From 16b4000996d1df2a00ecf5c8ff292e3047ed7a20 Mon Sep 17 00:00:00 2001 From: George Chen <72078254+jiajic@users.noreply.github.com> Date: Wed, 4 Sep 2024 09:33:28 -0400 Subject: [PATCH 01/12] chore: update GiottoUtils requirement --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 8cff1d619..aed8c9f48 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -40,7 +40,7 @@ Imports: data.table (>= 1.12.2), dbscan (>= 1.1-3), ggplot2 (>= 3.1.1), - GiottoUtils (>= 0.1.9), + GiottoUtils (>= 0.1.12), GiottoVisuals (>= 0.2.4), igraph (>= 1.2.4.1), jsonlite, From 86497bdc80a8c9b071c7f8f2d1c9522213f9e9a6 Mon Sep 17 00:00:00 2001 From: George Chen <72078254+jiajic@users.noreply.github.com> Date: Wed, 4 Sep 2024 09:35:45 -0400 Subject: [PATCH 02/12] chore: remove deprecated functions & update PAGE example * remove deprecated `PAGEEnrich()`. Use `runPAGEEnrich()` instead * remove deprecated `rankEnrich()`. Use `runRankEnrich()` instead * remove deprecated `hyperGeometricEnrich()`. Use `runHyperGeometricEnrich()` instead * remove deprecated `createSpatialEnrich()`. Use `runSpatialEnrich()` instead * remove deprecated `heatmSpatialCorGenes()`. Use `heatmSpatialCorFeats()` instead * remove deprecated `runPAGEEnrich_OLD()`. Use `runPAGEEnrich()` instead * update PAGE example * start on reshape2 removal --- NAMESPACE | 6 - NEWS.md | 12 ++ R/clustering.R | 26 +-- R/spatial_enrichment.R | 400 ++++++------------------------------ R/spatial_genes.R | 13 -- man/PAGEEnrich.Rd | 41 ---- man/createSpatialEnrich.Rd | 46 ----- man/doKmeans.Rd | 4 +- man/enrichment_PAGE.Rd | 126 ++++++++++++ man/heatmSpatialCorGenes.Rd | 39 ---- man/hyperGeometricEnrich.Rd | 37 ---- man/makeSignMatrixPAGE.Rd | 50 ----- man/rankEnrich.Rd | 39 ---- man/reexports.Rd | 2 +- man/runPAGEEnrich.Rd | 100 --------- man/runPAGEEnrich_OLD.Rd | 64 ------ 16 files changed, 219 insertions(+), 786 deletions(-) delete mode 100644 man/PAGEEnrich.Rd delete mode 100644 man/createSpatialEnrich.Rd create mode 100644 man/enrichment_PAGE.Rd delete mode 100644 man/heatmSpatialCorGenes.Rd delete mode 100644 man/hyperGeometricEnrich.Rd delete mode 100644 man/makeSignMatrixPAGE.Rd delete mode 100644 man/rankEnrich.Rd delete mode 100644 man/runPAGEEnrich.Rd delete mode 100644 man/runPAGEEnrich_OLD.Rd diff --git a/NAMESPACE b/NAMESPACE index f9eddf16a..559afbf2c 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -14,7 +14,6 @@ export("prov<-") export("sankeyLabel<-") export("sankeyRelate<-") export("spatUnit<-") -export(PAGEEnrich) export(activeFeatType) export(activeSpatUnit) export(addCellIntMetadata) @@ -138,7 +137,6 @@ export(createSpatLocsObj) export(createSpatNetObj) export(createSpatialDefaultGrid) export(createSpatialDelaunayNetwork) -export(createSpatialEnrich) export(createSpatialFeaturesKNNnetwork) export(createSpatialGenomicsObject) export(createSpatialGrid) @@ -262,10 +260,8 @@ export(giottoToSeuratV4) export(giottoToSeuratV5) export(giottoToSpatialExperiment) export(heatmSpatialCorFeats) -export(heatmSpatialCorGenes) export(hexVertices) export(hist) -export(hyperGeometricEnrich) export(identifyTMAcores) export(importCosMx) export(importVisiumHD) @@ -339,7 +335,6 @@ export(print.combIcfObject) export(print.icfObject) export(processGiotto) export(prov) -export(rankEnrich) export(rankSpatialCorGroups) export(read10xAffineImage) export(readCellMetadata) @@ -375,7 +370,6 @@ export(runGiottoHarmony) export(runHyperGeometricEnrich) export(runIntegratedUMAP) export(runPAGEEnrich) -export(runPAGEEnrich_OLD) export(runPCA) export(runPCAprojection) export(runPCAprojectionBatch) diff --git a/NEWS.md b/NEWS.md index 5269365d9..1f4151739 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,16 @@ +# Giotto 4.1.2 + +## Breaking changes +* remove deprecated `PAGEEnrich()`. Use `runPAGEEnrich()` instead +* remove deprecated `rankEnrich()`. Use `runRankEnrich()` instead +* remove deprecated `hyperGeometricEnrich()`. Use `runHyperGeometricEnrich()` instead +* remove deprecated `createSpatialEnrich()`. Use `runSpatialEnrich()` instead +* remove deprecated `heatmSpatialCorGenes()`. Use `heatmSpatialCorFeats()` instead +* remove deprecated `runPAGEEnrich_OLD()`. Use `runPAGEEnrich()` instead + + + # Giotto 4.1.1 ## Bug fixes diff --git a/R/clustering.R b/R/clustering.R index 65c36846c..bd1b0ff66 100644 --- a/R/clustering.R +++ b/R/clustering.R @@ -1223,8 +1223,8 @@ doSNNCluster <- function( #' @param set_seed set seed (default = TRUE) #' @param seed_number number for seed #' @returns if return_gobject = TRUE: giotto object with new clusters appended to cell metadata -#' @details The default settings will use dimension reduction results as input. -#' Set dim_reduction_to_use = NULL if you want to directly use expression values as input. +#' @details The default settings will use dimension reduction results as input. +#' Set dim_reduction_to_use = NULL if you want to directly use expression values as input. #' By providing a feature vector to feats_to_use you can subset the expression matrix. #' @seealso \code{\link[stats]{kmeans}} #' @examples @@ -1254,7 +1254,7 @@ doKmeans <- function( return_gobject = TRUE, set_seed = TRUE, seed_number = 1234) { - + # Set feat_type and spat_unit spat_unit <- set_default_spat_unit( gobject = gobject, @@ -1276,7 +1276,7 @@ doKmeans <- function( ## using dimension reduction ## if(!is.null(dim_reduction_to_use)) { - + # use only available dimensions if dimensions < dimensions_to_use dim_coord <- get_dimReduction( gobject = gobject, @@ -1287,14 +1287,14 @@ doKmeans <- function( name = dim_reduction_name, output = "dimObj" ) - + dimensions_to_use <- dimensions_to_use[ dimensions_to_use %in% seq_len(ncol(dim_coord[])) ] matrix_to_use <- dim_coord[][, dimensions_to_use] - + } else { - + ## using original matrix ## expr_values <- getExpression( gobject = gobject, @@ -1303,21 +1303,21 @@ doKmeans <- function( values = expression_values, output = "exprObj" ) - + # subset expression matrix if (!is.null(feats_to_use)) { expr_values[] <- expr_values[][ rownames(expr_values[]) %in% feats_to_use, ] } - + # features as columns # cells as rows matrix_to_use <- t_flex(expr_values[]) - + } - - + + ## distance if (distance_method == "original") { celldist <- matrix_to_use @@ -2817,7 +2817,7 @@ getClusterSimilarity <- function( # correlation matrix cormatrix <- cor_flex(x = testmatrix, method = cor) - cor_table <- data.table::as.data.table(reshape2::melt(cormatrix)) + cor_table <- melt_matrix(cormatrix) data.table::setnames( cor_table, old = c("Var1", "Var2"), c("group1", "group2") diff --git a/R/spatial_enrichment.R b/R/spatial_enrichment.R index 1f95d9a53..b72db8759 100644 --- a/R/spatial_enrichment.R +++ b/R/spatial_enrichment.R @@ -1,19 +1,51 @@ ## create spatial enrichment matrix #### -#' @title makeSignMatrixPAGE -#' @description Function to convert a list of signature genes -#' (e.g. for cell types or processes) into -#' a binary matrix format that can be used with the PAGE enrichment option. -#' Each cell type or process should -#' have a vector of cell-type or process specific genes. These vectors need to -#' be combined into a list (sign_list). -#' The names of the cell types or processes that are provided in the list need -#' to be given (sign_names). -#' @param sign_names vector with names for each provided gene signature -#' @param sign_list list of genes (signature) -#' @returns matrix -#' @seealso \code{\link{PAGEEnrich}} +#' @title PAGE feature enrichment +#' @name enrichment_PAGE +#' @description +#' Expression feature-based enrichment scoring of labels. \cr +#' A binary matrix of signature features (e.g. for cell types or processes) can +#' either be directly provided or converted from a list using +#' `makeSignMatrixPAGE()`. This matrix is then used with `runPAGEEnrich()` in +#' order to calculate feature signature enrichment scores per spatial position +#' using PAGE. +#' @param sign_names `character` vector with names (labels) for each provided +#' feat signature +#' @param sign_list list of feats in signature +#' @param gobject Giotto object +#' @param spat_unit spatial unit +#' @param feat_type feature type +#' @param sign_matrix binary matrix of signature feats for each cell type / +#' process. Alternatively a list of signature feats can be provided to +#' `makeSignMatrixPAGE()`, which will create the matrix for you. +#' @param expression_values expression values to use +#' @param min_overlap_genes minimum number of overlapping feats in +#' `sign_matrix` required to calculate enrichment +#' @param reverse_log_scale reverse expression values from log scale +#' @param logbase log base to use if reverse_log_scale = TRUE +#' @param output_enrichment how to return enrichment output +#' @param p_value logical. Default = `FALSE`. calculate p-values +#' @param include_depletion calculate both enrichment and depletion +#' @param n_times number of permutations to calculate for p_value +#' @param max_block number of lines to process together (default = 20e6) +#' @param name to give to spatial enrichment results, default = PAGE +#' @param verbose be verbose +#' @param return_gobject return giotto object +#' @returns `matrix` (`makeSignMatrixPAGE()`) and +#' `giotto` (`runPAGEEnrich(return_gobject = TRUE)`) or +#' `data.table` (`runPAGEEnrich(return_gobject = FALSE)`) +#' @details +#' The enrichment Z score is calculated by using method (PAGE) from +#' Kim SY et al., BMC bioinformatics, 2005 as \cr +#' \eqn{Z = ((Sm – mu)*m^(1/2)) / delta}. \cr +#' For each gene in each spot, mu is the fold change values versus the mean +#' expression and delta is the standard deviation. Sm is the mean fold change +#' value of a specific marker gene set and m is the size of a given marker +#' gene set. +#' @md #' @examples +#' g <- GiottoData::loadGiottoMini("visium") +#' #' sign_list <- list( #' cell_type1 = c( #' "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", @@ -29,10 +61,24 @@ #' ) #' ) #' -#' makeSignMatrixPAGE( +#' sm <- makeSignMatrixPAGE( #' sign_names = c("cell_type1", "cell_type2", "cell_type3"), #' sign_list = sign_list #' ) +#' +#' g <- runPAGEEnrich(gobject = g, +#' sign_matrix = sign_matrix, +#' min_overlap_genes = 2 +#' ) +#' +#' spatPlot2D(g, +#' cell_color = "cell_type2", +#' spat_enr_names = "PAGE", +#' color_as_factor = FALSE +#' ) +NULL + +#' @rdname enrichment_PAGE #' @export makeSignMatrixPAGE <- function( sign_names, @@ -420,218 +466,6 @@ makeSignMatrixRank <- function( -#' @title runPAGEEnrich_OLD -#' @description Function to calculate gene signature enrichment scores per -#' spatial position using PAGE. -#' @param gobject Giotto object -#' @param sign_matrix Matrix of signature genes for each cell type / process -#' @param expression_values expression values to use -#' @param reverse_log_scale reverse expression values from log scale -#' @param logbase log base to use if reverse_log_scale = TRUE -#' @param output_enrichment how to return enrichment output -#' @param p_value calculate p-values (boolean, default = FALSE) -#' @param n_times number of permutations to calculate for p_value -#' @param name to give to spatial enrichment results, default = PAGE -#' @param return_gobject return giotto object -#' @returns data.table with enrichment results -#' @details -#' sign_matrix: a binary matrix with genes as row names and cell-types as -#' column names. -#' Alternatively a list of signature genes can be provided to -#' makeSignMatrixPAGE, which will create the matrix for you. \cr -#' -#' The enrichment Z score is calculated by using method (PAGE) from -#' Kim SY et al., BMC bioinformatics, 2005 as -#' \eqn{Z = ((Sm – mu)*m^(1/2)) / delta}. -#' For each gene in each spot, mu is the fold change values versus the mean -#' expression and delta is the standard deviation. Sm is the mean fold change -#' value of a specific marker gene set and m is the size of a given marker -#' gene set. -#' @seealso \code{\link{makeSignMatrixPAGE}} -#' @export -runPAGEEnrich_OLD <- function( - gobject, - sign_matrix, - expression_values = c("normalized", "scaled", "custom"), - reverse_log_scale = TRUE, - logbase = 2, - output_enrichment = c("original", "zscore"), - p_value = FALSE, - n_times = 1000, - name = NULL, - return_gobject = TRUE) { - # expression values to be used - values <- match.arg(expression_values, c("normalized", "scaled", "custom")) - expr_values <- get_expression_values(gobject = gobject, values = values) - - # check parameters - if (is.null(name)) name <- "PAGE" - - # check available gene - available_ct <- c() - for (i in colnames(sign_matrix)) { - gene_i <- rownames(sign_matrix)[which(sign_matrix[, i] == 1)] - overlap_i <- intersect(gene_i, rownames(expr_values)) - if (length(overlap_i) <= 5) { - output <- paste0( - "Warning, ", i, " only has ", length(overlap_i), - " overlapped genes. Will remove it." - ) - } else { - available_ct <- c(available_ct, i) - } - } - - if (length(available_ct) == 1) { - stop("Only one cell type available.") - } - - # output enrichment - output_enrichment <- match.arg( - output_enrichment, - choices = c("original", "zscore") - ) - - # only continue with genes present in both datasets - interGene <- intersect(rownames(sign_matrix), rownames(expr_values)) - filterSig <- sign_matrix[interGene, available_ct] - signames <- rownames(filterSig)[which(filterSig[, 1] == 1)] - - # calculate mean gene expression - if (reverse_log_scale == TRUE) { - mean_gene_expr <- log(rowMeans(logbase^expr_values - 1, dims = 1) + 1) - } else { - mean_gene_expr <- rowMeans(expr_values) - } - geneFold <- expr_values - mean_gene_expr - - # calculate sample/spot mean and sd - cellColMean <- apply(geneFold, 2, mean) - cellColSd <- apply(geneFold, 2, stats::sd) - - # get enrichment scores - enrichment <- matrix( - data = NA, nrow = dim(filterSig)[2], ncol = length(cellColMean) - ) - for (i in seq_len(dim(filterSig)[2])) { - signames <- rownames(filterSig)[which(filterSig[, i] == 1)] - sigColMean <- apply(geneFold[signames, ], 2, mean) - m <- length(signames) - vectorX <- NULL - for (j in (seq_along(cellColMean))) { - Sm <- sigColMean[j] - u <- cellColMean[j] - sigma <- cellColSd[j] - zscore <- (Sm - u) * m^(1 / 2) / sigma - vectorX <- append(vectorX, zscore) - } - enrichment[i, ] <- vectorX - } - - rownames(enrichment) <- colnames(filterSig) - colnames(enrichment) <- names(cellColMean) - enrichment <- t(enrichment) - - if (output_enrichment == "zscore") { - enrichment <- scale(enrichment) - } - - enrichmentDT <- data.table::data.table(cell_ID = rownames(enrichment)) - enrichmentDT <- cbind(enrichmentDT, data.table::as.data.table(enrichment)) - - - - ## calculate p-values if requested - if (p_value == TRUE) { - # check available gene - available_ct <- c() - for (i in colnames(sign_matrix)) { - gene_i <- rownames(sign_matrix)[which(sign_matrix[, i] == 1)] - overlap_i <- intersect( - gene_i, rownames(gobject@expression$rna$normalized) - ) - - if (length(overlap_i) <= 5) { - output <- paste0( - "Warning, ", i, " only has ", length(overlap_i), - " overlapped genes. It will be removed." - ) - } else { - available_ct <- c(available_ct, i) - } - } - - if (length(available_ct) == 1) { - stop("Only one cell type available.") - } - - # only continue with genes present in both datasets - interGene <- intersect( - rownames(sign_matrix), rownames(gobject@expression$rna$normalized) - ) - filter_sign_matrix <- sign_matrix[interGene, available_ct] - - background_mean_sd <- .do_page_permutation( - gobject = gobject, - sig_gene = filter_sign_matrix, - ntimes = n_times - ) - - for (i in seq_len(dim(filter_sign_matrix)[2])) { - length_gene <- length(which(filter_sign_matrix[, i] == 1)) - join_gene_with_length <- paste("gene_num_", length_gene, sep = "") - mean_i <- as.numeric(as.character( - background_mean_sd[join_gene_with_length, ][[1]] - )) - sd_i <- as.numeric(as.character( - background_mean_sd[join_gene_with_length, ][[2]] - )) - j <- i + 1 - enrichmentDT[[j]] <- stats::pnorm( - enrichmentDT[[j]], - mean = mean_i, sd = sd_i, - lower.tail = FALSE, log.p = FALSE - ) - } - } - - - - ## return object or results ## - if (return_gobject == TRUE) { - spenr_names <- names(gobject@spatial_enrichment) - - if (name %in% spenr_names) { - cat(name, " has already been used, will be overwritten") - } - - ## update parameters used ## - parameters_list <- gobject@parameters - number_of_rounds <- length(parameters_list) - update_name <- paste0(number_of_rounds, "_spatial_enrichment") - - # parameters to include - parameters_list[[update_name]] <- c( - "method used" = "PAGE", - "enrichment