SparseDC (version 0.1.17)

lambda1_calculator: Lambda 1 Calculator.

Description

Calculates the lambda 1 value for the sparseDC algorithm. The lambda 1 value controls the number of marker genes selected for each cluster in the output from SparseDC. It is calculated as the value of lambda 1 that results in no marker genes being selected when then are no meaningful clusters present in the data. Please see the original manuscript for further details.

Usage

lambda1_calculator(pdat1, pdat2, ncluster, alpha1 = 0.5, nboot1 = 1000)

Arguments

pdat1

The centered data from condition 1, columns should be samples (cells) and rows should be features (genes).

pdat2

The centered data from condition 2, columns should be samples (cells) and rows should be features (genes). The number of genes should be the same as pdat1. as in pdat1.

ncluster

The number of clusters present in the data.

alpha1

The quantile of the bootstrapped lambda 1 values to use, range is (0,1). The default value is 0.5, the median of the calculated lambda 1 values.

nboot1

The number of bootstrap repetitions used for estimating lambda 1, the default value is 1000.

Value

The calculated value of lambda 1 to use in the main SparseDC algorithm.

See Also

lambda2_calculator for how to calculate the lambda 2 parameter. sparsedc_cluster for the main sparse differential clustering function.

Examples

# NOT RUN {
set.seed(10)
# Select small dataset for example
data_test <- data_biase[1:100,]
# Split data into conditions A and B
data_A <- data_test[ , which(condition_biase == "A")]
data_B <- data_test[ , which(condition_biase == "B")]
# Pre-process the data
pre_data <- pre_proc_data(data_A, data_B, norm = FALSE, log = TRUE,
center = TRUE)
# Calculate the lambda 1 value
lambda1_calculator(pdat1 = pre_data[[1]], pdat2 = pre_data[[2]], ncluster=3,
 alpha1 = 0.5, nboot1 = 1000)

# Can also run

# Pre-process the data
pre_data <- pre_proc_data(data_A, data_B, norm = FALSE, log = TRUE,
center = TRUE)
pdata_A <- pre_data[[1]]
pdata_B <- pre_data[[2]]
# Calculate the lambda 1 value
lambda1_calculator(pdat1 = pdata_A, pdat2 = pdata_B , ncluster=3,
 alpha1 = 0.5, nboot1 = 1000)

# }