perform.seurat.cca | R Documentation |
Performs Seurat integration on the supplied assay names. Results are saved under integration_reductions
perform.seurat.integration( object, object.list, assay, reduction.save.suffix = NULL, nfeatures = 2000, anchors.dims = 1:30, l2.norm = T, k.anchor = 5, k.filter = 200, k.score = 30, max.features = 200, nn.method = "annoy", n.trees = 50, anchor.eps = 0, features = NULL, integrate.dims = 1:30, k.weight = 100, sd.weight = 1, sample.tree = NULL, integrate.eps = 0, print.variance = TRUE, verbose = FALSE, seed = 1234, ... )
object |
IBRAP S4 class object |
object.list |
list of individual sample IBRAP S4 class objects. |
assay |
Character. String containing indicating which assay to use |
nfeatures |
Numerical. How many features should be found as integration anchors. Default = 3000 |
l2.norm |
Logical. Perform L2 normalization on the CCA cell embeddings after dimensional reduction. Default = TRUE |
k.anchor |
Numerical. How many neighbors (k) to use when picking anchors. Default = 5 |
k.filter |
Numerical. How many neighbors (k) to use when filtering anchors. Default = 200 |
k.score |
Numerical. How many neighbors (k) to use when scoring anchors. Default = 30 |
nn.method |
Character. Method for nearest neighbor finding. Options include: rann, annoy. Default = annoy |
anchor.eps |
Numerical. Error bound on the neighbor finding algorithm (from RANN/Annoy) when finding integration genes. |
features |
Character. Vector of features to use when computing the PCA to determine the weights. Only set if you want a different set from those used in the anchor finding process. Default = NULL |
integrate.dims |
Numerical. Number of dimensions to use in the anchor weighting procedure. Default = 1:30 |
k.weight |
Numerical. Number of neighbors to consider when weighting anchors. Default = 100 |
sd.weight |
Numerical. Controls the bandwidth of the Gaussian kernel for weighting. Default = 1 |
sample.tree |
Character. Specify the order of integration. If NULL, will compute automatically. Default = NULL |
integrate.eps |
Numerical. Error bound on the neighbor finding algorithm (from RANN) |
reduction.save.suffix. |
Character. What should be added as a suffix to reduction name. Default = ” |
max.features. |
Numerical. The maximum number of features to use when specifying the neighborhood search space in the anchor filtering. Default = 200 |
n.tree |
Numerical. More trees gives higher precision when using annoy approximate nearest neighbor search. Default = 50 |
save.plot |
Boolean. Should the automatically genewrated plot be saved? Default = TRUE |
Produces a new 'methods' assay containing normalised, scaled and HVGs.
perform.seurat.cca <- function(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'), reduction.save.suffix=NULL, nfeatures = 3000, reduction = 'cca', anchors.dims = 1:30, l2.norm = T, k.anchor = 5, k.filter = 200, k.score = 30, max.features = 200, nn.method = 'annoy', n.trees = 50, anchor.eps = 0, features = NULL, integrate.dims = 1:30, k.weight = 100, sd.weight = 1, sample.tree = NULL, integrate.eps = 0)
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