sparse_mdc: SparseDC Multi

In SparseMDC: Implementation of SparseMDC Algorithm

Description

Applies sparse clustering to data from multiple conditions, linking the clusters across conditions and selecting a set of marker variables for each cluster and condition. See the manuscript for descriptions of the different categories of marker genes.

Usage

sparse_mdc(pdat, nclust, dim, lambda1, lambda2, nitter = 20,
  nstarts = 50, init_iter = 5, delta = 1e-07)

Arguments

pdat	list with D entries, each entry contains data d, p * n matrix. This data should be centered and log-transformed.
nclust	Number of clusters in the data.
dim	Total number of conditions, D.
lambda1	The lambda 1 value to use in the SparseMDC function. This value controls the number of marker genes detected for each of the clusters in the final result. This can be calculated using the "lambda1_calculator" function or supplied by the user.
lambda2	The lambda 2 value to use in the SparseMDC function. This value controls the number of genes that show condition-dependent expression within each cell type. This can be calculated using the "lambda2_calculator" function or supplied by the user.
nitter	The max number of iterations for each of the start values, the default value is 20.
nstarts	The max number of possible starts. The default value is 50.
init_iter	The number of iterations used to initialize the algorithm. Higher values result in less starts but more accurate and vice versa. Default is 5.
delta	Small term to ensure existance of solution, default is 0.0000001.

Value

A list containing cluster assignments, center values and the scores for each start.

Examples

set.seed(10)
# Select small dataset for example
data_test <- data_biase[1:100,]
# Split data into condition A and B
data_A <- data_test[ , which(condition_biase == "A")]
data_B <- data_test[ , which(condition_biase == "B")]
data_C <- data_test[ , which(condition_biase == "C")]
# Store data as list
dat_l <- list(data_A, data_B, data_C)
# Pre-process the data
pdat <- pre_proc_data(dat_l, dim=3, norm = FALSE, log = TRUE,
center = TRUE)
lambda1 <- lambda1_calculator(pdat, dim = 3, nclust = 3)
lambda2 <- lambda2_calculator(pdat, dim = 3, nclust = 3, lambda1 = lambda1)
smdc_res <- sparse_mdc(pdat, nclust = 3, dim = 3, lambda1 = lambda1,
lambda2 = lambda2)

SparseMDC documentation built on May 2, 2019, 4:01 a.m.