Package 'dawnn'

Title: Differential Abundance with Neural Networks
Description: Detects regions of differential abundance in single-cell transcriptomic data by applying a pre-trained neural network model to the labels of each cell's nearest neighbours. Tests for both local and global differential abundance, controlling the false discovery rate with the Benjamini-Yekutieli procedure. The method is described in Hall and Castellano (2023) <doi:10.1101/2023.05.05.539427>.
Authors: George Hall [aut, cre] (ORCID: <https://orcid.org/0000-0002-4828-0668>), Sergi Castellano [aut] (ORCID: <https://orcid.org/0000-0002-5819-4210>), University College London [cph]
Maintainer: George Hall <[email protected]>
License: GPL (>= 3)
Version: 2.2.0
Built: 2026-09-28 16:33:20 UTC
Source: https://github.com/george-hall-ucl/dawnn

Help Index

Download the neural network model used by Dawnn.

Description

download_model() downloads the neural network model used by Dawnn, which is too large to be bundled with the package. This function must be used once before run_dawnn() can be executed. After this, the path to the model can be passed to this function.

Usage

download_model(
  model_url = NULL,
  model_file_path = NULL,
  download_method = "auto",
  download_timeout = 600
)

Arguments

model_url

String url from which to download the model.
model_file_path

String path at which to save the downloaded model.
download_method

String download program to use (e.g. wget, curl etc).
download_timeout

Integer number of seconds before download times out (optional, default = 600).

Value

Message confirming the absolute path to the downloaded model.

Examples

## Not run: 
download_model()

## End(Not run)

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

Description

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

## Not run: 
# 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"
)

## End(Not run)