View source: R/make_bulk_matrix_from_seurat.R
make_bulk_matrix_from_seurat | R Documentation |
This function creates a gene expression matrix from a list of files, each containing bulk gene expression profiles. It returns a samples-by-genes matrix.
make_bulk_matrix_from_seurat(
scrna_folder,
scrna_name,
scrna_group_cells_by = "seurat_clusters",
scrna_groups_to_keep = NULL,
scrna_assay_markers = "RNA",
scrna_assay_vars = "RNA",
scrna_test_markers = "MAST",
scrna_logfc = 0.585,
scrna_minpct = 0.25,
scrna_var_nfeatures = 3000,
scrna_label_prefix = "cluster",
pseudocount = 0.083,
qvalue_cutoff = 0.05,
force_find_markers = FALSE
)
scrna_folder |
Folder that hosts the Seurat object. |
scrna_name |
Name of the Seurat object (without .rds). |
scrna_group_cells_by |
The name of the column in the metadata to group by (default = "seurat_clusters"). |
scrna_assay_markers |
The assay (e.g. "RNA", "SCT", "alra") used for differential expression analysis. |
scrna_assay_vars |
The assay (e.g. "RNA", "SCT", "alra") used for pre-calculated variable features. |
scrna_test_markers |
The method used (e.g. "MAST", "wilcox", "t") used for differential expression analysis. See help for FindAllMarkers() in Seurat. |
scrna_logfc |
See help for FindAllMarkers() in Seurat. |
scrna_minpct |
See help for FindAllMarkers() in Seurat. |
scrna_label_prefix |
When creating a pseudobulk profile for each subgroup, this is a prefix to add to their labels (default = "cluster"). |
pseudocount |
An arbitrary small number to add to gene counts before log transformation. 1 works well for 10X data, 0.083 works well for Drop-seq data (default = 1). |
qvalue_cutoff |
FDR cutoff to consider a marker significant. |
force_find_markers |
By default, finding markers will be skipped if done previously (saved as scrna_name.scrna_group_cells_by.markers inside the scrna_folder folder). If TRUE, finding markers will always happen. |
make_bulk_matrix_from_seurat("./data","my_seurat_object")
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