├── DESCRIPTION ├── LICENSE ├── NAMESPACE ├── R ├── functions.R ├── loom_utils.R └── methods.R └── README.md /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: sceasy 2 | Type: Package 3 | Title: A package to help convert different single-cell data formats to each other 4 | Version: 0.0.7 5 | Authors@R: c( 6 | person('Vladimir', 'Kiselev', email='wikiselev@users.noreply.github.com', role='cre'), 7 | person('Ni', 'Huang', email='nh3@users.noreply.github.com', role='aut')) 8 | Description: Convert Seurat, SingleCellExperiment, Loom object to AnnData object. 9 | Convert SingleCellExperiment object to and from Loom object. 10 | Depends: 11 | R (>= 3.5.1), 12 | reticulate 13 | Suggests: 14 | Seurat (>= 3.0.1), 15 | SingleCellExperiment (>= 1.4.0), 16 | LoomExperiment (>= 1.1.5), 17 | monocle3 (>= 0.2.0) 18 | SystemRequirements: 19 | anndata, 20 | loompy 21 | License: GPL-3 22 | LazyDate: true 23 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | export(convertFormat) 2 | import(reticulate) 3 | import(Matrix) 4 | -------------------------------------------------------------------------------- /R/functions.R: -------------------------------------------------------------------------------- 1 | # Functions for converting between different objects 2 | 3 | 4 | #' Regularise dataframe 5 | #' 6 | #' This function checks if certain columns of a dataframe is of a single value 7 | #' and drop them if required 8 | #' 9 | #' @param df Input data frame, usually cell metadata table (data.frame-like 10 | #' object) 11 | #' @param drop_single_values Drop columns with only a single value (logical) 12 | #' 13 | #' @return Dataframe 14 | .regularise_df <- function(df, drop_single_values = TRUE) { 15 | if (ncol(df) == 0) df[["name"]] <- rownames(df) 16 | if (drop_single_values) { 17 | k_singular <- sapply(df, function(x) length(unique(x)) == 1) 18 | if (sum(k_singular) > 0) { 19 | warning( 20 | paste("Dropping single category variables:"), 21 | paste(colnames(df)[k_singular], collapse = ", ") 22 | ) 23 | } 24 | df <- df[, !k_singular, drop = F] 25 | if (ncol(df) == 0) df[["name"]] <- rownames(df) 26 | } 27 | return(df) 28 | } 29 | 30 | #' Convert Seurat object to AnnData 31 | #' 32 | #' This function converts a Seurat object to an Anndata object 33 | #' 34 | #' @param obj Input Seurat object 35 | #' @param outFile Save output AnnData to this file if specified (str or NULL) 36 | #' @param assay Assay to be converted, default "RNA" (str) 37 | #' @param main_layer Slot in `assay` to be converted to AnnData.X, may be 38 | #' "counts", "data", "scale.data", default "data" (str) 39 | #' @param transfer_layers If specified, convert slots to AnnData.layers[], 40 | #' (vector of str) 41 | #' @param drop_single_values Drop single value columns in cell metadata table, 42 | #' default TRUE (logical) 43 | #' 44 | #' @return AnnData object 45 | #' 46 | #' @import reticulate 47 | #' @import Matrix 48 | seurat2anndata <- function(obj, outFile = NULL, assay = "RNA", main_layer = "data", transfer_layers = NULL, drop_single_values = TRUE) { 49 | if (!requireNamespace("Seurat")) { 50 | stop("This function requires the 'Seurat' package.") 51 | } 52 | main_layer <- match.arg(main_layer, c("data", "counts", "scale.data")) 53 | transfer_layers <- transfer_layers[ 54 | transfer_layers %in% c("data", "counts", "scale.data") 55 | ] 56 | transfer_layers <- transfer_layers[transfer_layers != main_layer] 57 | 58 | if (compareVersion(as.character(obj@version), "3.0.0") < 0) { 59 | obj <- Seurat::UpdateSeuratObject(object = obj) 60 | } 61 | 62 | X <- Seurat::GetAssayData(object = obj, assay = assay, slot = main_layer) 63 | 64 | obs <- .regularise_df(obj@meta.data, drop_single_values = drop_single_values) 65 | 66 | var <- .regularise_df(Seurat::GetAssay(obj, assay = assay)@meta.features, drop_single_values = drop_single_values) 67 | 68 | obsm <- NULL 69 | reductions <- names(obj@reductions) 70 | if (length(reductions) > 0) { 71 | obsm <- sapply( 72 | reductions, 73 | function(name) as.matrix(Seurat::Embeddings(obj, reduction = name)), 74 | simplify = FALSE 75 | ) 76 | names(obsm) <- paste0("X_", tolower(names(obj@reductions))) 77 | } 78 | 79 | layers <- list() 80 | for (layer in transfer_layers) { 81 | mat <- Seurat::GetAssayData(object = obj, assay = assay, slot = layer) 82 | if (all(dim(mat) == dim(X))) layers[[layer]] <- Matrix::t(mat) 83 | } 84 | 85 | anndata <- reticulate::import("anndata", convert = FALSE) 86 | 87 | adata <- anndata$AnnData( 88 | X = Matrix::t(X), 89 | obs = obs, 90 | var = var, 91 | obsm = obsm, 92 | layers = layers 93 | ) 94 | 95 | if (!is.null(outFile)) { 96 | adata$write(outFile, compression = "gzip") 97 | } 98 | 99 | adata 100 | } 101 | 102 | #' Convert SingleCellExperiment object to AnnData 103 | #' 104 | #' This function converts a SingleCellExperiment object to an Anndata object 105 | #' 106 | #' @param obj Input SingleCellExperiment object 107 | #' @param outFile Save output AnnData to this file if specified (str or NULL) 108 | #' @param main_layer Assay to be converted to AnnData.X, default "counts" (str) 109 | #' @param transfer_layers If specified, convert additional assays to 110 | #' AnnData.layers[], (vector of str) 111 | #' @param drop_single_values Drop single value columns in cell metadata table, 112 | #' default TRUE (logical) 113 | #' 114 | #' @return AnnData object 115 | #' 116 | #' @import reticulate 117 | #' @import Matrix 118 | sce2anndata <- function(obj, outFile = NULL, main_layer = "counts", transfer_layers = NULL, drop_single_values = TRUE) { 119 | if (!requireNamespace("SummarizedExperiment")) { 120 | stop("This function requires the 'SummarizedExperiment' package.") 