RunDimensionReduction {IRISFGM} | R Documentation |
This function is based on the Seurat package to perform dimension reduction. The input matirx is the LTMG signaling matrix.
RunDimensionReduction(object, ...) .runDimensionReduction( object, mat.source = c("LTMG", "UMImatrix"), reduction = "umap", dims = seq_len(15), perplexity = 15, seed = 1 ) ## S4 method for signature 'IRISFGM' RunDimensionReduction( object, mat.source = c("LTMG", "UMImatrix"), reduction = "umap", dims = seq_len(15), perplexity = 15, seed = 1 )
object |
Input IRIS-FGM object. |
... |
other arguments passed to methods |
mat.source |
choose source data for running this function either from LTMG signal matrix or from processed data. Values of this parameter are 'LTMG' and 'UMImatrix' |
reduction |
select a method for dimension reduction, including umap, tsne, and pca. |
dims |
select the number of PCs from PCA results to perform the following dimension reduction and cell clustering. |
perplexity |
Perplexity parameter as optimal number of neighbors. |
seed |
Set the seed of R‘s random number generator, which is useful for creating simulations or random objects that can be reproduced. |
This function will generate pca, tsne, or umap dimension reduction results.
data(example_object) example_obejct <- RunDimensionReduction(example_object, mat.source= 'LTMG', reduction = 'umap', dims = 1:15 , perplexity = 15, seed = 1)