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Scrublet R6 Class

ScrubletR.Rd

The Scrublet R6 class represents a single-cell RNA-seq scrubbing tool.

Public Fields

  • E_obs: Observed expression matrix.

  • E_sim: Simulated expression matrix.

  • E_obs_norm: Normalized observed expression matrix.

  • E_sim_norm: Normalized simulated expression matrix.

  • gene_filter: Filter for highly variable genes.

  • embeddings: List of embeddings.

  • total_counts_obs: Total counts for observed cells.

  • total_counts_sim: Total counts for simulated cells.

  • sim_doublet_ratio: Ratio of simulated doublets to observed cells.

  • n_neighbors: Number of neighbors for calculations.

  • expected_doublet_rate: Expected doublet rate.

  • stdev_doublet_rate: Standard deviation of doublet rate.

  • random_state: Seed for reproducibility.

  • doublet_parents: Matrix of doublet parents.

  • manifold_obs_: Observed manifold data.

  • manifold_sim_: Simulated manifold data.

  • doublet_scores_obs_: Doublet scores for observed cells.

  • doublet_scores_sim_: Doublet scores for simulated cells.

  • doublet_errors_obs_: Doublet errors for observed cells.

  • doublet_errors_sim_: Doublet errors for simulated cells.

  • doublet_neighbor_parents_: Parents of doublet neighbors.

  • predicted_doublets: Predicted doublets.

  • z_scores_: Z-scores for doublet predictions.

  • threshold_: Threshold for doublet predictions.

  • detected_doublet_rate_: Detected doublet rate.

  • detectable_doublet_fraction_: Detectable doublet fraction.

  • overall_doublet_rate_: Overall doublet rate.

Public Methods

  • get_dims: Display dimensions of expression matrices.

  • scrub_doublets: Perform scrubbing to identify and remove doublets.

  • simulate_doublets: Simulate doublets based on observed data.

  • set_manifold: Set manifold data.

  • calculate_doublet_scores: Calculate doublet scores using nearest neighbors.

  • call_doublets: Identify doublets based on scores and threshold.

  • nearest_neighbor_classifier: Nearest neighbor classification for doublet scores.

  • plot_histogram: Plot histogram of doublet scores.

  • set_embedding: Set embedding data.

  • plot_embedding: Plot embeddings.

  • pipeline_normalize: Normalize total counts.

  • pipeline_get_gene_filter: Identify highly variable genes.

  • pipeline_apply_gene_filter: Apply gene filter to expression matrices.

  • pipeline_mean_center: Mean center expression matrix.

  • pipeline_normalize_variance: Variance normalization of expression matrices.

  • pipeline_zscore: Z-score normalization of expression matrices.

  • pipeline_log_transform: Log transform expression matrices.

  • pipeline_truncated_svd: Truncated Singular Value Decomposition.

  • pipeline_pca: Principal Component Analysis.

Methods

Public methods

Method get_dims()

Usage

ScrubletR$get_dims()

Method new()

Usage

ScrubletR$new(
  counts_matrix,
  total_counts = NULL,
  sim_doublet_ratio = 2,
  n_neighbors = NULL,
  expected_doublet_rate = 0.1,
  stdev_doublet_rate = 0.02,
  random_state = 0,
  show_gene_filter_plot = TRUE
)

Method scrub_doublets()

Usage

ScrubletR$scrub_doublets(
  synthetic_doublet_umi_subsampling = 1,
  use_approx_neighbors = TRUE,
  distance_metric = "euclidean",
  get_doublet_neighbor_parents = FALSE,
  min_counts = 3,
  min_cells = 3,
  min_gene_variability_pctl = 85,
  log_transform = FALSE,
  mean_center = T,
  normalize_variance = T,
  n_prin_comps = 30,
  verbose = TRUE
)

Method simulate_doublets()

Usage

ScrubletR$simulate_doublets(
  sim_doublet_ratio = NULL,
  synthetic_doublet_umi_subsampling = 1
)

Method set_manifold()

Usage

ScrubletR$set_manifold(manifold_obs, manifold_sim)

Method calculate_doublet_scores()

Usage

ScrubletR$calculate_doublet_scores(
  use_approx_neighbors = TRUE,
  distance_metric = "euclidean",
  get_doublet_neighbor_parents = FALSE
)

Method call_doublets()

Usage

ScrubletR$call_doublets(threshold = NULL, verbose = TRUE)

Method nearest_neighbor_classifier()

Usage

ScrubletR$nearest_neighbor_classifier(
  k = 40,
  use_approx_nn = TRUE,
  distance_metric = "euclidean",
  exp_doub_rate = 0.1,
  stdev_doub_rate = 0.03,
  get_neighbor_parents = FALSE
)

Method plot_histogram()

Usage

ScrubletR$plot_histogram(
  scale_hist_obs = "log",
  scale_hist_sim = "linear",
  fig_size = c(8, 3)
)

Method set_embedding()

Usage

ScrubletR$set_embedding(embedding_name, coordinates)

Method plot_embedding()

Usage

ScrubletR$plot_embedding(
  embedding_name,
  score = "raw",
  marker_size = 5,
  order_points = FALSE,
  fig_size = c(8, 4),
  color_map = NULL
)

Method pipeline_normalize()

Usage

ScrubletR$pipeline_normalize(postnorm_total = NULL)

Method pipeline_get_gene_filter()

Usage

ScrubletR$pipeline_get_gene_filter(
  min_counts = 3,
  min_cells = 3,
  min_gene_variability_pctl = 85,
  plot = T
)

Method pipeline_apply_gene_filter()

Usage

ScrubletR$pipeline_apply_gene_filter()

Method pipeline_mean_center()

Usage

ScrubletR$pipeline_mean_center()

Method pipeline_normalize_variance()

Usage

ScrubletR$pipeline_normalize_variance()

Method pipeline_zscore()

Usage

ScrubletR$pipeline_zscore()

Method pipeline_log_transform()

Usage

ScrubletR$pipeline_log_transform(pseudocount = 1)

Method pipeline_truncated_svd()

Usage

ScrubletR$pipeline_truncated_svd(
  n_prin_comps = 30,
  random_state = 0,
  algorithm = "arpack"
)

Method pipeline_pca()

Usage

ScrubletR$pipeline_pca(
  n_prin_comps = 50,
  random_state = 0,
  svd_solver = "arpack"
)

Method clone()

The objects of this class are cloneable with this method.

Usage

ScrubletR$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.