| scVIIntegration | R Documentation | 
scVI Integration
scVIIntegration(
  object,
  features = NULL,
  layers = "counts",
  conda_env = NULL,
  new.reduction = "integrated.dr",
  ndims = 30,
  nlayers = 2,
  gene_likelihood = "nb",
  max_epochs = NULL,
  ...
)
object | 
 A   | 
features | 
 Features to integrate  | 
layers | 
 Layers to integrate  | 
conda_env | 
 conda environment to run scVI  | 
new.reduction | 
 Name under which to store resulting DimReduc object  | 
ndims | 
 Dimensionality of the latent space  | 
nlayers | 
 Number of hidden layers used for encoder and decoder NNs  | 
gene_likelihood | 
 Distribution to use for modelling expression data: "zinb", "nb", "poisson"  | 
max_epochs | 
 Number of passes through the dataset taken while training the model  | 
... | 
 Unused - currently just capturing parameters passed in from
  | 
A single-element named list DimReduc elements containing
the integrated data
This function requires the scvi-tools package to be installed
## Not run: 
# Preprocessing
obj <- SeuratData::LoadData("pbmcsca")
obj[["RNA"]] <- split(obj[["RNA"]], f = obj$Method)
obj <- NormalizeData(obj)
obj <- FindVariableFeatures(obj)
obj <- ScaleData(obj)
obj <- RunPCA(obj)
# After preprocessing, we integrate layers, specifying a conda environment
obj <- IntegrateLayers(
  object = obj,
  method = scVIIntegration,
  new.reduction = "integrated.scvi",
  conda_env = "../miniconda3/envs/scvi-env",
  verbose = FALSE
)
# Alternatively, we can integrate SCTransformed data
obj <- SCTransform(object = obj)
obj <- IntegrateLayers(
  object = obj, method = scVIIntegration,
  orig.reduction = "pca", new.reduction = "integrated.scvi",
  assay = "SCT", conda_env = "../miniconda3/envs/scvi-env", verbose = FALSE
)
## End(Not run)
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