Identify which cells are in regions of differential abundance using Dawnn.

run_dawnn() is the main function used to run Dawnn. It takes a Seurat dataset and identifies which cells are in regions of differential abundance. Dawnn requires at least 1,001 cells.

Usage

run_dawnn(
  cells,
  label_names,
  label_pos_lfc,
  reduced_dim,
  n_dims = 10,
  nn_model = dawnn_default_model_file(),
  recalculate_graph = TRUE,
  alpha = 0.1,
  verbosity = 1,
  seed = 123,
  tf_conda_env = NULL
)

Arguments

cells

Seurat object containing the dataset.

label_names

String containing the name of the meta.data slot in cells containing the labels of each cell.

label_pos_lfc

String containing the name of the label associated with positive log-fold change.

reduced_dim

String containing the name of the dimensionality reduction to use.

n_dims

Integer number of dimensions to use if computing graph (optional, default 10).

nn_model

String containing the path to the model's .hdf5 file (optional, defaults to the location used by download_model()).

recalculate_graph

Boolean whether to recalculate the KNN graph. If FALSE, then the one stored in the cells object will be used (optional, default = TRUE).

alpha

Numeric target false discovery rate supplied to the Benjamini–Yekutieli procedure (optional, default 0.1, i.e. 10%).

verbosity

Integer how much output to print. 0: silent; 1: normal output; 2: display messages from predict() function.

seed

Integer random seed (optional, default 123).

tf_conda_env

Conda environment with TensorFlow installed, useful if it is unavailable in the current environment (optional, default NULL).

Value

Seurat dataset cells with added metadata: dawnn_scores (output of Dawnn's model for each cell); dawnn_lfc (estimated log2-fold change in the neighbourhood of each cell); dawnn_p_vals_lda and dawnn_p_vals_gda (p-values associated with the hypothesis tests for whether a cell is in a region of local or global differential abundance, respectively); dawnn_lda_verdict and dawnn_gda_verdict (Boolean output of Dawnn indicating whether it considers a cell to be in a region of local or global differential abundance, respectively).

Examples

if (FALSE) { # \dontrun{
# Only required options
run_dawnn(
    cells = dataset, label_names = "condition", label_pos_lfc = "Condition_1",
    nn_model = "my_model.h5", reduced_dim = "pca"
)
# All options
run_dawnn(
    cells = dataset, label_names = "condition", label_pos_lfc = "Condition_1",
    nn_model = "my_model.h5", reduced_dim = "pca", n_dims = 50,
    recalculate_graph = FALSE, alpha = 0.2, verbosity = 0, seed = 42,
    tf_conda_env = "my_tensorflow_env"
)
} # }