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image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2026
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Single-cell transcriptomics of melanoma sentinel lymph nodes identifies immune cell signatures associated with metastasis

Authors: Engelbrecht, Eric;

Single-cell transcriptomics of melanoma sentinel lymph nodes identifies immune cell signatures associated with metastasis

Abstract

Cell-level raw counts for RNA and ADT (cell-surface protein) expression. A seurat object can be created from these files in R, for example: ## 1. libraries --------------------------------------------------------------if (!requireNamespace("Seurat", quietly = TRUE)) stop("install.packages('Seurat')")if (!requireNamespace("Matrix", quietly = TRUE)) stop("install.packages('Matrix')")if (!requireNamespace("data.table", quietly = TRUE)) install.packages("data.table") library(data.table)library(Seurat)library(data.table) ## check versionspkgs <- c("Seurat", "data.table", "Matrix")v <- vapply(pkgs, function(p) as.character(packageVersion(p)), character(1))print(v)# Seurat data.table Matrix # "5.0.0" "1.14.8" "1.6.3" ## 2. helper to read a MatrixMarket triple + row/col names -------------------- read_mtx <- function(mtx_path, row_csv, barcode_csv) { mat <- Matrix::readMM(mtx_path) rows <- data.table::fread(row_csv, header = FALSE)[[1]] if (length(rows) == nrow(mat) + 1) rows <- rows[-1] # drop header stopifnot(length(rows) == nrow(mat)) cols <- data.table::fread(barcode_csv, header = FALSE)[[1]] if (length(cols) == ncol(mat) + 1) cols <- cols[-1] # drop header stopifnot(length(cols) == ncol(mat)) rownames(mat) <- rows colnames(mat) <- cols mat} ## 3. load counts and metadata ----------------------------------------------rna_counts <- read_mtx("RNA_counts.mtx", "RNA_genes.csv", "RNA_barcodes.csv")adt_counts <- read_mtx("ADT_counts.mtx", "ADT_features.csv","RNA_barcodes.csv") # same barcodesmeta <- data.table::fread("seurat_metadata_full.csv", data.table = FALSE)rownames(meta) <- colnames(rna_counts) # ensure 1-to-1 alignment ## 4. build Seurat object ----------------------------------------------------seu <- Seurat::CreateSeuratObject( counts = rna_counts, assay = "RNA", project = "Rebuilt", meta.data = meta) # add ADT as a separate assayadt_assay <- Seurat::CreateAssayObject(counts = adt_counts)Seurat::DefaultAssay(adt_assay) <- "ADT"seu[["ADT"]] <- adt_assay # tidy upSeurat::Key(seu[["ADT"]]) <- "adt_"Seurat::DefaultAssay(seu) <- "RNA" ## 5. save -------------------------------------------------------------------saveRDS(seu, file = "seurat_rebuilt.rds")# peek at first 5×5 slice of RNA & ADT count layers## RNA -----------------------------------------------------------------------rna_slice <- Seurat::GetAssayData(seu[["RNA"]], layer = "counts")[1:5, 1:5]cat("\n── RNA (first 5 genes × 5 cells) ──\n")print(as.matrix(rna_slice)) # coercion only for nicer console display ## ADT -----------------------------------------------------------------------adt_slice <- Seurat::GetAssayData(seu[["ADT"]], layer = "counts")[1:5, 1:5]cat("\n── ADT (first 5 features × 5 cells) ──\n")print(as.matrix(adt_slice))

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average