Description Usage Arguments Details Note Examples
View source: R/6.5_ISS_ClustCompare.R
Compare two cluster and find their similarity (Supervised).
1 2 | ISS_clustCompare(preditorData, prediction, ntree = 100,
method = "prob")
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preditorData |
Data to be used to compare. Input data in class MolDiaISS. Output of readISS and must be clustered. |
prediction |
Data that need to compare. Input data in class MolDiaISS. Output of readISS and must be clustered. |
ntree |
Number of trees to grow. randomForest. See This should not be set to too small a number, to ensure that every input row gets predicted at least a few times. |
method |
Methods to return comarison results. Possible values are "vote" and "prob". Defaut is "prob". |
This function will be apply to compare Two cluster by Random forest algorithm.
This function need optimization to run properly. Work fine with small data but not with big data .
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ## Read data: Left and right HC
left_hypo <- readISS(file = system.file("extdata", "Hypocampus_left.csv", package="MolDia"),
cellid = "CellId",centX = "centroid_x", centY = "centroid_y")
right_hypo <- readISS(file = system.file("extdata", "Hypocampus_right.csv", package="MolDia"),
cellid = "CellId",centX = "centroid_x", centY = "centroid_y")
## Data preprocessing
left_hypo <- ISS_preprocess(data = left_hypo, normalization.method = "LogNormalize",
do.scale = TRUE, do.center = TRUE)
right_hypo <- ISS_preprocess(data = right_hypo, normalization.method = "LogNormalize",
do.scale = TRUE, do.center = TRUE)
## Cluster data based on SEURAT pipeline
left_hypo <- ISS_cluster(data = left_hypo, pc = 0.9, resolution = 0.05)
right_hypo <- ISS_cluster(data = right_hypo, pc = 0.9, resolution = 0.05)
## Cluster compare
comapre_left_right <- ISS_clustCompare(preditorData = left_hypo, prediction = right_hypo, ntree = 10)
comapre_right_left <- ISS_clustCompare(preditorData = right_hypo, prediction = left_hypo, ntree = 10)
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