121 | } 122 | if (!requireNamespace("SingleCellExperiment")) { 123 | stop("This function requires the 'SingleCellExperiment' package.") 124 | } 125 | assay_names <- SummarizedExperiment::assayNames(obj) 126 | main_layer <- match.arg(main_layer, assay_names) 127 | transfer_layers <- transfer_layers[transfer_layers %in% assay_names] 128 | transfer_layers <- transfer_layers[transfer_layers != main_layer] 129 | 130 | X <- SummarizedExperiment::assay(obj, main_layer) 131 | 132 | obs <- .regularise_df(as.data.frame(SummarizedExperiment::colData(obj)), drop_single_values = drop_single_values) 133 | 134 | var <- .regularise_df(as.data.frame(SummarizedExperiment::rowData(obj)), drop_single_values = drop_single_values) 135 | 136 | obsm <- NULL 137 | reductions <- SingleCellExperiment::reducedDimNames(obj) 138 | if (length(reductions) > 0) { 139 | obsm <- sapply( 140 | reductions, 141 | function(name) { 142 | as.matrix( 143 | SingleCellExperiment::reducedDim(obj, type = name) 144 | ) 145 | }, 146 | simplify = FALSE 147 | ) 148 | names(obsm) <- paste0( 149 | "X_", tolower(SingleCellExperiment::reducedDimNames(obj)) 150 | ) 151 | } 152 | 153 | layers <- list() 154 | for (layer in transfer_layers) { 155 | mat <- SummarizedExperiment::assay(obj, layer) 156 | if (all(dim(mat) == dim(X))) layers[[layer]] <- Matrix::t(mat) 157 | } 158 | 159 | anndata <- reticulate::import("anndata", convert = FALSE) 160 | 161 | adata <- anndata$AnnData( 162 | X = Matrix::t(X), 163 | obs = obs, 164 | var = var, 165 | obsm = obsm, 166 | layers = layers 167 | ) 168 | 169 | if (!is.null(outFile)) { 170 | adata$write(outFile, compression = "gzip") 171 | } 172 | 173 | adata 174 | } 175 | 176 | #' Convert Loom object to AnnData 177 | #' 178 | #' This function converts a Loom object to an Anndata object 179 | #' 180 | #' @param inFile Path to the input Loom file on disk (str) 181 | #' @param outFile Save output AnnData to this file if specified (str or NULL) 182 | #' @param main_layer Assay to be converted to AnnData.X, can be "spliced" or 183 | #' "unspliced", default "spliced" (str) 184 | #' @param obs_names Column in cell metadata table that contains cell IDs (str) 185 | #' @param var_names Column in gene metadata table that contains gene names 186 | #' 187 | #' @return AnnData object 188 | #' 189 | #' @import reticulate 190 | loom2anndata <- function(inFile, outFile = NULL, main_layer = c("spliced", "unspliced"), 191 | obs_names = "CellID", var_names = "Gene") { 192 | main_layer <- match.arg(main_layer) 193 | 194 | anndata <- reticulate::import("anndata", convert = FALSE) 195 | 196 | if (compareVersion(as.character(anndata[["__version__"]]), "0.6.20") < 0) { 197 | message(paste( 198 | "Warning: anndata <0.6.20 detected.", 199 | "Upgrade to handle multi-dimensional embeddings." 200 | )) 201 | } 202 | 203 | adata <- anndata$read_loom( 204 | inFile, 205 | sparse = TRUE, cleanup = TRUE, X_name = main_layer, 206 | obs_names = obs_names, var_names = var_names 207 | ) 208 | 209 | anndata$AnnData$obs_names_make_unique(adata) 210 | anndata$AnnData$var_names_make_unique(adata) 211 | 212 | if (!is.null(outFile)) { 213 | adata$write(outFile, compression = "gzip") 214 | } 215 | 216 | adata 217 | } 218 | 219 | #' Convert Seurat object to SingleCellExperiment 220 | #' 221 | #' This function converts a Seurat object to a SingleCellExperiment object 222 | #' 223 | #' @param obj Input Seurat object 224 | #' @param outFile Save output SingleCellExperiment object to this file if 225 | #' specified (str or NULL) 226 | #' @param assay Assay to be converted to AnnData.X, default "RNA" (str) 227 | #' 228 | #' @return AnnData object 229 | seurat2sce <- function(obj, outFile = NULL, main_layer = NULL, assay = "RNA", ...) { 230 | if (!requireNamespace("Seurat")) { 231 | stop("This function requires the 'Seurat' package.") 232 | } 233 | sce <- Seurat::as.SingleCellExperiment(obj, assay = assay, ...) 