Description Usage Arguments Value Examples
View source: R/preprocesssubsetdata.R
The wrapper does FindVeriableGenes, ScaleData, RunPCA, JackStraw to determine how many PCs to use, ProjectPCA and FindClusters and retrun a fully processed Seurat object. The input subsetted seurat object is supposed to be fully processed as well. So the NormalizeData step is not necessary.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | PreprocessSubsetData(
object,
variable.features.n = 3000,
num.pc = 20,
pc.use = NULL,
workers = 2,
score.thresh = 1e-05,
sig.pc.thresh = 0.05,
n.start = 100,
nn.eps = 0,
resolution = 0.8,
k.param = 30,
...
)
|
object |
A subsetted Seurat object created by RandomSubsetData |
variable.features.n |
number of variable features for |
num.pc |
number of PCs to calculate in RunPCA, JackStraw and JackStrawPlot step. The optimal PCs for FindClusters will be determined by only significant PCs from JackStrawPlot or if pc.use is set, JackStraw step will be skipped and use pc.use for FindClusters. |
pc.use |
number of PCs used for FindClusters. if pc.use is set, JackStraw step will be skipped and use pc.use for FindClusters. score.thresh and sig.pc.thresh will be ignored. |
workers |
number of CPUs to use for |
score.thresh |
Threshold to use for the proportion test of PC significance. |
sig.pc.thresh |
Threshold for the significance of a particular PC. |
n.start |
Number of random start. |
nn.eps |
Error bound when performing nearest neighbor seach using RANN; default of 0.0 implies exact nearest neighbor search. See FindClusters. |
resolution |
Value of the resolution parameter, use a value above (below) 1.0 if you want to obtain a larger (smaller) number of communities. see FIndClusters. |
k.param |
Defines k for the k-nearest neighbor algorithm. |
... |
any other parameters |
a fully processed Seurat object
1 2 3 4 5 6 | ## Not run:
pbmc_small_subset<- RandomSubsetData(pbmc_small, 0.8)
pbmc_small_subset_processed<- PreprocessSubsetData(pbmc_small_subset)
pbmc_small_subset_processed@meta.data
## End(Not run)
|
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