Create Seurat object based on sample metadata.
create_seurat_object.Rd
This function creates Seurat object for each sample in the metadata file. It also allows the user to perform SoupX (ambient RNA removal) and Scrublet (doublet detection) analysis.
Usage
create_seurat_object(
data_dir,
sample_meta = NULL,
sample_meta_filename = NULL,
meta_colnames = c("donor", "condition", "pass_qc"),
plot_dir = NULL,
use_scrublet = TRUE,
use_soupx = FALSE,
tenx_dir = "premrna_outs",
tenx_counts_dir = "filtered_feature_bc_matrix",
expected_doublet_rate = 0.06,
min.cells = 10,
min.features = 200,
...
)
Arguments
- data_dir
Parent directory where all sample 10x files are stored. Think of it as project directory.
- sample_meta
Sample metadata information in a Data.frame like object. Columns should at least contain 'sample', 'donor', 'condition' and 'pass_qc'.
- sample_meta_filename
Filename of sample metadata information, same as 'meta' parameter above. User should provide one of 'meta' or 'meta_filename'.
- meta_colnames
Sample metadata column names to store in Seurat metadata.
- plot_dir
Directory for storing QC plots. Used if use_soupx = TRUE.
- use_scrublet
Logical, wether to use Scrublet for doublet detection.
- use_soupx
Logical, wether to use SoupX for ambient RNA removal.
- tenx_dir
Name of 10x base directory, e.g. with outputs after running cellranger. Default 'premrna_outs', i.e. assumes single-nuclei RNA-seq.
- tenx_counts_dir
Name of 10x directory where count matrices are stored. Default 'filtered_feature_bc_matrix'
- expected_doublet_rate
The expected fraction of transcriptomes that are doublets, typically 0.05 - 0.1
- min.cells
Include features/genes detected in at least this many cells.
- min.features
Include cells where at least this many features/genes are detected.
- ...
Additional named parameters passed to Seurat, Scrublet or SoupX.
Value
List of Seurat objects equal the number of samples in the sample metadata file. If a single sample, returns a Seurat object.
Author
C.A.Kapourani C.A.Kapourani@ed.ac.uk