View source: R/cytof_dimensionReduction.R
| cytof_dimReduction | R Documentation | 
Apply dimension reduction on the cytof expression data, 
with method pca, tsne, umap or isomap.
cytof_dimReduction(
  data,
  markers = NULL,
  method = c("umap", "tsne", "pca", "isomap", "NULL"),
  distMethod = "euclidean",
  out_dim = 2,
  umap_neighbor = 30,
  umap_min_dist = 0.3,
  tsneSeed = 42,
  isomap_k = 5,
  isomap_ndim = NULL,
  isomapFragmentOK = TRUE,
  ...
)
| data | Input expression data matrix. | 
| markers | Selected markers for dimension reduction, either marker names/descriptions or marker IDs. | 
| method | Method chosen for dimensition reduction, must be one of  | 
| distMethod | Method for distance calcualtion, default is "euclidean", other choices like "manhattan", "cosine", "rankcor".... | 
| out_dim | The dimensionality of the output. | 
| umap_neighbor | This parameter controls how UMAP balances local versus global structure in the data. | 
| umap_min_dist | Controls how tightly UMAP is allowed to pack points together. | 
| tsneSeed | Set a seed if you want reproducible t-SNE results. | 
| isomap_k | Number of shortest dissimilarities retained for a point, parameter for  | 
| isomap_ndim | Number of axes in metric scaling, parameter for  | 
| isomapFragmentOK | What to do if dissimilarity matrix is fragmented, parameter for  | 
| ... | Other parameters passed to the method, check  | 
A matrix of the dimension reduced data, with colnames method_ID, and rownames same as the input data.
data(iris) in_data <- iris[, 1:4] markers <- colnames(in_data[, 1:4]) out_data <- cytof_dimReduction(in_data, markers = markers, method = "tsne")
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