tgs_graph_cover_resample {tgstat}

Clusters directed graph multiple times with randomized sample subset

Description

Clusters directed graph multiple times with randomized sample subset.

Usage

tgs_graph_cover_resample(
  graph,
  knn,
  min_cluster_size,
  cooling = 1.05,
  burn_in = 10,
  p_resamp = 0.75,
  n_resamp = 500,
  method = "hash"
)

Arguments

graph

directed graph in the format returned by tgs_graph

knn

maximal number of edges used per node for each sample subset

min_cluster_size

used to determine the candidates for seeding (= min weight)

cooling

factor that is used to gradually increase the chance of a node to stay in the cluster

burn_in

number of node reassignments after which cooling is applied

p_resamp

fraction of total number of nodes used in each sample subset

n_resamp

number iterations the clustering is run on different sample subsets

method

method for calculating co_cluster and co_sample; valid values: "hash", "full", "edges"

Details

The algorithm is explained in a "MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions" paper, published in "Genome Biology" #20: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1812-2

Value

If method == "hash", a list with two members. The first member is a data frame with 3 columns: "node1", "node2" and "cnt". "cnt" indicates the number of times "node1" and "node2" appeared in the same cluster. The second member of the list is a vector of number of nodes length reflecting how many times each node was used in the subset.

If method == "full", a list containing two matrices: co_cluster and co_sample.

If method == "edges", a list containing two data frames: co_cluster and co_sample.

See Also

tgs_graph()

Examples

# Note: all the available CPU cores might be used

set.seed(seed = 0)
rows <- 100
cols <- 200
vals <- sample(1:(rows * cols / 2), rows * cols, replace = TRUE)
m <- matrix(vals, nrow = rows, ncol = cols)

r1 <- tgs_cor(m, pairwise.complete.obs = FALSE, spearman = TRUE)
r2 <- tgs_knn(r1, knn = 20, diag = FALSE)
r3 <- tgs_graph(r2, knn = 3, k_expand = 10)
r4 <- tgs_graph_cover_resample(r3, 10, 1)