name" = name, - "expression values" = expression_values, - "reverse log scale" = reverse_log_scale, - "logbase" = logbase, - "p-values calculated" = p_value, - "output enrichment scores" = output_enrichment, - "p values calculated" = p_value, - "nr permutations" = n_times - ) - gobject@parameters <- parameters_list - - gobject@spatial_enrichment[[name]] <- enrichmentDT - - return(gobject) - } else { - return(enrichmentDT) - } -} - #' @title PAGE data.table method @@ -664,7 +498,7 @@ runPAGEEnrich_OLD <- function( ## identify available cell types all_genes <- rownames(expr_values) sign_matrix <- as.matrix(sign_matrix) - sign_matrix_DT <- data.table::as.data.table(reshape2::melt(sign_matrix)) + sign_matrix_DT <- melt_matrix(sign_matrix) sign_matrix_DT <- sign_matrix_DT[Var1 %in% all_genes] detected_DT <- sign_matrix_DT[, sum(value), by = Var2] @@ -685,7 +519,7 @@ runPAGEEnrich_OLD <- function( stop("Only one cell type available.") } - # create subset of sinature matrix + # create subset of signature matrix interGene <- intersect(rownames(sign_matrix), rownames(expr_values)) filterSig <- sign_matrix[interGene, available_ct] @@ -707,13 +541,13 @@ runPAGEEnrich_OLD <- function( colSd = cellColSd ) - filterSig_DT <- data.table::as.data.table(reshape2::melt(filterSig)) + filterSig_DT <- melt_matrix(filterSig) colnames(filterSig_DT) <- c("gene", "cell_type", "marker") sub_ct_DT <- filterSig_DT[marker == 1] sub_ct_DT[, nr_markers := .N, by = cell_type] ## reshape gene fold-expression - geneFold_DT <- data.table::as.data.table(reshape2::melt(geneFold)) + geneFold_DT <- melt_matrix(geneFold) colnames(geneFold_DT) <- c("gene", "cell_ID", "fc") mergetest <- data.table::merge.data.table( @@ -882,57 +716,7 @@ runPAGEEnrich_OLD <- function( -#' @title runPAGEEnrich -#' @description Function to calculate gene signature enrichment scores per -#' spatial position using PAGE. -#' @param gobject Giotto object -#' @param spat_unit spatial unit -#' @param feat_type feature type -#' @param sign_matrix Matrix of signature genes for each cell type / process -#' @param expression_values expression values to use -#' @param min_overlap_genes minimum number of overlapping genes in sign_matrix -#' required to calculate enrichment -#' @param reverse_log_scale reverse expression values from log scale -#' @param logbase log base to use if reverse_log_scale = TRUE -#' @param output_enrichment how to return enrichment output -#' @param p_value calculate p-values (boolean, default = FALSE) -#' @param include_depletion calculate both enrichment and depletion -#' @param n_times number of permutations to calculate for p_value -#' @param max_block number of lines to process together (default = 20e6) -#' @param name to give to spatial enrichment results, default = PAGE -#' @param verbose be verbose -#' @param return_gobject return giotto object -#' @returns data.table with enrichment results -#' @details -#' sign_matrix: a binary matrix with genes as row names and cell-types as -#' column names. -#' Alternatively a list of signature genes can be provided to -#' makeSignMatrixPAGE, which will create the matrix for you. \cr -#' -#' The enrichment Z score is calculated by using method (PAGE) from -#' Kim SY et al., BMC bioinformatics, 2005 as -#' \eqn{Z = ((Sm – mu)*m^(1/2)) / delta}. -#' For each gene in each spot, mu is the fold change values versus the mean -#' expression and delta is the standard deviation. Sm is the mean fold change -#' value of a specific marker gene set and m is the size of a given marker -#' gene set. -#' @seealso \code{\link{makeSignMatrixPAGE}} -#' @examples -#' g <- GiottoData::loadGiottoMini("visium") -#' sign_gene <- c( -#' "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap", -#' "Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2", -#' "Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5", -#' "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b", "Ipcef1" -#' ) -#' -#' sign_matrix <- matrix(rnorm(length(sign_gene) * 3, mean = 10), -#' nrow = length(sign_gene) -#' ) -#' rownames(sign_matrix) <- sign_gene -#' colnames(sign_matrix) <- c("cell_type1", "cell_type2", "cell_type3") -#' -#' runPAGEEnrich(gobject = g, sign_matrix = sign_matrix) +#' @rdname enrichment_PAGE #' @export runPAGEEnrich <- function( gobject, @@ -1064,21 +848,6 @@ runPAGEEnrich <- function( -#' @title PAGEEnrich -#' @description Function to calculate gene signature enrichment scores per -#' spatial position using PAGE. -#' @inheritDotParams runPAGEEnrich -#' @seealso \code{\link{runPAGEEnrich}} -#' @returns gene enrichment -#' @export -PAGEEnrich <- function(...) { - .Deprecated(new = "runPAGEEnrich") - - runPAGEEnrich(...) -} - - - #' @title Rank permutation @@ -1377,20 +1146,6 @@ runRankEnrich <- function( -#' @title rankEnrich -#' @description Function to calculate gene signature enrichment scores per -#' spatial position using a rank based approach. -#' @inheritDotParams runRankEnrich -#' @seealso \code{\link{runRankEnrich}} -#' @returns gene enrichment -#' @export -rankEnrich <- function(...) { - .Deprecated(new = "runRankEnrich") - - runRankEnrich(...) -} - - #' @title runHyperGeometricEnrich #' @description Function to calculate gene signature enrichment scores per @@ -1609,19 +1364,6 @@ runHyperGeometricEnrich <- function( } -#' @title hyperGeometricEnrich -#' @description Function to calculate gene signature enrichment scores per -#' spatial position using a hypergeometric test. -#' @inheritDotParams runHyperGeometricEnrich -#' @seealso \code{\link{runHyperGeometricEnrich}} -#' @returns enrichment scores -#' @export -hyperGeometricEnrich <- function(...) { - .Deprecated(new = "runHyperGeometricEnrich") - - runHyperGeometricEnrich(...) -} - @@ -1755,18 +1497,6 @@ runSpatialEnrich <- function( } -#' @title createSpatialEnrich -#' @description Function to calculate gene signature enrichment scores per -#' spatial position using an enrichment test. -#' @inheritDotParams runSpatialEnrich -#' @returns gene signature enrichment scores -#' @seealso \code{\link{runSpatialEnrich}} -#' @export -createSpatialEnrich <- function(...) { - .Deprecated(new = "runSpatialEnrich") - - runSpatialEnrich(...) -} diff --git a/R/spatial_genes.R b/R/spatial_genes.R index f77cc1527..473ecaadc 100644 --- a/R/spatial_genes.R +++ b/R/spatial_genes.R @@ -4104,19 +4104,6 @@ heatmSpatialCorFeats <- function( -#' @title heatmSpatialCorGenes -#' @name heatmSpatialCorGenes -#' @description Create heatmap of spatially correlated genes -#' @inheritDotParams heatmSpatialCorFeats -#' @returns heatmap -#' @seealso \code{\link{heatmSpatialCorFeats}} -#' @export -heatmSpatialCorGenes <- function(...) { - .Deprecated(new = "heatmSpatialCorFeats") - - heatmSpatialCorFeats(...) -} - diff --git a/man/PAGEEnrich.Rd b/man/PAGEEnrich.Rd deleted file mode 100644 index 50596ada6..000000000 --- a/man/PAGEEnrich.Rd +++ /dev/null @@ -1,41 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/spatial_enrichment.R -\name{PAGEEnrich} -\alias{PAGEEnrich} -\title{PAGEEnrich} -\usage{ -PAGEEnrich(...) -} -\arguments{ -\item{...}{ - Arguments passed on to \code{\link[=runPAGEEnrich]{runPAGEEnrich}} - \describe{ - \item{\code{gobject}}{Giotto object} - \item{\code{spat_unit}}{spatial unit} - \item{\code{feat_type}}{feature type} - \item{\code{sign_matrix}}{Matrix of signature genes for each cell type / process} - \item{\code{expression_values}}{expression values to use} - \item{\code{min_overlap_genes}}{minimum number of overlapping genes in sign_matrix -required to calculate enrichment} - \item{\code{reverse_log_scale}}{reverse expression values from log scale} - \item{\code{logbase}}{log base to use if reverse_log_scale = TRUE} - \item{\code{output_enrichment}}{how to return enrichment output} - \item{\code{p_value}}{calculate p-values (boolean, default = FALSE)} - \item{\code{include_depletion}}{calculate both enrichment and depletion} - \item{\code{n_times}}{number of permutations to calculate for p_value} - \item{\code{max_block}}{number of lines to process together (default = 20e6)} - \item{\code{name}}{to give to spatial enrichment results, default = PAGE} - \item{\code{verbose}}{be verbose} - \item{\code{return_gobject}}{return giotto object} - }} -} -\value{ -gene enrichment -} -\description{ -Function to calculate gene signature enrichment scores per -spatial position using PAGE. -} -\seealso{ -\code{\link{runPAGEEnrich}} -} diff --git a/man/createSpatialEnrich.Rd b/man/createSpatialEnrich.Rd deleted file mode 100644 index c62e820da..000000000 --- a/man/createSpatialEnrich.Rd +++ /dev/null @@ -1,46 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/spatial_enrichment.R -\name{createSpatialEnrich} -\alias{createSpatialEnrich} -\title{createSpatialEnrich} -\usage{ -createSpatialEnrich(...) -} -\arguments{ -\item{...}{ - Arguments passed on to \code{\link[=runSpatialEnrich]{runSpatialEnrich}} - \describe{ - \item{\code{gobject}}{Giotto object} - \item{\code{spat_unit}}{spatial unit} - \item{\code{feat_type}}{feature type} - \item{\code{enrich_method}}{method for gene signature enrichment calculation} - \item{\code{sign_matrix}}{Matrix of signature genes for each cell type / process} - \item{\code{expression_values}}{expression values to use} - \item{\code{reverse_log_scale}}{reverse expression values from log scale} - \item{\code{min_overlap_genes}}{minimum number of overlapping genes in sign_matrix -required to calculate enrichment (PAGE)} - \item{\code{logbase}}{log base to use if reverse_log_scale = TRUE} - \item{\code{p_value}}{calculate p-value (default = FALSE)} - \item{\code{n_times}}{(page/rank) number of permutation iterations to calculate -p-value} - \item{\code{rbp_p}}{(rank) fractional binarization threshold (default = 0.99)} - \item{\code{num_agg}}{(rank) number of top genes to aggregate (default = 100)} - \item{\code{max_block}}{number of lines to process together (default = 20e6)} - \item{\code{top_percentage}}{(hyper) percentage of cells that will be considered -to have gene expression with matrix binarization} - \item{\code{output_enrichment}}{how to return enrichment output} - \item{\code{name}}{to give to spatial enrichment results, default = PAGE} - \item{\code{verbose}}{be verbose} - \item{\code{return_gobject}}{return giotto object} - }} -} -\value{ -gene signature enrichment scores -} -\description{ -Function to calculate gene signature enrichment scores per -spatial position using an enrichment test. -} -\seealso{ -\code{\link{runSpatialEnrich}} -} diff --git a/man/doKmeans.Rd b/man/doKmeans.Rd index 3bfc8552d..ca9fde554 100644 --- a/man/doKmeans.Rd +++ b/man/doKmeans.Rd @@ -71,8 +71,8 @@ if return_gobject = TRUE: giotto object with new clusters appended to cell metad cluster cells using kmeans algorithm } \details{ -The default settings will use dimension reduction results as input. -Set dim_reduction_to_use = NULL if you want to directly use expression values as input. +The default settings will use dimension reduction results as input. +Set dim_reduction_to_use = NULL if you want to directly use expression values as input. By providing a feature vector to feats_to_use you can subset the expression matrix. } \examples{ diff --git a/man/enrichment_PAGE.Rd b/man/enrichment_PAGE.Rd new file mode 100644 index 000000000..fc1af2b2d --- /dev/null +++ b/man/enrichment_PAGE.Rd @@ -0,0 +1,126 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/spatial_enrichment.R +\name{enrichment_PAGE} +\alias{enrichment_PAGE} +\alias{makeSignMatrixPAGE} +\alias{runPAGEEnrich} +\title{PAGE feature enrichment} +\usage{ +makeSignMatrixPAGE(sign_names, sign_list) + +runPAGEEnrich( + gobject, + spat_unit = NULL, + feat_type = NULL, + sign_matrix, + expression_values = c("normalized", "scaled", "custom"), + min_overlap_genes = 5, + reverse_log_scale = TRUE, + logbase = 2, + output_enrichment = c("original", "zscore"), + p_value = FALSE, + include_depletion = FALSE, + n_times = 1000, + max_block = 2e+07, + name = NULL, + verbose = TRUE, + return_gobject = TRUE +) +} +\arguments{ +\item{sign_names}{\code{character} vector with names (labels) for each provided +feat signature} + +\item{sign_list}{list of feats in signature} + +\item{gobject}{Giotto object} + +\item{spat_unit}{spatial unit} + +\item{feat_type}{feature type} + +\item{sign_matrix}{binary matrix of signature feats for each cell type / +process. Alternatively a list of signature feats can be provided to +\code{makeSignMatrixPAGE()}, which will create the matrix for you.} + +\item{expression_values}{expression values to use} + +\item{min_overlap_genes}{minimum number of overlapping feats in +\code{sign_matrix} required to calculate enrichment} + +\item{reverse_log_scale}{reverse expression values from log scale} + +\item{logbase}{log base to use if reverse_log_scale = TRUE} + +\item{output_enrichment}{how to return enrichment output} + +\item{p_value}{logical. Default = \code{FALSE}. calculate p-values} + +\item{include_depletion}{calculate both enrichment and depletion} + +\item{n_times}{number of permutations to calculate for p_value} + +\item{max_block}{number of lines to process together (default = 20e6)} + +\item{name}{to give to spatial enrichment results, default = PAGE} + +\item{verbose}{be verbose} + +\item{return_gobject}{return giotto object} +} +\value{ +\code{matrix} (\code{makeSignMatrixPAGE()}) and +\code{giotto} (\code{runPAGEEnrich(return_gobject = TRUE)}) or +\code{data.table} (\code{runPAGEEnrich(return_gobject = FALSE)}) +} +\description{ +Expression feature-based enrichment scoring of labels. \cr +A binary matrix of signature features (e.g. for cell types or processes) can +either be directly provided or converted from a list using +\code{makeSignMatrixPAGE()}. This matrix is then used with \code{runPAGEEnrich()} in +order to calculate feature signature enrichment scores per spatial position +using PAGE. +} +\details{ +The enrichment Z score is calculated by using method (PAGE) from +Kim SY et al., BMC bioinformatics, 2005 as \cr +\eqn{Z = ((Sm – mu)*m^(1/2)) / delta}. \cr +For each gene in each spot, mu is the fold change values versus the mean +expression and delta is the standard deviation. Sm is the mean fold change +value of a specific marker gene set and m is the size of a given marker +gene set. +} +\examples{ +g <- GiottoData::loadGiottoMini("visium") + +sign_list <- list( + cell_type1 = c( + "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", + "Rims2", "Gfap", "Gjc3", "Chrna4", "Prkcd" + ), + cell_type2 = c( + "Prr18", "Grb14", "Tprn", "Clic1", "Olig2", "Hrh3", "Tmbim1", + "Carhsp1", "Tmem88b", "Ugt8a" + ), + cell_type2 = c( + "Arpp19", "Lamp5", "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", + "Cygb", "Ttc9b", "Ipcef1" + ) +) + +sm <- makeSignMatrixPAGE( + sign_names = c("cell_type1", "cell_type2", "cell_type3"), + sign_list = sign_list +) + +g <- runPAGEEnrich(gobject = g, + sign_matrix = sign_matrix, + min_overlap_genes = 2 +) + +spatPlot2D(g, + cell_color = "cell_type2", + spat_enr_names = "PAGE", + color_as_factor = FALSE +) +} diff --git a/man/heatmSpatialCorGenes.Rd b/man/heatmSpatialCorGenes.Rd deleted file mode 100644 index bbf314b0a..000000000 --- a/man/heatmSpatialCorGenes.Rd +++ /dev/null @@ -1,39 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/spatial_genes.R -\name{heatmSpatialCorGenes} -\alias{heatmSpatialCorGenes} -\title{heatmSpatialCorGenes} -\usage{ -heatmSpatialCorGenes(...) -} -\arguments{ -\item{...}{ - Arguments passed on to \code{\link[=heatmSpatialCorFeats]{heatmSpatialCorFeats}} - \describe{ - \item{\code{gobject}}{giotto object} - \item{\code{spatCorObject}}{spatial correlation object} - \item{\code{use_clus_name}}{name of clusters to visualize -(from clusterSpatialCorFeats())} - \item{\code{show_cluster_annot}}{show cluster annotation on top of heatmap} - \item{\code{show_row_dend}}{show row dendrogram} - \item{\code{show_column_dend}}{show column dendrogram} - \item{\code{show_row_names}}{show row names} - \item{\code{show_column_names}}{show column names} - \item{\code{show_plot}}{show plot} - \item{\code{return_plot}}{return ggplot object} - \item{\code{save_plot}}{directly save the plot [boolean]} - \item{\code{save_param}}{list of saving parameters, see -\code{\link{showSaveParameters}}} - \item{\code{default_save_name}}{default save name for saving, don't change, -change save_name in save_param} - }} -} -\value{ -heatmap -} -\description{ -Create heatmap of spatially correlated genes -} -\seealso{ -\code{\link{heatmSpatialCorFeats}} -} diff --git a/man/hyperGeometricEnrich.Rd b/man/hyperGeometricEnrich.Rd deleted file mode 100644 index c4dd29587..000000000 --- a/man/hyperGeometricEnrich.Rd +++ /dev/null @@ -1,37 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/spatial_enrichment.R -\name{hyperGeometricEnrich} -\alias{hyperGeometricEnrich} -\title{hyperGeometricEnrich} -\usage{ -hyperGeometricEnrich(...) -} -\arguments{ -\item{...