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Create a usable Seurat object from a sample.

create_seurat_object.Rd

Everything needed to take a sample from a counts file to an object ready for clustering, in one call.

Usage

create_seurat_object(
  path,
  sample_name,
  num_mads = 3,
  min_genes = 500,
  max_genes = NULL,
  min_counts = NULL,
  max_counts = NULL,
  max_mt = 10,
  normalization_method = "log",
  num_variable_genes = 3000,
  num_pcs = 50,
  num_dim = 30,
  log_file = NULL
)

Arguments

path

Path to a 10x directory or a flat counts file.

sample_name

Sample/library name, used as the cell ID prefix.

num_mads, min_genes, max_genes, min_counts, max_counts, max_mt

Quality cutoffs, passed to filter_cells() — see there for how the outlier stage and the fixed cutoffs combine.

normalization_method

Normalization method, passed to normalize_counts() (as its own method argument): "log" or "sct".

num_variable_genes

Number of variable features.

num_pcs

Principal components to compute. Reduced automatically when the object is too small to support that many.

num_dim

Number of dimensions for the first-pass tSNE/UMAP, capped at num_pcs.

log_file

Filename for the log file.

Value

A Seurat object with normalized data, variable features, scaled data, and "pca", "tsne", and "umap" reductions — ready for cluster_seurat_object(). Also writes the variable-feature, PCA, tSNE, and UMAP diagnostic plots (via normalize_counts(), run_pca(), run_tsne(), and run_umap()) and the metadata+embeddings table (via save_metadata(), as metadata.csv.gz) to the working directory.

Details

The function runs these steps:

  1. Read the counts (read_counts_file()).

  2. Build the object (initialize_seurat_object()).

  3. Attach any antibody capture data as an "ADT" assay (add_seurat_assay()).

  4. Apply the quality cutoffs (filter_cells()).

  5. Normalize, select variable features, and scale (normalize_counts(), which does all three for either method).

  6. Run PCA (run_pca()).

  7. Compute a first-pass tSNE/UMAP (run_tsne(), run_umap()).

This function attaches the ADT assay before filtering, on purpose. add_seurat_assay() restricts the object to the barcodes both matrices share. Attaching ADT after filtering would apply the cutoffs to a different set of cells.

PCA, and the tSNE/UMAP computed from it, runs on whichever assay normalization left active. SCTransform() creates and activates "SCT". The log path leaves "RNA" active. So dr_assay is decided directly from normalization_method, not read back off the object. Reading it back off DefaultAssay() would reintroduce the implicit dependency an explicit assay argument exists to avoid.

The tSNE/UMAP computed here are a first-pass look, capped at num_dim dimensions. cluster_seurat_object() recomputes both once a clustering resolution's own num_dim is chosen. So this pair is not the final one used for the actual clustering plots.