234 | if (!is.null(outFile)) { 235 | saveRDS(sce, outFile) 236 | } 237 | 238 | sce 239 | } 240 | 241 | #' Convert SingleCellExperiment object to Loom file 242 | #' 243 | #' This function converts a SingleCellExperiment object to a LoomExperiment 244 | #' object 245 | #' 246 | #' @param obj Input SingleCellExperiment object 247 | #' @param outFile Save output Loom to this file if specified (str or NULL) 248 | #' @param drop_single_values Drop single value columns in cell metadata table, 249 | #' default TRUE (logical) 250 | #' 251 | #' @return LoomExperiment object 252 | sce2loom <- function(obj, outFile, main_layer = NULL, drop_single_values = TRUE, ...) { 253 | if (!requireNamespace("LoomExperiment")) { 254 | stop("This function requires the 'LoomExperiment' package.") 255 | } 256 | scle <- LoomExperiment::SingleCellLoomExperiment(obj) 257 | 258 | if (!is.null(outFile)) { 259 | LoomExperiment::export( 260 | scle, outFile, 261 | matrix = ifelse(!is.null(main_layer) && main_layer %in% SummarizedExperiment::assayNames(scle), main_layer, SummarizedExperiment::assayNames(scle)[1]), 262 | colnames_attr = "obs_names", rownames_attr = "var_names" 263 | ) 264 | } 265 | 266 | scle 267 | } 268 | 269 | #' Read a Loom file into a SingleCellExperiment object 270 | #' 271 | #' This function reads a Loom file object into a SingleCellExperiment object 272 | #' 273 | #' @param inFile Path to input Loom file on disk (str) 274 | #' @param outFile Save output SingleCellExperiment object to this file if 275 | #' specified (str or NULL) 276 | #' 277 | #' @return LoomExperiment object 278 | loom2sce <- function(inFile, outFile = NULL, main_layer = NULL, main_layer_name = NULL, ...) { 279 | if (!requireNamespace("LoomExperiment")) { 280 | stop("This function requires the 'LoomExperiment' package.") 281 | } 282 | if (!requireNamespace("SingleCellExperiment")) { 283 | stop("This function requires the 'LoomExperiment' package.") 284 | } 285 | scle <- LoomExperiment::import(inFile) 286 | sce <- as(scle, "SingleCellExperiment") 287 | 288 | if (!is.null(outFile)) { 289 | saveRDS(sce, outFile) 290 | } 291 | 292 | sce 293 | } 294 | 295 | #' Prepare cell metadata 296 | #' 297 | #' This function prepare cell metadata from AnnData.obs 298 | #' 299 | #' @param obs_pd Input AnnData.obs dataframe 300 | #' @param assay Assay name, default "RNA" (str) 301 | #' 302 | #' @return AnnData object 303 | #' 304 | #' @import reticulate 305 | .obs2metadata <- function(obs_pd, assay = "RNA") { 306 | obs_df <- .regularise_df(reticulate::py_to_r(obs_pd), drop_single_values = FALSE) 307 | attr(obs_df, "pandas.index") <- NULL 308 | colnames(obs_df) <- sub("n_counts", paste0("nCounts_", assay), colnames(obs_df)) 309 | colnames(obs_df) <- sub("n_genes", paste0("nFeaturess_", assay), colnames(obs_df)) 310 | return(obs_df) 311 | } 312 | 313 | #' Prepare feature metadata 314 | #' 315 | #' This function prepare feature metadata from AnnData.var 316 | #' 317 | #' @param var_pd Input AnnData.var dataframe 318 | #' 319 | #' @return AnnData object 320 | #' 321 | #' @import reticulate 322 | .var2feature_metadata <- function(var_pd) { 323 | var_df <- .regularise_df(reticulate::py_to_r(var_pd), drop_single_values = FALSE) 324 | attr(var_df, "pandas.index") <- NULL 325 | colnames(var_df) <- sub("dispersions_norm", "mvp.dispersion.scaled", colnames(var_df)) 326 | colnames(var_df) <- sub("dispersions", "mvp.dispersion", colnames(var_df)) 327 | colnames(var_df) <- sub("means", "mvp.mean", colnames(var_df)) 328 | colnames(var_df) <- sub("highly_variable", "highly.variable", colnames(var_df)) 329 | return(var_df) 330 | } 331 | 332 | .uns2misc <- function(ad, target_uns_keys = list()) { 333 | uns_keys <- intersect(target_uns_keys, reticulate::py_to_r(ad$uns_keys())) 334 | misc <- sapply(uns_keys, function(x) reticulate::py_to_r(ad$uns[x]), simplify = FALSE, USE.NAMES = TRUE) 335 | return(misc) 336 | } 337 | 338 | #' Convert AnnData object to Seurat object 339 | #' 340 | #' This function converts an AnnData object to a Seurat object 341 | #' 342 | #' @param inFile Path to an input AnnData object on disk (str) 343 | #' @param outFile Save output Seurat to this file if specified (str or NULL) 344 | #' @param assay Name of assay in Seurat object to store expression values, 345 | #' default "RNA" (str) 346 | #' @param main_layer Name of slot in `assay` to store AnnData.X, can be 347 | #' "counts", "data", "scale.data", default "counts" (str) 348 | #' @param use_seurat Use Seurat::ReadH5AD() to do the conversion, default FALSE (logical) 349 | #' @param lzf Whether AnnData is compressed by `lzf`, default FALSE (logical) 350 | #' 351 | #' @return Seurat object 352 | #' 353 | #' @import reticulate 354 | #' @import Matrix 355 | anndata2seurat <- function(inFile, outFile = NULL, main_layer = "counts", assay = "RNA", use_seurat = FALSE, lzf = FALSE, target_uns_keys = list()) { 356 | if (!requireNamespace("Seurat")) { 357 | stop("This function requires the 'Seurat' package.") 