}{ - Arguments passed on to \code{\link[=runHyperGeometricEnrich]{runHyperGeometricEnrich}} - \describe{ - \item{\code{gobject}}{Giotto object} - \item{\code{spat_unit}}{spatial unit} - \item{\code{feat_type}}{feature type} - \item{\code{sign_matrix}}{Matrix of signature genes for each cell type / process} - \item{\code{expression_values}}{expression values to use} - \item{\code{reverse_log_scale}}{reverse expression values from log scale} - \item{\code{logbase}}{log base to use if reverse_log_scale = TRUE} - \item{\code{top_percentage}}{percentage of cells that will be considered to have -gene expression with matrix binarization} - \item{\code{output_enrichment}}{how to return enrichment output} - \item{\code{p_value}}{calculate p-values (boolean, default = FALSE)} - \item{\code{name}}{to give to spatial enrichment results, default = hypergeometric} - \item{\code{return_gobject}}{return giotto object} - }} -} -\value{ -enrichment scores -} -\description{ -Function to calculate gene signature enrichment scores per -spatial position using a hypergeometric test. -} -\seealso{ -\code{\link{runHyperGeometricEnrich}} -} diff --git a/man/makeSignMatrixPAGE.Rd b/man/makeSignMatrixPAGE.Rd deleted file mode 100644 index 805dde245..000000000 --- a/man/makeSignMatrixPAGE.Rd +++ /dev/null @@ -1,50 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/spatial_enrichment.R -\name{makeSignMatrixPAGE} -\alias{makeSignMatrixPAGE} -\title{makeSignMatrixPAGE} -\usage{ -makeSignMatrixPAGE(sign_names, sign_list) -} -\arguments{ -\item{sign_names}{vector with names for each provided gene signature} - -\item{sign_list}{list of genes (signature)} -} -\value{ -matrix -} -\description{ -Function to convert a list of signature genes -(e.g. for cell types or processes) into -a binary matrix format that can be used with the PAGE enrichment option. -Each cell type or process should -have a vector of cell-type or process specific genes. These vectors need to -be combined into a list (sign_list). -The names of the cell types or processes that are provided in the list need -to be given (sign_names). -} -\examples{ -sign_list <- list( - cell_type1 = c( - "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", - "Rims2", "Gfap", "Gjc3", "Chrna4", "Prkcd" - ), - cell_type2 = c( - "Prr18", "Grb14", "Tprn", "Clic1", "Olig2", "Hrh3", "Tmbim1", - "Carhsp1", "Tmem88b", "Ugt8a" - ), - cell_type2 = c( - "Arpp19", "Lamp5", "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", - "Cygb", "Ttc9b", "Ipcef1" - ) -) - -makeSignMatrixPAGE( - sign_names = c("cell_type1", "cell_type2", "cell_type3"), - sign_list = sign_list -) -} -\seealso{ -\code{\link{PAGEEnrich}} -} diff --git a/man/rankEnrich.Rd b/man/rankEnrich.Rd deleted file mode 100644 index 7835c0802..000000000 --- a/man/rankEnrich.Rd +++ /dev/null @@ -1,39 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/spatial_enrichment.R -\name{rankEnrich} -\alias{rankEnrich} -\title{rankEnrich} -\usage{ -rankEnrich(...) -} -\arguments{ -\item{...}{ - Arguments passed on to \code{\link[=runRankEnrich]{runRankEnrich}} - \describe{ - \item{\code{gobject}}{Giotto object} - \item{\code{spat_unit}}{spatial unit} - \item{\code{feat_type}}{feature type} - \item{\code{sign_matrix}}{Matrix of signature genes for each cell type / process} - \item{\code{expression_values}}{expression values to use} - \item{\code{reverse_log_scale}}{reverse expression values from log scale} - \item{\code{logbase}}{log base to use if reverse_log_scale = TRUE} - \item{\code{output_enrichment}}{how to return enrichment output} - \item{\code{ties_method}}{how to handle rank ties} - \item{\code{p_value}}{calculate p-values (boolean, default = FALSE)} - \item{\code{n_times}}{number of permutations to calculate for p_value} - \item{\code{rbp_p}}{fractional binarization threshold (default = 0.99)} - \item{\code{num_agg}}{number of top genes to aggregate (default = 100)} - \item{\code{name}}{to give to spatial enrichment results, default = rank} - \item{\code{return_gobject}}{return giotto object} - }} -} -\value{ -gene enrichment -} -\description{ -Function to calculate gene signature enrichment scores per -spatial position using a rank based approach. -} -\seealso{ -\code{\link{runRankEnrich}} -} diff --git a/man/reexports.Rd b/man/reexports.Rd index 6b000402a..d82383743 100644 --- a/man/reexports.Rd +++ b/man/reexports.Rd @@ -300,7 +300,7 @@ below to see their documentation. \describe{ \item{GiottoClass}{\code{\link[GiottoClass:activeFeatType-generic]{activeFeatType}}, \code{\link[GiottoClass:activeFeatType-generic]{activeFeatType<-}}, \code{\link[GiottoClass:activeSpatUnit-generic]{activeSpatUnit}}, \code{\link[GiottoClass:activeSpatUnit-generic]{activeSpatUnit<-}}, \code{\link[GiottoClass]{addCellMetadata}}, \code{\link[GiottoClass]{addFeatMetadata}}, \code{\link[GiottoClass]{addGiottoImage}}, \code{\link[GiottoClass]{addGiottoImageMG}}, \code{\link[GiottoClass]{addGiottoLargeImage}}, \code{\link[GiottoClass]{addGiottoPoints}}, \code{\link[GiottoClass:addGiottoPoints]{addGiottoPoints3D}}, \code{\link[GiottoClass]{addGiottoPolygons}}, \code{\link[GiottoClass]{addNetworkLayout}}, \code{\link[GiottoClass]{addSpatialCentroidLocations}}, \code{\link[GiottoClass]{addSpatialCentroidLocationsLayer}}, \code{\link[GiottoClass]{aggregateStacks}}, \code{\link[GiottoClass]{aggregateStacksExpression}}, \code{\link[GiottoClass]{aggregateStacksLocations}}, \code{\link[GiottoClass]{aggregateStacksPolygonOverlaps}}, \code{\link[GiottoClass]{aggregateStacksPolygons}}, \code{\link[GiottoClass]{anndataToGiotto}}, \code{\link[GiottoClass]{annotateGiotto}}, \code{\link[GiottoClass]{annotateSpatialGrid}}, \code{\link[GiottoClass]{annotateSpatialNetwork}}, \code{\link[GiottoClass]{as.points}}, \code{\link[GiottoClass]{as.polygons}}, \code{\link[GiottoClass:r_spatial_conversions]{as.sf}}, \code{\link[GiottoClass:r_spatial_conversions]{as.sp}}, \code{\link[GiottoClass:r_spatial_conversions]{as.stars}}, \code{\link[GiottoClass:r_spatial_conversions]{as.terra}}, \code{\link[GiottoClass]{calculateMetaTable}}, \code{\link[GiottoClass]{calculateMetaTableCells}}, \code{\link[GiottoClass]{calculateOverlap}}, \code{\link[GiottoClass]{calculateOverlapParallel}}, \code{\link[GiottoClass]{calculateOverlapPolygonImages}}, \code{\link[GiottoClass]{calculateOverlapRaster}}, \code{\link[GiottoClass]{calculateOverlapSerial}}, \code{\link[GiottoClass]{calculateSpatCellMetadataProportions}}, \code{\link[GiottoClass:centroids-generic]{centroids}}, \code{\link[GiottoClass]{changeGiottoInstructions}}, \code{\link[GiottoClass]{changeImageBg}}, \code{\link[GiottoClass:giotto_python]{checkGiottoEnvironment}}, \code{\link[GiottoClass]{circleVertices}}, \code{\link[GiottoClass]{combineCellData}}, \code{\link[GiottoClass]{combineFeatureData}}, \code{\link[GiottoClass]{combineFeatureOverlapData}}, \code{\link[GiottoClass]{combineMetadata}}, \code{\link[GiottoClass]{combineSpatialCellFeatureInfo}}, \code{\link[GiottoClass]{combineSpatialCellMetadataInfo}}, \code{\link[GiottoClass]{combineToMultiPolygon}}, \code{\link[GiottoClass]{convertGiottoLargeImageToMG}}, \code{\link[GiottoClass]{copy}}, \code{\link[GiottoClass]{createBentoAdata}}, \code{\link[GiottoClass]{createCellMetaObj}}, \code{\link[GiottoClass]{createDimObj}}, \code{\link[GiottoClass]{createExprObj}}, \code{\link[GiottoClass]{createFeatMetaObj}}, \code{\link[GiottoClass]{createGiottoImage}}, \code{\link[GiottoClass:giotto_instructions]{createGiottoInstructions}}, \code{\link[GiottoClass]{createGiottoLargeImage}}, \code{\link[GiottoClass]{createGiottoLargeImageList}}, \code{\link[GiottoClass]{createGiottoObject}}, \code{\link[GiottoClass]{createGiottoObjectSubcellular}}, \code{\link[GiottoClass]{createGiottoPoints}}, \code{\link[GiottoClass]{createGiottoPolygon}}, \code{\link[GiottoClass:createGiottoPolygon]{createGiottoPolygonsFromDfr}}, \code{\link[GiottoClass:createGiottoPolygon]{createGiottoPolygonsFromGeoJSON}}, \code{\link[GiottoClass:createGiottoPolygon]{createGiottoPolygonsFromMask}}, \code{\link[GiottoClass]{createMetafeats}}, \code{\link[GiottoClass]{createNearestNetObj}}, \code{\link[GiottoClass]{createNearestNetwork}}, \code{\link[GiottoClass]{createSpatEnrObj}}, \code{\link[GiottoClass]{createSpatialDefaultGrid}}, \code{\link[GiottoClass]{createSpatialDelaunayNetwork}}, \code{\link[GiottoClass]{createSpatialFeaturesKNNnetwork}}, \code{\link[GiottoClass]{createSpatialGrid}}, \code{\link[GiottoClass]{createSpatialKNNnetwork}}, \code{\link[GiottoClass]{createSpatialNetwork}}, \code{\link[GiottoClass]{createSpatialWeightMatrix}}, \code{\link[GiottoClass]{createSpatLocsObj}}, \code{\link[GiottoClass]{createSpatNetObj}}, \code{\link[GiottoClass]{crop}}, \code{\link[GiottoClass]{cropGiottoLargeImage}}, \code{\link[GiottoClass]{density}}, \code{\link[GiottoClass]{distGiottoImage}}, \code{\link[GiottoClass]{estimateImageBg}}, \code{\link[GiottoClass]{ext}}, \code{\link[GiottoClass:ext]{ext<-}}, \code{\link[GiottoClass]{fDataDT}}, \code{\link[GiottoClass:spatIDs-generic]{featIDs}}, \code{\link[GiottoClass:featType-generic]{featType}}, \code{\link[GiottoClass:featType-generic]{featType<-}}, \code{\link[GiottoClass:featureNetwork-class]{featureNetwork}}, \code{\link[GiottoClass]{flip}}, \code{\link[GiottoClass]{gefToGiotto}}, \code{\link[GiottoClass]{getCellMetadata}}, \code{\link[GiottoClass]{getDimReduction}}, \code{\link[GiottoClass]{getExpression}}, \code{\link[GiottoClass]{getFeatureInfo}}, \code{\link[GiottoClass]{getFeatureMetadata}}, \code{\link[GiottoClass]{getGiottoImage}}, \code{\link[GiottoClass]{getMultiomics}}, \code{\link[GiottoClass]{getNearestNetwork}}, \code{\link[GiottoClass]{getPolygonInfo}}, \code{\link[GiottoClass]{getSpatialEnrichment}}, \code{\link[GiottoClass]{getSpatialEnrichment}}, \code{\link[GiottoClass]{getSpatialGrid}}, \code{\link[GiottoClass]{getSpatialGrid}}, \code{\link[GiottoClass]{getSpatialLocations}}, \code{\link[GiottoClass]{getSpatialLocations}}, \code{\link[GiottoClass]{getSpatialNetwork}}, \code{\link[GiottoClass]{getSpatialNetwork}}, \code{\link[GiottoClass:giotto-class]{giotto}}, \code{\link[GiottoClass:giottoImage-class]{giottoImage}}, \code{\link[GiottoClass:giottoLargeImage-class]{giottoLargeImage}}, \code{\link[GiottoClass]{giottoMasterToSuite}}, \code{\link[GiottoClass:giottoPoints-class]{giottoPoints}}, \code{\link[GiottoClass:giottoPolygon-class]{giottoPolygon}}, \code{\link[GiottoClass]{giottoToAnnData}}, \code{\link[GiottoClass]{giottoToSeurat}}, \code{\link[GiottoClass]{giottoToSeuratV4}}, \code{\link[GiottoClass]{giottoToSeuratV5}}, \code{\link[GiottoClass]{giottoToSpatialExperiment}}, \code{\link[GiottoClass]{hexVertices}}, \code{\link[GiottoClass]{hist}}, \code{\link[GiottoClass:giotto_python]{installGiottoEnvironment}}, \code{\link[GiottoClass:giotto_instructions]{instructions}}, \code{\link[GiottoClass:giotto_instructions]{instructions<-}}, \code{\link[GiottoClass]{joinGiottoObjects}}, \code{\link[GiottoClass]{loadGiotto}}, \code{\link[GiottoClass]{makePseudoVisium}}, \code{\link[GiottoClass]{objHistory}}, \code{\link[GiottoClass:objName-generic]{objName}}, \code{\link[GiottoClass:objName-generic]{objName<-}}, \code{\link[GiottoClass:generate_grid]{orthoGrid}}, \code{\link[GiottoClass]{overlapImagesToMatrix}}, \code{\link[GiottoClass:overlaps-generic]{overlaps}}, \code{\link[GiottoClass]{overlapToMatrix}}, \code{\link[GiottoClass]{overlapToMatrixMultiPoly}}, \code{\link[GiottoClass]{pDataDT}}, \code{\link[GiottoClass]{plotGiottoImage}}, \code{\link[GiottoClass]{polyStamp}}, \code{\link[GiottoClass:prov-generic]{prov}}, \code{\link[GiottoClass:prov-generic]{prov<-}}, \code{\link[GiottoClass]{readCellMetadata}}, \code{\link[GiottoClass]{readDimReducData}}, \code{\link[GiottoClass]{readExprData}}, \code{\link[GiottoClass]{readExprMatrix}}, \code{\link[GiottoClass]{readFeatData}}, \code{\link[GiottoClass]{readFeatMetadata}}, \code{\link[GiottoClass]{readGiottoInstructions}}, \code{\link[GiottoClass]{readNearestNetData}}, \code{\link[GiottoClass]{readPolygonData}}, \code{\link[GiottoClass]{readSpatEnrichData}}, \code{\link[GiottoClass]{readSpatLocsData}}, \code{\link[GiottoClass]{readSpatNetData}}, \code{\link[GiottoClass]{reconnectGiottoImage}}, \code{\link[GiottoClass]{rectVertices}}, \code{\link[GiottoClass]{removeCellAnnotation}}, \code{\link[GiottoClass]{removeFeatAnnotation}}, \code{\link[GiottoClass:giotto_python]{removeGiottoEnvironment}}, \code{\link[GiottoClass]{replaceGiottoInstructions}}, \code{\link[GiottoClass]{rescale}}, \code{\link[GiottoClass]{rescalePolygons}}, \code{\link[GiottoClass]{saveGiotto}}, \code{\link[GiottoClass]{setCellMetadata}}, \code{\link[GiottoClass]{setDimReduction}}, \code{\link[GiottoClass]{setExpression}}, \code{\link[GiottoClass]{setFeatureInfo}}, \code{\link[GiottoClass]{setFeatureMetadata}}, \code{\link[GiottoClass]{setGiotto}}, \code{\link[GiottoClass]{setGiottoImage}}, \code{\link[GiottoClass]{setMultiomics}}, \code{\link[GiottoClass]{setNearestNetwork}}, \code{\link[GiottoClass]{setPolygonInfo}}, \code{\link[GiottoClass]{setSpatialEnrichment}}, \code{\link[GiottoClass]{setSpatialGrid}}, \code{\link[GiottoClass]{setSpatialLocations}}, \code{\link[GiottoClass]{setSpatialNetwork}}, \code{\link[GiottoClass]{seuratToGiotto}}, \code{\link[GiottoClass]{seuratToGiottoV4}}, \code{\link[GiottoClass]{seuratToGiottoV5}}, \code{\link[GiottoClass]{showGiottoCellMetadata}}, \code{\link[GiottoClass]{showGiottoDimRed}}, \code{\link[GiottoClass]{showGiottoExpression}}, \code{\link[GiottoClass]{showGiottoFeatInfo}}, \code{\link[GiottoClass]{showGiottoFeatMetadata}}, \code{\link[GiottoClass]{showGiottoImageNames}}, \code{\link[GiottoClass]{showGiottoInstructions}}, \code{\link[GiottoClass]{showGiottoNearestNetworks}}, \code{\link[GiottoClass]{showGiottoSpatEnrichments}}, \code{\link[GiottoClass]{showGiottoSpatGrids}}, \code{\link[GiottoClass]{showGiottoSpatialInfo}}, \code{\link[GiottoClass]{showGiottoSpatLocs}}, \code{\link[GiottoClass]{showGiottoSpatNetworks}}, \code{\link[GiottoClass]{showProcessingSteps}}, \code{\link[GiottoClass]{smoothGiottoPolygons}}, \code{\link[GiottoClass]{spatialExperimentToGiotto}}, \code{\link[GiottoClass:spatIDs-generic]{spatIDs}}, \code{\link[GiottoClass]{spatQueryGiottoPolygons}}, \code{\link[GiottoClass]{spatShift}}, \code{\link[GiottoClass:spatUnit-generic]{spatUnit}}, \code{\link[GiottoClass:spatUnit-generic]{spatUnit<-}}, \code{\link[GiottoClass]{spin}}, \code{\link[GiottoClass]{stitchFieldCoordinates}}, \code{\link[GiottoClass]{stitchFieldCoordinates}}, \code{\link[GiottoClass]{stitchGiottoLargeImage}}, \code{\link[GiottoClass]{subsetGiotto}}, \code{\link[GiottoClass]{subsetGiottoLocs}}, \code{\link[GiottoClass]{subsetGiottoLocsMulti}}, \code{\link[GiottoClass]{subsetGiottoLocsSubcellular}}, \code{\link[GiottoClass]{tessellate}}, \code{\link[GiottoClass:generate_grid]{triGrid}}, \code{\link[GiottoClass]{updateGiottoImage}}, \code{\link[GiottoClass]{updateGiottoImageMG}}, \code{\link[GiottoClass]{updateGiottoLargeImage}}, \code{\link[GiottoClass]{updateGiottoObject}}, \code{\link[GiottoClass]{updateGiottoPointsObject}}, \code{\link[GiottoClass]{updateGiottoPolygonObject}}, \code{\link[GiottoClass:wrap]{vect}}, \code{\link[GiottoClass]{wrap}}, \code{\link[GiottoClass]{writeGiottoLargeImage}}} - \item{GiottoUtils}{\code{\link[GiottoUtils:pipe]{\%>\%}}, \code{\link[GiottoUtils]{getDistinctColors}}, \code{\link[GiottoUtils]{getRainbowColors}}} + \item{GiottoUtils}{\code{\link[GiottoUtils]{\%>\%}}, \code{\link[GiottoUtils]{getDistinctColors}}, \code{\link[GiottoUtils]{getRainbowColors}}} \item{GiottoVisuals}{\code{\link[GiottoVisuals]{addGiottoImageToSpatPlot}}, \code{\link[GiottoVisuals]{dimCellPlot}}, \code{\link[GiottoVisuals:dimCellPlot]{dimCellPlot2D}}, \code{\link[GiottoVisuals]{dimFeatPlot2D}}, \code{\link[GiottoVisuals]{dimFeatPlot3D}}, \code{\link[GiottoVisuals:dimFeatPlot3D]{dimGenePlot3D}}, \code{\link[GiottoVisuals]{dimPlot}}, \code{\link[GiottoVisuals:dimPlot]{dimPlot2D}}, \code{\link[GiottoVisuals:dimPlot]{dimPlot3D}}, \code{\link[GiottoVisuals]{getColors}}, \code{\link[GiottoVisuals]{giottoSankeyPlan}}, \code{\link[GiottoVisuals]{plotHeatmap}}, \code{\link[GiottoVisuals]{plotMetaDataCellsHeatmap}}, \code{\link[GiottoVisuals]{plotMetaDataHeatmap}}, \code{\link[GiottoVisuals]{plotPCA}}, \code{\link[GiottoVisuals]{plotPCA_2D}}, \code{\link[GiottoVisuals]{plotPCA_3D}}, \code{\link[GiottoVisuals]{plotStatDelaunayNetwork}}, \code{\link[GiottoVisuals]{plotTSNE}}, \code{\link[GiottoVisuals]{plotTSNE_2D}}, \code{\link[GiottoVisuals]{plotTSNE_3D}}, \code{\link[GiottoVisuals]{plotUMAP}}, \code{\link[GiottoVisuals]{plotUMAP_2D}}, \code{\link[GiottoVisuals]{plotUMAP_3D}}, \code{\link[GiottoVisuals]{sankeyLabel}}, \code{\link[GiottoVisuals:sankeyLabel]{sankeyLabel<-}}, \code{\link[GiottoVisuals]{sankeyPlot}}, \code{\link[GiottoVisuals]{sankeyRelate}}, \code{\link[GiottoVisuals:sankeyRelate]{sankeyRelate<-}}, \code{\link[GiottoVisuals]{sankeySet}}, \code{\link[GiottoVisuals]{sankeySetAddresses}}, \code{\link[GiottoVisuals]{showClusterDendrogram}}, \code{\link[GiottoVisuals]{showClusterHeatmap}}, \code{\link[GiottoVisuals]{showColorInstructions}}, \code{\link[GiottoVisuals]{showSaveParameters}}, \code{\link[GiottoVisuals]{spatCellPlot}}, \code{\link[GiottoVisuals:spatCellPlot]{spatCellPlot2D}}, \code{\link[GiottoVisuals]{spatDeconvPlot}}, \code{\link[GiottoVisuals]{spatDimCellPlot}}, \code{\link[GiottoVisuals]{spatDimCellPlot2D}}, \code{\link[GiottoVisuals]{spatDimFeatPlot2D}}, \code{\link[GiottoVisuals]{spatDimFeatPlot3D}}, \code{\link[GiottoVisuals:spatDimFeatPlot3D]{spatDimGenePlot3D}}, \code{\link[GiottoVisuals]{spatDimPlot}}, \code{\link[GiottoVisuals:spatDimPlot]{spatDimPlot2D}}, \code{\link[GiottoVisuals]{spatDimPlot3D}}, \code{\link[GiottoVisuals]{spatFeatPlot2D}}, \code{\link[GiottoVisuals]{spatFeatPlot2D_single}}, \code{\link[GiottoVisuals]{spatFeatPlot3D}}, \code{\link[GiottoVisuals:spatFeatPlot3D]{spatGenePlot3D}}, \code{\link[GiottoVisuals]{spatInSituPlotDensity}}, \code{\link[GiottoVisuals]{spatInSituPlotHex}}, \code{\link[GiottoVisuals]{spatInSituPlotPoints}}, \code{\link[GiottoVisuals]{spatNetwDistributions}}, \code{\link[GiottoVisuals]{spatNetwDistributionsDistance}}, \code{\link[GiottoVisuals]{spatNetwDistributionsKneighbors}}, \code{\link[GiottoVisuals]{spatPlot}}, \code{\link[GiottoVisuals:spatPlot]{spatPlot2D}}, \code{\link[GiottoVisuals:spatPlot]{spatPlot3D}}, \code{\link[GiottoVisuals]{subsetSankeySet}}, \code{\link[GiottoVisuals]{violinPlot}}} }} diff --git a/man/runPAGEEnrich.Rd b/man/runPAGEEnrich.Rd deleted file mode 100644 index 8d1ec1818..000000000 --- a/man/runPAGEEnrich.Rd +++ /dev/null @@ -1,100 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/spatial_enrichment.R -\name{runPAGEEnrich} -\alias{runPAGEEnrich} -\title{runPAGEEnrich} -\usage{ -runPAGEEnrich( - gobject, - spat_unit = NULL, - feat_type = NULL, - sign_matrix, - expression_values = c("normalized", "scaled", "custom"), - min_overlap_genes = 5, - reverse_log_scale = TRUE, - logbase = 2, - output_enrichment = c("original", "zscore"), - p_value = FALSE, - include_depletion = FALSE, - n_times = 1000, - max_block = 2e+07, - name = NULL, - verbose = TRUE, - return_gobject = TRUE -) -} -\arguments{ -\item{gobject}{Giotto object} - -\item{spat_unit}{spatial unit} - -\item{feat_type}{feature type} - -\item{sign_matrix}{Matrix of signature genes for each cell type / process} - -\item{expression_values}{expression values to use} - -\item{min_overlap_genes}{minimum number of overlapping genes in sign_matrix -required to calculate enrichment} - -\item{reverse_log_scale}{reverse expression values from log scale} - -\item{logbase}{log base to use if reverse_log_scale = TRUE} - -\item{output_enrichment}{how to return enrichment output} - -\item{p_value}{calculate p-values (boolean, default = FALSE)} - -\item{include_depletion}{calculate both enrichment and depletion} - -\item{n_times}{number of permutations to calculate for p_value} - -\item{max_block}{number of lines to process together (default = 20e6)} - -\item{name}{to give to spatial enrichment results, default = PAGE} - -\item{verbose}{be verbose} - -\item{return_gobject}{return giotto object} -} -\value{ -data.table with enrichment results -} -\description{ -Function to calculate gene signature enrichment scores per -spatial position using PAGE. -} -\details{ -sign_matrix: a binary matrix with genes as row names and cell-types as -column names. -Alternatively a list of signature genes can be provided to -makeSignMatrixPAGE, which will create the matrix for you. \cr - -The enrichment Z score is calculated by using method (PAGE) from -Kim SY et al., BMC bioinformatics, 2005 as -\eqn{Z = ((Sm – mu)*m^(1/2)) / delta}. -For each gene in each spot, mu is the fold change values versus the mean -expression and delta is the standard deviation. Sm is the mean fold change -value of a specific marker gene set and m is the size of a given marker -gene set. -} -\examples{ -g <- GiottoData::loadGiottoMini("visium") -sign_gene <- c( - "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap", - "Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2", - "Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5", - "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b", "Ipcef1" -) - -sign_matrix <- matrix(rnorm(length(sign_gene) * 3, mean = 10), - nrow = length(sign_gene) -) -rownames(sign_matrix) <- sign_gene -colnames(sign_matrix) <- c("cell_type1", "cell_type2", "cell_type3") - -runPAGEEnrich(gobject = g, sign_matrix = sign_matrix) -} -\seealso{ -\code{\link{makeSignMatrixPAGE}} -} diff --git a/man/runPAGEEnrich_OLD.Rd b/man/runPAGEEnrich_OLD.Rd deleted file mode 100644 index e6e826493..000000000 --- a/man/runPAGEEnrich_OLD.Rd +++ /dev/null @@ -1,64 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/spatial_enrichment.R -\name{runPAGEEnrich_OLD} -\alias{runPAGEEnrich_OLD} -\title{runPAGEEnrich_OLD} -\usage{ -runPAGEEnrich_OLD( - gobject, - sign_matrix, - expression_values = c("normalized", "scaled", "custom"), - reverse_log_scale = TRUE, - logbase = 2, - output_enrichment = c("original", "zscore"), - p_value = FALSE, - n_times = 1000, - name = NULL, - return_gobject = TRUE -) -} -\arguments{ -\item{gobject}{Giotto object} - -\item{sign_matrix}{Matrix of signature genes for each cell type / process} - -\item{expression_values}{expression values to use} - -\item{reverse_log_scale}{reverse expression values from log scale} - -\item{logbase}{log base to use if reverse_log_scale = TRUE} - -\item{output_enrichment}{how to return enrichment output} - -\item{p_value}{calculate p-values (boolean, default = FALSE)} - -\item{n_times}{number of permutations to calculate for p_value} - -\item{name}{to give to spatial enrichment results, default = PAGE} - -\item{return_gobject}{return giotto object} -} -\value{ -data.table with enrichment results -} -\description{ -Function to calculate gene signature enrichment scores per -spatial position using PAGE. -} -\details{ -sign_matrix: a binary matrix with genes as row names and cell-types as -column names. -Alternatively a list of signature genes can be provided to -makeSignMatrixPAGE, which will create the matrix for you. \cr - -The enrichment Z score is calculated by using method (PAGE) from -Kim SY et al., BMC bioinformatics, 2005 as -\eqn{Z = ((Sm – mu)*m^(1/2)) / delta}. -For each gene in each spot, mu is the fold change values versus the mean -expression and delta is the standard deviation. Sm is the mean fold change -value of a specific marker gene set and m is the size of a given marker -gene set. -} -\seealso{ -\code{\link{makeSignMatrixPAGE}} -} From 27d5e7816a2c557e7d5421fa7322d9300beaf68e Mon Sep 17 00:00:00 2001 From: George Chen <72078254+jiajic@users.noreply.github.com> Date: Wed, 4 Sep 2024 09:38:03 -0400 Subject: [PATCH 03/12] chore: reflow example --- R/spatial_enrichment.R | 8 ++++---- man/enrichment_PAGE.Rd | 8 ++++---- man/reexports.Rd | 2 +- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/R/spatial_enrichment.R b/R/spatial_enrichment.R index b72db8759..488a6ab41 100644 --- a/R/spatial_enrichment.R +++ b/R/spatial_enrichment.R @@ -52,12 +52,12 @@ #' "Rims2", "Gfap", "Gjc3", "Chrna4", "Prkcd" #' ), #' cell_type2 = c( -#' "Prr18", "Grb14", "Tprn", "Clic1", "Olig2", "Hrh3", "Tmbim1", -#' "Carhsp1", "Tmem88b", "Ugt8a" +#' "Prr18", "Grb14", "Tprn", "Clic1", "Olig2", "Hrh3", +#' "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a" #' ), #' cell_type2 = c( -#' "Arpp19", "Lamp5", "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", -#' "Cygb", "Ttc9b", "Ipcef1" +#' "Arpp19", "Lamp5", "Galnt6", "Hlf", "Hs3st2", +#' "Tbr1", "Myl4", "Cygb", "Ttc9b", "Ipcef1" #' ) #' ) #' diff --git a/man/enrichment_PAGE.Rd b/man/enrichment_PAGE.Rd index fc1af2b2d..220a05060 100644 --- a/man/enrichment_PAGE.Rd +++ b/man/enrichment_PAGE.Rd @@ -99,12 +99,12 @@ sign_list <- list( "Rims2", "Gfap", "Gjc3", "Chrna4", "Prkcd" ), cell_type2 = c( - "Prr18", "Grb14", "Tprn", "Clic1", "Olig2", "Hrh3", "Tmbim1", - "Carhsp1", "Tmem88b", "Ugt8a" + "Prr18", "Grb14", "Tprn", "Clic1", "Olig2", "Hrh3", + "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a" ), cell_type2 = c( - "Arpp19", "Lamp5", "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", - "Cygb", "Ttc9b", "Ipcef1" + "Arpp19", "Lamp5", "Galnt6", "Hlf", "Hs3st2", + "Tbr1", "Myl4", "Cygb", "Ttc9b", "Ipcef1" ) ) diff --git a/man/reexports.Rd b/man/reexports.Rd index d82383743..6b000402a 100644 --- a/man/reexports.Rd +++ b/man/reexports.Rd @@ -300,7 +300,7 @@ below to see their documentation. \describe{ \item{GiottoClass}{\code{\link[GiottoClass:activeFeatType-generic]{activeFeatType}}, \code{\link[GiottoClass:activeFeatType-generic]{activeFeatType<-}}, \code{\link[GiottoClass:activeSpatUnit-generic]{activeSpatUnit}}, \code{\link[GiottoClass:activeSpatUnit-generic]{activeSpatUnit<-}}, \code{\link[GiottoClass]{addCellMetadata}}, \code{\link[GiottoClass]{addFeatMetadata}}, \code{\link[GiottoClass]{addGiottoImage}}, \code{\link[GiottoClass]{addGiottoImageMG}}, \code{\link[GiottoClass]{addGiottoLargeImage}}, \code{\link[GiottoClass]{addGiottoPoints}}, \code{\link[GiottoClass:addGiottoPoints]{addGiottoPoints3D}}, \code{\link[GiottoClass]{addGiottoPolygons}}, \code{\link[GiottoClass]{addNetworkLayout}}, \code{\link[GiottoClass]{addSpatialCentroidLocations}}, \code{\link[GiottoClass]{addSpatialCentroidLocationsLayer}}, \code{\link[GiottoClass]{aggregateStacks}}, \code{\link[GiottoClass]{aggregateStacksExpression}}, \code{\link[GiottoClass]{aggregateStacksLocations}}, \code{\link[GiottoClass]{aggregateStacksPolygonOverlaps}}, \code{\link[GiottoClass]{aggregateStacksPolygons}}, \code{\link[GiottoClass]{anndataToGiotto}}, \code{\link[GiottoClass]{annotateGiotto}}, \code{\link[GiottoClass]{annotateSpatialGrid}}, \code{\link[GiottoClass]{annotateSpatialNetwork}}, \code{\link[GiottoClass]{as.points}}, \code{\link[GiottoClass]{as.polygons}}, \code{\link[GiottoClass:r_spatial_conversions]{as.sf}}, \code{\link[GiottoClass:r_spatial_conversions]{as.sp}}, \code{\link[GiottoClass:r_spatial_conversions]{as.stars}}, \code{\link[GiottoClass:r_spatial_conversions]{as.terra}}, \code{\link[GiottoClass]{calculateMetaTable}}, \code{\link[GiottoClass]{calculateMetaTableCells}}, \code{\link[GiottoClass]{calculateOverlap}}, \code{\link[GiottoClass]{calculateOverlapParallel}}, \code{\link[GiottoClass]{calculateOverlapPolygonImages}}, \code{\link[GiottoClass]{calculateOverlapRaster}}, \code{\link[GiottoClass]{calculateOverlapSerial}}, \code{\link[GiottoClass]{calculateSpatCellMetadataProportions}}, \code{\link[GiottoClass:centroids-generic]{centroids}}, \code{\link[GiottoClass]{changeGiottoInstructions}}, \code{\link[GiottoClass]{changeImageBg}}, \code{\link[GiottoClass:giotto_python]{checkGiottoEnvironment}}, \code{\link[GiottoClass]{circleVertices}}, \code{\link[GiottoClass]{combineCellData}}, \code{\link[GiottoClass]{combineFeatureData}}, \code{\link[GiottoClass]{combineFeatureOverlapData}}, \code{\link[GiottoClass]{combineMetadata}}, \code{\link[GiottoClass]{combineSpatialCellFeatureInfo}}, \code{\link[GiottoClass]{combineSpatialCellMetadataInfo}}, \code{\link[GiottoClass]{combineToMultiPolygon}}, \code{\link[GiottoClass]{convertGiottoLargeImageToMG}}, \code{\link[GiottoClass]{copy}}, \code{\link[GiottoClass]{createBentoAdata}}, \code{\link[GiottoClass]{createCellMetaObj}}, \code{\link[GiottoClass]{createDimObj}}, \code{\link[GiottoClass]{createExprObj}}, \code{\link[GiottoClass]{createFeatMetaObj}}, \code{\link[GiottoClass]{createGiottoImage}}, \code{\link[GiottoClass:giotto_instructions]{createGiottoInstructions}}, \code{\link[GiottoClass]{createGiottoLargeImage}}, \code{\link[GiottoClass]{createGiottoLargeImageList}}, \code{\link[GiottoClass]{createGiottoObject}}, \code{\link[GiottoClass]{createGiottoObjectSubcellular}}, \code{\link[GiottoClass]{createGiottoPoints}}, \code{\link[GiottoClass]{createGiottoPolygon}}, \code{\link[GiottoClass:createGiottoPolygon]{createGiottoPolygonsFromDfr}}, \code{\link[GiottoClass:createGiottoPolygon]{createGiottoPolygonsFromGeoJSON}}, \code{\link[GiottoClass:createGiottoPolygon]{createGiottoPolygonsFromMask}}, \code{\link[GiottoClass]{createMetafeats}}, \code{\link[GiottoClass]{createNearestNetObj}}, \code{\link[GiottoClass]{createNearestNetwork}}, \code{\link[GiottoClass]{createSpatEnrObj}}, \code{\link[GiottoClass]{createSpatialDefaultGrid}}, \code{\link[GiottoClass]{createSpatialDelaunayNetwork}}, \code{\link[GiottoClass]{createSpatialFeaturesKNNnetwork}}, \code{\link[GiottoClass]{createSpatialGrid}}, \code{\link[GiottoClass]{createSpatialKNNnetwork}}, \code{\link[GiottoClass]{createSpatialNetwork}}, \code{\link[GiottoClass]{createSpatialWeightMatrix}}, \code{\link[GiottoClass]{createSpatLocsObj}}, \code{\link[GiottoClass]{createSpatNetObj}}, \code{\link[GiottoClass]{crop}}, \code{\link[GiottoClass]{cropGiottoLargeImage}}, \code{\link[GiottoClass]{density}}, \code{\link[GiottoClass]{distGiottoImage}}, \code{\link[GiottoClass]{estimateImageBg}}, \code{\link[GiottoClass]{ext}}, \code{\link[GiottoClass:ext]{ext<-}}, \code{\link[GiottoClass]{fDataDT}}, \code{\link[GiottoClass:spatIDs-generic]{featIDs}}, \code{\link[GiottoClass:featType-generic]{featType}}, \code{\link[GiottoClass:featType-generic]{featType<-}}, \code{\link[GiottoClass:featureNetwork-class]{featureNetwork}}, \code{\link[GiottoClass]{flip}}, \code{\link[GiottoClass]{gefToGiotto}}, \code{\link[GiottoClass]{getCellMetadata}}, \code{\link[GiottoClass]{getDimReduction}}, \code{\link[GiottoClass]{getExpression}}, \code{\link[GiottoClass]{getFeatureInfo}}, \code{\link[GiottoClass]{getFeatureMetadata}}, \code{\link[GiottoClass]{getGiottoImage}}, \code{\link[GiottoClass]{getMultiomics}}, \code{\link[GiottoClass]{getNearestNetwork}}, \code{\link[GiottoClass]{getPolygonInfo}}, \code{\link[GiottoClass]{getSpatialEnrichment}}, \code{\link[GiottoClass]{getSpatialEnrichment}}, \code{\link[GiottoClass]{getSpatialGrid}}, \code{\link[GiottoClass]{getSpatialGrid}}, \code{\link[GiottoClass]{getSpatialLocations}}, \code{\link[GiottoClass]{getSpatialLocations}}, \code{\link[GiottoClass]{getSpatialNetwork}}, \code{\link[GiottoClass]{getSpatialNetwork}}, \code{\link[GiottoClass:giotto-class]{giotto}}, \code{\link[GiottoClass:giottoImage-class]{giottoImage}}, \code{\link[GiottoClass:giottoLargeImage-class]{giottoLargeImage}}, \code{\link[GiottoClass]{giottoMasterToSuite}}, \code{\link[GiottoClass:giottoPoints-class]{giottoPoints}}, \code{\link[GiottoClass:giottoPolygon-class]{giottoPolygon}}, \code{\link[GiottoClass]{giottoToAnnData}}, \code{\link[GiottoClass]{giottoToSeurat}}, \code{\link[GiottoClass]{giottoToSeuratV4}}, \code{\link[GiottoClass]{giottoToSeuratV5}}, \code{\link[GiottoClass]{giottoToSpatialExperiment}}, \code{\link[GiottoClass]{hexVertices}}, \code{\link[GiottoClass]{hist}}, \code{\link[GiottoClass:giotto_python]{installGiottoEnvironment}}, \code{\link[GiottoClass:giotto_instructions]{instructions}}, \code{\link[GiottoClass:giotto_instructions]{instructions<-}}, \code{\link[GiottoClass]{joinGiottoObjects}}, \code{\link[GiottoClass]{loadGiotto}}, \code{\link[GiottoClass]{makePseudoVisium}}, \code{\link[GiottoClass]{objHistory}}, \code{\link[GiottoClass:objName-generic]{objName}}, \code{\link[GiottoClass:objName-generic]{objName<-}}, \code{\link[GiottoClass:generate_grid]{orthoGrid}}, \code{\link[GiottoClass]{overlapImagesToMatrix}}, \code{\link[GiottoClass:overlaps-generic]{overlaps}}, \code{\link[GiottoClass]{overlapToMatrix}}, \code{\link[GiottoClass]{overlapToMatrixMultiPoly}}, \code{\link[GiottoClass]{pDataDT}}, \code{\link[GiottoClass]{plotGiottoImage}}, \code{\link[GiottoClass]{polyStamp}}, \code{\link[GiottoClass:prov-generic]{prov}}, \code{\link[GiottoClass:prov-generic]{prov<-}}, \code{\link[GiottoClass]{readCellMetadata}}, \code{\link[GiottoClass]{readDimReducData}}, \code{\link[GiottoClass]{readExprData}}, \code{\link[GiottoClass]{readExprMatrix}}, \code{\link[GiottoClass]{readFeatData}}, \code{\link[GiottoClass]{readFeatMetadata}}, \code{\link[GiottoClass]{readGiottoInstructions}}, \code{\link[GiottoClass]{readNearestNetData}}, \code{\link[GiottoClass]{readPolygonData}}, \code{\link[GiottoClass]{readSpatEnrichData}}, \code{\link[GiottoClass]{readSpatLocsData}}, \code{\link[GiottoClass]{readSpatNetData}}, \code{\link[GiottoClass]{reconnectGiottoImage}}, \code{\link[GiottoClass]{rectVertices}}, \code{\link[GiottoClass]{removeCellAnnotation}}, \code{\link[GiottoClass]{removeFeatAnnotation}}, \code{\link[GiottoClass:giotto_python]{removeGiottoEnvironment}}, \code{\link[GiottoClass]{replaceGiottoInstructions}}, \code{\link[GiottoClass]{rescale}}, \code{\link[GiottoClass]{rescalePolygons}}, \code{\link[GiottoClass]{saveGiotto}}, \code{\link[GiottoClass]{setCellMetadata}}, \code{\link[GiottoClass]{setDimReduction}}, \code{\link[GiottoClass]{setExpression}}, \code{\link[GiottoClass]{setFeatureInfo}}, \code{\link[GiottoClass]{setFeatureMetadata}}, \code{\link[GiottoClass]{setGiotto}}, \code{\link[GiottoClass]{setGiottoImage}}, \code{\link[GiottoClass]{setMultiomics}}, \code{\link[GiottoClass]{setNearestNetwork}}, \code{\link[GiottoClass]{setPolygonInfo}}, \code{\link[GiottoClass]{setSpatialEnrichment}}, \code{\link[GiottoClass]{setSpatialGrid}}, \code{\link[GiottoClass]{setSpatialLocations}}, \code{\link[GiottoClass]{setSpatialNetwork}}, \code{\link[GiottoClass]{seuratToGiotto}}, \code{\link[GiottoClass]{seuratToGiottoV4}}, \code{\link[GiottoClass]{seuratToGiottoV5}}, \code{\link[GiottoClass]{showGiottoCellMetadata}}, \code{\link[GiottoClass]{showGiottoDimRed}}, \code{\link[GiottoClass]{showGiottoExpression}}, \code{\link[GiottoClass]{showGiottoFeatInfo}}, \code{\link[GiottoClass]{showGiottoFeatMetadata}}, \code{\link[GiottoClass]{showGiottoImageNames}}, \code{\link[GiottoClass]{showGiottoInstructions}}, \code{\link[GiottoClass]{showGiottoNearestNetworks}}, \code{\link[GiottoClass]{showGiottoSpatEnrichments}}, \code{\link[GiottoClass]{showGiottoSpatGrids}}, \code{\link[GiottoClass]{showGiottoSpatialInfo}}, \code{\link[GiottoClass]{showGiottoSpatLocs}}, \code{\link[GiottoClass]{showGiottoSpatNetworks}}, \code{\link[GiottoClass]{showProcessingSteps}}, \code{\link[GiottoClass]{smoothGiottoPolygons}}, \code{\link[GiottoClass]{spatialExperimentToGiotto}}, \code{\link[GiottoClass:spatIDs-generic]{spatIDs}}, \code{\link[GiottoClass]{spatQueryGiottoPolygons}}, \code{\link[GiottoClass]{spatShift}}, \code{\link[GiottoClass:spatUnit-generic]{spatUnit}}, \code{\link[GiottoClass:spatUnit-generic]{spatUnit<-}}, \code{\link[GiottoClass]{spin}}, \code{\link[GiottoClass]{stitchFieldCoordinates}}, \code{\link[GiottoClass]{stitchFieldCoordinates}}, \code{\link[GiottoClass]{stitchGiottoLargeImage}}, \code{\link[GiottoClass]{subsetGiotto}}, \code{\link[GiottoClass]{subsetGiottoLocs}}, \code{\link[GiottoClass]{subsetGiottoLocsMulti}}, \code{\link[GiottoClass]{subsetGiottoLocsSubcellular}}, \code{\link[GiottoClass]{tessellate}}, \code{\link[GiottoClass:generate_grid]{triGrid}}, \code{\link[GiottoClass]{updateGiottoImage}}, \code{\link[GiottoClass]{updateGiottoImageMG}}, \code{\link[GiottoClass]{updateGiottoLargeImage}}, \code{\link[GiottoClass]{updateGiottoObject}}, \code{\link[GiottoClass]{updateGiottoPointsObject}}, \code{\link[GiottoClass]{updateGiottoPolygonObject}}, \code{\link[GiottoClass:wrap]{vect}}, \code{\link[GiottoClass]{wrap}}, \code{\link[GiottoClass]{writeGiottoLargeImage}}} - \item{GiottoUtils}{\code{\link[GiottoUtils]{\%>\%}}, \code{\link[GiottoUtils]{getDistinctColors}}, \code{\link[GiottoUtils]{getRainbowColors}}} + \item{GiottoUtils}{\code{\link[GiottoUtils:pipe]{\%>\%}}, \code{\link[GiottoUtils]{getDistinctColors}}, \code{\link[GiottoUtils]{getRainbowColors}}} \item{GiottoVisuals}{\code{\link[GiottoVisuals]{addGiottoImageToSpatPlot}}, \code{\link[GiottoVisuals]{dimCellPlot}}, \code{\link[GiottoVisuals:dimCellPlot]{dimCellPlot2D}}, \code{\link[GiottoVisuals]{dimFeatPlot2D}}, \code{\link[GiottoVisuals]{dimFeatPlot3D}}, \code{\link[GiottoVisuals:dimFeatPlot3D]{dimGenePlot3D}}, \code{\link[GiottoVisuals]{dimPlot}}, \code{\link[GiottoVisuals:dimPlot]{dimPlot2D}}, \code{\link[GiottoVisuals:dimPlot]{dimPlot3D}}, \code{\link[GiottoVisuals]{getColors}}, \code{\link[GiottoVisuals]{giottoSankeyPlan}}, \code{\link[GiottoVisuals]{plotHeatmap}}, \code{\link[GiottoVisuals]{plotMetaDataCellsHeatmap}}, \code{\link[GiottoVisuals]{plotMetaDataHeatmap}}, \code{\link[GiottoVisuals]{plotPCA}}, \code{\link[GiottoVisuals]{plotPCA_2D}}, \code{\link[GiottoVisuals]{plotPCA_3D}}, \code{\link[GiottoVisuals]{plotStatDelaunayNetwork}}, \code{\link[GiottoVisuals]{plotTSNE}}, \code{\link[GiottoVisuals]{plotTSNE_2D}}, \code{\link[GiottoVisuals]{plotTSNE_3D}}, \code{\link[GiottoVisuals]{plotUMAP}}, \code{\link[GiottoVisuals]{plotUMAP_2D}}, \code{\link[GiottoVisuals]{plotUMAP_3D}}, \code{\link[GiottoVisuals]{sankeyLabel}}, \code{\link[GiottoVisuals:sankeyLabel]{sankeyLabel<-}}, \code{\link[GiottoVisuals]{sankeyPlot}}, \code{\link[GiottoVisuals]{sankeyRelate}}, \code{\link[GiottoVisuals:sankeyRelate]{sankeyRelate<-}}, \code{\link[GiottoVisuals]{sankeySet}}, \code{\link[GiottoVisuals]{sankeySetAddresses}}, \code{\link[GiottoVisuals]{showClusterDendrogram}}, \code{\link[GiottoVisuals]{showClusterHeatmap}}, \code{\link[GiottoVisuals]{showColorInstructions}}, \code{\link[GiottoVisuals]{showSaveParameters}}, \code{\link[GiottoVisuals]{spatCellPlot}}, \code{\link[GiottoVisuals:spatCellPlot]{spatCellPlot2D}}, \code{\link[GiottoVisuals]{spatDeconvPlot}}, \code{\link[GiottoVisuals]{spatDimCellPlot}}, \code{\link[GiottoVisuals]{spatDimCellPlot2D}}, \code{\link[GiottoVisuals]{spatDimFeatPlot2D}}, \code{\link[GiottoVisuals]{spatDimFeatPlot3D}}, \code{\link[GiottoVisuals:spatDimFeatPlot3D]{spatDimGenePlot3D}}, \code{\link[GiottoVisuals]{spatDimPlot}}, \code{\link[GiottoVisuals:spatDimPlot]{spatDimPlot2D}}, \code{\link[GiottoVisuals]{spatDimPlot3D}}, \code{\link[GiottoVisuals]{spatFeatPlot2D}}, \code{\link[GiottoVisuals]{spatFeatPlot2D_single}}, \code{\link[GiottoVisuals]{spatFeatPlot3D}}, \code{\link[GiottoVisuals:spatFeatPlot3D]{spatGenePlot3D}}, \code{\link[GiottoVisuals]{spatInSituPlotDensity}}, \code{\link[GiottoVisuals]{spatInSituPlotHex}}, \code{\link[GiottoVisuals]{spatInSituPlotPoints}}, \code{\link[GiottoVisuals]{spatNetwDistributions}}, \code{\link[GiottoVisuals]{spatNetwDistributionsDistance}}, \code{\link[GiottoVisuals]{spatNetwDistributionsKneighbors}}, \code{\link[GiottoVisuals]{spatPlot}}, \code{\link[GiottoVisuals:spatPlot]{spatPlot2D}}, \code{\link[GiottoVisuals:spatPlot]{spatPlot3D}}, \code{\link[GiottoVisuals]{subsetSankeySet}}, \code{\link[GiottoVisuals]{violinPlot}}} }} From b5cc77d957707ccc9c1ed06df1a6694383b1967c Mon Sep 17 00:00:00 2001 From: George Chen <72078254+jiajic@users.noreply.github.com> Date: Wed, 4 Sep 2024 10:08:09 -0400 Subject: [PATCH 04/12] chore: remove `progressr`. Use giottoutils export --- DESCRIPTION | 1 - R/convenience_cosmx.R | 8 ++++---- R/convenience_xenium.R | 4 ++-- R/differential_expression.R | 8 ++++---- R/general_help.R | 24 ++++++++++++------------ R/kriging.R | 4 ++-- R/spatial_enrichment.R | 8 ++++---- R/spatial_interaction.R | 4 ++-- R/spdep.R | 4 ++-- 9 files changed, 32 insertions(+), 33 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index aed8c9f48..acc600a9f 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -46,7 +46,6 @@ Imports: jsonlite, Matrix (>= 1.6-2), MatrixGenerics, - progressr, reshape2, reticulate (>= 1.25), scales (>= 1.0.0), diff --git a/R/convenience_cosmx.R b/R/convenience_cosmx.R index 4a107002f..f22ed482d 100644 --- a/R/convenience_cosmx.R +++ b/R/convenience_cosmx.R @@ -723,8 +723,8 @@ setMethod("$<-", signature("CosmxReader"), function(x, name, value) { ) fovs <- fovs %null% .cosmx_imgname_fovparser(path) # ALL if NULL - progressr::with_progress({ - p <- progressr::progressor(along = fovs) + with_pbar({ + p <- pbar(along = fovs) gpolys <- lapply(fovs, function(f) { segfile <- Sys.glob(paths = sprintf("%s/*F%03d*", path, f)) @@ -925,8 +925,8 @@ setMethod("$<-", signature("CosmxReader"), function(x, name, value) { fovs <- fovs %null% .cosmx_imgname_fovparser(path) # ALL if NULL verbose <- verbose %null% TRUE - progressr::with_progress({ - p <- progressr::progressor(along = fovs) + with_pbar({ + p <- pbar(along = fovs) gimg_list <- lapply(fovs, function(f) { imgfile <- Sys.glob(paths = sprintf("%s/*F%03d*", path, f)) diff --git a/R/convenience_xenium.R b/R/convenience_xenium.R index e8b437286..a5048b9f8 100644 --- a/R/convenience_xenium.R +++ b/R/convenience_xenium.R @@ -1102,8 +1102,8 @@ importXenium <- function( # micron checkmate::assert_numeric(micron) - progressr::with_progress({ - p <- progressr::progressor(along = path) + with_pbar({ + p <- pbar(along = path) gimg_list <- lapply(seq_along(path), function(img_i) { gimg <- .xenium_image_single( diff --git a/R/differential_expression.R b/R/differential_expression.R index 5eead79a0..d84d06f53 100644 --- a/R/differential_expression.R +++ b/R/differential_expression.R @@ -279,8 +279,8 @@ findScranMarkers_one_vs_all <- function( # save list - progressr::with_progress({ - pb <- progressr::progressor(along = uniq_clusters) + with_pbar({ + pb <- pbar(along = uniq_clusters) result_list <- lapply( seq_along(uniq_clusters), function(clus_i) { @@ -734,8 +734,8 @@ findGiniMarkers_one_vs_all <- function( # GINI - progressr::with_progress({ - pb <- progressr::progressor(along = uniq_clusters) + with_pbar({ + pb <- pbar(along = uniq_clusters) result_list <- lapply( seq_along(uniq_clusters), function(clus_i) { diff --git a/R/general_help.R b/R/general_help.R index c10ae4ffb..97bcc7c8c 100644 --- a/R/general_help.R +++ b/R/general_help.R @@ -859,8 +859,8 @@ readPolygonFilesVizgenHDF5_old <- function( # append data from all FOVs to single list init <- proc.time() - progressr::with_progress({ - pb <- progressr::progressor(along = hdf5_boundary_selected_list) + with_pbar({ + pb <- pbar(along = hdf5_boundary_selected_list) read_list <- lapply_flex(seq_along(hdf5_boundary_selected_list), cores = cores, future.packages = c("rhdf5", "Rhdf5lib"), @@ -933,8 +933,8 @@ readPolygonFilesVizgenHDF5_old <- function( # create Giotto polygons and add them to gobject - progressr::with_progress({ - pb <- progressr::progressor(along = result_list_rbind) + with_pbar({ + pb <- pbar(along = result_list_rbind) smooth_cell_polygons_list <- lapply_flex(seq_along(result_list_rbind), cores = cores, function(i) { dfr_subset <- result_list_rbind[[i]][, .(x, y, cell_id)] @@ -1074,8 +1074,8 @@ readPolygonFilesVizgenHDF5 <- function( # append data from all FOVs to single list init <- Sys.time() - progressr::with_progress({ - pb <- progressr::progressor(length(hdf5_boundary_selected_list) / 5) + with_pbar({ + pb <- pbar(length(hdf5_boundary_selected_list) / 5) read_list <- lapply_flex(seq_along(hdf5_boundary_selected_list), future.packages = c("rhdf5", "Rhdf5lib"), function(init, z_indices, segm_to_use, bound_i) { @@ -1167,8 +1167,8 @@ readPolygonFilesVizgenHDF5 <- function( # **** sequential method **** if (!isTRUE(create_gpoly_parallel)) { - progressr::with_progress({ - pb <- progressr::progressor(along = z_read_DT) + with_pbar({ + pb <- pbar(along = z_read_DT) smooth_cell_polygons_list <- lapply( seq_along(z_read_DT), function(i) { dfr_subset <- z_read_DT[[i]][, .(x, y, cell_id)] @@ -1213,8 +1213,8 @@ readPolygonFilesVizgenHDF5 <- function( # **** parallel methods **** # no binning if (!is.numeric(create_gpoly_bin)) { - progressr::with_progress({ - pb <- progressr::progressor(along = z_read_DT) + with_pbar({ + pb <- pbar(along = z_read_DT) smooth_cell_polygons_list <- lapply_flex( seq_along(z_read_DT), future.packages = c("terra", "stats", "data.table"), @@ -1282,8 +1282,8 @@ readPolygonFilesVizgenHDF5 <- function( bin_steps <- sum(unlist(lapply(dfr_subset, length))) - progressr::with_progress({ - pb <- progressr::progressor(steps = bin_steps) + with_pbar({ + pb <- pbar(steps = bin_steps) smooth_cell_polygons_list <- lapply( # sequential across z index seq_along(dfr_subset), function(i) { diff --git a/R/kriging.R b/R/kriging.R index 2c0c8b204..95612a41f 100644 --- a/R/kriging.R +++ b/R/kriging.R @@ -182,8 +182,8 @@ setMethod( e_numeric <- ext[] - progressr::with_progress({ - pb <- progressr::progressor(along = feats) + with_pbar({ + pb <- pbar(along = feats) interp_img_list <- lapply_flex( feats, diff --git a/R/spatial_enrichment.R b/R/spatial_enrichment.R index 488a6ab41..e378fd762 100644 --- a/R/spatial_enrichment.R +++ b/R/spatial_enrichment.R @@ -1948,9 +1948,9 @@ spatialAutoCorLocal <- function( step_size <- step_size <- ceiling(nfeats / 10L) } - progressr::with_progress({ + with_pbar({ if (step_size > 1) { - pb <- progressr::progressor( + pb <- pbar( steps = nfeats / step_size ) } @@ -2031,9 +2031,9 @@ spatialAutoCorLocal <- function( step_size <- step_size <- ceiling(nfeats / 10L) } - progressr::with_progress({ + with_pbar({ if (step_size > 1) { - pb <- progressr::progressor( + pb <- pbar( steps = nfeats / step_size ) } diff --git a/R/spatial_interaction.R b/R/spatial_interaction.R index 27e82edea..b7139ee0e 100644 --- a/R/spatial_interaction.R +++ b/R/spatial_interaction.R @@ -2145,8 +2145,8 @@ exprCellCellcom <- function(gobject, # not yet available - progressr::with_progress({ - pb <- progressr::progressor(steps = random_iter) + with_pbar({ + pb <- pbar(steps = random_iter) for (sim in seq_len(random_iter)) { diff --git a/R/spdep.R b/R/spdep.R index 6abbe9cad..84b1b9ed9 100644 --- a/R/spdep.R +++ b/R/spdep.R @@ -77,9 +77,9 @@ spdepAutoCorr <- function( step_size <- ceiling(nfeats / 10L) result_list <- list() - progressr::with_progress({ + with_pbar({ if (step_size > 1) { - pb <- progressr::progressor( + pb <- pbar( steps = nfeats / step_size ) } From 0c50bac0e998ab376dbdd11246fdaef2f17e58dc Mon Sep 17 00:00:00 2001 From: George Chen <72078254+jiajic@users.noreply.github.com> Date: Wed, 4 Sep 2024 10:55:57 -0400 Subject: [PATCH 05/12] chore: fix `cellProximityEnrichmentSpots()` example --- R/spatial_interaction_spot.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/spatial_interaction_spot.R b/R/spatial_interaction_spot.R index ab8ed82d4..9e1b5ace1 100644 --- a/R/spatial_interaction_spot.R +++ b/R/spatial_interaction_spot.R @@ -225,7 +225,7 @@ NULL #' ) #' sign_gene <- x$feats #' -#' sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10), +#' sign_matrix <- matrix(rnorm(length(sign_gene) * 7, mean = 10), #' nrow = length(sign_gene) #' ) #' rownames(sign_matrix) <- sign_gene From d94a29f5080700242b68474a819e9d6a108c405f Mon Sep 17 00:00:00 2001 From: George Chen <72078254+jiajic@users.noreply.github.com> Date: Wed, 4 Sep 2024 12:55:48 -0400 Subject: [PATCH 06/12] chore: finish removing `reshape2` - fix `findICFSpot()` example --- DESCRIPTION | 1 - NEWS.md | 5 ++++- R/spatial_interaction.R | 14 ++++++++----- R/spatial_interaction_spot.R | 31 ++++++++++++++++------------- man/cellProximityEnrichmentSpots.Rd | 2 +- man/exprCellCellcom.Rd | 2 +- man/findICFSpot.Rd | 16 +++++++++------ 7 files changed, 42 insertions(+), 29 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index acc600a9f..507dc13ed 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -46,7 +46,6 @@ Imports: jsonlite, Matrix (>= 1.6-2), MatrixGenerics, - reshape2, reticulate (>= 1.25), scales (>= 1.0.0), sparseMatrixStats, diff --git a/NEWS.md b/NEWS.md index 1f4151739..19ff86879 100644 --- a/NEWS.md +++ b/NEWS.md @@ -9,7 +9,10 @@ * remove deprecated `heatmSpatialCorGenes()`. Use `heatmSpatialCorFeats()` instead * remove deprecated `runPAGEEnrich_OLD()`. Use `runPAGEEnrich()` instead - +## Changes +* `limma`, `plotly`, and `Rtsne` moved to Suggests +* move `progressr` and `jsonlite` dependencies to GiottoUtils v0.1.12 +* remove `reshape2` dependency. # Giotto 4.1.1 diff --git a/R/spatial_interaction.R b/R/spatial_interaction.R index b7139ee0e..c009c84a2 100644 --- a/R/spatial_interaction.R +++ b/R/spatial_interaction.R @@ -1996,8 +1996,10 @@ print.combIcfObject <- function(x, ...) { lig_cell_type <- rec_cell_type <- NULL all_ligand_cols <- colnames(ligand_match) - lig_test <- data.table::as.data.table( - reshape2::melt(ligand_match, measure.vars = all_ligand_cols)) + lig_test <- data.table::melt( + data.table::as.data.table(ligand_match), + measure.vars = all_ligand_cols + ) lig_test[, ligand := rep(rownames(ligand_match), ncol(ligand_match))] lig_test[, ligand := strsplit(ligand, "\\.")