View source: R/preprocessing.R
ReadMtx | R Documentation |
Enables easy loading of sparse data matrices
ReadMtx(
mtx,
cells,
features,
cell.column = 1,
feature.column = 2,
cell.sep = "\t",
feature.sep = "\t",
skip.cell = 0,
skip.feature = 0,
mtx.transpose = FALSE,
unique.features = TRUE,
strip.suffix = FALSE
)
mtx |
Name or remote URL of the mtx file |
cells |
Name or remote URL of the cells/barcodes file |
features |
Name or remote URL of the features/genes file |
cell.column |
Specify which column of cells file to use for cell names; default is 1 |
feature.column |
Specify which column of features files to use for feature/gene names; default is 2 |
cell.sep |
Specify the delimiter in the cell name file |
feature.sep |
Specify the delimiter in the feature name file |
skip.cell |
Number of lines to skip in the cells file before beginning to read cell names |
skip.feature |
Number of lines to skip in the features file before beginning to gene names |
mtx.transpose |
Transpose the matrix after reading in |
unique.features |
Make feature names unique (default TRUE) |
strip.suffix |
Remove trailing "-1" if present in all cell barcodes. |
A sparse matrix containing the expression data.
## Not run:
# For local files:
expression_matrix <- ReadMtx(
mtx = "count_matrix.mtx.gz", features = "features.tsv.gz",
cells = "barcodes.tsv.gz"
)
seurat_object <- CreateSeuratObject(counts = expression_matrix)
# For remote files:
expression_matrix <- ReadMtx(mtx = "http://localhost/matrix.mtx",
cells = "http://localhost/barcodes.tsv",
features = "http://localhost/genes.tsv")
seurat_object <- CreateSeuratObject(counts = data)
## End(Not run)
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