358 | } 359 | main_layer <- match.arg(main_layer, c("counts", "data", "scale.data")) 360 | inFile <- path.expand(inFile) 361 | 362 | anndata <- reticulate::import("anndata", convert = FALSE) 363 | sp <- reticulate::import("scipy.sparse", convert = FALSE) 364 | 365 | if (use_seurat) { 366 | if (lzf) { 367 | tmpFile <- paste0(tools::file_path_sans_ext(inFile), ".decompressed.h5ad") 368 | ad <- anndata$read_h5ad(inFile) 369 | ad$write(tmpFile) 370 | tryCatch( 371 | { 372 | srt <- Seurat::ReadH5AD(tmpFile) 373 | }, 374 | finally = { 375 | file.remove(tmpFile) 376 | } 377 | ) 378 | } else { 379 | srt <- Seurat::ReadH5AD(inFile) 380 | } 381 | } else { 382 | ad <- anndata$read_h5ad(inFile) 383 | 384 | obs_df <- .obs2metadata(ad$obs) 385 | var_df <- .var2feature_metadata(ad$var) 386 | 387 | if (reticulate::py_to_r(sp$issparse(ad$X))) { 388 | X <- Matrix::t(reticulate::py_to_r(sp$csc_matrix(ad$X))) 389 | } else { 390 | X <- t(reticulate::py_to_r(ad$X)) 391 | } 392 | colnames(X) <- rownames(obs_df) 393 | rownames(X) <- rownames(var_df) 394 | 395 | if (!is.null(reticulate::py_to_r(ad$raw))) { 396 | raw_var_df <- .var2feature_metadata(ad$raw$var) 397 | raw_X <- Matrix::t(reticulate::py_to_r(sp$csc_matrix(ad$raw$X))) 398 | colnames(raw_X) <- rownames(obs_df) 399 | rownames(raw_X) <- rownames(raw_var_df) 400 | } else { 401 | raw_var_df <- NULL 402 | raw_X <- NULL 403 | } 404 | 405 | if (main_layer == "scale.data" && !is.null(raw_X)) { 406 | assays <- list(Seurat::CreateAssayObject(data = raw_X)) 407 | assays[[1]] <- Seurat::SetAssayData(assays[[1]], slot = "scale.data", new.data = X) 408 | message("X -> scale.data; raw.X -> data") 409 | } else if (main_layer == "data" && !is.null(raw_X)) { 410 | if (nrow(X) != nrow(raw_X)) { 411 | message("Raw layer was found with different number of genes than main layer, resizing X and raw.X to match dimensions") 412 | raw_X <- raw_X[rownames(raw_X) %in% rownames(X), , drop = F] 413 | X <- X[rownames(raw_X), , drop = F] 414 | } 415 | assays <- list(Seurat::CreateAssayObject(counts = raw_X)) 416 | assays[[1]] <- Seurat::SetAssayData(assays[[1]], slot = "data", new.data = X) 417 | message("X -> data; raw.X -> counts") 418 | } else if (main_layer == "counts") { 419 | assays <- list(Seurat::CreateAssayObject(counts = X)) 420 | message("X -> counts") 421 | } else { 422 | assays <- list(Seurat::CreateAssayObject(data = X)) 423 | message("X -> data") 424 | } 425 | names(assays) <- assay 426 | Seurat::Key(assays[[assay]]) <- paste0(tolower(assay), "_") 427 | 428 | if (main_layer == "scale.data" && !is.null(raw_X)) { 429 | assays[[assay]]@meta.features <- raw_var_df 430 | } else { 431 | assays[[assay]]@meta.features <- var_df 432 | } 433 | 434 | project_name <- sub("\\.h5ad$", "", basename(inFile)) 435 | srt <- new("Seurat", assays = assays, project.name = project_name, version = packageVersion("Seurat")) 436 | Seurat::DefaultAssay(srt) <- assay 437 | Seurat::Idents(srt) <- project_name 438 | 439 | srt@meta.data <- obs_df 440 | embed_names <- unlist(reticulate::py_to_r(ad$obsm_keys())) 441 | if (length(embed_names) > 0) { 442 | embeddings <- sapply(embed_names, function(x) as.matrix(reticulate::py_to_r(ad$obsm[x])), simplify = FALSE, USE.NAMES = TRUE) 443 | names(embeddings) <- embed_names 444 | for (name in embed_names) { 445 | rownames(embeddings[[name]]) <- colnames(assays[[assay]]) 446 | } 447 | 448 | dim.reducs <- vector(mode = "list", length = length(embeddings)) 449 | for (i in seq(length(embeddings))) { 450 | name <- embed_names[i] 451 | embed <- embeddings[[name]] 452 | key <- switch(name, 453 | sub("_(.*)", "\\L\\1", sub("^X_", "", toupper(name)), perl = T), 454 | "X_pca" = "PC", 455 | "X_tsne" = "tSNE", 456 | "X_umap" = "UMAP" 457 | ) 458 | colnames(embed) <- paste0(key, "_", seq(ncol(embed))) 459 | dim.reducs[[i]] <- Seurat::CreateDimReducObject( 460 | embeddings = embed, 461 | loadings = new("matrix"), 462 | assay = assay, 463 | stdev = numeric(0L), 464 | key = paste0(key, "_") 465 | ) 466 | } 467 | names(dim.reducs) <- sub("X_", "", embed_names) 468 | 469 | for (name in names(dim.reducs)) { 470 | srt[[name]] <- dim.reducs[[name]] 471 | } 472 | } 473 | } 474 | 475 | srt@misc <- .uns2misc(ad, target_uns_keys = target_uns_keys) 476 | 477 | if (!is.null(outFile)) saveRDS(object = srt, file = outFile) 478 | 479 | srt 480 | } 481 | 482 | #' Convert AnnData object to monocle3 CDS object 483 | #' 484 | #' This function converts an AnnData object to a monocle3 CDS object 485 | #' 486 | #' @param inFile Path to an input AnnData object on disk (str) 487 | #' @param outFile Save output CDS to this file if specified (str or NULL) 488 | #' @param main_layer Name of the slot in AnnData to be converted, can be "X", 489 | #' "raw", default "X" (str) 490 | #' @param pcaName Key of PCA in AnnData.obsm to be transferred (str or NULL) 491 | #' @param umapName Key of UMAP in AnnData.obsm to be transferred (str or NULL) 492 | #' 493 | #' @return CDS object 494 | #' 495 | #' @import reticulate 496 | #' @import Matrix 497 | anndata2cds <- function(inFile, outFile = NULL, main_layer = "X", pcaName = "X_pca", umapName = "X_umap") { 498 | builtins <- reticulate::import_builtins(convert = FALSE) 499 | anndata <- reticulate::import("anndata", convert = FALSE) 