hierarchical_partition: Hierarchical partition

In jokergoo/cola: A Framework for Consensus Partitioning

Hierarchical partition

Description

Hierarchical partition

Usage

hierarchical_partition(data,
    top_n = NULL,
    top_value_method = "ATC",
    partition_method = "skmeans",
    combination_method =  expand.grid(top_value_method, partition_method),
    anno = NULL, anno_col = NULL,
    mean_silhouette_cutoff = 0.9, min_samples = max(6, round(ncol(data)*0.01)),
    subset = Inf, predict_method = "centroid",
    group_diff = ifelse(scale_rows, 0.5, 0),
    fdr_cutoff = cola_opt$fdr_cutoff,
    min_n_signatures = NULL,
    filter_fun = function(mat) {
    s = rowSds(mat)
    s > quantile(unique(s[s > 1e-10]), 0.05, na.rm = TRUE)
    },
    max_k = 4, scale_rows = TRUE, verbose = TRUE, mc.cores = 1, cores = mc.cores, help = TRUE, ...)

Arguments

data	a numeric matrix where subgroups are found by columns.
top_n	Number of rows with top values.
top_value_method	a single or a vector of top-value methods. Available methods are in all_top_value_methods.
partition_method	a single or a vector of partition methods. Available methods are in all_partition_methods.
combination_method	A list of combinations of top-value methods and partitioning methods. The value can be a two-column data frame where the first column is the top-value methods and the second column is the partitioning methods. Or it can be a vector of combination names in a form of "top_value_method:partitioning_method".
anno	A data frame with known annotation of samples. The annotations will be plotted in heatmaps and the correlation to predicted subgroups will be tested.
anno_col	A list of colors (color is defined as a named vector) for the annotations. If anno is a data frame, anno_col should be a named list where names correspond to the column names in anno.
mean_silhouette_cutoff	The cutoff to test whether partition in current node is stable.
min_samples	the cutoff of number of samples to determine whether to continue looking for subgroups.
group_diff	Pass to get_signatures,ConsensusPartition-method.
fdr_cutoff	Pass to get_signatures,ConsensusPartition-method.
subset	Number of columns to randomly sample.
predict_method	Method for predicting class labels. Possible values are "centroid", "svm" and "randomForest".
min_n_signatures	Minimal number of signatures under the best classification.
filter_fun	A self-defined function which filters the original matrix and returns a submatrix for partitioning.
max_k	maximal number of partitions to try. The function will try 2:max_k partitions. Note this is the number of partitions that will be tried out on each node of the hierarchical partition. Since more subgroups will be found in the whole partition hierarchy, on each node, max_k should not be set to a large value.
scale_rows	Whether rows are scaled?
verbose	whether print message.
mc.cores	multiple cores to use. This argument will be removed in future versions.
cores	Number of cores, or a cluster object returned by makeCluster.
help	Whether to show the help message.
...	pass to consensus_partition

Details

The function looks for subgroups in a hierarchical way.

There is a special way to encode the node in the hierarchy. The length of the node name is the depth of the node in the hierarchy and the substring excluding the last digit is the name node of the parent node. E.g. for the node 0011, the depth is 4 and the parent node is 001.

Value

A HierarchicalPartition-class object. Simply type object in the interactive R session to see which functions can be applied on it.

Author(s)

Zuguang Gu <[e-mail]>

Examples

## Not run: 
set.seed(123)
m = cbind(rbind(matrix(rnorm(20*20, mean = 2, sd = 0.3), nr = 20),
                matrix(rnorm(20*20, mean = 0, sd = 0.3), nr = 20),
                matrix(rnorm(20*20, mean = 0, sd = 0.3), nr = 20)),
          rbind(matrix(rnorm(20*20, mean = 0, sd = 0.3), nr = 20),
                matrix(rnorm(20*20, mean = 1, sd = 0.3), nr = 20),
                matrix(rnorm(20*20, mean = 0, sd = 0.3), nr = 20)),
          rbind(matrix(rnorm(20*20, mean = 0, sd = 0.3), nr = 20),
                matrix(rnorm(20*20, mean = 0, sd = 0.3), nr = 20),
                matrix(rnorm(20*20, mean = 1, sd = 0.3), nr = 20))
         ) + matrix(rnorm(60*60, sd = 0.5), nr = 60)
rh = hierarchical_partition(m, top_value_method = "SD", partition_method = "kmeans")

## End(Not run)

jokergoo/cola documentation built on Feb. 29, 2024, 1:41 a.m.