lambda2_calculator: Lambda 2 Calculator.
In SparseDC: Implementation of SparseDC Algorithm

Description Usage Arguments Value See Also Examples

Calculates the lambda 2 values for use in the main SparseDC algorithm, the lambda 2 value controls the number of genes that show condition-dependent expression within each cell type. That is it controls the number of different mean values across the conditions for each cluster. It is calculated by estimating the value of lambda 2 that would result in no difference in mean values across conditions when there are no meaningful differences across between the conditions. For further details please see the original manuscript.

1	lambda2_calculator(pdat1, pdat2, ncluster, alpha2 = 0.5, nboot2 = 1000)

`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.
`alpha2`	The quantile of the bootstrapped lambda 2 values to use, range is (0,1). The default value is 0.5, the median of the calculated lambda 2 values.
`nboot2`	The number of bootstrap repetitions for estimating lambda 2, the default value is 1000.

The calculated value of lambda 2 to use in the main SparseDC algorithm.

lambda1_calculator sparsedc_cluster

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 2 value
lambda2_calculator(pdat1 = pre_data[[1]], pdat2 = pre_data[[2]], ncluster = 3,
 alpha2 = 0.5, nboot2 = 1000)

 # Can also run
 pdata_A <- pre_data[[1]]
 pdata_B <- pre_data[[2]]
lambda2_calculator(pdat1 = pdata_A, pdat2 = pdata_B, ncluster = 3,
 alpha2 = 0.5, nboot2 = 1000)