number of 5 nodes is required.
- verbose
A logical value. If TRUE, intermediate graphs will be displayed during the execution. In addition, a reduced graph with clusters as nodes will be fitted and showed to screen (see also
mergeNodes). By default,verbode = FALSE.- ...
Currently ignored.
Value
A list of 3 objects:
"fit", hidden module fitting as a lavaan object;
"membership", hidden module nodes membership;
clusterGraphfunction;"dataHM", data matrix with cluster scores in first columns.
References
Grassi M, Palluzzi F, Tarantino B (2022). SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation Models. Bioinformatics, 38 (20), 4829–4830 <https://doi.org/10.1093/bioinformatics/btac567>
See also
See clusterGraph and cplot
for graph clustering.
Author
Mario Grassi mario.grassi@unipv.it
Examples
# Nonparanormal(npn) transformation
als.npn <- transformData(alsData$exprs)$data
#> Conducting the nonparanormal transformation via shrunkun ECDF...done.
C <- clusterScore(graph = alsData$graph, data = als.npn,
group = alsData$group,
HM = "LV",
type = "wtc",
verbose = FALSE)
#> modularity = 0.528642
#>
#> Community sizes
#> 4 1 3 2
#> 3 8 9 11
#>
#> Model converged: TRUE
#> SRMR: 6.89267e-10
#>
summary(C$fit)
#> lavaan 0.6-20 ended normally after 16 iterations
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 9
#>
#> Number of observations 160
#>
#> Model Test User Model:
#>
#> Test statistic 0.000
#> Degrees of freedom 0
#>
#> Parameter Estimates:
#>
#> Standard errors Standard
#> Information Expected
#> Information saturated (h1) model Structured
#>
#> Regressions:
#> Estimate Std.Err z-value P(>|z|)
#> LV1 ~
#> group -0.848 0.251 -3.377 0.001
#> LV2 ~
#> group 0.869 0.226 3.847 0.000
#> LV3 ~
#> group -0.987 0.225 -4.382 0.000
#>
#> Covariances:
#> Estimate Std.Err z-value P(>|z|)
#> .LV1 ~~
#> .LV2 -0.201 0.083 -2.416 0.016
#> .LV3 0.787 0.103 7.667 0.000
#> .LV2 ~~
#> .LV3 -0.367 0.079 -4.650 0.000
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .LV1 1.152 0.129 8.944 0.000
#> .LV2 0.930 0.104 8.944 0.000
#> .LV3 0.925 0.103 8.944 0.000
#>
head(C$dataHM)
#> group LV1 LV2 LV3 317 572 581
#> ALS2 1 -0.7989464 -0.7527118 -0.42868026 1.0661487 -0.7577341 -1.1225852
#> ALS3 1 -0.8635143 -0.8612364 -0.68134940 1.6294444 -2.1342965 -0.2662029
#> ALS4 1 0.9117982 -1.1961490 0.73949684 0.9870911 0.3160400 -0.2006991
#> ALS5 1 -1.1279817 -0.2024923 -1.35460499 0.5799344 -0.9135118 -1.0127737
#> ALS6 1 -0.2946798 1.3716185 -0.07552589 0.1360061 -0.8900137 -1.4346740
#> ALS7 1 -1.7251688 0.4125732 -1.46126288 0.5987578 -1.6885942 -0.4181436
#> 596 598 836 842 54205 1616
#> ALS2 0.3666365 -0.007977011 -0.08785375 -0.8003346 0.2497352 -0.20069907
#> ALS3 1.3168559 -0.716404714 0.89001373 -0.1199272 0.6370285 -0.77886604
#> ALS4 1.2812248 -0.216987905 -0.48853105 0.9375004 -1.1521227 0.24973518
#> ALS5 1.6885942 -0.418143644 1.21426434 -0.2827396 0.8443656 -0.15211855
#> ALS6 0.6370285 0.656498936 0.10387774 -0.1038777 -0.2497352 0.05586618
#> ALS7 -0.6177855 -2.299257173 0.63702853 -1.2470790 1.2812248 -2.29925717
#> 79139 5606 5608 4217 5600 6300
#> ALS2 0.9870911 -0.03989473 0.8003346 -0.18446080 -1.0661487 -1.2142643
#> ALS3 1.3168559 -1.62944439 1.3933390 -0.03989473 -0.8900137 -0.8900137
#> ALS4 -0.8003346 0.28273958 -0.3666365 0.69617310 0.2827396 0.9870911
#> ALS5 2.0111279 0.02393297 0.9620141 1.57527382 -0.6177855 -2.5616910
#> ALS6 -0.2169879 0.10387774 0.5613048 0.75773406 -1.2470790 -1.3168559
#> ALS7 1.0127737 -0.57993445 2.2992572 1.21426434 0.1199272 0.4008634
#> 5603 1432 4744 4747 4741 5530
#> ALS2 0.332813985 0.8669746 -0.67620930 -0.5799344 -0.7577341 0.6961731
#> ALS3 -0.103877738 0.2497352 -0.89001373 -0.8003346 -0.8443656 0.8443656
#> ALS4 0.266202856 1.1225852 -1.47848512 -1.5251790 -1.1521227 -0.5064807
#> ALS5 1.316855924 2.1342965 0.15211855 0.2333318 -0.2497352 0.9870911
#> ALS6 -0.233331753 1.3933390 1.57527382 0.7577341 1.3541545 -0.8669746
#> ALS7 -0.007977011 1.0127737 -0.05586618 0.5245871 0.4181436 2.5616910
#> 5532 5533 5534 5535 5630 6647
#> ALS2 0.41814364 0.4530701 0.2993503 0.03989473 -1.31685592 -0.7369195
#> ALS3 0.57993445 -1.2142643 0.5987578 1.03910878 -0.54285880 0.7577341
#> ALS4 -0.73691953 -0.3666365 -0.6564989 1.62944439 1.18264983 -1.7539739
#> ALS5 1.21426434 0.3328140 1.4346740 0.71640471 -0.98709115 0.3328140
#> ALS6 0.07185121 -0.1199272 0.3836965 -0.47073008 -0.40086343 1.2812248
#> ALS7 1.06614870 -0.6177855 1.3168559 0.43554360 -0.03989473 -0.1844608
#> 7132 7133 10452 84134
#> ALS2 -0.07185121 -0.7788660 -0.9620141 -0.1199272
#> ALS3 -1.12258519 -0.9620141 -1.1521227 -1.1826498
#> ALS4 0.40086343 0.1038777 -1.2142643 -0.2993503
#> ALS5 0.20069907 -2.2992572 -1.9115966 -0.8669746
#> ALS6 0.91351183 -0.6961731 -1.6885942 -0.5245871
#> ALS7 0.96201413 0.8003346 -0.5799344 -0.1682687
table(C$membership)
#>
#> 1 2 3
#> 8 11 9