[[1]][1], by = seq_len(nrow(lig_test))] @@ -2006,8 +2008,10 @@ print.combIcfObject <- function(x, ...) { setnames(lig_test, "variable", "lig_cell_type") all_receptor_cols <- colnames(receptor_match) - rec_test <- data.table::as.data.table(reshape2::melt( - receptor_match, measure.vars = all_receptor_cols)) + rec_test <- data.table::melt( + data.table::as.data.table(receptor_match), + measure.vars = all_receptor_cols + ) rec_test[, receptor := rep(rownames(receptor_match), ncol(receptor_match))] rec_test[, receptor := strsplit( receptor, "\\.")[[1]][1], by = seq_len(nrow(rec_test))] @@ -2045,7 +2049,7 @@ print.combIcfObject <- function(x, ...) { #' (random variance and z-score) #' @param adjust_method which method to adjust p-values #' @param adjust_target adjust multiple hypotheses at the cell or feature level -#' @param set_seed set seed for random simulations (default = TRUE) +#' @param set_seed `logical`. set seed for random simulations (default = TRUE) #' @param seed_number seed number #' @param verbose verbose #' @returns Cell-Cell communication scores for feature pairs based on diff --git a/R/spatial_interaction_spot.R b/R/spatial_interaction_spot.R index 9e1b5ace1..9fe9f0740 100644 --- a/R/spatial_interaction_spot.R +++ b/R/spatial_interaction_spot.R @@ -54,7 +54,7 @@ NULL diag(diff_ct) <- NA diff_ct[lower.tri(diff_ct)] <- NA # transfer format to data.table - diff_ct <- data.table::as.data.table(reshape2::melt(diff_ct)) + diff_ct <- melt_matrix(diff_ct) diff_ct <- diff_ct[value != "NA"] diff_ct[, c("Var1", "Var2") := lapply( .SD, as.character @@ -99,10 +99,9 @@ NULL pairs_for_mat <- pairs_for_mat[, .N, by = c("from", "to")] # make square matrix of interaction between spots - pairs_mat <- reshape2::acast( - pairs_for_mat, from ~ to, - value.var = "N", fill = 0 - ) + pairs_mat <- as.matrix(data.table::dcast( + pairs_for_mat, from ~ to, value.var = "N", fill = 0 + ), rownames = "from") pairs_mat <- pairs_mat[cell_IDs, cell_IDs] # calculate cell-type/cell-type interactions @@ -715,8 +714,8 @@ cellProximityEnrichmentEachSpot <- function( dimnames = list(names(dwls_target_cell), names(spot_proximity)) ) } - spot_proximity <- reshape2::melt(spot_proximity) - spot_proximity <- data.table::data.table(spot_proximity) + + spot_proximity <- melt_matrix(spot_proximity) spot_proximity[, c("Var1", "Var2") := lapply( .SD, as.character ), .SDcols = c("Var1", "Var2")] @@ -1146,7 +1145,9 @@ NULL #' @param spat_unit spatial unit (e.g. 'cell') #' @param feat_type feature type (e.g. 'rna') #' @param expression_values expression values to use -#' @param ave_celltype_exp average feature expression in each cell type +#' @param ave_celltype_exp `matrix` or `data.frame`. Average feature expression +#' in each cell type. Colnames should be the cell type and rownames are feat +#' names. #' @param selected_features subset of selected features (optional) #' @param spatial_network_name name of spatial network to use #' @param deconv_name name of deconvolution/spatial enrichment values to use @@ -1197,7 +1198,7 @@ NULL #' ) #' sign_gene <- x$feats #' -#' sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10), +#' sign_matrix <- matrix(rnorm(length(sign_gene) * 7, mean = 10), #' nrow = length(sign_gene) #' ) #' rownames(sign_matrix) <- sign_gene @@ -1205,17 +1206,19 @@ NULL #' #' g <- runDWLSDeconv(gobject = g, sign_matrix = sign_matrix) #' ave_celltype_exp <- calculateMetaTable(g, metadata_cols = "leiden_clus") -#' ave_celltype_exp <- reshape2::dcast(ave_celltype_exp, variable~leiden_clus) -#' rownames(ave_celltype_exp) <- ave_celltype_exp$variable -#' ave_celltype_exp <- ave_celltype_exp[,-1] +#' ave_celltype_exp <- data.table::dcast( +#' ave_celltype_exp, variable~leiden_clus +#' ) +#' ave_celltype_exp <- as.matrix(ave_celltype_exp, rownames = "variable") #' colnames(ave_celltype_exp) <- colnames(sign_matrix) -#' -#' findICFSpot(g, +#' +#' res <- findICFSpot(g, #' spat_unit = "cell", #' feat_type = "rna", #' ave_celltype_exp = ave_celltype_exp, #' spatial_network_name = "spatial_network" #' ) +#' force(res) #' @seealso [findInteractionChangedFeats()] #' @md #' @export diff --git a/man/cellProximityEnrichmentSpots.Rd b/man/cellProximityEnrichmentSpots.Rd index e618dbbc3..432921401 100644 --- a/man/cellProximityEnrichmentSpots.Rd +++ b/man/cellProximityEnrichmentSpots.Rd @@ -70,7 +70,7 @@ x <- findMarkers_one_vs_all(g, ) sign_gene <- x$feats -sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10), +sign_matrix <- matrix(rnorm(length(sign_gene) * 7, mean = 10), nrow = length(sign_gene) ) rownames(sign_matrix) <- sign_gene diff --git a/man/exprCellCellcom.Rd b/man/exprCellCellcom.Rd index e3d89cef8..ca167a8c1 100644 --- a/man/exprCellCellcom.Rd +++ b/man/exprCellCellcom.Rd @@ -46,7 +46,7 @@ exprCellCellcom( \item{adjust_target}{adjust multiple hypotheses at the cell or feature level} -\item{set_seed}{set seed for random simulations (default = TRUE)} +\item{set_seed}{`logical`. set seed for random simulations (default = TRUE)} \item{seed_number}{seed number} diff --git a/man/findICFSpot.Rd b/man/findICFSpot.Rd index 500f53e2d..91eeedf11 100644 --- a/man/findICFSpot.Rd +++ b/man/findICFSpot.Rd @@ -36,7 +36,9 @@ findICFSpot( \item{expression_values}{expression values to use} -\item{ave_celltype_exp}{average feature expression in each cell type} +\item{ave_celltype_exp}{\code{matrix} or \code{data.frame}. Average feature expression +in each cell type. Colnames should be the cell type and rownames are feat +names.} \item{selected_features}{subset of selected features (optional)} @@ -113,7 +115,7 @@ x <- findMarkers_one_vs_all(g, ) sign_gene <- x$feats -sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10), +sign_matrix <- matrix(rnorm(length(sign_gene) * 7, mean = 10), nrow = length(sign_gene) ) rownames(sign_matrix) <- sign_gene @@ -121,17 +123,19 @@ colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster)) g <- runDWLSDeconv(gobject = g, sign_matrix = sign_matrix) ave_celltype_exp <- calculateMetaTable(g, metadata_cols = "leiden_clus") -ave_celltype_exp <- reshape2::dcast(ave_celltype_exp, variable~leiden_clus) -rownames(ave_celltype_exp) <- ave_celltype_exp$variable -ave_celltype_exp <- ave_celltype_exp[,-1] +ave_celltype_exp <- data.table::dcast( + ave_celltype_exp, variable~leiden_clus +) +ave_celltype_exp <- as.matrix(ave_celltype_exp, rownames = "variable") colnames(ave_celltype_exp) <- colnames(sign_matrix) -findICFSpot(g, +res <- findICFSpot(g, spat_unit = "cell", feat_type = "rna", ave_celltype_exp = ave_celltype_exp, spatial_network_name = "spatial_network" ) +force(res) } \seealso{ \code{\link[=findInteractionChangedFeats]{findInteractionChangedFeats()}} From c80b58b44f629079ac45fb246425eaa81636e43d Mon Sep 17 00:00:00 2001 From: George Chen <72078254+jiajic@users.noreply.github.com> Date: Wed, 4 Sep 2024 14:34:42 -0400 Subject: [PATCH 07/12] chore: remove jsonlite references --- DESCRIPTION | 1 - R/convenience_general.R | 2 +- R/convenience_visiumHD.R | 2 +- R/convenience_xenium.R | 4 ++-- R/general_help.R | 2 +- 5 files changed, 5 insertions(+), 6 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 507dc13ed..1eeb3defb 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -43,7 +43,6 @@ Imports: GiottoUtils (>= 0.1.12), GiottoVisuals (>= 0.2.4), igraph (>= 1.2.4.1), - jsonlite, Matrix (>= 1.6-2), MatrixGenerics, reticulate (>= 1.25), diff --git a/R/convenience_general.R b/R/convenience_general.R index 1d626c5bf..d5815eaa4 100644 --- a/R/convenience_general.R +++ b/R/convenience_general.R @@ -656,7 +656,7 @@ addVisiumPolygons <- function(gobject, return(NULL) } - json_scalefactors <- jsonlite::read_json(json_path) + json_scalefactors <- read_json(json_path) # Intial assertion that json dimensions are appropriate checkmate::assert_list( diff --git a/R/convenience_visiumHD.R b/R/convenience_visiumHD.R index a0c3ca5fe..645b76ee4 100644 --- a/R/convenience_visiumHD.R +++ b/R/convenience_visiumHD.R @@ -746,7 +746,7 @@ setMethod("$<-", signature("VisiumHDReader"), function(x, name, value) { json_path = file.path(path, 'scalefactors_json.json') checkmate::assert_file_exists(json_path) - json_scalefactors <- jsonlite::read_json(json_path) + json_scalefactors <- read_json(json_path) expected_json_names <- c( "regist_target_img_scalef", # NEW as of 2023 diff --git a/R/convenience_xenium.R b/R/convenience_xenium.R index a5048b9f8..6fc5842b7 100644 --- a/R/convenience_xenium.R +++ b/R/convenience_xenium.R @@ -193,7 +193,7 @@ setMethod( # decide micron scaling if (length(obj@micron) == 0) { # if no value already set if (!is.null(experiment_info_path)) { - obj@micron <- jsonlite::fromJSON( + obj@micron <- fromJSON( experiment_info_path)$pixel_size } else { warning(wrap_txt("No .xenium file found. @@ -914,7 +914,7 @@ importXenium <- function( gene_ids <- match.arg(gene_ids, c("symbols", "ensembl")) # tested on v1.6 - j <- jsonlite::fromJSON(path) + j <- fromJSON(path) # j$metadata # dataset meta # j$payload # main content # j$payload$chemistry # panel chemistry used diff --git a/R/general_help.R b/R/general_help.R index 97bcc7c8c..cc0b3daed 100644 --- a/R/general_help.R +++ b/R/general_help.R @@ -744,7 +744,7 @@ read10xAffineImage <- function( checkmate::assert_file_exists(imagealignment_path) if (!is.numeric(micron)) { checkmate::assert_file_exists(micron) - micron <- jsonlite::read_json(micron)$pixel_size + micron <- read_json(micron)$pixel_size } aff <- data.table::fread(imagealignment_path) %>% From 6311dd4df03bdbe187d21eb6d0438de30eb13cfd Mon Sep 17 00:00:00 2001 From: George Chen <72078254+jiajic@users.noreply.github.com> Date: Wed, 4 Sep 2024 15:07:21 -0400 Subject: [PATCH 08/12] chore: remove packages moved to GiottoUtils --- DESCRIPTION | 3 --- 1 file changed, 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 8cff1d619..bb1a981c9 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -43,11 +43,8 @@ Imports: GiottoUtils (>= 0.1.9), GiottoVisuals (>= 0.2.4), igraph (>= 1.2.4.1), - jsonlite, Matrix (>= 1.6-2), MatrixGenerics, - progressr, - reshape2, reticulate (>= 1.25), scales (>= 1.0.0), sparseMatrixStats, From 8119be9018ff99aacba48cff6265dc508551bb4e Mon Sep 17 00:00:00 2001 From: George Chen <72078254+jiajic@users.noreply.github.com> Date: Fri, 6 Sep 2024 13:28:33 -0400 Subject: [PATCH 09/12] chore: isolate `cowplot` dep to GiottoVisuals --- DESCRIPTION | 3 +-- R/dimension_reduction.R | 2 +- R/python_hmrf.R | 8 ++++---- R/spatial_interaction_visuals.R | 2 +- 4 files changed, 7 insertions(+), 8 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index bb1a981c9..4b534c4a0 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -36,12 +36,11 @@ Imports: BiocParallel, BiocSingular, checkmate, - cowplot (>= 0.9.4), data.table (>= 1.12.2), dbscan (>= 1.1-3), ggplot2 (>= 3.1.1), GiottoUtils (>= 0.1.9), - GiottoVisuals (>= 0.2.4), + GiottoVisuals (>= 0.2.5), igraph (>= 1.2.4.1), Matrix (>= 1.6-2), MatrixGenerics, diff --git a/R/dimension_reduction.R b/R/dimension_reduction.R index 7b4f8bc18..77b5a44d3 100644 --- a/R/dimension_reduction.R +++ b/R/dimension_reduction.R @@ -1629,7 +1629,7 @@ create_screeplot <- function(eigs, ncp = 20, ylim = c(0, 20)) { savelist <- list(pl, cpl) ## combine plots with cowplot - combo_plot <- cowplot::plot_grid( + combo_plot <- plot_grid( plotlist = savelist, ncol = 1, rel_heights = c(1), diff --git a/R/python_hmrf.R b/R/python_hmrf.R index 6b0b597d0..6d1177ed0 100644 --- a/R/python_hmrf.R +++ b/R/python_hmrf.R @@ -2034,7 +2034,7 @@ addHMRF_V2 <- function(gobject, HMRFoutput, name = "hmrf") { spat_unit = spat_unit, feat_type = feat_type, new_metadata = HMRFoutput[[i]]$class[match( - ordered_cell_IDs, + ordered_cell_IDs, rownames(HMRFoutput[[i]]$prob))], vector_name = paste(name, names(HMRFoutput)[i]) # ,column_cell_ID = 'cell_ID', @@ -2044,8 +2044,8 @@ addHMRF_V2 <- function(gobject, HMRFoutput, name = "hmrf") { return(gobject) } - - + + #' @title viewHMRFresults_V2 #' @name viewHMRFresults_V2 #' @description function to view HMRF results with multiple betas @@ -2151,7 +2151,7 @@ viewHMRFresults_V2 <- } # combine plots with cowplot - combo_plot <- cowplot::plot_grid( + combo_plot <- plot_grid( plotlist = savelist, ncol = cow_n_col, rel_heights = cow_rel_h, diff --git a/R/spatial_interaction_visuals.R b/R/spatial_interaction_visuals.R index c74da4fc3..8f192b778 100644 --- a/R/spatial_interaction_visuals.R +++ b/R/spatial_interaction_visuals.R @@ -68,7 +68,7 @@ cellProximityBarplot <- function( bpl <- bpl + ggplot2::labs(y = "# of interactions") bpl - combo_plot <- cowplot::plot_grid( + combo_plot <- plot_grid( pl, bpl, ncol = 2, rel_heights = c(1), rel_widths = c(3, 1.5), align = "h" From 9e81c0a373d313bf57688cd1fa901a6b25edd05a Mon Sep 17 00:00:00 2001 From: George Chen <72078254+jiajic@users.noreply.github.com> Date: Wed, 11 Sep 2024 12:21:33 -0400 Subject: [PATCH 10/12] change: deprecate `name` arg in `screePlot()` --- NEWS.md | 3 +++ R/dimension_reduction.R | 17 +++++++++++++++-- 2 files changed, 18 insertions(+), 2 deletions(-) diff --git a/NEWS.md b/NEWS.md index 5269365d9..38ef76098 100644 --- a/NEWS.md +++ b/NEWS.md @@ -4,6 +4,9 @@ ## Bug fixes * Allow `giottoInstructions` passing for Xenium convenience functions +## Changes +* Deprecate `screePlot()` `name` in favor of `dim_reduction_name` param + # Giotto 4.1.0 (2024/07/31) diff --git a/R/dimension_reduction.R b/R/dimension_reduction.R index 77b5a44d3..8825258c4 100644 --- a/R/dimension_reduction.R +++ b/R/dimension_reduction.R @@ -1376,7 +1376,8 @@ runPCAprojectionBatch <- function( #' @inheritParams data_access_params #' @inheritParams plot_output_params #' @inheritParams create_screeplot -#' @param name name of PCA object if available +#' @param dim_reduction_name name of PCA +#' @param name deprecated #' @param expression_values expression values to use #' @param reduction cells or features #' @param method which implementation to use @@ -1402,7 +1403,8 @@ screePlot <- function( gobject, spat_unit = NULL, feat_type = NULL, - name = NULL, + dim_reduction_name = NULL, + name = deprecated(), expression_values = c("normalized", "scaled", "custom"), reduction = c("cells", "feats"), method = c("irlba", "exact", "random", "factominer"), @@ -1419,6 +1421,17 @@ screePlot <- function( save_param = list(), default_save_name = "screePlot", ...) { + + if (is_present(name)) { + deprecate_warn( + when = "4.1.1", + what = "screePlot(name = )", + with = "screePlot(dim_reduction_name = )" + ) + } else { + name <- dim_reduction_name # shorter varname + } + # Set feat_type and spat_unit spat_unit <- set_default_spat_unit( gobject = gobject, From 3b738f72b14e0b6fb37bb6dc60738a635defca2a Mon Sep 17 00:00:00 2001 From: George Chen <72078254+jiajic@users.noreply.github.com> Date: Thu, 12 Sep 2024 08:16:10 -0400 Subject: [PATCH 11/12] chore: bump version for dev --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index ab156986f..5bd133691 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: Giotto Title: Spatial Single-Cell Transcriptomics Toolbox -Version: 4.1.1 +Version: 4.1.2 Authors@R: c( person("Ruben", "Dries", email = "rubendries@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-7650-7754")), From efdf9c0163dd8e1a270e4a7da8319929e945610a Mon Sep 17 00:00:00 2001 From: George Chen <72078254+jiajic@users.noreply.github.com> Date: Thu, 12 Sep 2024 10:30:19 -0400 Subject: [PATCH 12/12] chore: update deprecated accessors --- R/ONTraC_wrapper.R | 4 +- R/auxiliary_giotto.R | 38 ++------ R/clustering.R | 2 +- R/convenience_cosmx.R | 7 +- R/dimension_reduction.R | 37 +++----- R/giotto_viewer.R | 11 ++- R/image_registration.R | 162 +++++++++++++++++------------------ R/interactivity.R | 4 +- R/python_hmrf.R | 4 +- R/spatial_enrichment.R | 29 ++----- R/spatial_interaction_spot.R | 7 +- R/wnn.R | 21 ++--- man/enrichment_PAGE.Rd | 2 +- man/screePlot.Rd | 7 +- man/spatCellCellcomSpots.Rd | 58 +++++++------ 15 files changed, 178 insertions(+), 215 deletions(-) diff --git a/R/ONTraC_wrapper.R b/R/ONTraC_wrapper.R index e33abeac7..bf11d6075 100644 --- a/R/ONTraC_wrapper.R +++ b/R/ONTraC_wrapper.R @@ -398,7 +398,7 @@ plotNicheClusterConnectivity <- function( # nolint: object_name_linter. ) gpl <- gpl + ggraph::scale_edge_alpha(range = c(0.1, 1)) gpl <- gpl + ggraph::scale_edge_colour_gradientn( - colours = RColorBrewer::brewer.pal(9, "Reds"), + colours = getColors("Reds", 9, src = "RColorBrewer"), name = "Value" ) @@ -764,4 +764,4 @@ plotCellTypeNTScore <- function(gobject, # nolint: object_name_linter. save_param = save_param, else_return = NULL )) -} \ No newline at end of file +} diff --git a/R/auxiliary_giotto.R b/R/auxiliary_giotto.R index 3276c90f5..8fbb26857 100644 --- a/R/auxiliary_giotto.R +++ b/R/auxiliary_giotto.R @@ -933,19 +933,12 @@ filterGiotto <- function( ) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_expression_values( - gobject = gobject, - values = norm_expr - ) - - gobject <- set_expression_values( - gobject = gobject, - values = norm_scaled_expr - ) + gobject <- setGiotto(gobject, norm_expr, initialize = FALSE) + gobject <- setGiotto(gobject, norm_scaled_expr, initialize = FALSE) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## 6. return Giotto object - return(gobject) + return(initialize(gobject)) } @@ -991,13 +984,9 @@ filterGiotto <- function( ) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_expression_values( - gobject = gobject, - values = norm_feats_cells - ) + gobject <- setGiotto(giotto, norm_feats_cells) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - return(gobject) } @@ -1088,10 +1077,7 @@ filterGiotto <- function( ) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_expression_values( - gobject = gobject, - values = z - ) + gobject <- setGiotto(gobject, z) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### return(gobject) @@ -1391,10 +1377,7 @@ adjustGiottoMatrix <- function( ) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_expression_values( - gobject = gobject, - values = adjusted_matrix - ) + gobject <- setGiotto(gobject, adjusted_matrix) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## update parameters used ## @@ -1456,14 +1439,14 @@ processGiotto <- function( adjust_params = list(), verbose = TRUE) { # filter Giotto - if (verbose == TRUE) message("1. start filter step") + vmsg(.v = verbose, "1. start filter step") if (!inherits(filter_params, "list")) { stop("filter_params need to be a list of parameters for filterGiotto") } gobject <- do.call("filterGiotto", c(gobject = gobject, filter_params)) # normalize Giotto - if (verbose == TRUE) message("2. start normalization step") + vmsg(.v = verbose, "2. start normalization step") if (!inherits(norm_params, "list")) { stop("norm_params need to be a list of parameters for normalizeGiotto") } @@ -1623,10 +1606,7 @@ addFeatStatistics <- function( "mean_expr_det" ) := NULL] ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_feature_metadata(gobject, - metadata = feat_metadata, - verbose = FALSE - ) + gobject <- setGiotto(gobject, feat_metadata, verbose = FALSE) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### } diff --git a/R/clustering.R b/R/clustering.R index bd1b0ff66..c0f767dbf 100644 --- a/R/clustering.R +++ b/R/clustering.R @@ -1278,7 +1278,7 @@ doKmeans <- function( if(!is.null(dim_reduction_to_use)) { # use only available dimensions if dimensions < dimensions_to_use - dim_coord <- get_dimReduction( + dim_coord <- getDimReduction( gobject = gobject, spat_unit = spat_unit, feat_type = feat_type, diff --git a/R/convenience_cosmx.R b/R/convenience_cosmx.R index f22ed482d..c3a503a6a 100644 --- a/R/convenience_cosmx.R +++ b/R/convenience_cosmx.R @@ -1645,7 +1645,7 @@ createGiottoCosMxObject <- function( provenance = "cell_agg" ) - cosmx_gobject <- set_expression_values(cosmx_gobject, values = s4_expr) + cosmx_gobject <- setGiotto(cosmx_gobject, s4_expr) # Add spatial locations if (isTRUE(verbose)) wrap_msg( @@ -1669,9 +1669,8 @@ createGiottoCosMxObject <- function( provenance = "cell_agg" ) - cosmx_gobject <- set_spatial_locations(cosmx_gobject, spatlocs = locsObj) - cosmx_gobject <- set_spatial_locations(cosmx_gobject, - spatlocs = locsObj_fov) + cosmx_gobject <- setGiotto(cosmx_gobject, locsObj, initialize = FALSE) + cosmx_gobject <- setGiotto(cosmx_gobject, locsObj_fov, initialize = FALSE) # initialize cell and feat IDs and metadata slots for 'cell_agg' spat_unit agg_cell_ID <- colnames(s4_expr[]) diff --git a/R/dimension_reduction.R b/R/dimension_reduction.R index 8825258c4..de7906d1c 100644 --- a/R/dimension_reduction.R +++ b/R/dimension_reduction.R @@ -541,9 +541,7 @@ runPCA <- function( ) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_dimReduction( - gobject = gobject, dimObject = dimObject, verbose = verbose - ) + gobject <- setGiotto(gobject, dimObject, verbose = verbose) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### @@ -945,7 +943,7 @@ runPCAprojection <- function( ) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_dimReduction(gobject = gobject, dimObject = dimObject) + gobject <- setGiotto(gobject, dimObject, verbose = verbose) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### @@ -1349,7 +1347,7 @@ runPCAprojectionBatch <- function( ) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_dimReduction(gobject = gobject, dimObject = dimObject) + gobject <- setGiotto(gobject, dimObject, verbose = verbose) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### @@ -2168,7 +2166,7 @@ runUMAP <- function( ## using dimension reduction ## if (!is.null(dim_reduction_to_use)) { ## TODO: check if reduction exists - dimObj_to_use <- get_dimReduction( + dimObj_to_use <- getDimReduction( gobject = gobject, spat_unit = spat_unit, feat_type = feat_type, @@ -2291,10 +2289,7 @@ runUMAP <- function( ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_dimReduction( - gobject = gobject, - dimObject = dimObject - ) + gobject <- setGiotto(gobject, dimObject, verbose = verbose) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### @@ -2566,7 +2561,7 @@ runUMAPprojection <- function( ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_dimReduction(gobject = gobject, dimObject = dimObject) + gobject <- setGiotto(gobject, dimObject, verbose = verbose) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## update parameters used ## @@ -2794,10 +2789,7 @@ runtSNE <- function( ) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_dimReduction( - gobject = gobject, - dimObject = dimObject - ) + gobject <- setGiotto(gobject, dimObject, verbose = verbose) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## update parameters used ## @@ -2931,7 +2923,7 @@ runGiottoHarmony <- function( ## using dimension reduction ## if (!is.null(dim_reduction_to_use)) { ## TODO: check if reduction exists - matrix_to_use <- get_dimReduction( + matrix_to_use <- getDimReduction( gobject = gobject, spat_unit = spat_unit, feat_type = feat_type, @@ -3000,14 +2992,14 @@ runGiottoHarmony <- function( colnames(harmony_results) <- paste0("Dim.", seq_len(ncol(harmony_results))) rownames(harmony_results) <- rownames(matrix_to_use) - harmdimObject <- create_dim_obj( + harmdimObject <- createDimObj( + coordinates = harmony_results, name = name, spat_unit = spat_unit, feat_type = feat_type, - provenance = provenance, + method = "harmony", reduction = "cells", # set to spat_unit? - reduction_method = "harmony", - coordinates = harmony_results, + provenance = provenance, misc = NULL ) @@ -3029,10 +3021,7 @@ runGiottoHarmony <- function( } ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_dimReduction( - gobject = gobject, - dimObject = harmdimObject - ) + gobject <- setGiotto(gobject, harmdimObject, verbose = verbose) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### diff --git a/R/giotto_viewer.R b/R/giotto_viewer.R index c21f66f75..8b044855d 100644 --- a/R/giotto_viewer.R +++ b/R/giotto_viewer.R @@ -212,9 +212,10 @@ exportGiottoViewer <- function( # data.table variables sdimx <- sdimy <- NULL - spatial_location <- get_spatial_locations( + spatial_location <- getSpatialLocations( gobject = gobject, - spat_loc_name = spat_loc_name + spat_unit = spat_unit, + name = spat_loc_name ) spatial_location <- spatial_location[, .(sdimx, sdimy)] write.table(spatial_location, @@ -241,6 +242,7 @@ exportGiottoViewer <- function( cell_metadata <- combineMetadata( gobject = gobject, feat_type = feat, + spat_unit = spat_unit, spat_enr_names = spat_enr_names ) @@ -361,11 +363,12 @@ exportGiottoViewer <- function( values <- match.arg(expression_values, unique(c("scaled", "normalized", "custom", expression_values))) for (feat in feat_type) { - expr_values <- get_expression_values( + expr_values <- getExpression( gobject = gobject, spat_unit = spat_unit, feat_type = feat, - values = values + values = values, + output = "matrix" ) expr_values <- as.matrix(expr_values) diff --git a/R/image_registration.R b/R/image_registration.R index a98e9a280..f163b30f2 100644 --- a/R/image_registration.R +++ b/R/image_registration.R @@ -163,7 +163,7 @@ img_type = img_type )))) { giottoImage_list <- lapply( - X = gobject_list, FUN = get_giottoImage, name = image_unreg, + X = gobject_list, FUN = getGiottoImage, name = image_unreg, image_type = img_type ) image_corners <- lapply(giottoImage_list, .get_img_corners) @@ -431,10 +431,10 @@ registerGiottoObjectListFiji <- function( spatloc_list <- list() for (gobj_i in seq_along(gobject_list)) { gobj <- gobject_list[[gobj_i]] - spatloc <- get_spatial_locations( + spatloc <- getSpatialLocations( gobject = gobj, spat_unit = spat_unit, - spat_loc_name = spatloc_unreg + name = spatloc_unreg ) #------ Put all spatial location data together spatloc_list[[gobj_i]] <- spatloc @@ -526,24 +526,24 @@ registerGiottoObjectListFiji <- function( # Rename original spatial locations to 'unregistered' if conflicting # with output if (spatloc_unreg == spatloc_reg_name) { - gobj <- set_spatial_locations( + gobj <- setSpatialLocations( gobject = gobj, spat_unit = spat_unit, - spat_loc_name = spatloc_replace_name, - spatlocs = get_spatial_locations( + name = spatloc_replace_name, + spatlocs = getSpatialLocations( gobject = gobj, spat_unit = spat_unit, - spat_loc_name = spatloc_unreg + name = spatloc_unreg ) ) } # Assign registered spatial locations from spatloc_list to gobject_list - gobj <- set_spatial_locations( + gobj <- setSpatialLocations( gobject = gobj, spat_unit = spat_unit, - spat_loc_name = spatloc_reg_name, + name = spatloc_reg_name, spatlocs = spatloc_list[[gobj_i]] ) @@ -637,9 +637,9 @@ registerGiottoObjectListRvision <- function( spatloc_list <- list() for (gobj_i in seq_along(gobject_list)) { gobj <- gobject_list[[gobj_i]] - spatloc <- get_spatial_locations( + spatloc <- getSpatialLocations( gobject = gobj, - spat_loc_name = spatloc_unreg, + name = spatloc_unreg, output = "spatLocsObj", copy_obj = TRUE ) @@ -744,22 +744,22 @@ registerGiottoObjectListRvision <- function( gobj <- gobject_list[[gobj_i]] # Rename original spatial locations to 'unregistered' - unreg_locs <- get_spatial_locations(gobj, - spat_loc_name = spatloc_unreg, + unreg_locs <- getSpatialLocations(gobj, + name = spatloc_unreg, copy_obj = FALSE, output = "spatLocsObj" ) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobj <- set_spatial_locations(gobj, + gobj <- setSpatialLocations(gobj, spatlocs = unreg_locs, - spat_loc_name = "unregistered" + name = "unregistered" ) # Assign registered spatial locations from spatloc_list to gobject_list - gobj <- set_spatial_locations(gobj, + gobj <- setSpatialLocations(gobj, spatlocs = spatloc_list[[gobj_i]], - spat_loc_name = spatloc_reg_name + name = spatloc_reg_name ) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### @@ -1033,7 +1033,7 @@ interactiveLandmarkSelection <- function(source, target) { GiottoUtils::package_check("shiny") GiottoUtils::package_check("ggplot2") GiottoUtils::package_check("miniUI") - + .create_image_to_plot <- function(x){ if (inherits(x, "gg")){ return(x) @@ -1052,7 +1052,7 @@ interactiveLandmarkSelection <- function(source, target) { } source_image <- .create_image_to_plot(source) target_image <- .create_image_to_plot(target) - + # Function to extract the range of x and y values from a ggplot object .extract_plot_ranges <- function(plot) { data <- ggplot2::ggplot_build(plot)$data[[1]] @@ -1060,11 +1060,11 @@ interactiveLandmarkSelection <- function(source, target) { y_range <- range(data$y, na.rm = TRUE) list(x_range = x_range, y_range = y_range) } - + # Extract ranges for the input plots source_ranges <- .extract_plot_ranges(source_image) target_ranges <- .extract_plot_ranges(target_image) - + ui <- miniUI::miniPage( miniUI::gadgetTitleBar("Select Extents and Points"), miniUI::miniContentPanel( @@ -1073,11 +1073,11 @@ interactiveLandmarkSelection <- function(source, target) { shiny::column(6, shiny::plotOutput("plot2", click = "plot2_click")) ), shiny::fluidRow( - shiny::column(6, + shiny::column(6, shiny::sliderInput("xrange1", "X Range for Plot 1", min = source_ranges$x_range[1], max = source_ranges$x_range[2], value = source_ranges$x_range), shiny::sliderInput("yrange1", "Y Range for Plot 1", min = source_ranges$y_range[1], max = source_ranges$y_range[2], value = source_ranges$y_range) ), - shiny::column(6, + shiny::column(6, shiny::sliderInput("xrange2", "X Range for Plot 2", min = target_ranges$x_range[1], max = target_ranges$x_range[2], value = target_ranges$x_range), shiny::sliderInput("yrange2", "Y Range for Plot 2", min = target_ranges$y_range[1], max = target_ranges$y_range[2], value = target_ranges$y_range) ) @@ -1092,63 +1092,63 @@ interactiveLandmarkSelection <- function(source, target) { ) ) ) - + server <- function(input, output, session) { click_history1 <- shiny::reactiveVal(data.frame(x = numeric(), y = numeric())) click_history2 <- shiny::reactiveVal(data.frame(x = numeric(), y = numeric())) - + output$plot1 <- shiny::renderPlot({ source_image + ggplot2::coord_cartesian(xlim = input$xrange1, ylim = input$yrange1) + ggplot2::geom_point(data = click_history1(), ggplot2::aes(x = x, y = y), color = "red", size = 4.5) }) - + output$plot2 <- shiny::renderPlot({ target_image + ggplot2::coord_cartesian(xlim = input$xrange2, ylim = input$yrange2) + ggplot2::geom_point(data = click_history2(), ggplot2::aes(x = x, y = y), color = "blue",size = 4.5) }) - + shiny::observeEvent(input$plot1_click, { click <- input$plot1_click new_coords <- rbind(click_history1(), data.frame(x = click$x, y = click$y)) click_history1(new_coords) }) - + shiny::observeEvent(input$plot2_click, { click <- input$plot2_click new_coords <- rbind(click_history2(), data.frame(x = click$x, y = click$y)) click_history2(new_coords) }) - + shiny::observeEvent(input$undo1, { if (nrow(click_history1()) > 0) { new_coords <- click_history1()[-nrow(click_history1()), , drop = FALSE] click_history1(new_coords) } }) - + shiny::observeEvent(input$undo2, { if (nrow(click_history2()) > 0) { new_coords <- click_history2()[-nrow(click_history2()), , drop = FALSE] click_history2(new_coords) } }) - + output$click_info1 <- shiny::renderPrint({ click_history1() }) - + output$click_info2 <- shiny::renderPrint({ click_history2() }) - + shiny::observeEvent(input$done, { returnValue <- list(click_history1(),click_history2()) shiny::stopApp(returnValue) }) } - + shiny::runGadget(ui, server) } @@ -1184,32 +1184,32 @@ calculateAffineMatrixFromLandmarks <- function(source_df,target_df){ #' @name .sift_detect -#' @title Run SIFT feature detector and descriptor extractor +#' @title Run SIFT feature detector and descriptor extractor #' @description #' Perform feature detector and descriptor extractor on a matrix object or preprocessed image object #' @param x input matrix or preprocessed image to extract feature and descriptor from #' @param ... additional params to pass to `skimage.feature.SIFT()` #' @returns list of keypoints and descriptors -#' +#' .sift_detect <- function(x, ..., pkg_ptr) { - + if (missing(pkg_ptr)) { GiottoUtils::package_check("skimage", repository = "pip:scikit-image") SKI <- reticulate::import("skimage", convert = TRUE, delay_load = TRUE) } else { SKI <- pkg_ptr } - + # sift object SIFT <- SKI$feature$SIFT() - + SIFT$detect_and_extract(x) - + out <- list( keypoints = SIFT$keypoints, descriptors = SIFT$descriptors ) - + return(out) } @@ -1230,7 +1230,7 @@ calculateAffineMatrixFromLandmarks <- function(source_df,target_df){ #' Computer Vision, 2004. #' @param ... additional params to pass to `skimage.feature.match_descriptors()` #' @returns list -#' +#' .match_descriptor <- function( descriptor_list, target_idx = 1L, @@ -1239,19 +1239,19 @@ calculateAffineMatrixFromLandmarks <- function(source_df,target_df){ ..., pkg_ptr ) { - + checkmate::assert_list(descriptor_list, min.len = 2L) target_idx <- as.integer(target_idx) - + if (missing(pkg_ptr)) { package_check("skimage", repository = "pip:scikit-image") SKI <- reticulate::import("skimage", convert = TRUE, delay_load = TRUE) } else { SKI <- pkg_ptr } - + target <- descriptor_list[[target_idx]] - + out <- lapply( seq_along(descriptor_list), function(moving_idx) { @@ -1263,9 +1263,9 @@ calculateAffineMatrixFromLandmarks <- function(source_df,target_df){ )) # directly return all as matches } - + moving <- descriptor_list[[moving_idx]] - + m <- .match_descriptor_single( x = target, y = moving, @@ -1275,7 +1275,7 @@ calculateAffineMatrixFromLandmarks <- function(source_df,target_df){ m + 1 # since it is 0 indexed } ) - + return(out) } @@ -1283,17 +1283,17 @@ calculateAffineMatrixFromLandmarks <- function(source_df,target_df){ # wrapper for sklearn-image match_descriptors # returns a 2 col matrix of x to y index matches .match_descriptor_single <- function(x, y,max_ratio, ..., pkg_ptr) { - + checkmate::assert_class(x, "matrix") checkmate::assert_class(y, "matrix") - + if (missing(pkg_ptr)) { GiottoUtils::package_check("skimage", repository = "pip:scikit-image") SKI <- reticulate::import("skimage", convert = TRUE, delay_load = TRUE) } else { SKI <- pkg_ptr } - + match_descriptors <- SKI$feature$match_descriptors m <- match_descriptors( descriptors1 = x, @@ -1301,7 +1301,7 @@ calculateAffineMatrixFromLandmarks <- function(source_df,target_df){ max_ratio = max_ratio, ... # max_ratio of 0.6 - 0.8 recommended for sift, cross_check = TRUE ) - + return(m) } @@ -1319,7 +1319,7 @@ calculateAffineMatrixFromLandmarks <- function(source_df,target_df){ #' @param use_single_channel If input is a multichannel image, whether or not to extract single channel, default FALSE #' @param single_channel_number Channel number in the multichannel image, required if use_single_channel = TRUE #' @returns a matrix array to input to .sift_detect -#' +#' #' @export preprocessImageToMatrix <- function(x, invert = F, @@ -1330,7 +1330,7 @@ preprocessImageToMatrix <- function(x, use_single_channel = F, single_channel_number = NULL, pkg_ptr) { - + if (missing(pkg_ptr)) { GiottoUtils::package_check("skimage", repository = "pip:scikit-image") SKI <- reticulate::import("skimage", convert = TRUE, delay_load = TRUE) @@ -1339,18 +1339,18 @@ preprocessImageToMatrix <- function(x, } GiottoUtils::package_check("numpy", repository = "pip:scikit-image") np <- reticulate::import("numpy", convert = TRUE, delay_load = TRUE) - + image = SKI$io$imread(x) - + if (length(dim(image)) >2 & use_single_channel == FALSE){ image = SKI$color$rgb2gray(image) - } + } if (use_single_channel == TRUE) { if (is.null(single_channel_number)) {stop("Set use single channel == TRUE, please provide a channel number to continue")} image <- image[,,single_channel_number] } - - + + if (flip_vertical == T){ image = np$flipud(image) } @@ -1390,20 +1390,20 @@ preprocessImageToMatrix <- function(x, } else { SKI <- pkg_ptr } - + # Extract matched keypoints src_pts <- keypoints1[match[, 1] + 1, , drop = FALSE] dst_pts <- keypoints2[match[, 2] + 1, , drop = FALSE] - + estimate_fun <- match.arg(estimate_fun, unique(c('euclidean', 'similarity', 'affine', 'piecewise-affine', 'projective', 'polynomial', estimate_fun))) - - # Estimate homography matrix + + # Estimate homography matrix ransac_result <- SKI$transform$estimate_transform( ttype = estimate_fun, src = src_pts, dst = dst_pts, ) - + return(ransac_result) } @@ -1427,10 +1427,10 @@ preprocessImageToMatrix <- function(x, } else { SKI <- pkg_ptr } - + # Ensure the source image array is writable by making a copy x_copy <- reticulate::r_to_py(x)$copy() - + # Warp the source image to align with the destination image warped_image <- SKI$transform$warp(x_copy, model, output_shape = dim(y)) SKI$io$imsave(outpath,warped_image) @@ -1455,22 +1455,22 @@ preprocessImageToMatrix <- function(x, } else { SKI <- pkg_ptr } - + matplotlib <-reticulate::import("matplotlib", convert = TRUE, delay_load = TRUE) np <- reticulate::import("numpy",convert = T, delay_load = T) plt <- matplotlib$pyplot - + match_py <- reticulate::r_to_py(match) - match_py <- np$array(match_py, dtype = np$int32) - + match_py <- np$array(match_py, dtype = np$int32) + # Create a subplot fig_ax <- plt$subplots(nrows = 1L, ncols = 1L, figsize = c(11, 8)) fig <- fig_ax[[1]] ax <- fig_ax[[2]] - + # Plot the matches SKI$feature$plot_matches(ax, x, y, keypoints1, keypoints2, match_py, only_matches = TRUE) - + ax$axis('off') plt$show() plt$close() @@ -1497,31 +1497,31 @@ estimateAutomatedImageRegistrationWithSIFT <- function(x, estimate_fun = 'affine', save_warp = NULL, verbose = T){ - + GiottoUtils::vmsg(.v = verbose, .is_debug = T,'Detecting features via SIFT... ') x_sift <- .sift_detect(x) y_sift <- .sift_detect(y) - + GiottoUtils::vmsg(.v = verbose, .is_debug = T,'Matching Descriptors via SIFT... ') matched <- .match_descriptor_single(x_sift$descriptor, y_sift$descriptor,max_ratio = max_ratio) - + if (plot_match == TRUE){ .plot_matched_descriptors(x, y, x_sift$keypoints, y_sift$keypoints, matched) } - - + + GiottoUtils::vmsg(.v = verbose, .is_debug = T,'Estimating transformation matrix from matched descriptor... ') - estimation <- .estimate_transform_from_matched_descriptor(x_sift$keypoints, + estimation <- .estimate_transform_from_matched_descriptor(x_sift$keypoints, y_sift$keypoints, matched, estimate_fun = estimate_fun) - + if (!is.null(save_warp)){ .warp_transformed_image(x = x, y = y, model = estimation$inverse, outpath = save_warp) } - + return(estimation) } diff --git a/R/interactivity.R b/R/interactivity.R index 27dc3ad5c..2ce831111 100644 --- a/R/interactivity.R +++ b/R/interactivity.R @@ -380,7 +380,7 @@ comparePolygonExpression <- function( } # get expression - my_expression <- get_expression_values(gobject, + my_expression <- getExpression(gobject, values = expression_values, spat_unit = spat_unit, feat_type = feat_type, @@ -587,7 +587,7 @@ plotPolygons <- function( if (is.null(x)) stop("A plot object must be provided") ## get polygons spatial info - polygon_spatVector <- get_polygon_info( + polygon_spatVector <- getPolygonInfo( gobject = gobject, polygon_name = polygon_name, return_giottoPolygon = FALSE diff --git a/R/python_hmrf.R b/R/python_hmrf.R index 6d1177ed0..a4ac65ed8 100644 --- a/R/python_hmrf.R +++ b/R/python_hmrf.R @@ -1498,7 +1498,7 @@ initHMRF_V2 <- "scaled", "normalized", "custom", expression_values ))) - expr_values <- get_expression_values( + expr_values <- getExpression( gobject = gobject, spat_unit = spat_unit, feat_type = feat_type, @@ -1506,7 +1506,7 @@ initHMRF_V2 <- ) if (zscore != "none") { zscore <- match.arg(zscore, c("none", "colrow", "rowcol")) - expr_values <- get_expression_values( + expr_values <- getExpression( gobject = gobject, spat_unit = spat_unit, feat_type = feat_type, diff --git a/R/spatial_enrichment.R b/R/spatial_enrichment.R index e378fd762..5c92d38a0 100644 --- a/R/spatial_enrichment.R +++ b/R/spatial_enrichment.R @@ -67,7 +67,7 @@ #' ) #' #' g <- runPAGEEnrich(gobject = g, -#' sign_matrix = sign_matrix, +#' sign_matrix = sm, #' min_overlap_genes = 2 #' ) #' @@ -256,7 +256,7 @@ makeSignMatrixDWLS <- function( expression_values, unique(c("normalized", "scaled", "custom", expression_values)) ) - expr_values <- get_expression_values( + expr_values <- getExpression( gobject = gobject, spat_unit = spat_unit, feat_type = feat_type, @@ -828,10 +828,7 @@ runPAGEEnrich <- function( gobject@parameters <- parameters_list ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_spatial_enrichment( - gobject = gobject, - spatenrichment = enrObj - ) + gobject <- setGiotto(gobject, enrObj) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### @@ -1132,10 +1129,7 @@ runRankEnrich <- function( gobject@parameters <- parameters_list ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_spatial_enrichment( - gobject = gobject, - spatenrichment = enrObj - ) + gobject <- setGiotto(gobject, enrObj) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### return(gobject) @@ -1351,10 +1345,7 @@ runHyperGeometricEnrich <- function( gobject@parameters <- parameters_list ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_spatial_enrichment( - gobject = gobject, - spatenrichment = enrObj - ) + gobject <- setGiotto(gobject, enrObj) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### return(gobject) @@ -1904,10 +1895,7 @@ spatialAutoCorLocal <- function( ) } ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_spatial_enrichment( - gobject = gobject, - spatenrichment = enr - ) + gobject <- setGiotto(gobject, enr) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### return(gobject) @@ -2928,10 +2916,7 @@ runDWLSDeconv <- function( gobject@parameters <- parameters_list ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### - gobject <- set_spatial_enrichment( - gobject = gobject, - spatenrichment = enrObj - ) + gobject <- setGiotto(gobject, enrObj) ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### return(gobject) diff --git a/R/spatial_interaction_spot.R b/R/spatial_interaction_spot.R index 9fe9f0740..267c426c8 100644 --- a/R/spatial_interaction_spot.R +++ b/R/spatial_interaction_spot.R @@ -2255,6 +2255,8 @@ plotCellProximityFeatSpot <- function( #' @param expression_values (e.g. 'normalized', 'scaled', 'custom') #' @param spatial_network_name spatial network to use for identifying #' interacting cells +#' @param spat_enr_name name of spatial enrichment containing DWLS results. +#' Default = `"DWLS"` #' @param cluster_column cluster column with cell type information #' @param random_iter number of iterations #' @param feature_set_1 first specific feature set from feature pairs @@ -2306,6 +2308,7 @@ plotCellProximityFeatSpot <- function( #' * p.adj: adjusted p-value #' * PI: significanc score: log2fc \* -log10(p.adj) #' } +#' @md #' @export spatCellCellcomSpots <- function( gobject, @@ -2313,6 +2316,7 @@ spatCellCellcomSpots <- function( feat_type = NULL, ave_celltype_exp, spatial_network_name = "Delaunay_network", + spat_enr_name = "DWLS", cluster_column = "cell_ID", random_iter = 1000, feature_set_1, @@ -2381,10 +2385,11 @@ spatCellCellcomSpots <- function( proximityMat <- proximityMat[, intersect_cell_IDs] # exact spatial_enrichment matrix - dwls_values <- get_spatial_enrichment( + dwls_values <- getSpatialEnrichment( gobject = gobject, spat_unit = spat_unit, feat_type = feat_type, + name = spat_enr_name, output = "data.table" ) data.table::setDF(dwls_values) diff --git a/R/wnn.R b/R/wnn.R index 588d11888..25b2cb829 100644 --- a/R/wnn.R +++ b/R/wnn.R @@ -458,7 +458,7 @@ runWNN <- function( matrix_result_name <- "theta_weighted_matrix" } - gobject <- set_multiomics( + gobject <- setMultiomics( gobject = gobject, result = theta_weighted, spat_unit = spat_unit, @@ -476,7 +476,7 @@ runWNN <- function( w_name_modality_1 <- paste0("w_", modality_1) } - gobject <- set_multiomics( + gobject <- setMultiomics( gobject = gobject, result = w_modality1, spat_unit = spat_unit, @@ -491,7 +491,7 @@ runWNN <- function( w_name_modality_2 <- paste0("w_", modality_2) } - gobject <- set_multiomics( + gobject <- setMultiomics( gobject = gobject, result = w_modality2, spat_unit = spat_unit, @@ -539,7 +539,7 @@ runIntegratedUMAP <- function( integrated_feat_type <- paste0(modality1, "_", modality2) } - theta_weighted <- get_multiomics(gobject, + theta_weighted <- getMultiomics(gobject, spat_unit = spat_unit, feat_type = integrated_feat_type, integration_method = integration_method, @@ -594,17 +594,10 @@ runIntegratedUMAP <- function( feat_type = modality1 ) - gobject <- set_NearestNetwork( - gobject = gobject, - nn_network = nnNetObj, - spat_unit = spat_unit, - feat_type = modality1, - nn_network_to_use = "kNN", - network_name = "integrated_kNN" - ) + gobject <- setGiotto(gobject, nnNetObj) ## store nn_network id - gobject <- set_multiomics( + gobject <- setMultiomics( gobject = gobject, result = nn_network$id, spat_unit = spat_unit, @@ -615,7 +608,7 @@ runIntegratedUMAP <- function( ) ## store nn_network dist - gobject <- set_multiomics( + gobject <- setMultiomics( gobject = gobject, result = nn_network$dist, spat_unit = spat_unit, diff --git a/man/enrichment_PAGE.Rd b/man/enrichment_PAGE.Rd index 220a05060..226fcb31a 100644 --- a/man/enrichment_PAGE.Rd +++ b/man/enrichment_PAGE.Rd @@ -114,7 +114,7 @@ sm <- makeSignMatrixPAGE( ) g <- runPAGEEnrich(gobject = g, - sign_matrix = sign_matrix, + sign_matrix = sm, min_overlap_genes = 2 ) diff --git a/man/screePlot.Rd b/man/screePlot.Rd index 2f65fb4f9..a8477d3b1 100644 --- a/man/screePlot.Rd +++ b/man/screePlot.Rd @@ -8,7 +8,8 @@ screePlot( gobject, spat_unit = NULL, feat_type = NULL, - name = NULL, + dim_reduction_name = NULL, + name = deprecated(), expression_values = c("normalized", "scaled", "custom"), reduction = c("cells", "feats"), method = c("irlba", "exact", "random", "factominer"), @@ -34,7 +35,9 @@ screePlot( \item{feat_type}{feature type (e.g. "rna", "dna", "protein")} -\item{name}{name of PCA object if available} +\item{dim_reduction_name}{name of PCA} + +\item{name}{deprecated} \item{expression_values}{expression values to use} diff --git a/man/spatCellCellcomSpots.Rd b/man/spatCellCellcomSpots.Rd index ac6e46389..dee251301 100644 --- a/man/spatCellCellcomSpots.Rd +++ b/man/spatCellCellcomSpots.Rd @@ -10,6 +10,7 @@ spatCellCellcomSpots( feat_type = NULL, ave_celltype_exp, spatial_network_name = "Delaunay_network", + spat_enr_name = "DWLS", cluster_column = "cell_ID", random_iter = 1000, feature_set_1, @@ -39,6 +40,9 @@ spatCellCellcomSpots( \item{spatial_network_name}{spatial network to use for identifying interacting cells} +\item{spat_enr_name}{name of spatial enrichment containing DWLS results. +Default = \code{"DWLS"}} + \item{cluster_column}{cluster column with cell type information} \item{random_iter}{number of iterations} @@ -84,31 +88,33 @@ Statistical framework to identify if pairs of features expected based on a reshuffled null distribution of feature expression values in cells that are spatially in proximity to each other. \itemize{ - * LR_comb:Pair of ligand and receptor - * lig_cell_type: cell type to assess expression level of ligand - * lig_expr: average expression residual(observed - DWLS_predicted) of - ligand in lig_cell_type - * ligand: ligand name - * rec_cell_type: cell type to assess expression level of receptor - * rec_expr: average expression residual(observed - DWLS_predicted) of - receptor in rec_cell_type - * receptor: receptor name - * LR_expr: combined average ligand and receptor expression residual - * lig_nr: total number of cells from lig_cell_type that spatially interact - with cells from rec_cell_type - * rec_nr: total number of cells from rec_cell_type that spatially interact - with cells from lig_cell_type - * rand_expr: average combined ligand and receptor expression residual from - random spatial permutations - * av_diff: average difference between LR_expr and rand_expr over all random - spatial permutations - * sd_diff: (optional) standard deviation of the difference between LR_expr - and rand_expr over all random spatial permutations - * z_score: (optional) z-score - * log2fc: LR_expr - rand_expr - * pvalue: p-value - * LR_cell_comb: cell type pair combination - * p.adj: adjusted p-value - * PI: significanc score: log2fc \* -log10(p.adj) +\itemize{ +\item LR_comb:Pair of ligand and receptor +\item lig_cell_type: cell type to assess expression level of ligand +\item lig_expr: average expression residual(observed - DWLS_predicted) of +ligand in lig_cell_type +\item ligand: ligand name +\item rec_cell_type: cell type to assess expression level of receptor +\item rec_expr: average expression residual(observed - DWLS_predicted) of +receptor in rec_cell_type +\item receptor: receptor name +\item LR_expr: combined average ligand and receptor expression residual +\item lig_nr: total number of cells from lig_cell_type that spatially interact +with cells from rec_cell_type +\item rec_nr: total number of cells from rec_cell_type that spatially interact +with cells from lig_cell_type +\item rand_expr: average combined ligand and receptor expression residual from +random spatial permutations +\item av_diff: average difference between LR_expr and rand_expr over all random +spatial permutations +\item sd_diff: (optional) standard deviation of the difference between LR_expr +and rand_expr over all random spatial permutations +\item z_score: (optional) z-score +\item log2fc: LR_expr - rand_expr +\item pvalue: p-value +\item LR_cell_comb: cell type pair combination +\item p.adj: adjusted p-value +\item PI: significanc score: log2fc \* -log10(p.adj) +} } }