500 | sp <- reticulate::import("scipy.sparse", convert = FALSE) 501 | 502 | ad <- anndata$read_h5ad(inFile) 503 | obs_df <- .obs2metadata(ad$obs) 504 | 505 | if (is.null(main_layer)) main_layer <- "X" 506 | 507 | if ((main_layer == "raw") && (!is.null(reticulate::py_to_r(ad$raw)))) { 508 | var_df <- .var2feature_metadata(ad$raw$var) 509 | var_df$gene_short_name <- rownames(var_df) 510 | } else { 511 | var_df <- .var2feature_metadata(ad$var) 512 | var_df$gene_short_name <- rownames(var_df) 513 | } 514 | 515 | if (main_layer == "raw") { 516 | count_x <- tryCatch( 517 | { 518 | ss <- reticulate::import("scanpy_scripts", convert = FALSE) 519 | ss$lib$lognorm_to_counts(ad$raw$X) 520 | }, 521 | error = function(e) { 522 | return(tryCatch( 523 | { 524 | ad$raw$X 525 | }, 526 | error = function(ee) { 527 | return(ad$X) 528 | }, 529 | warning = function(ww) {} 530 | )) 531 | }, 532 | warning = function(w) {} 533 | ) 534 | } else { 535 | count_x <- ad$X 536 | } 537 | X <- Matrix::t(reticulate::py_to_r(sp$csc_matrix(count_x))) 538 | colnames(X) <- rownames(obs_df) 539 | rownames(X) <- rownames(var_df) 540 | 541 | suppressPackageStartupMessages(library(SingleCellExperiment)) 542 | cds1 <- monocle3::new_cell_data_set(expression_data = X, cell_metadata = obs_df, gene_metadata = var_df) 543 | 544 | embed_names <- reticulate::py_to_r(builtins$list(ad$obsm$keys())) 545 | if ((!is.null(pcaName) && pcaName %in% embed_names) || (!is.null(umapName) && umapName %in% embed_names)) { 546 | embeds <- SimpleList() 547 | if (!is.null(pcaName) && pcaName %in% embed_names) { 548 | pcs <- reticulate::py_to_r(ad$obsm[pcaName]) 549 | embeds$PCA <- pcs 550 | } 551 | if (!is.null(umapName) && umapName %in% embed_names) { 552 | umap <- reticulate::py_to_r(ad$obsm[umapName]) 553 | embeds$UMAP <- umap 554 | } 555 | SingleCellExperiment::reducedDims(cds1) <- embeds 556 | } 557 | 558 | if (!is.null(outFile)) saveRDS(object = cds1, file = outFile) 559 | 560 | cds1 561 | } 562 | -------------------------------------------------------------------------------- /R/loom_utils.R: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env Rscript 2 | 3 | exchangeable_loom_version <- "3.0.0" 4 | 5 | isExchangeableLoom <- function(h5f) { 6 | attrs <- rhdf5::h5readAttributes(h5f, "/") 7 | version <- attrs[["LOOM_SPEC_VERSION"]] 8 | return(!is.null(version) && version == exchangeable_loom_version) 9 | } 10 | 11 | readDimNames <- function(h5f) { 12 | cell_attr <- rhdf5::h5readAttributes(h5f, "/col_attrs")[["CellID"]] 13 | gene_attr <- rhdf5::h5readAttributes(h5f, "/row_attrs")[["Gene"]] 14 | source <- rhdf5::h5readAttributes(h5f, "/")[["created_from"]] 15 | if (!is.null(source) && source == "anndata") { 16 | if (is.null(cell_attr)) { 17 | cell_attr <- "obs_names" 18 | } 19 | if (is.null(gene_attr)) { 20 | gene_attr <- "var_names" 21 | } 22 | } 23 | return(list(col = cell_attr, row = gene_attr)) 24 | } 25 | 26 | readManifest <- function(h5f) { 27 | tryCatch( 28 | { 29 | manifest <- data.frame(t(rhdf5::h5read(h5f, "/attr/manifest")), stringsAsFactors = FALSE) 30 | colnames(manifest) <- c("loom_path", "dtype", "anndata_path", "sce_path") 31 | return(manifest) 32 | }, 33 | error = function(e) { 34 | manifest <- NULL 35 | return(manifest) 36 | } 37 | ) 38 | } 39 | 40 | nestedEnvAsList <- function(x) { 41 | out <- as.list(x) 42 | lapply(out, function(e) if (is.environment(e)) nestedEnvAsList(e) else e) 43 | } 44 | 45 | nestedEnv <- function(root_env, paths, v) { 46 | n <- length(paths) 47 | var <- paths[1] 48 | if (n == 1) { 49 | root_env[[var]] <- v 50 | } else { 51 | if (is.null(root_env[[var]])) { 52 | root_env[[var]] <- new.env(parent = emptyenv()) 53 | } 54 | nestedEnv(root_env[[var]], paths[2:n], v) 55 | } 56 | invisible() 57 | } 58 | 59 | flattenNestedListToEnv <- function(x, e, prefix = NULL) { 60 | entry_names <- names(x) 61 | if (is.null(entry_names)) entry_names <- seq(length(x)) 62 | sapply(entry_names, function(name) { 63 | if (is.null(prefix)) { 64 | full_name <- name 65 | } else { 66 | full_name <- paste(prefix, name, sep = "__") 67 | } 68 | if (is.list(x[[name]])) { 69 | flattenNestedListToEnv(x[[name]], e, full_name) 70 | } else { 71 | e[[full_name]] <- x[[name]] 72 | } 73 | }) 74 | invisible() 75 | } 76 | 77 | makeManifest <- function(entries, dtype = "array", loom_prefix = "/attr/", anndata_prefix = "/uns/", 78 | sce_prefix = "@metadata$") { 79 | n <- length(entries) 80 | if (is.list(entries)) { 81 | entry_names <- names(entries) 82 | is_scalar <- sapply(entries, function(x) is.vector(x) && length(x) == 1) 83 | dtypes <- ifelse(is_scalar, "scalar", "array") 84 | } else { 85 | entry_names <- entries 86 | dtypes <- rep(dtype, n) 87 | } 88 | loom_paths <- paste0(loom_prefix, entry_names) 89 | if (endsWith(loom_prefix, "[")) { 90 | loom_paths <- paste0(loom_paths, "]") 91 | } 92 | if (is.null(anndata_prefix)) { 93 | anndata_paths <- rep("", n) 94 | } else { 95 | if (anndata_prefix == "/obsm/X_") { 96 | ad_names <- tolower(entry_names) 97 | } else { 98 | ad_names <- gsub("__", "/", entry_names) 99 | } 100 | anndata_paths <- paste0(anndata_prefix, ad_names) 101 | } 102 | if (startsWith(sce_prefix, "@metadata$")) { 103 | entry_names <- gsub("__", "$", entry_names) 104 | } 105 | sce_paths <- paste0(sce_prefix, entry_names) 106 | return(data.frame( 107 | loom_path = loom_paths, dtype = dtypes, anndata_path = anndata_paths, 108 | sce_path = sce_paths, stringsAsFactors = FALSE 109 | )) 110 | } 111 | 112 | readExchangeableLoom <- function(filename, backed = TRUE, main_layer_name = NULL) { 113 | stopifnot(file.exists(filename), rhdf5::H5Fis_hdf5(filename)) 114 | h5f <- rhdf5::H5Fopen(filename, flag = "H5F_ACC_RDONLY") 115 | if (!isExchangeableLoom(h5f)) { 116 | rhdf5::H5Fclose(h5f) 117 | return(LoomExperiment::import(filename, type = "SingleCellLoomExperiment")) 118 | } 119 | dim_names <- readDimNames(h5f) 120 | manifest <- readManifest(h5f) 121 | rhdf5::h5closeAll() 122 | 123 | suppressWarnings(scle <- LoomExperiment::import( 124 | filename, 125 | type = "SingleCellLoomExperiment", 126 | rownames_attr = dim_names$row, 127 | colnames_attr = dim_names$col 128 | )) 129 | if (!backed) { 130 | for (i in seq_along(SummarizedExperiment::assays(scle))) { 131 | SummarizedExperiment::assays(scle)[[i]] <- as(SummarizedExperiment::assays(scle)[[i]], "dgCMatrix") 132 | } 133 | } 134 | 135 | h5f <- rhdf5::H5Fopen(filename, flag = "H5F_ACC_RDONLY") 136 | 137 | # Add appropriate assay name 138 | mx_attrs <- rhdf5::h5readAttributes(h5f, "/matrix") 139 | if (!is.null(main_layer_name)) { 140 | names(SummarizedExperiment::assays(scle))[1] <- main_layer_name 141 | } else if ("assay" %in% names(mx_attrs)) { 142 | names(SummarizedExperiment::assays(scle))[1] <- mx_attrs["assay"] 143 | } else { 144 | names(SummarizedExperiment::assays(scle))[1] <- "counts" 145 | } 146 | 147 | if (!is.null(manifest)) { 148 | 149 | # Graphs are already handled by import(), just record entries 150 | is_graph <- (startsWith(manifest$loom_path, "/col_graphs/") | 151 | startsWith(manifest$loom_path, "/row_graphs/")) 152 | 153 | # Handle reducedDims 154 | is_rd <- startsWith(manifest$loom_path, "/attr/reducedDims__") 155 | rd_paths <- manifest$loom_path[is_rd] 156 | names(rd_paths) <- sub("^@reducedDims@listData[$]", "", manifest$sce_path[is_rd]) 157 | SingleCellExperiment::reducedDims(scle) <- S4Vectors::SimpleList(lapply(rd_paths, function(path) { 158 | mat <- t(rhdf5::h5read(h5f, path)) 159 | rownames(mat) <- colnames(scle) 160 | mat 161 | })) 162 | 163 | # Handle global attributes 164 | is_attr <- startsWith(manifest$loom_path, "/.attrs[") 165 | src_paths <- manifest$loom_path[is_attr] 166 | tgt_paths <- manifest$sce_path[is_attr] 167 | attr_names <- substr(src_paths, 9, nchar(src_paths) - 1) 168 | global_attrs <- rhdf5::h5readAttributes(h5f, "/") 169 | mtdt <- new.env(parent = emptyenv(), size = length(tgt_paths)) 170 | for (i in seq_along(attr_names)) { 171 | v <- global_attrs[[attr_names[i]]] 172 | paths <- unlist(strsplit(sub("^@metadata[$]", "", tgt_paths[i]), "$", fixed = TRUE)) 173 | nestedEnv(mtdt, paths, v) 174 | scle@metadata[[attr_names[i]]] <- NULL 175 | } 176 | 177 | # Handle global datasets 178 | is_ds <- (!(is_graph | is_rd | is_attr) & manifest$sce_path != "") 179 | src_paths <- manifest$loom_path[is_ds] 180 | tgt_paths <- manifest$sce_path[is_ds] 181 | for (i in seq_along(tgt_paths)) { 182 | v <- rhdf5::h5read(h5f, src_paths[i]) 183 | paths <- unlist(strsplit(sub("^@metadata[$]", "", tgt_paths[i]), "$", fixed = TRUE)) 184 | nestedEnv(mtdt, paths, v) 185 | } 186 | mtdt <- nestedEnvAsList(mtdt) 187 | for (name in names(mtdt)) { 188 | scle@metadata[[name]] <- mtdt[[name]] 189 | } 190 | } 191 | rhdf5::h5closeAll() 192 | 193 | return(as(scle, "SingleCellExperiment")) 194 | } 195 | 196 | writeExchangeableLoom <- function(sce, filename, main_layer = NULL, return_manifest = FALSE) { 197 | scle <- LoomExperiment::SingleCellLoomExperiment(sce) 198 | 199 | # Clean rowData and colData 200 | row_fct_idx <- sapply(SummarizedExperiment::rowData(scle), is.factor) 201 | SummarizedExperiment::rowData(scle)[row_fct_idx] <- lapply( 202 | SummarizedExperiment::rowData(scle)[row_fct_idx], 203 | function(x) type.convert(as.character(x), as.is = TRUE) 204 | ) 205 | col_fct_idx <- sapply(SummarizedExperiment::colData(scle), is.factor) 206 | SummarizedExperiment::colData(scle)[col_fct_idx] <- lapply( 207 | SummarizedExperiment::colData(scle)[col_fct_idx], 208 | function(x) type.convert(as.character(x), as.is = TRUE) 209 | ) 210 | 211 | # Handle reducedDims. Move embeddings out of reducedDims so they don't get 212 | # written to unwanted location by export(). 213 | rdims <- SingleCellExperiment::reducedDims(scle) 214 | SingleCellExperiment::reducedDims(scle) <- S4Vectors::SimpleList() 215 | if (!S4Vectors::isEmpty(rdims)) { 216 | rdim_manifest <- makeManifest( 217 | names(rdims), 218 | dtype = "array", 219 | loom_prefix = "/attr/reducedDims__", 220 | anndata_prefix = "/obsm/X_", 221 | sce_prefix = "@reducedDims@listData$" 222 | ) 223 | } else { 224 | rdim_manifest <- NULL 225 | } 226 | 227 | # Handle graphs. They get written by export() but we still need to record the paths. 