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Author: Wei Liu [aut, cre], Xiao Zhang [aut], Yi Yang [aut], Peng Xie [aut], Chengqi Lin [aut], Jin Liu [aut]
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Maintainer: Wei Liu <liuweideng@gmail.com>
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Description:coFAST is aspatially-aware cell clustering algorithm with clustersignificant assessment. It comprises four key modules: spatially-aware cell-gene co-embedding, cell clustering, signature gene identification, and cluster significant assessment.
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Description:Aspatially-aware cell clustering algorithmis providedwith clustersignificance assessment. It comprises four key modules: spatially-aware cell-gene co-embedding, cell clustering, signature gene identification, and cluster significant assessment. More details can be referred to Peng Xie, et al. (2025) <doi:10.1016/j.cell.2025.05.035>.
#' @param platform a string, specify the platform of the provided data, default as "Others". There are more platforms to be chosen, including "Visuim", "ST" and "Others" ("Others" represents the other SRT platforms except for 'Visium' and 'ST')
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#' The platform helps to calculate the adjacency matrix by defining the neighborhoods when type="fixed_distance" is chosen.
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#' @param neighbors an optional postive integer, specify how many neighbors used in calculation, default as 6.
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#' @param ... Other arguments passed to \code{\link{getAdj_auto}}.
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#' @param ... Other arguments passed to \code{\link[DR.SC]{getAdj_auto}}.
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#' @return return a sparse matrix, representing the adjacency matrix.
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#' @details When the type = "fixed_distance", then the spots within the Euclidean distance cutoffs from one spot are regarded as the neighbors of this spot. When the type = "fixed_number", the K-nearest spots are regarded as the neighbors of each spot.
#' @param weighted an optional logical value, specify whether use weighted method.
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#' @param var.features an optional string vector, specify the variable features used to calculate cell embedding.
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#'
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#' @return return a revised Seurat object with a new reduction slot reduction.name obtained by NCFM co-embedding method, where reduction.name is default as `ncfm`.
#' @param q an optional positive integer, specify the dimension of low dimensional embeddings to compute and store. Default is 10.
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#' @param reduction.name an optional string, dimensional reduction name, `cofast` by default.
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#' @param var.features an optional string vector, specify the variable features, used to calculate cell embedding.
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#' @param ... Other argument passed to the \code{\link{FAST_run}}.
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#' @param ... Other argument passed to the \code{\link[ProFAST]{FAST_run}}.
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#'
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#' @return return a revised Seurat object with a new reduction slot reduction.name obtained by coFAST co-embedding, where default reduction.name is `cofast`.
#' @param assay.name aoptional string, specify the new generated assay name, `distce` by default.
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#' @param eta an optionalpositive real, a quantity to avoid numerical errors. 1e-10 by default.
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#'
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#' @return return a revised Seurat object with a assay slot `assay.name`.
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#' @details This function calculate the distance matrix between cells/spots and features, and then put the distance matrix in a new generated assay. This
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#' distance matrix will be used in the siganture gene identification.
#' @description Identify clusters of spots by a shared nearest neighbor (SNN) modularity optimization based on coFAST's embeddings.
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#' @paramdf.list alist that is obtained by the function \code{\link{find.signature.genes}}.
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#' @paramntop an optionalpositive integer, specify the how many top signature genes extracted, default as 5.
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#' @paramexpr.prop.cutoff an optionalpostive real ranging from 0 to 1,specifycutoff of expression proportion of features, default as 0.1.
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#' @return return a `data.frame` object with four columns: `distance`,`expr.prop`, `label` and `gene`.
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#' @details Using this funciton, we obtainthetop signature genes and organize them into a data.frame. The `row.names` are gene names.
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#'The colname `distance` means the distance between gene (i.e., VPREB3) and cells with the specific cell type (i.e., B cell),
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#' which is calculated based on the coembedding of genes and cells in the coembedding space. The distance is smaller, the association between gene and the cell type is stronger.
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#' The colname `expr.prop` represents the expression proportion of the gene (i.e., VPREB3) within the cell type (i.e., B cell).
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#' The colname `label` means the cell types and colname `gene` denotes the genename.
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#' By the data.frame object, we know `VPREB3` is the one of the top signature gene of B cell.
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#' @paramseu aSeurat object.
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#' @paramreduction a optionalstring, dimensional reduction name, `cofast` by default.
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#' @paramcluster.name an optionalstring,specifythe colname in meta.data for clusters, `cofast.cluster` by default.
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#' @param res a positive real, speficy the resolution parameter for Louvain clustering, default as 0.8.
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#' @param K a positive integer or NULL, specifythenumber of clusters, default as NULL that indicates not specify the number of clusters.
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#'@param res.start a positive real, when K is not NULL, starting value of resolution to be searched, default as 0.2.
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#'@param res.end a positive real, when K is not NULL, ending value of resolution to be searched, default as 2.
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#'@param step a positive real, when K is not NULL, step size of resolution to be searched, default as 0.02.
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#'@return return a revised Seurat object with a new column in meta.data named cluster.name.