pg_embedding: Co-embeddings of Peaks and Genes.

In scPOEM: Single-Cell Meta-Path Based Omic Embedding

Co-embeddings of Peaks and Genes.

Description

Learn the low-dimensional representations for peaks and genes with a meta-path based method.

Usage

pg_embedding(
  gg_net,
  pp_net,
  pg_net_list,
  dirpath = tempdir(),
  relearn_pg_embedding = TRUE,
  save_file = TRUE,
  d = 100,
  numwalks = 5,
  walklength = 3,
  epochs = 100,
  neg_sample = 5,
  batch_size = 32,
  weighted = TRUE,
  exclude_pos = FALSE,
  seed = NULL,
  python_env = "scPOEM_env"
)

Arguments

gg_net	The gene-gene network.
pp_net	The peak-peak network.
pg_net_list	A list of peak-gene networks, constructed via different methods.
dirpath	The folder path to read or write file.
relearn_pg_embedding	Logical. Whether to relearn the low-dimensional representations for peaks and genes from scratch. If FALSE, the function will attempt to read from node_embeddings.mtx, node_used_peak.csv, node_used_gene.csv under dirpath/embedding in single mode or dirpath/state_name/embedding in compare mode.
save_file	Logical, whether to save the output to a file.
d	Dimension of the latent space. Default is 100.
numwalks	Number of random walks per node. Default is 5.
walklength	Length of walk depth. Default is 3.
epochs	Number of training epochs. Default is 100.
neg_sample	Number of negative samples per positive sample. Default is 5.
batch_size	Batch size for training. Default is 32.
weighted	Whether the sampling network is weighted. Default is TRUE.
exclude_pos	Whether to exclude positive samples from negative sampling. Default is FALSE.
seed	An integer specifying the random seed to ensure reproducible results.
python_env	Name or path of the Python environment to be used.

Value

A list containing the following:

E

Low-dimensional representations of peaks and genes

peak_node

Peak ids that are associated with other peaks or genes.

gene_node

Gene ids that are associated with other peaks or genes.

Examples

library(scPOEM)
library(monocle)
dirpath <- "./example_data"
# Download single mode example data
data(example_data_single)
gg_net <- GGN(example_data_single$Y,
              file.path(dirpath, "single"),
              save_file=FALSE)
pp_net <- PPN(example_data_single$X, example_data_single$peak_data,
              example_data_single$cell_data, example_data_single$genome,
              file.path(dirpath, "single"), save_file=FALSE)
net_Lasso <- PGN_Lasso(example_data_single$X, example_data_single$Y,
                       example_data_single$gene_data, example_data_single$neibor_peak,
                       file.path(dirpath, "single"), save_file=FALSE)
net_RF <- PGN_RF(example_data_single$X, example_data_single$Y,
                 example_data_single$gene_data, example_data_single$neibor_peak,
                 file.path(dirpath, "single"), save_file=FALSE)
net_XGB <- PGN_XGBoost(example_data_single$X, example_data_single$Y,
                       example_data_single$gene_data, example_data_single$neibor_peak,
                       file.path(dirpath, "single"), save_file=FALSE)
E_result <- pg_embedding(gg_net, pp_net, list(net_Lasso, net_RF, net_XGB),
                         file.path(dirpath, "single"), save_file=FALSE)

scPOEM documentation built on Aug. 28, 2025, 9:09 a.m.