Initializes a Vision object from an existing Seurat object taking any existing expression data, meta-data, and dimensionality reductions if they exist already

Initializes a Vision object from an existing Seurat object taking any existing expression data, meta-data, and dimensionality reductions if they exist already

Vision(data, ...)

# S4 method for matrixORSparse
  signatures = list(),
  proteinData = NULL,
  unnormalizedData = NULL,
  meta = NULL,
  projection_genes = c("fano"),
  min_signature_genes = 5,
  sig_gene_threshold = 0.001,
  threshold = 0.05,
  perm_wPCA = FALSE,
  projection_methods = c("tSNE30"),
  sig_norm_method = c("znorm_columns", "none", "znorm_rows", "znorm_rows_then_columns",
  pool = "auto",
  cellsPerPartition = 10,
  name = NULL,
  num_neighbors = NULL,
  latentSpace = NULL,
  latentSpaceName = NULL,
  latentTrajectory = NULL,
  tree = NULL,
  modData = list(),
  hotspot = NULL,
  pools = list()

# S4 method for data.frame
Vision(data, ...)

# S4 method for sparseMatrix
Vision(data, ...)

# S4 method for dgeMatrix
Vision(data, ...)

# S4 method for ExpressionSet
Vision(data, ...)

# S4 method for SummarizedExperiment
Vision(data, ...)

# S4 method for seurat
Vision(data, dimRed = NULL, dimRedComponents = NULL, ...)

# S4 method for Seurat
Vision(data, assay = "RNA", dimRed = NULL, dimRedComponents = NULL, ...)



expression data - can be one of these:

  • numeric matrix or sparse matrix (GENES x CELLS)

  • data.frame (GENES x CELLS)

  • ExpressionSet object

  • SummzrizedExperiment object (or extending classes)

Expression data should be scaled and normalized, but not log-transformed.


arguments passed to the base Vision constructor


list of file paths to signature files (.gmt or .txt) or Signature objects. See the createGeneSignature(...) method for information on creating Signature objects.


additional protein abundance data (such as ADT counts). Can be either a data.frame or numeric matrix. Should be of shape (CELLS x PROTEINS)


data.frame or numeric matrix (dense or sparse) - used when displaying gene expression values in the output report. If supplied this overrides the input in `data` but only when visualizing data.


data.frame with meta-data for cells. Rows in this data.frame should correspond with columns in the expression data matrix


name of filtering method ('threshold' or 'fano') or list of genes to use when computing projections.


Signature that match less than this number of genes in the supplied expression matrix are removed.


Proportion of cells that a gene must be detected in (nonzero) to be used in signature score calculations.


Threshold to apply when using the 'threshold' or 'fano' projection genes filter. If greater than 1, this specifies the number of cells in which a gene must be detected for it to be used when computing PCA. If less than 1, this instead specifies the proportion of cells needed


If TRUE, apply permutation procedure to calculate significant number of PCs when running PCA. If FALSE (default), retain the top 30.


a character vector specifying which projection methods to apply. Can be:

  • tSNE10 (tSNE with perplexity 10)

  • tSNE30 (tSNE with perplexity 30)

  • ICA

  • ISOMap


  • UMAP

By default will perform tSNE and PCA on the data.


Method to apply to normalize the expression matrix before calculating signature scores. Valid options are: "znorm_columns" (default), "none", "znorm_rows", "znorm_rows_then_columns", or "rank_norm_columns"


indicates whether or not to pool cells into supercells. Acceptable values are TRUE, FALSE, or 'auto', the last of which is the default and enables pooling if there are more than 100000 cells.


the target number of cells to put into a supercell when pooling


a name for the sample - shown on the output report


the number of neighbors to consider for downstream analyses.'


latent space for expression data. Numeric matrix or dataframe with dimensions CELLS x COMPONENTS


a name for the latent space method (used in output report)


trajectory to model cell progression. Wrapped result of a trajectory inference method by the dynverse/dynwrap library


a phylo object


a list of signature objects for user defined modules


a list containing one hotspot object precomputed in python and loaded in via reticulate


assignments of cell to micropool. Used when microclustering batches separately and then combining. See vignette for usage.


Dimensionality reduction to use for the latentSpace. Default is to look for "pca" and use that if it exists


number of components to use for the selected dimensionality reduction. Default is to use all components


The assay slot in the Seurat object to use for expression data


A VISION object


if (FALSE) {
expMat <- read.csv("expressionMatrix.csv", row.names=1)
meta <- read.csv("metaData.csv", row.names=1)

sigs <- c("/path/to/signatures/msigdb_Hallmark.gmt",

vis <- Vision(data = expMat,
              signatures = sigs,
              meta = meta)