228 | if (!S4Vectors::isEmpty(LoomExperiment::colGraphs(scle))) { 229 | colgraph_manifest <- makeManifest( 230 | names(LoomExperiment::colGraphs(scle)), 231 | dtype = "graph", 232 | loom_prefix = "/col_graphs/", 233 | anndata_prefix = "/uns/", 234 | sce_prefix = "@colGraphs$" 235 | ) 236 | } else { 237 | colgraph_manifest <- NULL 238 | } 239 | if (!S4Vectors::isEmpty(LoomExperiment::rowGraphs(scle))) { 240 | rowgraph_manifest <- makeManifest( 241 | names(LoomExperiment::rowGraphs(scle)), 242 | dtype = "graph", 243 | loom_prefix = "/row_graphs/", 244 | anndata_prefix = NULL, 245 | sce_prefix = "@rowGraphs$" 246 | ) 247 | } else { 248 | rowgraph_manifest <- NULL 249 | } 250 | 251 | # Handle metadata. Flatten nested lists to make export() happy. Scalars go 252 | # to /.attrs, others go to /attr 253 | if (length(S4Vectors::metadata(scle)) > 0) { 254 | mtdt <- new.env(parent = emptyenv()) 255 | flattenNestedListToEnv(scle@metadata, mtdt) 256 | mtdt <- lapply(mtdt, function(x) { 257 | if (!is.numeric(x) && !is.character(x) && !is.logical(x)) { 258 | x <- type.convert(as.character(x), as.is = TRUE) 259 | } 260 | if ("class" %in% names(attributes(x))) { 261 | attributes(x)$class <- NULL 262 | } 263 | x 264 | }) 265 | is_attr <- rep(FALSE, length(mtdt)) 266 | names(is_attr) <- names(mtdt) 267 | for (i in seq_along(mtdt)) { 268 | x <- mtdt[[i]] 269 | if ((is.vector(x) || is.array(x)) && (length(x) == 1)) { 270 | is_attr[i] <- TRUE 271 | mtdt[[i]] <- x[1] 272 | } 273 | } 274 | is_attr <- is_attr & !grepl("__", names(mtdt)) 275 | 276 | # Let export handle attributes 277 | S4Vectors::metadata(scle) <- mtdt[is_attr] 278 | 279 | excluded_from_manifest <- c( 280 | "LOOM_SPEC_VERSION", "CreationDate", "last_modified", 281 | "CreatedWith", "LoomExperiment-class", "created_from", 282 | "last_modified_by" 283 | ) 284 | attr_names <- names(S4Vectors::metadata(scle)) 285 | attr_names <- attr_names[!attr_names %in% excluded_from_manifest] 286 | if (length(attr_names) > 0) { 287 | attr_manifest <- makeManifest( 288 | attr_names, 289 | dtype = "scalar", 290 | loom_prefix = "/.attrs[", 291 | anndata_prefix = "/uns/", 292 | sce_prefix = "@metadata$" 293 | ) 294 | } else { 295 | attr_manifest <- NULL 296 | } 297 | 298 | datasets <- mtdt[!is_attr] 299 | if (length(datasets) > 0) { 300 | dts_manifest <- makeManifest( 301 | datasets, 302 | dtype = NULL, 303 | loom_prefix = "/attr/", 304 | anndata_prefix = "/uns/", 305 | sce_prefix = "@metadata$" 306 | ) 307 | } else { 308 | dts_manifest <- NULL 309 | } 310 | } else { 311 | attr_manifest <- NULL 312 | dts_manifest <- NULL 313 | datasets <- list() 314 | } 315 | 316 | manifest <- rbind(attr_manifest, dts_manifest, rdim_manifest, colgraph_manifest, rowgraph_manifest) 317 | 318 | # LoomExperiment currently handles sparse matrix incorrectly, so convert to dense for now 319 | for (assay_name in SummarizedExperiment::assayNames(scle)) { 320 | if (class(SummarizedExperiment::assays(scle)[[assay_name]]) == "dgCMatrix") { 321 | SummarizedExperiment::assays(scle)[[assay_name]] <- as.matrix(SummarizedExperiment::assays(scle)[[assay_name]]) 322 | } 323 | } 324 | # Write to loom by LoomExperiment::export 325 | if (file.exists(filename)) file.remove(filename) 326 | suppressWarnings(LoomExperiment::export( 327 | scle, 328 | filename, 329 | matrix = ifelse(!is.null(main_layer) && main_layer %in% SummarizedExperiment::assayNames(scle), 330 | main_layer, SummarizedExperiment::assayNames(scle)[1] 331 | ), 332 | colnames_attr = "obs_names", 333 | rownames_attr = "var_names" 334 | )) 335 | rhdf5::h5closeAll() 336 | 337 | # Write extra bits 338 | h5f <- rhdf5::H5Fopen(filename) 339 | 340 | # Write extra global attributes 341 | rhdf5::h5writeAttribute(exchangeable_loom_version, h5f, "LOOM_SPEC_VERSION") 342 | rhdf5::h5writeAttribute("sce", h5f, "created_from") 343 | 344 | # Write column names of 'CellID' and 'Gene' as attributes of '/col_attrs' and '/row_attrs' 345 | h5g_ca <- rhdf5::H5Gopen(h5f, "/col_attrs") 346 | rhdf5::h5writeAttribute("obs_names", h5g_ca, "CellID") 347 | h5g_ra <- rhdf5::H5Gopen(h5f, "/row_attrs") 348 | rhdf5::h5writeAttribute("var_names", h5g_ra, "Gene") 349 | 350 | # Write primary asssay name as attribute of '/matrix' 351 | h5d_mx <- rhdf5::H5Dopen(h5f, "/matrix") 352 | rhdf5::h5writeAttribute("assay", h5d_mx, names(SummarizedExperiment::assays(scle))[1]) 353 | 354 | # Write manifest 355 | rhdf5::h5createGroup(h5f, "/attr") 356 | if (!is.null(manifest)) { 357 | manifest <- manifest[order(manifest$dtype, manifest$loom_path), ] 358 | rhdf5::h5write(t(manifest), h5f, "/attr/manifest") 359 | } 360 | 361 | # Write reducedDims 362 | for (i in seq_along(rdims)) { 363 | rdim <- rdims[[i]] 364 | loom_path <- rdim_manifest$loom_path[i] 365 | rhdf5::h5write(t(rdim), h5f, loom_path) 366 | } 367 | 368 | # Write extra global datasets 369 | for (i in seq_along(datasets)) { 370 | dts <- datasets[[i]] 371 | loom_path <- dts_manifest$loom_path[i] 372 | rhdf5::h5write(dts, h5f, loom_path) 373 | } 374 | 375 | # Remove '/col_attrs/reducedDims' to make anndata happy 376 | rhdf5::h5delete(h5f, "/col_attrs/reducedDims") 377 | rhdf5::h5closeAll() 378 | 379 | if (return_manifest) { 380 | return(manifest) 381 | } else { 382 | invisible() 383 | } 384 | } 385 | -------------------------------------------------------------------------------- /R/methods.R: -------------------------------------------------------------------------------- 1 | #' Convert between data objects 2 | #' 3 | #' This function converts between data format frequently used to hold single 4 | #' cell data 5 | #' 6 | #' @param obj Input Seurat object 7 | #' @param from Format of input object, e.g. "anndata", "seurat", "sce", "loom", 8 | #' etc (str) 9 | #' @param to Format of output object (str) 10 | #' @param main_layer Required by some formats, may be "counts", "data", 11 | #' "scale.data", etc (str) 12 | #' 13 | #' @return Output object 14 | convertFormat <- function(obj, from = c("anndata", "seurat", "sce", "loom"), to = c("anndata", "loom", "sce", "seurat", "cds"), outFile = NULL, 15 | main_layer = NULL, ...) { 16 | from <- match.arg(from) 17 | to <- match.arg(to) 18 | 19 | tryCatch( 20 | { 21 | func <- eval(parse(text = paste(from, to, sep = "2"))) 22 | }, 23 | error = function(e) { 24 | stop(paste0('Unsupported conversion from "', from, '" to "', to, '"'), call. = FALSE) 25 | }, 26 | finally = {} 27 | ) 28 | 29 | return(func(obj, outFile = outFile, main_layer = main_layer, ...)) 30 | } 31 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # sceasy 2 | 3 | `sceasy` is a package that helps easy conversion of different single-cell data formats to each other. Converting to AnnData creates a file that can be directly used in [cellxgene](https://github.com/chanzuckerberg/cellxgene) which is an interactive explorer for single-cell transcriptomics datasets. 4 | 5 | 6 | | 💡 for h5da to rds conversion also see [https://github.com/cellgeni/schard](https://github.com/cellgeni/schard) | 7 | | ----------------------------------------------------------------------------------------------- | 8 | 9 | 10 | > ### Warning 11 | > Before installing the conda packages below please first create a new conda environment EnvironmentName and activate it. Everything else can be installed in R. 12 | 13 | 14 | ## Installation 15 | 16 | sceasy is installable either as a bioconda package: 17 | 18 | ```conda install -c bioconda r-sceasy``` 19 | 20 | or as an R package: 21 | 22 | ```devtools::install_github("cellgeni/sceasy")``` 23 | 24 | which will require the biconductor packages BiocManager and LoomExperiment: 25 | 26 | ``` 27 | if (!requireNamespace("BiocManager", quietly = TRUE)) 28 | install.packages("BiocManager") 29 | 30 | BiocManager::install(c("LoomExperiment", "SingleCellExperiment")) 31 | ``` 32 | 33 | To use sceasy ensure the anndata package is installed: 34 | 35 | ```conda install anndata -c bioconda``` 36 | 37 | Optionally, if you plan to convert between loom and anndata, please also ensure that the `loompy` package is installed: 38 | 39 | ```conda install loompy -c bioconda``` 40 | 41 | You will also need to install reticulate package: 42 | 43 | ```install.packages('reticulate')``` 44 | 45 | 46 | ## Usage 47 | 48 | Before converting your data please load the following libraries in your R session: 49 | 50 | ``` 51 | library(sceasy) 52 | library(reticulate) 53 | use_condaenv('EnvironmentName') 54 | loompy <- reticulate::import('loompy') 55 | ``` 56 | 57 | **Seurat to AnnData** 58 | 59 | ``` 60 | sceasy::convertFormat(seurat_object, from="seurat", to="anndata", 61 | outFile='filename.h5ad') 62 | ``` 63 | 64 | **AnnData to Seurat** 65 | 66 | ``` 67 | sceasy::convertFormat(h5ad_file, from="anndata", to="seurat", 68 | outFile='filename.rds') 69 | ``` 70 | 71 | **Seurat to SingleCellExperiment** 72 | 73 | ``` 74 | sceasy::convertFormat(seurat_object, from="seurat", to="sce", 75 | outFile='filename.rds') 76 | ``` 77 | 78 | **SingleCellExperiment to AnnData** 79 | 80 | ``` 81 | sceasy::convertFormat(sce_object, from="sce", to="anndata", 82 | outFile='filename.h5ad') 83 | ``` 84 | 85 | **SingleCellExperiment to Loom** 86 | 87 | ``` 88 | sceasy::convertFormat(sce_object, from="sce", to="loom", 89 | outFile='filename.loom') 90 | ``` 91 | 92 | **Loom to AnnData** 93 | 94 | ``` 95 | sceasy::convertFormat('filename.loom', from="loom", to="anndata", 96 | outFile='filename.h5ad') 97 | ``` 98 | 99 | **Loom to SingleCellExperiment** 100 | 101 | ``` 102 | sceasy::convertFormat('filename.loom', from="loom", to="sce", 103 | outFile='filename.rds') 104 | ``` 105 | --------------------------------------------------------------------------------