Detects functional communities from EMG coherence and compares them with structural communities from the MSK network.
Arguments
- pe
A SummarizedExperiment-like object or numeric signal matrix.
- hg
An MSKHypergraph object (NULL loads default).
- gamma
Resolution parameter for MSK community detection (default: 4.3).
- mapping
Optional data.frame from
emgToMSKMapping().
Value
A list with:
- emg_communities
Named integer vector of EMG community assignments
- msk_communities
Named integer vector of MSK community assignments
- z_rand
z-Rand score comparing the two partitions
- mapping
The channel-to-muscle mapping used
Examples
C <- matrix(c(1,1,0,0,0, 0,1,1,0,0, 0,0,1,1,0, 0,0,0,1,1, 1,0,0,0,1, 0,1,0,1,0),
nrow = 6, byrow = TRUE,
dimnames = list(paste0("bone", 1:6),
c("Biceps Brachii", "Deltoid", "Trapezius",
"Triceps Brachii", "Brachialis")))
hg <- MSKHypergraph(C)
set.seed(1)
emg <- matrix(abs(rnorm(300 * 4)), 300, 4)
colnames(emg) <- c("Biceps Brachii", "Deltoid", "Trapezius", "Triceps Brachii")
emgCommunityCompare(emg, hg = hg)
#> $emg_communities
#> Biceps Brachii Deltoid Trapezius Triceps Brachii
#> 1 1 1 1
#>
#> $msk_communities
#> Biceps Brachii Deltoid Trapezius Triceps Brachii
#> 1 2 3 4
#>
#> $z_rand
#> [1] 0
#>
#> $mapping
#> channel_idx channel_name muscle_idx muscle_name match_quality
#> 1 1 Biceps Brachii 1 Biceps Brachii 1
#> 2 2 Deltoid 2 Deltoid 1
#> 3 3 Trapezius 3 Trapezius 1
#> 4 4 Triceps Brachii 4 Triceps Brachii 1
#>