mnn_opt: Search for the optimal parameters for MNN
In WeiqiangZhou/SINTER: Single-cell genomic data integration

Description Usage Arguments Value Examples

This function is used for searching for the optimal parameters used in MNN. The goal is find parameters that maximize the average of p-values from the neighbor_test function.

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mnn_opt(data_ref, data_in, k_range = c(16:25), sigma_range = c(0.01,
  1), dim = 3, dist_scale_in = 10, tol_er = 0.001, ncore = 10,
  fast = FALSE)

`data_ref`	Single cell data from one data type as the reference for matching (e.g., scATAC-seq data).
`data_in`	Single cell data from another data type as the input for matching (e.g., scRNA-seq data).
`k_range`	Searching space for k, the number of mutual nearest neighbor in MNN.
`sigma_range`	Searching space for sigma, the bandwidth of the Gaussian smoothing kernel used to compute the correction vector.
`dim`	Number of dimension used for matching the single cells. For example, the number of principal components.
`dist_scale_in`	Scale used to define the radius of the region for testing.
`tol_er`	The desired accuracy in function optimize.
`ncore`	Number of CPU cores used for parallel processing. Use ncore = 1 to run the function without parallel processing.
`fast`	A flag indicates whether or not to use a fast neighbor_test.

`k_opt`	The optimal value for k.
`sigma_opt`	The optimal value for sigma.
`max_obj`	The average p-value based on the optimal parameters.

## Not run: 
result_opt <- mnn_opt(data_ref,data_in,k_range=c(16:25),sigma_range=c(0.01,1),dim=3,dist_scale_in=10,tol_er=0.001,ncore=10)

## End(Not run)