predictTree: Predict labels of a dataset
In joseah/HierscPred: Supervised hierarchical classification for scPred
Description
Usage
Arguments
Value
Author(s)
Examples
Description
Predict the labels of a dataset using a tree object
Usage
1
predictTree(tree, newData, threshold = 0, max.iter.harmony = 10)
Arguments
tree
data.tree object storing the hierarchy and trained classifiers per layer
newData
Seurat object containing cells to annotate
threshold
Threshold used for probabilities to classify cells into classes. All cells below
this threshold value will be labels as "unassigned".
max.iter.harmony
Maximum number of clustering iterations by harmony
Value
A Seurat object with additional metadata columns with prediction probabilities associated to each
class, a prediction column, indicating the classification based on the provided threshold and
a generic_class column without "unassigned" labels.
Author(s)
Lieke Michielsen and Jose Alquicira-Hernandez
Examples
1
2
3
4
branches <- c("Myeloid/DC/pDC", "Myeloid/DC/cDC")
h <- create_hierarchy(branches)
train_tree(dataTrain, h)
dataTest <- predictTree(h, dataTest)
joseah/HierscPred documentation built on Nov. 25, 2021, 12:20 p.m.
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Description Usage Arguments Value Author(s) Examples
Description
Predict the labels of a dataset using a tree object
Usage
1 | predictTree(tree, newData, threshold = 0, max.iter.harmony = 10)
|
Arguments
tree |
data.tree object storing the hierarchy and trained classifiers per layer |
newData |
Seurat object containing cells to annotate |
threshold |
Threshold used for probabilities to classify cells into classes. All cells below this threshold value will be labels as "unassigned". |
max.iter.harmony |
Maximum number of clustering iterations by harmony |
Value
A Seurat object with additional metadata columns with prediction probabilities associated to each
class, a prediction column, indicating the classification based on the provided threshold and
a generic_class column without "unassigned" labels.
Author(s)
Lieke Michielsen and Jose Alquicira-Hernandez
Examples
1 2 3 4 | branches <- c("Myeloid/DC/pDC", "Myeloid/DC/cDC")
h <- create_hierarchy(branches)
train_tree(dataTrain, h)
dataTest <- predictTree(h, dataTest)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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